Molecular evolution of transcriptional regulation by Alan ... - Eisen Lab
Molecular evolution of transcriptional regulation by Alan ... - Eisen Lab
Molecular evolution of transcriptional regulation by Alan ... - Eisen Lab
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<strong>Molecular</strong> <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong><br />
<strong>by</strong><br />
<strong>Alan</strong> Michael Moses<br />
B.A. (Columbia University) 2000<br />
A dissertation submitted in partial satisfaction <strong>of</strong> the<br />
requirements for the degree <strong>of</strong><br />
Doctor <strong>of</strong> Philosophy<br />
in<br />
BIOPHYSICS<br />
in the<br />
GRADUATE DIVISION<br />
<strong>of</strong> the<br />
UNIVERSITY OF CALIFORNIA, BERKELEY<br />
Committee in charge:<br />
Pr<strong>of</strong>essor Michael B. <strong>Eisen</strong>, Chair<br />
Pr<strong>of</strong>essor Adam P. Arkin<br />
Pr<strong>of</strong>essor Daniel S. Rokhsar<br />
Pr<strong>of</strong>essor Michael Levine<br />
Spring 2005
The thesis <strong>of</strong> <strong>Alan</strong> Michael Moses is approved:<br />
______________________________________________________________<br />
Chair Date<br />
______________________________________________________________<br />
Date<br />
______________________________________________________________<br />
Date<br />
______________________________________________________________<br />
Date<br />
University <strong>of</strong> California, Berkeley<br />
Spring 2005
<strong>Molecular</strong> Evolution <strong>of</strong> Transcriptional Regulation<br />
© 2005<br />
<strong>by</strong><br />
<strong>Alan</strong> Michael Moses<br />
This thesis is licensed under the terms <strong>of</strong> the Creative Commons Attribution<br />
License, which permits unrestricted use, distribution, and reproduction in<br />
any medium, provided the original work is properly cited.
Abstract<br />
<strong>Molecular</strong> <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong><br />
<strong>by</strong><br />
<strong>Alan</strong> Michael Moses<br />
Doctor <strong>of</strong> Philosophy in Biophysics<br />
University <strong>of</strong> California, Berkeley<br />
Pr<strong>of</strong>essor Michael B. <strong>Eisen</strong>, Chair<br />
Regulation <strong>of</strong> transcription <strong>by</strong> sequence-specific DNA-binding proteins is an important<br />
component <strong>of</strong> many fundamental biological processes; these include autonomous cellular<br />
behavior, response to the environment, and the generation <strong>of</strong> functional and<br />
morphological diversity over development and <strong>evolution</strong>. Understanding how this<br />
regulatory information is encoded in genome sequences is therefore a major challenge.<br />
Taking advantage <strong>of</strong> the availability <strong>of</strong> the genome sequences <strong>of</strong> closely related<br />
organisms, I studied the <strong>evolution</strong> <strong>of</strong> the DNA binding sites recognized <strong>by</strong> these proteins<br />
(transcription factor binding sites) and found that, just as the pattern <strong>of</strong> <strong>evolution</strong> in<br />
protein coding sequences reflects the degeneracy <strong>of</strong> the genetic code, the <strong>evolution</strong> <strong>of</strong><br />
these binding sites reflects the constraints under which they function. Using models <strong>of</strong><br />
molecular <strong>evolution</strong> that described the observed patterns <strong>of</strong> <strong>evolution</strong>, I developed new<br />
computational approaches to determine both the sequence specificity <strong>of</strong> transcription<br />
factors and the sites in the genome to which these factors bind. These new methods<br />
exploit the specific patterns <strong>of</strong> <strong>evolution</strong> observed in transcription factor binding sites. I<br />
1
have applied these methods to test cases from the budding yeasts (sensu stricto<br />
Saccharomyces) and fruit fly (Drosophila) and to novel cases in the human genome, in<br />
order to discover the specificity <strong>of</strong> transcription factors and locations <strong>of</strong> binding sites.<br />
The <strong>evolution</strong> <strong>of</strong> transcription factor binding sites not only helps reveal the<br />
regulatory information encoded in the genome, but it also a key mechanism underlying<br />
the <strong>evolution</strong> <strong>of</strong> transcription networks. In order to study the <strong>evolution</strong> <strong>of</strong> these networks,<br />
I have developed and applied computational methods to characterize the patterns <strong>of</strong><br />
<strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> regulatory systems over the ascomycete fungi. In order to<br />
relate binding site <strong>evolution</strong> at the level <strong>of</strong> DNA sequence to the <strong>evolution</strong> <strong>of</strong><br />
<strong>transcriptional</strong> <strong>regulation</strong>, I have developed methods to systematically test for gains and<br />
losses <strong>of</strong> functional binding sites and to differentiate among <strong>evolution</strong>ary scenarios.<br />
Finally, I have suggested a general probabilistic framework to model the changing<br />
selective constraints on these sequences.<br />
______________________________________________________________<br />
Chair Date<br />
2
To California<br />
i
Table <strong>of</strong> contents<br />
Part I – Introduction<br />
1. Regulation <strong>of</strong> gene expression at the level <strong>of</strong> transcription … 2<br />
2. Understanding the <strong>evolution</strong> <strong>of</strong> non-coding DNA … 7<br />
3. <strong>Molecular</strong> mechanisms <strong>of</strong> <strong>transcriptional</strong> <strong>evolution</strong> … 9<br />
Part II – Identifying sequences controlling gene expression using data from a single<br />
species<br />
4. Searching for statistically enriched sequences … 15<br />
5. Methods that associate sequences with other data ... 23<br />
6. Improved methods <strong>of</strong> regulatory regions … 30<br />
7. Future challenges ... 35<br />
Part III – Including molecular <strong>evolution</strong> in the search for transcription factor binding sites<br />
8. Characterizing the constraints on transcription factor binding sites … 40<br />
9. Phylogenetic motif finding <strong>by</strong> expectation maximization on <strong>evolution</strong>ary mixtures<br />
… 67<br />
10. MONKEY: extending the probabilistic model to identify binding sites <strong>of</strong> factors<br />
with known specificity … 82<br />
11. Application to alignments <strong>of</strong> multiple primates and human-fugu conserved<br />
elements … 120<br />
12. Futures challenges … 131<br />
Part IV – Evolution <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong><br />
13. Evidence for <strong>evolution</strong> <strong>of</strong> transcription networks on a genome-wide scale … 132<br />
14. Evidence for losses and gains <strong>of</strong> functional binding sites … 176<br />
15. Future directions … 201<br />
Part V – Conclusions<br />
16. Insight from quantitative <strong>evolution</strong>ary analyses … 208<br />
ii
List <strong>of</strong> Figures<br />
4-1 Enrichment <strong>of</strong> predicted zeste binding sites in bound fragments in a Chip Chip<br />
experiment … 15<br />
4-2 Representing families <strong>of</strong> binding sites with probabilistic models … 21<br />
5-1 Z scores over the cell cycle … 23<br />
5-2 P-values from various statistical tests in the cell cycle dataset … 25<br />
5-3 Optimizing a matrix based on the KS-statistic … 26<br />
5-4 Clustering <strong>of</strong> 7-mers significantly associated with gene categories … 28<br />
6-1 Positional biases in binding sites associated with regulated genes … 30<br />
6-2 Upstream regions <strong>of</strong> the alpha cell type and other pheromone responsive genes … 33<br />
8-1 Characterized binding sites evolve more slowly than the promoters in which they are<br />
found … 44<br />
8-2 Comparison <strong>of</strong> rates <strong>of</strong> <strong>evolution</strong> to structures <strong>of</strong> protein-DNA complexes implies a<br />
model for the variation in the rate <strong>of</strong> <strong>evolution</strong> across binding motifs … 46<br />
8-3 Association between information pr<strong>of</strong>ile and rate <strong>of</strong> <strong>evolution</strong> in characterized<br />
binding sites from SCPD … 48<br />
8-4 Test <strong>of</strong> the Halpern-Bruno proportionality … 50<br />
8-5 Information and rate <strong>of</strong> <strong>evolution</strong> for the recently reported Crz1p motif … 53<br />
9-1 Effect <strong>of</strong> models for motif <strong>evolution</strong> on motif detection … 75<br />
9-2 Effect <strong>of</strong> <strong>evolution</strong>ary distance on motif detection … 77<br />
10-1 Accuracy <strong>of</strong> p-value estimations … 91<br />
10-2 Significance <strong>of</strong> matches increases with <strong>evolution</strong>ary distance … 96<br />
10-3 Significance <strong>of</strong> binding sites in pairwise or three-way comparisons at similar<br />
<strong>evolution</strong>ary distance … 99<br />
10-4 Relationship between conserved Rpn4p-binding sites and expression … 101<br />
iii
10-5 Some apparently functional Rpn4p-binding sites are not conserved … 103<br />
10-6 The <strong>evolution</strong>ary distance required to confidently identify conserved binding sites<br />
varies among transcription factors … 107<br />
10-7 Many characterized binding sites do not seem to be evolving under constraint …<br />
111<br />
10-8 Applying MONKEY to alignments <strong>of</strong> Drosophila … 111<br />
11-1 Sequence logo representation <strong>of</strong> the LXRE … 121<br />
11-2 Closely spaced, highly conserved matches suggest the presence <strong>of</strong> a downstream<br />
CYP7A enhancer … 123<br />
11-3 Discovery <strong>of</strong> a enriched, conserved homodomain-like motif using EMnEM … 124<br />
11-4 Identification <strong>of</strong> motifs specifically associated with enhancers that showed forebrain<br />
expression … 127<br />
13-1 Fungal Phylogeny … 134<br />
13-2 Conservation <strong>of</strong> Cis-Sequence Enrichment in Specific Gene Groups … 140<br />
13-3 Enrichment <strong>of</strong> Novel Sequences in Coregulated Genes from Other Species … 143<br />
13-4 Distribution <strong>of</strong> Cis-Regulatory Elements Upstream <strong>of</strong> Coregulated Genes … 146<br />
13-5 Spatial Relationships between Cis-Regulatory Elements … 148<br />
13-6 Position-Weight Matrices Representing Proteasome Cis-Regulatory Element … 150<br />
13-7 Sequence Alignment <strong>of</strong> the DNA-Binding Domain <strong>of</strong> Rpn4p and Its Orthologs …<br />
158<br />
14-1 The CYP7A1 LXRE in alignment <strong>of</strong> primates … 178<br />
14-2 LXRa PPRE sites in an alignment <strong>of</strong> primates and mouse … 179<br />
14-3 P-values for the T-statistic to detect binding site <strong>evolution</strong> … 183<br />
14-4 Non-conserved binding sites in the Kr 730 enhancer … 184<br />
14-5 185 A misaligned bcd site in the eve stripe 2 enhancer … 185<br />
iv
14-6 Enrichment <strong>of</strong> conserved and non-conserved binding sites in the regions bound <strong>by</strong><br />
zeste … 190<br />
14-7 Evolutionary scenarios consistent with the observation <strong>of</strong> a non-conserved binding<br />
in D. melanogaster … 193<br />
14-8 Enrichment <strong>of</strong> bindng sites classified <strong>by</strong> <strong>evolution</strong>ary scenario … 196<br />
14-9 Evidence for purifying selection on zeste binding sites … 197<br />
15-1 Modeling changing <strong>evolution</strong>ary constraints, such as binding site gain or loss … 202<br />
15-2 Probabilistic <strong>evolution</strong>ary model for binding sites in un-alignable promoters … 205<br />
v
List <strong>of</strong> Tables<br />
4-1 Running a discrete motif finder on the zeste data …19<br />
8-1 Correlation between information content and substitutions per site for the<br />
experimentally characterized binding sites in the SCPD database … 45<br />
8-2 Evolution <strong>of</strong> motifs with known consensus, but binding sites identified <strong>by</strong> MEME …<br />
51<br />
8-3 Motifs identified <strong>by</strong> MEME that do not correspond to the expected consensus<br />
sequences for the transcription factors thought to be regulating the cluster … 53<br />
9-1 Motif discovery using EMnEM and MEME … 79<br />
10-1 Definition <strong>of</strong> positive and negative sets <strong>of</strong> matrix matches … 94<br />
10-2 Performance <strong>of</strong> different scores in recognizing functional and nonfunctional sites …<br />
95<br />
13-1 Orthologues assigned to S. cerervisiage genes … 163<br />
vi
Acknowledgements<br />
I gratefully acknowledge the Biophysics Graduate Group for financial and administrative<br />
support, particularly Shizuka Gannon, Diane Sigman, Bob Glaeser and Ehud Isac<strong>of</strong>f. I<br />
received much scientific and academic advice from many people at Berkeley including<br />
Adam Arkin, Jasper Rine, Mark Van der Laan, Terry Speed, Mike Levine and Dan<br />
Rokhsar, and especially Casey Bergman and Paul Spellman. I was lucky to be inspired<br />
and challenged in discussions with Hunter Fraser and Justin Fay and in attending a course<br />
<strong>by</strong> Michael Jordan. I have also been extremely fortunate to find myself in the midst <strong>of</strong> a<br />
scientific environment where colleagues have been excited to collaborate and share<br />
resources and data. Particularly generous to me have been Nobuo Ogawa, Jeff Wang,<br />
Len Pennacchio and Eddy Rubin.<br />
Most important to the work presented here were my main collaborators in Mike <strong>Eisen</strong>’s<br />
<strong>Lab</strong>, Derek Chiang, Dan Pollard, Audrey Gasch, and <strong>of</strong> course Mike himself; I here<strong>by</strong><br />
acknowledge their guidance, insight, patience and kindness.<br />
Finally, I thank my parents, sisters, grandmother, friends, and Tom, Rima and Miranda<br />
for their support and love.<br />
vii
Part I<br />
Introduction<br />
1
1. Regulation <strong>of</strong> gene expression at the level <strong>of</strong> transcription<br />
T<br />
HE DISCOVERY OF DNA as the genetic material (Hershey and Chase<br />
1952) together with the mechanistic understanding <strong>of</strong> how the information<br />
encoded in DNA sequences is converted into protein sequences that carry out<br />
biological function (Crick et al. 1961) raises a fundamental question: How is the<br />
expression <strong>of</strong> the genetic material regulated (Orphanides and Reinberg 2002)?<br />
One <strong>of</strong> the major mechanisms for gene <strong>regulation</strong> is <strong>regulation</strong> at the level <strong>of</strong><br />
transcription (Monod and Jacob 1961). The best understood mechanism for <strong>regulation</strong> at<br />
the level <strong>of</strong> transcription is the action <strong>of</strong> sequence specific DNA binding proteins, which<br />
modulate the rate <strong>of</strong> transcript initiation through a variety <strong>of</strong> mechanisms (Gill 2001,<br />
Emerson 2002). These proteins recognize degenerate families <strong>of</strong> short sequences,<br />
(Stormo 2000) and in eukaryotes they <strong>of</strong>ten act 'combinatorially', such that the effects <strong>of</strong><br />
multiple factors are required to specify a given expression pattern (Wolberger 1999).<br />
Because the DNA binding proteins can themselves be regulated at the level <strong>of</strong><br />
transcription, they can be assembled into 'networks' that can transmit information through<br />
cascades or can set up arbitrarily complex patterns. The activity <strong>of</strong> transcription factors<br />
may also be regulated post-translationally, and they are <strong>of</strong>ten the effectors <strong>of</strong> classic<br />
signaling pathways. Because differences in gene expression programs are a major source<br />
<strong>of</strong> cellular diversity, it is perhaps not surprising (in retrospect) that many key molecules<br />
<strong>of</strong> developmental, <strong>evolution</strong>ary, and medical interest have turned out to be transcription<br />
factors.<br />
Regulation <strong>by</strong> sequence specific transcription factors is a major component <strong>of</strong><br />
2
many fundamental biological processes including:<br />
● Networks that produce autonomous cellular behaviors (Ptasne 1992), such as the<br />
eukaryotic cell-cycle (Koch and Nasmyth 1994)<br />
● Response to cellular environment (Gasch et al. 2000), such as response to oxysterols <strong>by</strong><br />
LXR in mammals (Janowski et al. 1996)<br />
● Cell-type differentiation, such as a/alpha determination in yeast (Johnson 1995)<br />
● Developmental patterning (St Johnston and Nusslein-Volhard 1992), such as the<br />
gap/pair-rule system in Drosophila (Akam 1987)<br />
● Evolutionary changes in morphology (Levine and Tjian 2003), such as changes in Ubx<br />
expression associated with appendage diversification in crustaceans (Aver<strong>of</strong> and Patel<br />
1997)<br />
The availability <strong>of</strong> complete genome sequences has greatly impacted the study <strong>of</strong><br />
<strong>transcriptional</strong> <strong>regulation</strong>. Technological innovations have allowed the measurement <strong>of</strong><br />
gene expression (De Risi et al. 1997) and in vivo binding <strong>of</strong> DNA binding proteins (Leib<br />
et al. 2001) on a genome wide scale. Although these techniques have been applied<br />
mostly to yeast, systematic measurements <strong>of</strong> this kind are rapidly becoming available for<br />
many organisms (Arbeitman et al. 2002, Odom et al. 2004).<br />
The purpose <strong>of</strong> this dissertation is tw<strong>of</strong>old: first, to examine in detail the <strong>evolution</strong>ary<br />
properties <strong>of</strong> transcription factor binding sites in relation to the development <strong>of</strong> new<br />
methods and tools for their identification and annotation, and second, to examine the role<br />
<strong>of</strong> the <strong>evolution</strong> <strong>of</strong> binding sites as a mechanism underlying the <strong>evolution</strong> <strong>of</strong> gene<br />
expression.<br />
3
Transcription factor binding sites in non-coding DNA<br />
Understanding how <strong>transcriptional</strong> <strong>regulation</strong> is encoded in the genome is a major<br />
challenge. Transcriptional regulatory regions are found in non-coding DNA near (linked<br />
in cis) the genes they affect. However, because the non-coding regions <strong>of</strong> eukaryotic<br />
genomes are typically very large and the binding sites for transcription factors are small<br />
and degenerate, (Stormo 2000) most <strong>of</strong> the sequences that seem to match the specificity<br />
<strong>of</strong> the binding proteins are not actually bound <strong>by</strong> the protein in vivo (Lieb et. al. 2001).<br />
In eukaryotes, binding sites are <strong>of</strong>ten organized into modular groups called enhancers,<br />
which typically contain ~10 binding sites for ~3 different factors and specify a particular<br />
portion <strong>of</strong> a gene’s expression pattern. Developing approaches to identify transcription<br />
factor binding sites is a major area <strong>of</strong> research in computational biology, and many<br />
approaches have been developed (Tompa et al. 2005). In addition, many computational<br />
approaches have been developed to identify conserved regions in non-coding DNA<br />
(Berezikov et al. 2004, Loots et al. 2004, Bigelow et al. 2004, Loots et al. 2002, Lenhard<br />
et al. 2003, Sandelin et al. 2004, Mrowka et al. 2003), in the hope that these will point to<br />
regulatory elements. A detailed understanding <strong>of</strong> the constraints under which<br />
transcription factor binding sites evolve will allow development <strong>of</strong> more accurate models<br />
and more effective computational methods, there<strong>by</strong> improving our ability to identify<br />
these regulatory regions.<br />
References<br />
Akam M. The molecular basis for metameric pattern in the Drosophila embryo. Development. 1987<br />
Sep;101(1):1-22.<br />
4
Arbeitman MN, Furlong EE, Imam F, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW,<br />
White KP. Gene expression during the life cycle <strong>of</strong> Drosophila melanogaster. Science. 2002 Sep<br />
27;297(5590):2270-5. Erratum in: Science 2002 Nov 8;298(5596):1172.<br />
Aver<strong>of</strong> M, Patel NH. Crustacean appendage <strong>evolution</strong> associated with changes in Hox gene expression.<br />
Nature. 1997 Aug 14;388(6643):682-6.<br />
Berezikov E, Guryev V, Plasterk RH, Cuppen E: CONREAL: conserved regulatory elements anchored<br />
alignment algorithm for identification <strong>of</strong> transcription factor binding sites <strong>by</strong> phylogenetic footprinting.<br />
Genome Res 2004, 14:170-178.<br />
Bigelow HR, Wenick AS, Wong A, Hobert O: CisOrtho: a program pipeline for genome-wide<br />
identification <strong>of</strong> transcription factor target genes using phylogenetic footprinting. BMC Bioinformatics<br />
2004, 5:27.<br />
Crick FHC, Barnett L, Brenner S, and Watts-Tobin RJ (1961) General nature <strong>of</strong> the genetic code for<br />
proteins. Nature 192:1227-1232<br />
DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control <strong>of</strong> gene expression on a<br />
genomic scale. Science. 1997 Oct 24;278(5338):680-6.<br />
Emerson BM. Specificity <strong>of</strong> gene <strong>regulation</strong>. Cell. 2002 May 3;109(3):267-70.<br />
F. Jacob and J. Monod. (1961). Genetic regulatory mechanisms in the synthesis <strong>of</strong> proteins J. Mol. Biol. 3:<br />
318-356.<br />
Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, <strong>Eisen</strong> MB, Storz G, Botstein D, Brown PO.Genomic<br />
expression programs in the response <strong>of</strong> yeast cells to environmental changes. Mol Biol Cell. 2000<br />
Dec;11(12):4241-57<br />
Gill G. Regulation <strong>of</strong> the initiation <strong>of</strong> eukaryotic transcription. Essays Biochem. 2001;37:33-43.<br />
Hershey, A.D. & Chase, M. (1952) Independent functions <strong>of</strong> viral protein and dna nucleic acid in growth <strong>of</strong><br />
bacteriophage. Journal <strong>of</strong> General Physiology 36:39-56<br />
Janowski BA, Willy PJ, Devi TR, Falck JR, Mangelsdorf DJ. An oxysterol signalling pathway mediated <strong>by</strong><br />
the nuclear receptor LXR alpha. Nature. 1996 Oct 24;383(6602):728-31.<br />
Johnson AD. <strong>Molecular</strong> mechanisms <strong>of</strong> cell-type determination in budding yeast. Curr Opin Genet Dev.<br />
1995 Oct;5(5):552-8<br />
Koch, C. and Nasmyth, K., 1994. Cell cycle regulated transcription in yeast. Curr. Opin. Cell Biol. 6, pp.<br />
451-459.<br />
Lenhard B, Sandelin A, Mendoza L, Engstrom P, Jareborg N, Wasserman WW: Identification <strong>of</strong> conserved<br />
regulatory elements <strong>by</strong> comparative genome analysis. J Biol 2003, 2:13.<br />
Levine M, Tjian R. Transcription <strong>regulation</strong> and animal diversity. Nature. 2003 Jul 10;424(6945):147-51.<br />
Lieb JD, Liu X, Botstein D, Brown PO. Promoter-specific binding <strong>of</strong> Rap1 revealed <strong>by</strong> genome-wide maps<br />
<strong>of</strong> protein-DNA association. Nat Genet. 2001 Aug;28(4):327-34. Erratum in: Nat Genet 2001<br />
Sep;29(1):100.<br />
Loots GG, Ovcharenko I, Pachter L, Dubchak I, Rubin EM: rVista for comparative sequence-based<br />
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discovery <strong>of</strong> functional transcription factor binding sites. Genome Res 2002, 12:832-839.<br />
Loots GG, Ovcharenko I: rVISTA 2.0: <strong>evolution</strong>ary analysis <strong>of</strong> transcription factor binding sites. Nucleic<br />
Acids Res 2004, 32(Web Server):W217-W221.<br />
Mrowka R, Steinhage K, Patzak A, Persson PB: An <strong>evolution</strong>ary approach for identifying potential<br />
transcription factor binding sites: the renin gene as an example. Am J Physiol Regul Integr Comp Physiol<br />
2003, 284:R1147-R1150.<br />
Odom DT, Zizlsperger N, Gordon DB, Bell GW, Rinaldi NJ, Murray HL, Volkert TL, Schreiber J, Rolfe<br />
PA, Gifford DK, Fraenkel E, Bell GI, Young RA Control <strong>of</strong> pancreas and liver gene expression <strong>by</strong> HNF<br />
transcription factors. Science. 2004 Feb 27;303(5662):1378-81.<br />
Orphanides G, Reinberg D. A unified theory <strong>of</strong> gene expression. Cell. 2002 Feb 22;108(4):439-51.<br />
Ptashne M (1992) A Genetic Switch, 2nd edn. Cambridge, MA: Cell Press and Blackwell Press.<br />
Sandelin A, Wasserman WW, Lenhard B: ConSite: web-based prediction <strong>of</strong> regulatory elements using<br />
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St Johnston D, Nusslein-Volhard C. The origin <strong>of</strong> pattern and polarity in the Drosophila embryo. Cell.<br />
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Stormo GD. DNA binding sites: representation and discovery. Bioinformatics. 2000 Jan;16(1):16-23.<br />
Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ,<br />
Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Regnier M, Simonis N, Sinha S, Thijs G, van<br />
Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z. Assessing computational tools for the<br />
discovery <strong>of</strong> transcription factor binding sites. Nat Biotechnol. 2005 Jan;23(1):137-44.<br />
Wolberger C: Multiprotein-DNA complexes in <strong>transcriptional</strong> <strong>regulation</strong>. Annu Rev Biophys Biomol<br />
Struct 1999, 28:29-56.<br />
6
2. Understanding the <strong>evolution</strong> <strong>of</strong> non-coding DNA<br />
An important reason for studying the <strong>evolution</strong> <strong>of</strong> transcription factor binding sites is to<br />
fill in the picture <strong>of</strong> the <strong>evolution</strong>ary constraints on non-coding DNA, about which<br />
relatively little is known. Historically, non-coding DNA has been assumed to be under<br />
much less functional constraint than coding sequences, as it was believed to be largely<br />
‘junk’ (Ohno 1972). While it is clear that transposons and other simple repetitive<br />
elements comprise a large portion <strong>of</strong> non-coding DNA, it is unclear what portion <strong>of</strong> the<br />
functional non-coding DNA is involved in the <strong>regulation</strong> <strong>of</strong> transcription. If cis-<br />
regulatory DNA makes up a large portion <strong>of</strong> non-coding DNA (Cameron et al. 2004),<br />
understanding the <strong>evolution</strong> <strong>of</strong> regulatory DNA will be critical in any understanding <strong>of</strong><br />
<strong>evolution</strong> at the molecular level. Recently, non-coding DNA from closely related<br />
organisms has become increasingly available (e.g., Thomas et al. 2003), and<br />
understanding the patterns <strong>of</strong> <strong>evolution</strong> in non-coding regions is an important challenge.<br />
Genome-wide screens for highly conserved elements have revealed non-coding elements<br />
showing extreme conservation (Bejerano et al. 2004, Dermitzakis et al. 2004), a large<br />
number <strong>of</strong> which may drive gene expression patterns (Wolfe et. al 2005); their extreme<br />
conservation may reflect the presence <strong>of</strong> multiple overlapping functional constraints or<br />
indicate that they represent a class <strong>of</strong> as <strong>of</strong> yet undiscovered non-coding elements. A<br />
better understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> transcription factor binding sites may help to<br />
clarify their relative contribution to these observations or to suggest new hypotheses.<br />
References<br />
Bejerano G, Pheasant M, Makunin I, Stephen S, Kent WJ, Mattick JS, Haussler D. Ultraconserved<br />
7
elements in the human genome. Science. 2004 May 28;304(5675):1321-5. Epub 2004 May 6.<br />
Cameron RA, Oliveri P, Wyllie J, Davidson EH. cis-Regulatory activity <strong>of</strong> randomly chosen genomic<br />
fragments from the sea urchin. Gene Expr Patterns. 2004 Mar;4(2):205-13.<br />
Dermitzakis ET, Reymond A, Scamuffa N, Ucla C, Kirkness E, Rossier C, Antonarakis SE. Evolutionary<br />
discrimination <strong>of</strong> mammalian conserved non-genic sequences (CNGs). Science. 2003 Nov<br />
7;302(5647):1033-5.<br />
Hardison RC. Conserved noncoding sequences are reliable guides to regulatory elements. Trends Genet.<br />
2000 Sep;16(9):369-72.<br />
Hunt C, Morimoto RI. Conserved features <strong>of</strong> eukaryotic hsp70 genes revealed <strong>by</strong> comparison with the<br />
nucleotide sequence <strong>of</strong> human hsp70. Proc Natl Acad Sci U S A. 1985 Oct;82(19):6455-9.<br />
Ohno, S. (1972). So much "junk" DNA in our genome. In: Evolution <strong>of</strong> Genetic Systems, edited <strong>by</strong> H.H.<br />
Smith. Gordon and Breach, New York, pp.366-370.<br />
Thomas JW, Touchman JW, Blakesley RW, Bouffard GG, Beckstrom-Sternberg SM, Margulies EH,<br />
Blanchette M, Siepel AC, Thomas PJ, McDowell JC, Maskeri B, Hansen NF, Schwartz MS, Weber RJ,<br />
Kent WJ, Karolchik D, Bruen TC, Bevan R, Cutler DJ, Schwartz S, Elnitski L, Idol JR, Prasad AB, Lee-<br />
Lin SQ, Maduro VV, Summers TJ, Portnoy ME, Dietrich NL, Akhter N, Ayele K, Benjamin B, Cariaga K,<br />
Brinkley CP, Brooks SY, Granite S, Guan X, Gupta J, Haghighi P, Ho SL, Huang MC, Karlins E, Laric PL,<br />
Legaspi R, Lim MJ, Maduro QL, Masiello CA, Mastrian SD, McCloskey JC, Pearson R, Stantripop S,<br />
Tiongson EE, Tran JT, Tsurgeon C, Vogt JL, Walker MA, Wether<strong>by</strong> KD, Wiggins LS, Young AC, Zhang<br />
LH, Osoegawa K, Zhu B, Zhao B, Shu CL, De Jong PJ, Lawrence CE, Smit AF, Chakravarti A, Haussler<br />
D, Green P, Miller W, Green ED. Comparative analyses <strong>of</strong> multi-species sequences from targeted genomic<br />
regions. Nature. 2003 Aug 14;424(6950):788-93.<br />
Woolfe A, Goodson M, Goode DK, Snell P, McEwen GK, Vavouri T, Smith SF, North P, Callaway H,<br />
Kelly K, Walter K, Abnizova I, Gilks W, Edwards YJ, Cooke JE, Elgar G. Highly conserved non-coding<br />
sequences are associated with vertebrate development. PLoS Biol. 2005 Jan;3(1):e7.<br />
8
3. <strong>Molecular</strong> mechanisms <strong>of</strong> <strong>transcriptional</strong> <strong>evolution</strong><br />
Although the importance <strong>of</strong> regulatory <strong>evolution</strong> has long been recognized (Wilson<br />
1976), recent elucidation <strong>of</strong> the role <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> in development has<br />
suggested a specific picture <strong>of</strong> how <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> can generate<br />
<strong>evolution</strong>ary diversity <strong>of</strong> morphology (Levine and Tjian, 2003). Differences in<br />
<strong>regulation</strong> <strong>of</strong> transcription can occur both in cis or trans, but most attention has been<br />
focused on changes in cis-regulatory sequences. Less is known about changes in trans,<br />
and they are <strong>of</strong>ten presumed to be much less common (and therefore less important)<br />
because they are more likely to be pleiotropic (Wray et al. 2003). Systematic studies <strong>of</strong><br />
the genetic variation in gene expression within or between closely related species (Brem<br />
et al. 2002, Wittkopp et al. 2004) support the idea that there is abundant genetic variation<br />
in gene expression, both in cis and trans. The few cases where regulatory changes have<br />
been characterized in detail suggest that <strong>evolution</strong>ary changes in gene expression <strong>of</strong>ten<br />
have both cis and trans components (Gompel et al. 2005, Ludwig et al. 2005), with the<br />
important caveat that a cis-regulatory change for one gene may represent a trans change<br />
for a gene downstream. Ultimately, it seems likely that an understanding <strong>of</strong> both cis and<br />
trans changes will be critical if we are to obtain a mechanistic picture <strong>of</strong> the <strong>evolution</strong> <strong>of</strong><br />
gene <strong>regulation</strong> at the <strong>transcriptional</strong> level.<br />
Evolution <strong>of</strong> cis-regulatory elements<br />
What is certainly clear is that cis-regulatory changes are much easier to study; while trans<br />
changes may occur at the loci <strong>of</strong> any number <strong>of</strong> regulatory proteins or in any protein that<br />
9
lies upstream genetically, cis-regulatory changes are confined to the locus <strong>of</strong> the gene<br />
whose expression has evolved. Further, because transcription factor binding sites may<br />
represent a major part <strong>of</strong> the cis-regulatory DNA for many well-studied genes, they are<br />
an appropriate class <strong>of</strong> regulatory sequences on which to focus <strong>evolution</strong>ary studies.<br />
Analysis <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> the expression <strong>of</strong> particular genes has yielded several<br />
interesting scenarios whose relative contributions to the <strong>evolution</strong> <strong>of</strong> gene expression<br />
remain unknown. For example, it has long been observed that regulatory elements may<br />
be conserved over long <strong>evolution</strong>ary distances (e.g., Hunt and Morimoto 1985) and that<br />
conserved non-coding sequences <strong>of</strong>ten turn out to function as regulatory sequences<br />
(Hardison 2000). On the other hand, landmark work has showed that lack <strong>of</strong> sequence<br />
conservation cannot be interpreted as change in function (Ludwig et al. 2000), as<br />
compensatory changes can apparently restore function and, conversely, that reasonably<br />
good sequence conservation can be associated with functional differences (Ludwig et al.<br />
2005). Finally, cases <strong>of</strong> cis-regulatory <strong>evolution</strong> over short <strong>evolution</strong>ary distances that<br />
lead to substantial functional differences have also been noted (e.g., Fang and Brennan<br />
1992), and simulations have suggested that transcription factor binding sites may emerge<br />
from random sequence <strong>by</strong> genetic drift on relatively short timescales (Stone and Wray<br />
2001).<br />
The availability <strong>of</strong> genome sequences that can be compared at the level <strong>of</strong> non-<br />
coding DNA (Kellis et al 2003, Cliften et al 2003, Richards et al 2005) allows<br />
interrogation <strong>of</strong> these issues systematically, on a genomic scale. Formulation <strong>of</strong> explicit<br />
models for these processes will allow tests for selection and will differentiate between<br />
<strong>evolution</strong>ary hypotheses.<br />
10
Evolution <strong>of</strong> transcription networks<br />
Understanding the <strong>evolution</strong> <strong>of</strong> individual binding sites, or even the <strong>evolution</strong> <strong>of</strong> the<br />
expression <strong>of</strong> individual genes, is not sufficient to understand the contribution <strong>of</strong><br />
<strong>transcriptional</strong> <strong>regulation</strong> to <strong>evolution</strong>ary diversity. Multiple transcription factors and<br />
their targets are <strong>of</strong>ten linked in complex networks that ultimately produce biological<br />
function and pattern. It is therefore important to consider the <strong>evolution</strong> <strong>of</strong> regulatory<br />
systems, rather than single genes. Because <strong>transcriptional</strong> <strong>regulation</strong> is multifactorial, its<br />
<strong>evolution</strong> may be complex, and multiple components may evolve simultaneously.<br />
Much <strong>of</strong> the thinking about the <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> regulatory networks<br />
has been from the following perspective (Schneider 2000, Sengupta et al. 2002, Gerland<br />
and Hwa 2002, Berg et al. 2004): the cell wants to express some set <strong>of</strong> genes at a<br />
particular time and, therefore, designs a DNA binding protein that binds to their<br />
promoters and to a minimum number <strong>of</strong> other promoters. Given assumptions about the<br />
relationship between protein-DNA binding energy to expression levels, expression levels<br />
to selection, and selection to constraints on DNA sequences, these models attempt to<br />
address the emergence <strong>of</strong> binding sites under selection or the variability in sequence<br />
specificity or in binding energy <strong>of</strong> transcription factors. While these models are<br />
conceptually interesting, there is little data to test the predictions they make and even less<br />
to support the assumptions on which they are based.<br />
On the other hand, due to advances in concepts and techniques as well as accurate<br />
quantitative data on which to base these models, modeling <strong>of</strong> regulatory networks in<br />
single species has advanced rapidly in recent years (Wolfe and Arkin 2003). This has<br />
11
allowed the first comparative studies <strong>of</strong> detailed models <strong>of</strong> regulatory networks (Rao et<br />
al. 2004). Genome-wide functional measurements and sequence data from closely<br />
related species, combined with an understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong><br />
regulatory sequences, will allow us to characterize patterns <strong>of</strong> <strong>evolution</strong> <strong>of</strong> regulatory<br />
networks.<br />
References<br />
Berg J, Willmann S, Lassig M. Adaptive <strong>evolution</strong> <strong>of</strong> transcription factor binding sites. BMC Evol Biol.<br />
2004 Oct 28;4(1):42.<br />
Brem RB, Yvert G, Clinton R, Kruglyak L. Genetic dissection <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> in budding<br />
yeast. Science. 2002 Apr 26;296(5568):752-5.<br />
Cliften P, Sudarsanam P, Desikan A, Fulton L, Fulton B, Majors J, Waterston R, Cohen BA, Johnston M.<br />
Finding functional features in Saccharomyces genomes <strong>by</strong> phylogenetic footprinting. Science. 2003 Jul<br />
4;301(5629):71-6. Epub 2003 May 29.<br />
Fang XM, Brennan MD. Multiple cis-acting sequences contribute to evolved regulatory variation for<br />
Drosophila Adh genes. Genetics. 1992 Jun;131(2):333-43.<br />
Gerland U, Hwa T. On the selection and <strong>evolution</strong> <strong>of</strong> regulatory DNA motifs. J Mol Evol. 2002<br />
Oct;55(4):386-400.<br />
Gompel N, Prud'homme B, Wittkopp PJ, Kassner VA, Carroll SB. Chance caught on the wing: cisregulatory<br />
<strong>evolution</strong> and the origin <strong>of</strong> pigment patterns in Drosophila. Nature. 2005 Feb 3;433(7025):481-<br />
7.<br />
Hardison RC. Conserved noncoding sequences are reliable guides to regulatory elements. Trends Genet.<br />
2000 Sep;16(9):369-72.<br />
Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES. Sequencing and comparison <strong>of</strong> yeast species to<br />
identify genes and regulatory elements. Nature. 2003 May 15;423(6937):241-54.<br />
Levine M, Tjian R. Transcription <strong>regulation</strong> and animal diversity. Nature. 2003 Jul 10;424(6945):147-51.<br />
Ludwig MZ, Bergman C, Patel NH, Kreitman M.Evidence for stabilizing selection in a eukaryotic<br />
enhancer element. Nature. 2000 Feb 3;403(6769):564-7.<br />
Ludwig MZ, Palsson A, Alekseeva E, Bergman CM, Nathan J, Kreitman M. Functional Evolution <strong>of</strong> a cis-<br />
Regulatory Module. PLoS Biol. 2005 Mar 15;3(4):e93<br />
Rao CV, Kir<strong>by</strong> JR, Arkin AP. Design and diversity in bacterial chemotaxis: a comparative study in<br />
Escherichia coli and Bacillus subtilis. PLoS Biol. 2004 Feb;2(2):E49.<br />
Richards S, Liu Y, Bettencourt BR, Hradecky P, Letovsky S, Nielsen R, Thornton K, Hubisz MJ, Chen R,<br />
Meisel RP, Couronne O, Hua S, Smith MA, Zhang P, Liu J, Bussemaker HJ, van Batenburg MF, Howells<br />
12
SL, Scherer SE, Sodergren E, Matthews BB, Cros<strong>by</strong> MA, Schroeder AJ, Ortiz-Barrientos D, Rives CM,<br />
Metzker ML, Muzny DM, Scott G, Steffen D, Wheeler DA, Worley KC, Havlak P, Durbin KJ, Egan A,<br />
Gill R, Hume J, Morgan MB, Miner G, Hamilton C, Huang Y, Waldron L, Verduzco D, Clerc-Blankenburg<br />
KP, Dubchak I, Noor MA, Anderson W, White KP, Clark AG, Schaeffer SW, Gelbart W, Weinstock GM,<br />
Gibbs RA. Comparative genome sequencing <strong>of</strong> Drosophila pseudoobscura: chromosomal, gene, and ciselement<br />
<strong>evolution</strong>. Genome Res. 2005 Jan;15(1):1-18.<br />
Schneider TD. Evolution <strong>of</strong> biological information. Nucleic Acids Res. 2000 Jul 15;28(14):2794-9.<br />
Sengupta AM, Djordjevic M, Shraiman BI. Specificity and robustness in transcription control networks.<br />
Proc Natl Acad Sci U S A. 2002 Feb 19;99(4):2072-7.<br />
Stone JR, Wray GA. Rapid <strong>evolution</strong> <strong>of</strong> cis-regulatory sequences via local point mutations. Mol Biol Evol.<br />
2001 Sep;18(9):1764-70.<br />
Wilson, A. C., 1976 Gene <strong>regulation</strong> and <strong>evolution</strong>, pp. 225-234 in <strong>Molecular</strong> Evolution, edited <strong>by</strong> F. J.<br />
Ayala. Sinauer Associates, Sunderland, Mass.<br />
Wittkopp PJ, Haerum BK, Clark AG. Evolutionary changes in cis and trans gene <strong>regulation</strong>. Nature. 2004<br />
Jul 1;430(6995):85-8.<br />
Wolf DM, Arkin AP. Motifs, modules and games in bacteria. Curr Opin Microbiol. 2003 Apr;6(2):125-34.<br />
Wray GA, Hahn MW, Abouheif E, Balh<strong>of</strong>f JP, Pizer M, Rockman MV, Romano LA. The <strong>evolution</strong> <strong>of</strong><br />
f<strong>transcriptional</strong> <strong>regulation</strong> in eukaryotes. Mol Biol Evol. 2003 Sep;20(9):1377-419. Epub 2003 May 30.<br />
13
Part III<br />
Including molecular <strong>evolution</strong> in the search for transcription factor binding sites<br />
39
8. Characterizing the constraints on transcription factor binding sites<br />
There are now closely related genomes available for nearly all experimental organisms. If<br />
the non-coding regions <strong>of</strong> a group <strong>of</strong> organisms can be aligned, it is possible to infer the<br />
<strong>evolution</strong>ary history <strong>of</strong> the sequences, and incorporate this information into the search for<br />
functional non-coding elements.<br />
A widely used approach is to search for regions <strong>of</strong> non-coding DNA that show<br />
slower <strong>evolution</strong> than the average for the locus or species being compared. This can be<br />
done using an <strong>evolution</strong>ary model. However, methods <strong>of</strong> this type identify generic<br />
conserved regions that may be any <strong>of</strong> several types <strong>of</strong> conserved non-coding DNA. In<br />
order to identify the binding sites for transcription factors, either <strong>of</strong> known specificity or<br />
to learn the specificity, a detailed understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> binding sites seems<br />
necessary. We therefore examine the <strong>evolution</strong> <strong>of</strong> characterized transcription factor<br />
binding sites from S. cerevisiae. This work was published as Moses et al. 2003.<br />
Position specific variation in the rate <strong>of</strong> <strong>evolution</strong> in transcription factor binding sites<br />
Transcription factors recognize degenerate families <strong>of</strong> short sequences (5–25 base pairs).<br />
The binding specificities <strong>of</strong> transcription factors are typically represented as consensus<br />
sequences or position weight matrices [1] that summarize their position-specific sequence<br />
preferences. In some cases, such 'motif' models <strong>of</strong> transcription factor binding sites can<br />
be inferred from genome sequences using computational methods [2–7]. Despite the<br />
absence <strong>of</strong> a detailed understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> transcription factor binding sites,<br />
the comparison <strong>of</strong> sequences from related species has been used to identify transcription<br />
factor binding sites en masse, with the guiding hypothesis that functional regulatory<br />
40
sequences will be more conserved than the surrounding DNA. Several methods [8–12]<br />
have been developed to identify conserved non-coding sequences that, when tested, <strong>of</strong>ten<br />
function as regulatory sequences in vivo (reviewed in [13]).<br />
Here we characterize the <strong>evolution</strong> <strong>of</strong> known transcription factor binding sites<br />
using the complete genome sequences <strong>of</strong> the closely related budding yeasts<br />
Saccharomyces mikatae, S. bayanus, S. paradoxus [12] and S. cerevisiae [14]. We limit<br />
our focus to the conservation <strong>of</strong> binding sites due to purifying selection [15–18], though<br />
binding site turnover [15,16] (the loss and reappearance <strong>of</strong> binding sites) and other<br />
processes also occur. Preferential conservation <strong>of</strong> transcription factor binding sites has<br />
been observed previously in the genomes <strong>of</strong> organisms from bacteria [10,11] to mammals<br />
[16–18], and we expect the same to be true <strong>of</strong> yeast. In addition to the availability <strong>of</strong><br />
complete genome sequences, the budding yeasts are a particularly appealing system in<br />
which to test these hypotheses because <strong>of</strong> the relative wealth and easy accessibility <strong>of</strong><br />
biochemical and genetic information [e.g., [20]].<br />
Characterizing the pattern <strong>of</strong> <strong>evolution</strong> within transcription factor binding sites<br />
allows us to explore the nature <strong>of</strong> functional constraints on these sequences. As is well<br />
known for protein sequences [21–23], we expect the pattern <strong>of</strong> <strong>evolution</strong> in transcription<br />
factor binding sites to reflect the particular patterns <strong>of</strong> constraint under which they<br />
function; important regions or residues should be constrained, while unimportant<br />
positions may show fixed changes. Unlike protein sequences, where the relationship <strong>of</strong><br />
the amino acid sequence to the functional constraint is <strong>of</strong>ten difficult to discern, in the<br />
case <strong>of</strong> transcription factor binding sites, we suggest that the <strong>evolution</strong>ary constraints can<br />
be interpreted directly with respect to the physical constraints imposed <strong>by</strong> the DNA-<br />
41
inding protein. Protein-DNA interactions are <strong>of</strong> much interest (e.g., [24– 27]) and an<br />
understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> the binding motifs may provide insight into these<br />
interactions. In particular, it has recently been shown that there is a relationship between<br />
the pattern <strong>of</strong> degeneracy in certain binding motifs and regions <strong>of</strong> contact between the<br />
DNA and the binding protein: positions with fewer points <strong>of</strong> contact in the structures <strong>of</strong><br />
protein-DNA complexes show greater variability among binding sites within a single<br />
genome [28]. If these degenerate positions are less important for the formation <strong>of</strong> the<br />
protein-DNA complex, they might be expected to show less constrained <strong>evolution</strong>, as<br />
changes at these positions have a smaller effect on the relative fitness <strong>of</strong> the organism,<br />
and therefore may become fixed in the population <strong>by</strong> drift with greater probability.<br />
Conversely, changes at positions in the motif that disrupt the recognition <strong>of</strong> the binding<br />
site <strong>by</strong> the binding-protein are likely to be deleterious, and therefore removed from the<br />
population <strong>by</strong> purifying selection. This intuition leads to a theoretical prediction that the<br />
rate <strong>of</strong> <strong>evolution</strong> at each position is a function <strong>of</strong> the frequencies in the position weight<br />
matrix (analogous to the predictions for protein sequences found in [29]).<br />
Characterized binding sites show fewer substitutions than background DNA<br />
We first sought to verify that functional non-coding regions evolve more slowly than<br />
'background sequences.' To do so, we selected several transcription factors for which<br />
there were multiple experimentally validated binding sites in the S. cerevisiae genome<br />
listed in the Promoter database <strong>of</strong> Saccharomyces cerevisiae (SCPD[20]), and compared<br />
the rate <strong>of</strong> <strong>evolution</strong> within these binding sites to that <strong>of</strong> the promoter regions in which<br />
they were found. We measured the rate <strong>of</strong> <strong>evolution</strong> in substitutions (i.e., inferred<br />
42
nucleotide changes) per site, where 'site' refers to a single nucleotide position, not the<br />
multi-basepair 'binding sites' <strong>of</strong> transcription factors. We first looked at Gal4p, a very<br />
well studied Zn[2]Cys[6] binuclear cluster domain <strong>transcriptional</strong> activator [30]. The<br />
average rate <strong>of</strong> <strong>evolution</strong> within known Gal4p binding sites is 0.32 (+0.12, - 0.09, n =<br />
119) substitutions per site, substantially slower than the 0.75 (± 0.03, n = 2760)<br />
substitutions per site observed in the promoters in which these Gal4p binding sites are<br />
found (fig. 1A, 1B compare Gal4 'motif' and 'background.')<br />
43
Figure 1.<br />
Characterized binding sites evolve more slowly than the promoters in which they<br />
are found.<br />
A. Histogram <strong>of</strong> the rate <strong>of</strong> <strong>evolution</strong> (estimated <strong>by</strong> maximum parsimony) in characterized Gal4p binding<br />
sites and randomly chosen sequences <strong>of</strong> the same length (17 basepairs) from the same promoters. B.<br />
Differences in the mean rate <strong>of</strong> <strong>evolution</strong> in motifs and the mean rate in the promoters in which they are<br />
found. Grey boxes represent the average in binding sites; unfilled boxes represent the average over the<br />
promoters in which the motifs are found (see methods). Error bars represent exact 95 % confidence<br />
intervals for a Poisson distribution.<br />
To test the generality <strong>of</strong> this observation, we chose six other transcription factors<br />
representing different types <strong>of</strong> DNA-binding domains (see table 1) with relatively many<br />
characterized binding sites in the SCPD database. In each case there are significantly<br />
fewer substitutions (p < 0.05, 1000 bootstraps) in the characterized binding sites than in<br />
44
the promoters in which they lie (figure 1B), suggesting that, in general, characterized<br />
transcription factor binding sites evolve more slowly than the surrounding intergenic<br />
sequences. This is consistent with the hypothesis that these sequences are under<br />
functional constraint and their <strong>evolution</strong> reflects purifying selection.<br />
Table 1: Correlation between information content and substitutions per site for the<br />
experimentally characterized binding sites in the SCPD database.<br />
Functionally important positions are preferentially conserved<br />
In order to further explore the functional constraints on transcription factor binding site<br />
<strong>evolution</strong>, we computed the rate <strong>of</strong> <strong>evolution</strong> at each position within the motif and<br />
observed that the rate <strong>of</strong> <strong>evolution</strong> is not constant over the binding sites. Some positions<br />
in the motif show fewer substitutions than background, while others do not. For example,<br />
in the Gal4p binding sites positions 1, 2, 3, 15, 16, and 17 show fewer substitutions than<br />
do positions 4– 14 (fig. 2, right panel). Functionally important positions are expected to<br />
be under stronger purifying selection and therefore show stronger conservation. Indeed,<br />
the conserved positions in the Gal4p binding sites correspond to the points <strong>of</strong> contact in<br />
the crystal structure <strong>of</strong> the protein-DNA complex (fig. 2, right panel) that are required for<br />
the recognition <strong>of</strong> the target sequence [30].<br />
45
Figure 2.<br />
Comparison <strong>of</strong> rates <strong>of</strong> <strong>evolution</strong> to structures <strong>of</strong> protein-DNA complexes implies a<br />
model for the variation in the rate <strong>of</strong> <strong>evolution</strong> across binding motifs.<br />
The DNA backbone appears as a red helix; proteins appear as linked coloured cylinders. We propose that<br />
the formation <strong>of</strong> the protein-DNA complex is the functional constraint that leads to purifying selection, and<br />
therefore fewer substitutions at certain positions in the binding motif. Images <strong>of</strong> protein-DNA complex<br />
structures are from the Protein Data Bank [47]. Rate <strong>of</strong> <strong>evolution</strong> is in substitutions per site (estimated <strong>by</strong><br />
maximum parsimony) and error bars represent exact 95 % confidence intervals for a Poisson distribution.<br />
Another particularly interesting example is the case <strong>of</strong> Mcm1p. Although there is no<br />
specific base in the consensus at positions 8, 9 and 10, there is a strong A/T bias in the<br />
matrix at these positions and mutagenesis studies [31] <strong>of</strong> the binding site have suggested<br />
that this is needed to allow the high degree <strong>of</strong> bending known to be necessary for the<br />
formation <strong>of</strong> Mcm1p-DNA complex [32–34]. The relative paucity <strong>of</strong> substitutions at<br />
positions 8, 9 and 10 (0.37, 0.22 and 0.5 respectively, compared to 0.70 over the entire<br />
46
promoters) further supports the notion that the constraint on functionally important<br />
positions slows their <strong>evolution</strong>.<br />
Positional variation within one genome is correlated to variation between genomes<br />
Noting that positions with fewer substitutions seem to coincide with the positions that are<br />
non-degenerate in the consensus, we constructed position weight matrices using the<br />
characterized binding sites from S. cerevisiae and, in order to quantify the degeneracy,<br />
computed the information content at each position. The information content <strong>of</strong> a position<br />
within a binding site has been shown to correlate with the importance <strong>of</strong> that position in<br />
the formation <strong>of</strong> the protein-DNA complex [28]. For the transcription factors used above<br />
(fig. 1B), we observe that positions <strong>of</strong> high information correspond to positions with<br />
fewer substitutions (e.g., Fig. 3). In 6 <strong>of</strong> 7 cases we found this correlation (Spearman's<br />
rank <strong>of</strong> -0.70 to -0.84) statistically significant (p < 0.01), the lone exception being Tbp1p,<br />
where a negative correlation was observed (-0.46), but was not significant (p = 0.11).<br />
(Table 1 & see discussion.) Thus the sequence variation in characterized transcription<br />
factor binding sites within one genome is directly related to the sequence variation at<br />
individual sites between genomes.<br />
47
Figure 3.<br />
Association between information pr<strong>of</strong>ile and rate <strong>of</strong> <strong>evolution</strong> in characterized<br />
binding sites from SCPD.<br />
A–D. Representative plots <strong>of</strong> information content and substitutions per site reveal a correspondence<br />
between positions <strong>of</strong> high information content and slower rates <strong>of</strong> <strong>evolution</strong>. Open symbols represent<br />
information content and filled symbols the number <strong>of</strong> substitutions per site (estimated <strong>by</strong> maximum<br />
parsimony). Consensus letters are included below the appropriate positions in the motif.<br />
Site-specific substitution rates are consistent with the proportionality <strong>of</strong> Halpern and<br />
Bruno<br />
If the nucleotide frequencies at each position <strong>of</strong> a position weight matrix accurately<br />
reflect the allowed sequence specificity for the formation <strong>of</strong> a functional protein-DNA<br />
complex, it is possible, under several assumptions, to predict the rates <strong>of</strong> <strong>evolution</strong> based<br />
on these frequencies, as has been done using the frequencies <strong>of</strong> residues in protein<br />
48
sequences [29]. The underlying intuition is that if, for example, at a given position in the<br />
motif, a transcription factor recognizes only guanine, i.e., (fA,fC,fG,fT) = (0,0,1,0), a<br />
mutation to any other nucleotide should prohibit formation <strong>of</strong> the protein-DNA complex,<br />
and therefore be deleterious. Such mutations should be removed from the population and<br />
therefore the number <strong>of</strong> observed substitutions at such a position is expected to be very<br />
small. Similarly, if the binding protein requires, say, A or T at a given position with no<br />
preference, i.e., (fA,fC,fG,fT) = (½ , 0, 0, ½), we expect changes between A and T to persist<br />
in the population, but changes to C or G to be removed; we should therefore observe<br />
somewhat more substitutions, but still fewer than at positions where there is no<br />
preference at all, i.e., (fA,fC,fG,fT) = (¼, ¼, ¼, ¼), and all types <strong>of</strong> substitutions are<br />
permitted. Under several assumptions, it is possible to write the following proportionality<br />
for the rates <strong>of</strong> substitution between various residues and a function <strong>of</strong> their frequencies<br />
([29] equation 10 & see methods).<br />
R<br />
abp<br />
∝ P<br />
ab<br />
⎛ f<br />
ln⎜<br />
⎜ f<br />
×<br />
⎝<br />
f<br />
1−<br />
f<br />
where Rabp is the observed rate <strong>of</strong> substitution from residue a to residue b at position p,<br />
Pab and Pba are the (position independent) underlying rates <strong>of</strong> mutation from residue a to<br />
residue b and b to a, respectively, and fap and fbp are the frequencies <strong>of</strong> residue a and b at<br />
position p in the position weight matrix. The predicted rate <strong>of</strong> <strong>evolution</strong> at each position,<br />
Kp, is just the sum <strong>of</strong> the Rabp times the probability that that base was observed, i.e.,<br />
p ∝ ∑∑<br />
a a≠<br />
b<br />
49<br />
ap<br />
bp<br />
ap<br />
ap<br />
bp<br />
P<br />
P<br />
ba<br />
ab<br />
P<br />
P<br />
abp<br />
ab<br />
ba<br />
K f R .<br />
⎞<br />
⎟<br />
⎠<br />
,
In order to test these predictions, we estimated a background mutation model (Pab) <strong>by</strong><br />
fitting the HKY85 model [34] to entire promoter sequences using the PAML package<br />
[35], treating all positions independently (see methods). Using the seven position weight<br />
matrices (fap) trained on the characterized binding sites (all from S. cerevisiae,) and<br />
scaling the proportionality <strong>by</strong> the total number <strong>of</strong> changes observed in the motif, we<br />
compared the predicted rates to the observed rates and the results are shown in figure 4.<br />
Although there is quite a bit <strong>of</strong> variability, the observed rates <strong>of</strong> <strong>evolution</strong> seem to agree<br />
with the predictions (R 2 = 0.67).<br />
Figure 4.<br />
Test <strong>of</strong> the Halpern-Bruno proportionality.<br />
Observed rate <strong>of</strong> <strong>evolution</strong> versus the predictions based on the nucleotide frequencies in the binding motif<br />
in S. cerevisiae. Each point represents the predicted and observed rates at a given position in a motif. For<br />
50
each factor the proportionality has been normalized <strong>by</strong> the total number <strong>of</strong> substitutions observed in the<br />
corresponding binding sites. See text for details.<br />
Computationally predicted binding sites show similar <strong>evolution</strong>ary properties<br />
There are relatively few transcription factors for which the number <strong>of</strong> experimentally<br />
characterized binding sites was sufficient to reliably estimate the information pr<strong>of</strong>ile and<br />
rate <strong>of</strong> <strong>evolution</strong> at each position. To further establish the generality <strong>of</strong> these observations<br />
we extended the analysis to include additional factors where some information regarding<br />
the consensus or target genes was available. We ran the MEME motif-finding program<br />
[3] on the promoter regions <strong>of</strong> groups <strong>of</strong> genes that showed similar expression patterns to<br />
the known targets <strong>of</strong> these factors in microarray experiments to derive models <strong>of</strong> their<br />
binding specificity and identify putative binding sites. As in the experimentally<br />
characterized cases, the rate <strong>of</strong> <strong>evolution</strong> in these binding sites was slower than that <strong>of</strong> the<br />
promoters in which the sequences were found (table 2.) Furthermore, most <strong>of</strong> the motifs<br />
showed the characteristic correlation between the information content at each position<br />
and the number <strong>of</strong> substitutions per site (table 2).<br />
Table 2.<br />
Evolution <strong>of</strong> motifs with known consensus, but binding sites identified <strong>by</strong> MEME<br />
51
Here binding sites are identified <strong>by</strong> running the MEME program [3] on genes that clustered with targets in<br />
micro-array gene expression data. Expected consensus sequences (from [20,40] or [7]) are underlined.<br />
'Motif subs.' and 'bg subs.' are the substitutions per site in the binding sites and the promoters in which they<br />
are found respectively. 'Corr.' and 'p-value' are the Spearman's rank correlation coefficient and the<br />
associated p-value between the rate <strong>of</strong> <strong>evolution</strong> at each position and the information content at each<br />
position. * Indicates significance at a per factor error rate <strong>of</strong> < 0.05. ** Indicates significance after<br />
Bonferoni correction for a global error rate < 0.05, assuming 50 tests were done in total. (?) indicates<br />
uncertainty as to the identity <strong>of</strong> the binding protein. + indicates clusters taken from hierarchical clustering<br />
[40] <strong>of</strong> yeast data from the Stanford Microarray database [42], ++ indicates clusters taken from hierarchical<br />
clustering <strong>of</strong> 300 genetic perturbations [43] and +++ indicates clusters taken from hierarchical clustering <strong>of</strong><br />
64 control experiments [43]<br />
The pattern <strong>of</strong> <strong>evolution</strong> may be useful in distinguishing real motifs from computational<br />
artifacts<br />
A challenge in computational motif detection is that algorithms <strong>of</strong>ten identify sequence<br />
motifs that do not represent real transcription factor binding sites. For example, in<br />
addition to the cases described above (table 2), there were several cases where the motif<br />
identified <strong>by</strong> MEME was not the binding motif for the factor known to regulate these<br />
genes. We computed the number <strong>of</strong> substitutions per site as well as the correlation<br />
between the number <strong>of</strong> substitutions and the information content for these motifs as well,<br />
and found no significant correlations (Table 3), suggesting that the reported motifs in<br />
these cases may be computational artefacts. It is possible that a reduction in the average<br />
number <strong>of</strong> substitutions per site, and a correlation between the information pr<strong>of</strong>ile and the<br />
substitutions across the motif will prove to be useful heuristics in assessing the support<br />
from comparative sequence data for computationally identified motifs. In order to further<br />
test this idea we ran MEME on the promoters <strong>of</strong> a group <strong>of</strong> proposed Crz1p target genes<br />
52
identified in a recent microarray study [36]. We found that the resulting motif (figure 5)<br />
was on average more conserved (0.38 subs. per site, n = 297) than the promoters <strong>of</strong> the<br />
genes in the group (0.65 subs. per site, n = 11832). In addition, it showed the<br />
characteristic correlation between the information pr<strong>of</strong>ile and rate <strong>of</strong> <strong>evolution</strong> across the<br />
motif (Spearman's rank = -0.78, p = 0.001). Thus, in this case, the comparative sequence<br />
data support the hypothesis that this is a functional binding motif in these genes.<br />
Table 3.<br />
Motifs identified <strong>by</strong> MEME that do not correspond to the expected consensus<br />
sequences for the transcription factors thought to be regulating the cluster.<br />
These motifs do not show the characteristic correlation with rate <strong>of</strong> substitution or the substantial decrease<br />
in substitution rate observed for the computationally identified motifs with the expected consensus. +<br />
indicates clusters taken from hierarchical clustering <strong>of</strong> yeast data from the Stanford Microarray database<br />
[42], ++ indicates clusters taken from hierarchical clustering <strong>of</strong> 300 genetic perturbations [43].<br />
53
Figure 5.<br />
Information and rate <strong>of</strong> <strong>evolution</strong> for the recently reported Crz1p motif.<br />
This motif shows the characteristic pattern <strong>of</strong> <strong>evolution</strong> observed for real motifs. Open symbols represent<br />
information content and filled symbols, the number <strong>of</strong> substitutions per site (estimated <strong>by</strong> maximum<br />
parsimony.) Consensus letters are included below the appropriate positions in the motif.<br />
Discussion<br />
Motifs are conserved on average, but individual binding sites are not perfectly conserved<br />
We confirm an important motivating assumption <strong>of</strong> comparative sequencing projects: the<br />
rate <strong>of</strong> <strong>evolution</strong> within functional non-coding sequences elements is slower than the<br />
surrounding intergenic DNA (fig. 1). While this means that on average binding sites are<br />
conserved, it is important to note, however, that in no case was the average number <strong>of</strong><br />
substitutions over the motif reduced to zero. Since substitutions do occur in characterized<br />
binding sites, simply searching through alignments for perfectly conserved segments<br />
would not have revealed all the real binding sites used in this study. Nevertheless,<br />
binding sites do show characteristic patterns <strong>of</strong> <strong>evolution</strong>, and it should be possible to<br />
take these into account in attempting to distinguish the functional instances <strong>of</strong> the motif.<br />
Position specific variation in the rate <strong>of</strong> <strong>evolution</strong> is consistent with models <strong>of</strong> functional<br />
constraint<br />
The observation that the rate <strong>of</strong> <strong>evolution</strong> is not constant over functional non-coding<br />
DNA sequences mirrors similar observations <strong>of</strong> regional variation in the number <strong>of</strong><br />
substitutions per site in peptide sequences; residues that are more important to the<br />
function or structure <strong>of</strong> the protein change much less rapidly, presumably because<br />
mutations at these positions are likely to be deleterious, and therefore do not drift to<br />
54
fixation [21–23]. By analogy to peptide sequences, the observation that the positions in<br />
functional non-coding DNA with high information content evolve more slowly is<br />
consistent with these positions being more important for the formation <strong>of</strong> the protein-<br />
DNA complex, and therefore under more functional constraint. Unlike peptide sequences,<br />
however, the purifying selection and accompanying reduction in the rate <strong>of</strong> substitution<br />
in transcription-factor binding sites seems to be a relatively straightforward mapping<br />
from the physical interaction <strong>of</strong> the DNA with the binding protein (as in fig. 2). Since the<br />
information content has been shown to correlate with the physical constraints imposed <strong>by</strong><br />
transcription factors on their motifs [28] it is consistent that we observe significant<br />
correlations between the information pr<strong>of</strong>iles and the rate <strong>of</strong> <strong>evolution</strong> as well. The<br />
binding sites <strong>of</strong> sequence specific transcription factors afford a rare opportunity to test<br />
theoretical predictions <strong>of</strong> the effects <strong>of</strong> purifying selection on site-specific rates <strong>of</strong><br />
<strong>evolution</strong>. By assuming the nucleotide frequencies from position specific weight matrices<br />
are the equilibrium frequencies under the purifying selection imposed on these sequences,<br />
we could make seemingly reasonable predictions for the rate <strong>of</strong> <strong>evolution</strong> at each position<br />
(figure 4). Although we do not have sufficient data to reliably estimate the rates for each<br />
type <strong>of</strong> substitution (e.g., A→T vs. A→G,) the results presented here are promising. The<br />
same intuition that allows us to construct position weight matrices (i.e., that we may<br />
average over all the binding sites to learn the average sequence specificity) allows us to<br />
compute the rate <strong>of</strong> <strong>evolution</strong> across the motif <strong>by</strong> averaging the changes observed in the<br />
individual binding sites.<br />
Improved understanding <strong>of</strong> binding site <strong>evolution</strong> can guide the use <strong>of</strong> comparative data<br />
55
An accurate understanding <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> functional regulatory sequences is critical<br />
to the optimal use <strong>of</strong> comparative sequence data in the analysis <strong>of</strong> <strong>transcriptional</strong><br />
<strong>regulation</strong>. Without such an understanding, it remains difficult to distinguish sequences<br />
under functional constraint from sequences that are similar because <strong>of</strong> shared descent, or<br />
to differentiate among the various classes <strong>of</strong> conserved non-coding sequences. We<br />
believe our observations linking position-specific variation in the rate <strong>of</strong> <strong>evolution</strong> within<br />
transcription factor binding sites to position-specific sequence variation within genomes<br />
(and to structural features <strong>of</strong> the protein-DNA complex) will be useful in comparative<br />
sequence analysis. For example, comparative sequence data can be used to verify the<br />
predictions <strong>of</strong> de novo motif finding algorithms that have been applied to single genomes,<br />
<strong>by</strong> allowing us to ascribe increased confidence to predicted motifs that are also<br />
conserved. However, simply assessing whether motifs are 'present' in other species can be<br />
ineffective as similar sequences are expected to be present in closely related species<br />
because they have had insufficient time to diverge or as the result <strong>of</strong> other functional<br />
constraints. We propose that the patterns <strong>of</strong> <strong>evolution</strong> we observe for known motifs –<br />
their conservation relative to flanking sequences and the correlation between position-<br />
specific rate <strong>of</strong> <strong>evolution</strong> and intragenomic degeneracy – can more accurately distinguish<br />
motif models that correspond to bona fide transcription factor binding sites from<br />
computational artefacts (compare tables 2 and 3). As a demonstration we show that<br />
comparative sequence supports the motif reported in [36] (fig. 5). Verification <strong>of</strong><br />
computationally predicted motifs may be an immediate practical application <strong>of</strong> our<br />
observations and computational methods that incorporate models <strong>of</strong> binding site<br />
<strong>evolution</strong> should take more effective advantage <strong>of</strong> comparative sequence data. More<br />
56
generally, just as faster <strong>evolution</strong> at synonymous sites is an <strong>evolution</strong>ary signature <strong>of</strong><br />
protein coding regions [21–23], the pattern <strong>of</strong> position-specific variation in <strong>evolution</strong>ary<br />
rates within binding sites can be thought <strong>of</strong> as an <strong>evolution</strong>ary signature <strong>of</strong> transcription<br />
factors. We have shown here how these <strong>evolution</strong>ary signatures might be used to identify<br />
sequences or motifs that collectively have the properties <strong>of</strong> transcription factor binding<br />
sites. With sufficient sequence data, it should ultimately be possible to estimate the rate<br />
<strong>of</strong> <strong>evolution</strong> at every base in a genome, and to identify individual short sequences with<br />
the <strong>evolution</strong>ary characteristics <strong>of</strong> functional transcription factor binding sites.<br />
Conclusions<br />
We show that the rate <strong>of</strong> <strong>evolution</strong> in characterized and predicted transcription factor<br />
binding sites is slower than that <strong>of</strong> the intergenic regions in which they are found. In<br />
addition we show that there is position specific variation in the rate <strong>of</strong> <strong>evolution</strong> across<br />
these binding sites. We show that this variation is correlated to the variability in the<br />
sequence specificity for that factor and can be modelled <strong>by</strong> assuming that purifying<br />
selection acts to maintain these specificities. Together this suggests that the variation in<br />
the rate <strong>of</strong> <strong>evolution</strong> is a direct reflection <strong>of</strong> differences in the strength <strong>of</strong> purifying<br />
selection due to differing physical constraints on the DNA imposed <strong>by</strong> the interaction<br />
with the binding protein. The characterization <strong>of</strong> the pattern <strong>of</strong> conservation over known<br />
binding sites is an important step in understanding the <strong>evolution</strong> <strong>of</strong> functional non-coding<br />
DNA, and perhaps also towards the general understanding <strong>of</strong> protein-DNA interactions.<br />
Our observations should contribute to the effectiveness <strong>of</strong> comparative non-coding<br />
sequence analysis<br />
57
Methods<br />
Rates <strong>of</strong> binding-site and intergenic <strong>evolution</strong><br />
Global alignments <strong>of</strong> intergenic regions from S. mikatae, S. paradoxus and S. bayanus<br />
were computed using clustalw (as described in [12]). Using the accepted species tree<br />
(Sbay, Smik,(Spar, Scer)) [8,12], we computed the minimal number <strong>of</strong> changes needed<br />
for each column <strong>of</strong> the alignment (the so called cost) using the classical parsimony<br />
algorithm (as described in [37]). We included only alignments where sequence from all<br />
four species was available; regions <strong>of</strong> ambiguity or missing sequence in the alignment,<br />
were treated as gaps. The average rate <strong>of</strong> <strong>evolution</strong> within a binding site (in fig. 1A) is the<br />
sum <strong>of</strong> the cost at each position in the binding site divided <strong>by</strong> its length. The average rate<br />
<strong>of</strong> <strong>evolution</strong> for a motif (in fig. 1B) is the sum <strong>of</strong> the cost in the binding sites divided <strong>by</strong><br />
the total number <strong>of</strong> ungapped positions in the binding sites. Although gaps are not<br />
expected in alignments <strong>of</strong> functional binding sites, we allow for them so that we can<br />
apply the same metrics to binding sites as the surrounding sequences. The background<br />
histogram in figure 1A was made <strong>by</strong> calculating the average rate <strong>of</strong> <strong>evolution</strong> in<br />
randomly drawn 17-mers from the promoters <strong>of</strong> the genes containing the binding sites.<br />
The rate <strong>of</strong> background <strong>evolution</strong> (in fig. 1B) is the sum <strong>of</strong> the cost over the entire<br />
alignment divided <strong>by</strong> the total number <strong>of</strong> ungapped positions. The average rate <strong>of</strong><br />
<strong>evolution</strong> at each position is the sum (over all the binding sites) <strong>of</strong> the cost at that position<br />
divided <strong>by</strong> the total number <strong>of</strong> binding sites that have no gap at that position. Although a<br />
maximum likelihood estimator for the number <strong>of</strong> substitutions per site in DNA sequences<br />
has been constructed [38] its performance is expected to be similar to parsimony methods<br />
58
for short <strong>evolution</strong>ary distances as are considered here. We note that the rate <strong>of</strong><br />
background <strong>evolution</strong> differed significantly among the groups <strong>of</strong> genes examined<br />
(characterized targets, expressions clusters.) We address this variation, and examine<br />
possible explanations in another manuscript (Hunter B Fraser, AMM and MBE in<br />
preparation).<br />
Statistics<br />
A Poisson distribution for the number <strong>of</strong> substitutions was used when reporting<br />
confidence intervals, and for error bars in figures 1 and 2, because this is thought to be a<br />
reasonable model for the underlying distribution for neutral substitution events. The<br />
significance <strong>of</strong> difference <strong>of</strong> means between motifs and background was estimated <strong>by</strong><br />
bootstrapping. We randomly selected sequences the same length as the motif (with<br />
replacement) from the upstream regions in which they were found, until we had the same<br />
number as we had characterized binding sites. We then calculated for these samples the<br />
mean number <strong>of</strong> substitutions exactly as for the characterized binding sites. Finally, we<br />
repeated this process 1000 times, and asked how <strong>of</strong>ten we observed an <strong>evolution</strong>ary rate<br />
smaller than for the characterized sites. Both the rate <strong>of</strong> <strong>evolution</strong> in promoter sequences<br />
and the locations <strong>of</strong> yeast transcription factor binding sites are known to show positional<br />
preferences ([39], AMM and MBE, unpublished data). To control for possible effects <strong>of</strong><br />
these biases, we also calculated the number <strong>of</strong> substitutions in sequences <strong>of</strong> the same size<br />
as the motif 5 basepairs away on either side <strong>of</strong> the binding sites, for each <strong>of</strong> the factors<br />
shown in fig. 1B. To be as conservative as possible, we simply computed the probability<br />
<strong>of</strong> observing the number <strong>of</strong> changes in the binding sites out <strong>of</strong> the total number <strong>of</strong><br />
59
changes in the binding sites and the flanking regions with no assumptions about the<br />
underlying distribution <strong>of</strong> the changes (using the hypergeometric distribution) and found<br />
that there were fewer substitutions in the binding sites than in the flanking regions (data<br />
not shown).<br />
Identification <strong>of</strong> binding sites and construction <strong>of</strong> position weight matrices<br />
Characterized binding sites were taken from SCPD [20] for Gal4p (n = 10), Mcm1p (n =<br />
35), Abf1p (n = 16), and Rap1p (n = 17). For some <strong>of</strong> the short Gcn4p (n = 15), Reb1p (n<br />
= 18) and Tbp1p (n = 15) sites up to 5 flanking base pairs were included. Although SCPD<br />
lists additional binding sites for many <strong>of</strong> these factors, we excluded many <strong>of</strong> these<br />
because they were redundant listings <strong>of</strong> binding sites that have been characterized<br />
multiple times (e.g., STE6 has 4 Mcm1p binding sites listed, but these are actually the<br />
same two listed twice) or they were found in divergently transcribed genes, and were<br />
listed independently for both genes (e.g., GAL1 and GAL10 both have 4 Gal4p binding<br />
sites listed, but in fact they share these sites). For each factor, the sequences were aligned<br />
using the MEME program [3] and the 'letter-probability-matrix' from its output was used<br />
as the position weight matrix. SCPD lists binding sites for many other regulatory<br />
elements and transcription factors, most <strong>of</strong> which have few sites, or have sites from a<br />
small number <strong>of</strong> target genes. For each transcription factor, we attempted to identify<br />
groups <strong>of</strong> genes with similar expression patterns as the known target genes, as well as<br />
known target genes <strong>of</strong> other transcription factors (from [40]). These groups were then<br />
chosen <strong>by</strong> hand from hierarchical clustering [41] <strong>of</strong> expression data from various<br />
experimental treatments and over the cell-cycle, downloaded from the Stanford<br />
60
Microarray Database [42] or from 300 publicly available deletion and drug treatment<br />
experiments or 64 control experiments [43]. We ran MEME on the putative promoter<br />
regions <strong>of</strong> genes in expression clusters with the following parameters: motif width was<br />
allowed to range between 8 and 16, 'zoops' and 'tcm' models were both tried for each<br />
case, and both strands <strong>of</strong> the promoter were searched. When the 'tcm' model was used, we<br />
specified between 0.5 n and 2 n for the number <strong>of</strong> occurrences where n is the number <strong>of</strong><br />
genes in the cluster. For MEME runs, promoter regions were taken to be the 600<br />
basepairs upstream <strong>of</strong> the translation start (basepairs in other coding regions were<br />
excluded), except in the case <strong>of</strong> the proteasome and the repressed stress genes where 300<br />
basepairs were used because <strong>of</strong> a positional bias in the location <strong>of</strong> those binding sites<br />
(AMM and MBE unpublished results.) For computationally predicted binding-sites,<br />
occurrences were taken to be those listed in the MEME output, and the 'letter-<br />
probability-matrix' was used as position weight matrix. In the case <strong>of</strong> Crz1p we used the<br />
starting consensus NNNNGGCNCNN, which was reported in [36].<br />
Correlation with information pr<strong>of</strong>iles<br />
Information at each position was calculated as<br />
I p = 2 − ∑ fbp<br />
log2<br />
fbp<br />
b<br />
where fbp is the frequency <strong>of</strong> base b at position p in the motif, with b є {A, C, G, T}, and<br />
p є [1, W] where W is the width <strong>of</strong> the motif. Spearman's rank-order correlation (the<br />
linear correlation <strong>of</strong> the ranks) was computed and the significance <strong>of</strong> the correlation<br />
coefficient was assigned as described in [44].<br />
61
Predictions <strong>of</strong> the rate <strong>of</strong> <strong>evolution</strong><br />
We follow exactly the derivation for protein sequences found in [29]. Briefly, if we<br />
assume that sites are independent, <strong>evolution</strong> is reversible, and underlying probabilities <strong>of</strong><br />
mutation are invariant across sites, we can write the rate <strong>of</strong> <strong>evolution</strong> at each position as<br />
R abp ∝ Pab<br />
× Fabp<br />
where Rabp is the rate <strong>of</strong> substitution from residue a to residue b at position p, Pab is the<br />
rate <strong>of</strong> mutation from residue a to residue b and Fabp is the probability <strong>of</strong> fixation <strong>of</strong> a<br />
mutation from residue a to residue b at position p. If we assume that the time <strong>of</strong> fixation<br />
is small relative to the time between fixations, a so-called weak-mutation model [45], we<br />
can use Kimura's equations [46] and write the following.<br />
−2s<br />
p<br />
1−<br />
e<br />
Fabp = −2<br />
Ns p<br />
1−<br />
e<br />
2s<br />
p<br />
≈ −2<br />
Ns p<br />
1−<br />
e<br />
2s<br />
p<br />
1−<br />
e<br />
Fbap = 2Ns<br />
p<br />
1−<br />
e<br />
− 2s<br />
p<br />
≈ 2Ns<br />
p<br />
1−<br />
e<br />
,<br />
where N is the effective population size and sp is the coefficient <strong>of</strong> selection at position p.<br />
As was noted in [29], if equilibrium has been reached, i.e., there has been sufficient time<br />
for all the possible mutations at that position to occur and either be fixed or removed,<br />
then<br />
fbpPba Fabp<br />
2Ns<br />
p<br />
=<br />
fapPab<br />
Fbap<br />
≈ e ,<br />
where fap is the equilibrium frequency <strong>of</strong> residue a at position p, in our case the frequency<br />
in the position weight matrix. This implies<br />
and therefore<br />
⎛ f<br />
2 Ns = ⎜<br />
p ln<br />
⎜<br />
⎝ f<br />
62<br />
bp<br />
ap<br />
P<br />
P<br />
ba<br />
ab<br />
⎞<br />
⎟<br />
⎠
F<br />
abp<br />
⎛ fbpP<br />
⎞ ba<br />
ln⎜<br />
⎟<br />
⎜ fapP<br />
⎟<br />
ab<br />
∝<br />
⎝ ⎠<br />
fapPab<br />
1−<br />
f P<br />
which can be substituted to give the proportionality used in results. To fit a background<br />
mutation model, we used PAML [35] to fit the HKY model [34] to the promoters that<br />
contain the characterized binding sites for each factor. We fixed the alpha parameter at 0<br />
to use a constant rate across sites. The HKY model accounts for equilibrium frequencies<br />
<strong>of</strong> nucleotides as well as transition-transversion mutation bias. The equilibrium<br />
frequencies differed from (¼,¼,¼,¼), and transitions were more probable than<br />
transversions (kappa between 3 and 4). We also tested the site specific predictions for the<br />
rate <strong>of</strong> <strong>evolution</strong> assuming that all types <strong>of</strong> substitutions were equally likely and<br />
qualitatively the results were very similar (data not shown).<br />
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66
9. Phylogenetic motif finding <strong>by</strong> expectation maximization on <strong>evolution</strong>ary mixtures<br />
The characteristic patterns <strong>of</strong> <strong>evolution</strong> in transcription factor binding sites described in<br />
the previous chapter suggest that we can develop new methods for both motif discovery<br />
and binding site identification that employ specific models <strong>of</strong> transcription factor binding<br />
site <strong>evolution</strong>. This work was published as Moses et al. 2004.<br />
The functional binding sites for a given protein are rarely identical, with most<br />
proteins binding to families <strong>of</strong> related sequences collectively referred to as their ‘motif’<br />
[1]. Although experimental methods exist to identify sequences bound <strong>by</strong> a specific<br />
protein, they have not been widely applied, and computational approaches [2,3,4] to<br />
‘motif discovery’ have proven to be a useful alternative. For example, the program<br />
MEME [5], models a collection <strong>of</strong> sequences as a mixture <strong>of</strong> multinomial models for<br />
motif and background and uses an Expectation-Maximization (EM) algorithm to estimate<br />
the parameters.<br />
Because functional binding sites are <strong>evolution</strong>arily constrained, their preferential<br />
conservation relative to background sequence has proven a useful approach for their<br />
identification [6]. With the availability <strong>of</strong> complete genomes for closely related species<br />
e.g., [7], it is possible to incorporate an understanding <strong>of</strong> binding site <strong>evolution</strong> into motif<br />
discovery as well. At present, few motif discovery methods simultaneously take<br />
advantage <strong>of</strong> both the statistical enrichment <strong>of</strong> motifs and the preferential conservation <strong>of</strong><br />
the sequences that match them. One recent study [7] enumerated spaced hexamers that<br />
were both preferentially conserved (in multiple sequence alignments) and statistically<br />
enriched. Another method, FootPrinter, [8] identifies sequences (with mismatches) with<br />
67
few changes over an <strong>evolution</strong>ary tree. Neither <strong>of</strong> these methods, however, makes use <strong>of</strong><br />
an explicit probabilistic model.<br />
Here we present a unified probabilistic framework that combines the mixture models<br />
<strong>of</strong> MEME with probabilistic models <strong>of</strong> <strong>evolution</strong>, and can thus be viewed as an<br />
<strong>evolution</strong>ary extension <strong>of</strong> MEME. These <strong>evolution</strong>ary models (used in the maximum<br />
likelihood estimation <strong>of</strong> phylogeny [9]) consider observed sequences to have been<br />
generated <strong>by</strong> a continuous time Markov substitution process from unobserved ancestral<br />
sequences, and can accurately model the complicated statistical relationship between<br />
sequences that have diverged along a tree from a common ancestor. Our approach<br />
considers observed sequences to have been generated from ancestral sequences that are<br />
two component mixtures <strong>of</strong> motif and background, each with their own <strong>evolution</strong>ary<br />
model. The value <strong>of</strong> varying <strong>evolution</strong>ary models has been realized in other contexts as<br />
well, e.g., [10] and such models have been successfully trained using EM [11]. A mixture<br />
<strong>of</strong> <strong>evolution</strong>ary models has been used previously to identify slowly evolving non-coding<br />
sequences [12], and this work can equally be regarded as an extension <strong>of</strong> that approach.<br />
Given a set <strong>of</strong> aligned sequences, we use an EM algorithm to obtain the maximum<br />
likelihood estimates <strong>of</strong> the motif matrix and a corresponding <strong>evolution</strong>ary model.<br />
We first describe the probabilistic framework used to model aligned non-coding<br />
sequences. We employ a mixture model, which can be written generically as<br />
p(data) = Σp(data|model)p(model),<br />
where p(x) is the probability density function for the random variable x. The sum over<br />
models indicates that the data is distributed as some mixture <strong>of</strong> component models, where<br />
68
the prior, p(model), is the mixing proportion. For simplicity, we first address the case <strong>of</strong><br />
pair-wise sequence alignments. Given some motif size, w, we treat the entire alignment as<br />
a series <strong>of</strong> alignments <strong>of</strong> length w, each <strong>of</strong> which may be an instance <strong>of</strong> the motif or a<br />
piece <strong>of</strong> background sequence. We denote the pair <strong>of</strong> aligned sequences as X and Y,<br />
where the ith position in the sequence as a vector <strong>of</strong> length 4, (for each <strong>of</strong> ACGT), where<br />
Xib=1 if the bth base is observed, and 0 otherwise. We denote the unobserved ancestral<br />
sequence, A, similarly, except that the values <strong>of</strong> Aib are not observed. For a series <strong>of</strong><br />
alignments <strong>of</strong> total length N, the likelihood, L, is given <strong>by</strong><br />
L =<br />
N −w<br />
∏∑ p(<br />
mi<br />
) ∏∑<br />
i= 0 mi<br />
i+<br />
w−1<br />
3<br />
k = i b=<br />
0<br />
p(<br />
X , Y | A , m ) p(<br />
A | m )<br />
where the m i are unobserved indicator variables indexing the component models; in our<br />
case m is either motif or background. Generically, we let<br />
p(m i ) = π m ,<br />
the prior probability for each component.<br />
We incorporate the sequence specificity <strong>of</strong> the motif <strong>by</strong> letting the prior probabilities<br />
<strong>of</strong> observing each base in the ancestral sequence, p(A kb |m i ), be the frequency <strong>of</strong> each base<br />
at each position in the motif (the frequency matrix). We write<br />
p(A kb |m i ) = f mkb ,<br />
such that if m is motif, f mkb gives the probability <strong>of</strong> observing the b th<br />
base at the k-i th<br />
position. For the background model we use the average base frequencies for each<br />
alignment, and assume that they are independent <strong>of</strong> position. This allows us to run our<br />
algorithm on several alignments simultaneously [15] and the densities are therefore<br />
conditioned on the alignment as well, but omit this here for notational clarity.<br />
69<br />
k<br />
k<br />
kb<br />
i<br />
kb<br />
i
Finally, noting that because the two sequences descended independently from the<br />
ancestor, we can write p(X k ,Y k |A kb ,m i ) = p(X k |A kb ,m i ) p(Y k |A kb ,m i ), where p(X k |A kb ,m i ) is<br />
the probability <strong>of</strong> the residue X k , given that the ancestral sequence, A, was base b at that<br />
position – a substitution matrix for each component model. For simplicity we use the<br />
Jukes-Cantor [16] substitution matrix, which is, in our notation,<br />
p(<br />
X<br />
k<br />
|<br />
A<br />
kb<br />
⎛<br />
m ⎜<br />
1<br />
, i ) =<br />
⎜<br />
+<br />
⎝ 4<br />
3<br />
4<br />
e<br />
4<br />
− α<br />
3<br />
where α mk is the rate parameter at position k.<br />
mk<br />
⎞<br />
⎟<br />
⎠<br />
X<br />
kb<br />
70<br />
⎛<br />
⎜<br />
1<br />
⎜<br />
−<br />
⎝ 4<br />
3<br />
4<br />
e<br />
4<br />
− α<br />
3<br />
mk<br />
⎞<br />
⎟<br />
⎠<br />
1−<br />
X<br />
It is here that we incorporate differences in <strong>evolution</strong> between the motif and<br />
background <strong>by</strong> specifying different substitution matrices for each component. For<br />
example, if we set α m smaller for the motif than for background, the motif evolves at a<br />
slower rate than the background – it is conserved. We test a variety <strong>of</strong> different<br />
substitution models for the motif and summarize the implications for motif discovery in<br />
the Gcn4p targets. (See results) Unfortunately, as the dependence <strong>of</strong> these models on the<br />
equilibrium frequencies becomes more complicated, deriving ML estimators for the<br />
parameters becomes more difficult, and more general optimization methods may be<br />
necessary. Once again, we can allow each alignment its own background rate, [15] and<br />
express the motif rate as a proportion <strong>of</strong> background.<br />
3.2 An EM algorithm to train parameters<br />
Following the example <strong>of</strong> the MEME program [5] which uses an EM (an iterative<br />
optimization scheme guaranteed to find local maxima in the likelihood) algorithm to fit<br />
kb
mixtures to unrelated sequences, we now derive an EM algorithm to train the parameters<br />
<strong>of</strong> the model described above. We write the ‘expected complete log likelihood’ [17]<br />
ln Lc<br />
N −w<br />
= ∑∑ mi<br />
i= 0 mi<br />
i+<br />
w−1<br />
3<br />
⎡<br />
⎢lnπ<br />
m + ∑∑<br />
⎣<br />
k= i b=<br />
0<br />
⎤<br />
Akb<br />
( ln p(<br />
X k , Yk<br />
| Akb,<br />
mi<br />
) p(<br />
Akb<br />
| mi<br />
) + ln fmkb<br />
) ⎥⎦ ,<br />
where ln denotes the natural logarithm, and maximize <strong>by</strong> setting the derivatives with<br />
respect to the parameters to zero at each iteration. Setting<br />
and solving gives<br />
∂ ln L<br />
∂π<br />
1<br />
π m = ∑ mi<br />
,<br />
N − w<br />
m<br />
c<br />
∂ ln Lc<br />
∂ ln Lc<br />
= 0 , = 0 and = 0<br />
∂f<br />
∂α<br />
mkb<br />
∑<br />
i<br />
∑<br />
mi<br />
Akb<br />
f mkb =<br />
m<br />
and α<br />
3 1−<br />
1<br />
3 Rmk<br />
= −<br />
⎜<br />
⎟<br />
mk ln<br />
i<br />
i<br />
4 ⎝ 1+<br />
Rmk<br />
⎠<br />
i<br />
where Rkm is the ratio <strong>of</strong> expected changed to identical residues under each model, and is<br />
given <strong>by</strong><br />
R<br />
N −w<br />
∑ mi<br />
∑ ∑<br />
i=<br />
0<br />
m = N −w<br />
∑ mi<br />
∑ ∑<br />
i=<br />
0<br />
i+<br />
w−1<br />
3<br />
k= i b=<br />
0<br />
i+<br />
w−1<br />
3<br />
k= i b=<br />
0<br />
71<br />
A<br />
kb<br />
A<br />
( 2<br />
kb<br />
−Y<br />
( Y<br />
kb<br />
kb<br />
mk<br />
− X<br />
+ X<br />
for all k in the case <strong>of</strong> a constant rate across the motif. The sufficient statistics ‹A kb › and<br />
‹m i ›, are derived <strong>by</strong> applying Bayes’ theorem and are computed using the values <strong>of</strong> the<br />
parameters from the previous iteration. We have<br />
where<br />
and<br />
m<br />
i<br />
p(<br />
mi<br />
) p(<br />
X , Y | mi<br />
)<br />
= p(<br />
mi<br />
| X , Y ) =<br />
p(<br />
X , Y )<br />
i w 1 3 − +<br />
p( X , Y | mi<br />
) = ∏∑ p(<br />
X k , Yk<br />
| Akb,<br />
mi<br />
) p(<br />
Akb<br />
| mi<br />
)<br />
k = i b=<br />
0<br />
kb<br />
kb<br />
)<br />
)<br />
⎛<br />
⎞
Similarly,<br />
∑<br />
p ( X , Y | m ) = p(<br />
X , Y | m ) p(<br />
m )<br />
i<br />
mi<br />
p(<br />
Aib)<br />
p(<br />
X i,<br />
Yi<br />
| Aib,<br />
mi<br />
)<br />
A ib = p(<br />
Aib<br />
| X i,<br />
Yi<br />
) = ∑ p(<br />
Aib<br />
| X i,<br />
Yi<br />
, mi<br />
) p(<br />
mi<br />
) = ∑<br />
p(<br />
mi<br />
)<br />
p(<br />
X , Y | m )<br />
mi<br />
In order to extend these results beyond pair-wise alignments, we can simply replace<br />
the two sequences X and Y with the probability <strong>of</strong> the entire tree below conditioned on<br />
having observed base b in the ancestral sequence. The likelihood becomes<br />
L =<br />
N −w<br />
∏∑ p mi<br />
) ∏∑<br />
i= 0 m<br />
i<br />
i+<br />
w−1<br />
3<br />
k = i b=<br />
0<br />
72<br />
kb<br />
i<br />
mi<br />
( p(<br />
tree | A , m ) p(<br />
A | m ) ,<br />
where p(tree|A kb ,mi) are computed using the ‘pruning’ algorithm [9]. Of course, a tree<br />
topology is needed in these cases and we used the accepted topology for the sensu stricto<br />
Saccharomyces [7] and computed for each alignment the maximum likelihood branch<br />
lengths using the paml package [18].<br />
3.3 Implementation<br />
We implemented a C++ program (EMnEM: Expectation-Maximization on Evolutionary<br />
Mixtures) to execute the algorithm described above, with the following extensions.<br />
Because instances <strong>of</strong> a motif may occur on either strand <strong>of</strong> DNA sequence, we also treat<br />
the strand <strong>of</strong> each occurrence as a hidden variable, and sum over the two possible<br />
orientations. In addition, because the mixture model treats each position in the alignment<br />
independently, we down-weight overlapping matches <strong>by</strong> limiting the total expected<br />
number <strong>of</strong> matches in any window <strong>of</strong> 2w to be less than one. Finally, because EM is<br />
guaranteed only to converge to a local optimum in the likelihood, we need to initialize the<br />
model in the region <strong>of</strong> the likelihood space where we believe the global optimum lies.<br />
i<br />
i<br />
kb<br />
i<br />
i<br />
i<br />
i
Similar to the strategy used in the MEME program [5], we initialize the motif matrix with<br />
the reconstructed ancestral sequence <strong>of</strong> length w at each position in the alignments, and<br />
perform the full EM starting with the sequence at the position that had the greatest<br />
likelihood. EMnEM will be made available at http://rana.lbl.gov.<br />
3.4 Time complexity<br />
The time complexity <strong>of</strong> the EM algorithm is linear with total length <strong>of</strong> the data, and<br />
the initialization heuristic we have implemented is quadratic with the length.<br />
Interestingly, because our algorithm runs on aligned sequences, relative to MEME, which<br />
that treats sequences independently, the total length is reduced <strong>by</strong> a factor <strong>of</strong> 1<br />
/ S , where S<br />
is the number <strong>of</strong> sequences in the alignment. Usually, we lose this factor in each iteration<br />
when calculating p(tree|A kb ) using the ‘pruning’ algorithm [9], as it is linear in S. We<br />
note, however, that for <strong>evolution</strong>ary models (e.g., Juckes-Cantor) where p(tree|A kb ) is<br />
independent <strong>of</strong> p(A kb |m i ), we may learn the PSPM without re estimating the sufficient<br />
statistics ‹A kb › (the reconstructed ancestral sequence) at each iteration. In these cases the<br />
complexity <strong>of</strong> EMnEM will indeed be linear in the length <strong>of</strong> the aligned sequence, a<br />
considerable speedup, especially in the quadratic initialization step.<br />
4 Results and Discussion<br />
4.1 A test case from the budding yeasts<br />
In order to compare our algorithm under various <strong>evolution</strong>ary models as well as to other<br />
motif discovery strategies, we chose to compare all methods on a single test case: the<br />
upstream regions from 5 sensu stricto Saccharomyces (S. bayanus, S. cerevisiae, S.<br />
73
kudriavzevii, S. mikatae, and S. paradoxus) <strong>of</strong> 9 known Gcn4p targets that are listed in<br />
SCPD [19]. In order to control for variability in alignment quality at different<br />
<strong>evolution</strong>ary distances, we made multiple alignments <strong>of</strong> all available upstream regions<br />
using T-c<strong>of</strong>fee [20] and then extracted the appropriate sequences for any subset <strong>of</strong> the<br />
species. The Gcn4p targets from SCPD are a good set on which to test our method<br />
because there are a relatively high number <strong>of</strong> characterized sites in these promoters. In<br />
addition, the upstream regions <strong>of</strong> these genes contain stretches <strong>of</strong> poly T, which are not<br />
known to be binding sites. As a result, MEME (“tcm” model, w 10) assigns a lower<br />
(better) evalue to a ‘polyT’ motif (e=2.7e-03) than to the known Gcn4p motif (e=1.6e06)<br />
when run on the S. cerevisiae upstream regions. Because this is typical <strong>of</strong> the types <strong>of</strong><br />
false positives that motif finding algorithms produce, we use as an indicator <strong>of</strong> the<br />
success <strong>of</strong> our method the log ratio <strong>of</strong> the likelihood <strong>of</strong> the <strong>evolution</strong>ary mixture model<br />
using the real Gcn4p matrix, to that using the polyT matrix. If this indicator is greater<br />
than zero, i.e.,<br />
⎡ p(<br />
data | Gcn4<br />
p)<br />
⎤<br />
log ⎢<br />
⎥ > 0,<br />
⎣ p(<br />
data | polyT ) ⎦<br />
the real motif has a greater likelihood than the false positive, and should be returned as<br />
the top motif.<br />
4.2 Incorporating a model <strong>of</strong> motif <strong>evolution</strong> can eliminate false positives<br />
In order to explore the effects <strong>of</strong> incorporating models <strong>of</strong> motif <strong>evolution</strong> into motif<br />
detection, we tested several <strong>evolution</strong>ary models. In particular we were interested in the<br />
effect <strong>of</strong> incorporating <strong>evolution</strong>ary rate, as real motifs evolve slower than surrounding<br />
sequences. Using alignments <strong>of</strong> S. cerevisiae and S. mikatae, we calculated the log ratio<br />
74
<strong>of</strong> the likelihood using the real Gcn4p matrix to the likelihood using the polyT matrix<br />
with Jukes-Cantor substitution under several assumptions about the rate <strong>of</strong> <strong>evolution</strong> in<br />
the motif (Figure 1). Interestingly, slower <strong>evolution</strong> in the motif, either ¼ or 0.03 (the ML<br />
estimate) times background rate, is enough to assign a higher likelihood to the Gcn4p<br />
motif and thus eliminate the false positive. We tried two additional <strong>evolution</strong>ary models,<br />
in which the rate <strong>of</strong> substitution at each position depends on the frequency matrix. In the<br />
Felsenstein ’81 model (F81) the different types <strong>of</strong> changes occur at different rates, but the<br />
overall rate at each position is constant, while the Halpern-Bruno model (HB) assumes<br />
there is purifying selection at each position and can account for positional variation in<br />
overall rate [21,22]. In each case, these more realistic models further favored the Gcn4p<br />
matrix over the polyT.<br />
Figure 1.<br />
Effect <strong>of</strong> models for motif <strong>evolution</strong> on motif detection<br />
Plotted is the log ratio <strong>of</strong> the likelihood using the Gcn4p PSPM to the likelihood using polyT PSPM under<br />
various <strong>evolution</strong>ary models in alignments <strong>of</strong> S. cerevisiae to S. mikatae. Models that allow the motif to<br />
evolve more slowly than background, JC (0.25), JC (ML) and JC (HB), and models in which the rates <strong>of</strong><br />
<strong>evolution</strong> take into account the deviation from equilibrium base frequencies, F81 and JC (HB), assign<br />
75
higher likelihood to the Gcn4p PSPM. Also plotted is the negative log ratio <strong>of</strong> the e-values from MEME<br />
(‘tcm’ model, w 10). JC are Jukes-Cantor models with rate parameter equal to background (bg), ¼ <strong>of</strong><br />
background (0.25) or set to the maximum-likelihood estimate below background (ML).<br />
4.3 Success <strong>of</strong> motif discovery is dependent on <strong>evolution</strong>ary distance<br />
In order to test the generality <strong>of</strong> the results achieved for the S. cerevisiae S. mikatae<br />
alignments, we calculated the log ratio <strong>of</strong> the likelihood <strong>of</strong> the <strong>evolution</strong>ary mixture using<br />
the real Gcn4p matrix to the polyT matrix over a range <strong>of</strong> <strong>evolution</strong>ary distances and<br />
rates <strong>of</strong> <strong>evolution</strong> (figure 2, filled symbols). At closer distances, more <strong>of</strong> the data is<br />
redundant, while over longer comparisons, conserved sequences should stand out more<br />
against the background. Indeed, at the distance <strong>of</strong> S. cerevisiae to S. paradoxus (~0.13<br />
substitutions per site), the likelihood <strong>of</strong> polyT is greater, while at the distance <strong>of</strong> S.<br />
cerevisiae, S. mikatae, and S. paradoxus (~0.31 subs. per site) the Gcn4p matrix is<br />
favored. Interestingly, this is true regardless <strong>of</strong> the rate <strong>of</strong> <strong>evolution</strong> assumed for the<br />
motif. While at all <strong>evolution</strong>ary distances slow <strong>evolution</strong> favors the Gcn4p matrix more<br />
than when the motif evolves at the background rate, the effect <strong>of</strong> including slower<br />
<strong>evolution</strong> is smaller than the effect <strong>of</strong> the varying <strong>evolution</strong>ary distance. Only at the<br />
borderline distance <strong>of</strong> S. cerevisiae to S. mikatae (~0.25 subs. per site), do the models<br />
perform differently. We also ran MEME (with the “tcm” model, w set at 10) on the all<br />
sequences (from all genes and all species) and calculated the negative log ratio <strong>of</strong> the<br />
MEME e-values for the two motifs (figure 2, heavy trace). MEME treats all the<br />
sequences independently, and continues to assign the polyT matrix a lower e-value over<br />
all the <strong>evolution</strong>ary distances. At least for this case, it seems more important to accurately<br />
76
model the phylogenetic relationships between the sequences (i.e., using a tree) than to<br />
accurately model the <strong>evolution</strong> within the motif.<br />
Figure 2.<br />
Effect <strong>of</strong> <strong>evolution</strong>ary distance on motif detection.<br />
Log ratio <strong>of</strong> the likelihood using the Gcn4p matrix to the likelihood using polyT matrix and alignments that<br />
span increasing <strong>evolution</strong>ary distance. At distances greater than S. cerevisiae to S. mikatae the <strong>evolution</strong>ary<br />
mixture assigns the Gcn4p matrix a greater likelihood whether the rate <strong>of</strong> <strong>evolution</strong> in the motif is equal to,<br />
½, ¼ or ⅛ <strong>of</strong> the background rate, (diamonds, squares, triangles and circles, respectively). Also plotted are<br />
negative log ratios <strong>of</strong> the MEME evalues for the Gcn4p to polyT, using the entire sequences, or pre-<br />
filtering alignments for 20 base pair windows <strong>of</strong> at least 70% or 50% identity to a reference genome<br />
(heavy, lighter and lightest traces, respectively.)<br />
4.4 The unified framework is preferable to using <strong>evolution</strong>ary information separately<br />
In order to compare our method, which incorporates <strong>evolution</strong>ary information directly<br />
into motif discovery, to approaches that use such information separately, we scanned the<br />
alignments at each <strong>evolution</strong>ary distance and removed regions than were less than 50 or<br />
70 % identical to a reference genome in a 20 base pair window. This allows MEME,<br />
which does take into account phylogenic information, to focus on the conserved regions.<br />
77
We ran MEME and computed the negative log ratio <strong>of</strong> the e-values for the Gcn4p matrix<br />
and the polyT matrix. While in both cases there were distances where the real motif was<br />
favored (figure 2, lighter traces), the effect <strong>of</strong> the filtering was not consistent. At<br />
distances too close, not enough is filtered out, and the polyT is still preferred, while at<br />
distances too far, real instances <strong>of</strong> the motif will no longer pass the cut<strong>of</strong>f and the real<br />
motif is no longer recovered (figure 2, lighter traces). Thus, while incorporating<br />
<strong>evolution</strong>ary information separately can help recover the real motif, it depends critically<br />
on the choice <strong>of</strong> percent identity cut<strong>of</strong>f.<br />
4.5 Examples <strong>of</strong> other discovered motifs<br />
We ran both our program and MEME on the upstream regions <strong>of</strong> target genes <strong>of</strong> some<br />
transcription factors with few characterized targets and/or poorly defined motifs In<br />
several cases, for a given motif size, our algorithm ranked a plausible motif first, and<br />
MEME ranked a polyT motif first (see Table 1).<br />
78
Table 1.<br />
Motif discovery using EMnEM and MEME.<br />
The EMnEM program was run using the Jukes Cantor model for motif <strong>evolution</strong> with the rate set to ¼<br />
background (JC 0.25) on S. cerevisiae S. mikatae alignments in each case. For cases where EMnEM ranked<br />
the motif higher, the consensus sequence and a plot <strong>of</strong> the information content is shown. MEME was run<br />
on the unaligned sequences from both species simultaneously. Target genes are from SCPD[20] (+) or YPD<br />
[23] (++). – indicates that a plausible motif was not found.<br />
5 Conclusions and future directions<br />
We have provided an <strong>evolution</strong>ary mixture model for transcription factor binding sites in<br />
aligned sequences, and a motif finding algorithm based on this framework. We believe<br />
that our approach has many advantages over current methods; it produces probabilistic<br />
models <strong>of</strong> motifs, can be applied directly to multiple or pair-wise alignments, and can be<br />
applied simultaneously at multiple loci. Our method should be applicable to any group <strong>of</strong><br />
species whose intergenic regions can be aligned, though because alignments may not be<br />
79
possible at large <strong>evolution</strong>ary distances, our reliance on them is a disadvantage <strong>of</strong> our<br />
method relative to FootPrinter [18]. It is not difficult to conceive <strong>of</strong> extending this<br />
framework to unaligned sequences <strong>by</strong> treating the alignment as a hidden variable as well;<br />
unfortunately, the space <strong>of</strong> multiple alignments is large, and improved optimization<br />
methods would certainly be needed.<br />
In addition to motif discovery, our probabilistic framework is also applicable to<br />
binding site identification. Current methods that search genome sequence for matches to<br />
motifs are also plagued <strong>by</strong> false positives, but optimally combining sequence specificity<br />
and <strong>evolution</strong>ary constraint may lead to considerable improvement.<br />
7 References<br />
1. Stormo GD. DNA binding sites: representation and discovery. Bioinformatics. (2000) Jan;16(1):16-23.<br />
2. Stormo GD, Hartzell GW 3rd. Identifying protein-binding sites from unaligned DNA fragments. Proc<br />
Natl Acad Sci U S A. (1989) Feb;86(4):1183-7.<br />
3. Lawrence CE, Reilly AA. An expectation maximization (EM) algorithm for the identification and<br />
characterization <strong>of</strong> common sites in unaligned biopolymer sequences. Proteins. 1990;7(1):41-51.<br />
4. Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC. Detecting subtle sequence<br />
signals: a Gibbs sampling strategy for multiple alignment. Science. (1993) Oct 8;262(5131):208-14.<br />
5. Bailey TL, Elkan C, Fitting a mixture model <strong>by</strong> expectation maximization to discover motifs in<br />
biopolymers, Proceedings <strong>of</strong> the Second International Conference on Intelligent Systems for <strong>Molecular</strong><br />
Biology, pp. 28-36, AAAI Press, Menlo Park, California, (1994.)<br />
6. Hardison, Conserved noncoding sequences are reliable guides to regulatory elements, Trends in<br />
Genetics, (2000) Sep;16(9):369-372<br />
7. Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES. Sequencing and comparison <strong>of</strong> yeast species to<br />
identify genes and regulatory elements. Nature. (2003) May 15;423(6937):241-54.<br />
8. Blanchette M, Schwikowski B, Tompa M. Algorithms for phylogenetic footprinting. J Comput Biol.<br />
(2002);9(2):211-23.<br />
9. Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol.<br />
(1981);17(6):368-76.<br />
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10. Ng PC, Henik<strong>of</strong>f JG, Henik<strong>of</strong>f S. PHAT: a transmembrane-specific substitution matrix. Predicted<br />
hydrophobic and transmembrane. Bioinformatics. 2000 Sep;16(9):760-6. Erratum in: Bioinformatics<br />
2001 Mar;17(3):290<br />
11. Holmes I, Rubin GM. An expectation maximization algorithm for training hidden substitution models.<br />
J Mol Biol. (2002) Apr 12;317(5):753-64.<br />
12. B<strong>of</strong>felli D, McAuliffe J, Ovcharenko D, Lewis KD, Ovcharenko I, Pachter L, Rubin EM. Phylogenetic<br />
shadowing <strong>of</strong> primate sequences to find functional regions <strong>of</strong> the human genome. Science. (2003) Feb<br />
28;299(5611):1391-4.<br />
13. Yang Z. PAML: a program package for phylogenetic analysis <strong>by</strong> maximum likelihood. Comput Appl<br />
Biosci. (1997) Oct;13(5):555-6.<br />
14. Hastie T, Tibshirani R, Friedman J. The Elements <strong>of</strong> Statistical Learning Springer-verlag NY, (2001)<br />
15. Yang, Z. Maximum likelihood models for combined analyses <strong>of</strong> multiple sequence data. J Mol Evol.<br />
42:587-596 (1996.)<br />
16. Yang, Z., N. Goldman, and A. E. Friday. Comparison <strong>of</strong> models for nucleotide substitution used in<br />
maximum likelihood phylogenetic estimation. Mol Biol Evol. 11:316-324 (1994)<br />
17. M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation.<br />
18. Yang Z: PAML: a program package for phylogenetic analysis <strong>by</strong> maximum likelihood. Comput. Appl.<br />
Biosci. (1997) 13(5):555-556<br />
19. Zhu J, Zhang MQ. SCPD: a promoter database <strong>of</strong> the yeast Saccharomyces cerevisiae. Bioinformatics.<br />
(1999) Jul-Aug;15(7-8):607-611.<br />
20. Notredame C, Higgins DG, Heringa J. T-C<strong>of</strong>fee: A novel method for fast and accurate multiple<br />
sequence alignment. J Mol Biol. (2000) Sep 8;302(1):205-17.<br />
21. Halpern AL, Bruno WJ. Evolutionary distances for protein-coding sequences: modeling site-specific<br />
residue frequencies. Mol Biol Evol. 1998 Jul;15(7):910-917.<br />
22. Moses AM, Chiang DY, Kellis M, Lander ES, <strong>Eisen</strong> MB. Position specific variation in the rate <strong>of</strong><br />
<strong>evolution</strong> in transcription factor binding sites. BMC Evol Biol. (2003) 3:18<br />
23. Hodges PE, Payne WE, Garrels JI. The Yeast Protein Database (YPD): a curated proteome database for<br />
Saccharomyces cerevisiae. Nucleic Acids Res. (1998) Jan 1;26(1):68-72.<br />
81
10. MONKEY – extending the probabilistic model to identify binding sites <strong>of</strong> factors<br />
with known specificity.<br />
This work was published as Moses et al. 2004.<br />
Different types <strong>of</strong> genomic features have characteristic patterns <strong>of</strong> <strong>evolution</strong> that, when<br />
sequences from closely related organisms are available, can be exploited to annotate<br />
genomes [1]. Methods for comparative sequence analysis that exploit variation in rates<br />
and patterns <strong>of</strong> nucleotide <strong>evolution</strong> can identify coding exons [1,2], noncoding<br />
sequences involved in the <strong>regulation</strong> <strong>of</strong> transcription [3,4] and various types <strong>of</strong> RNAs [5-<br />
7]. While most <strong>of</strong> these methods have been developed for and applied to pairwise<br />
comparisons, sequence data are increasingly available for multiple closely related species<br />
[8]. It is therefore <strong>of</strong> considerable importance to develop sequence-analysis methods that<br />
optimally exploit <strong>evolution</strong>ary information, and to explore the dependence <strong>of</strong> these<br />
methods on the <strong>evolution</strong>ary relationships <strong>of</strong> the species in comparison.<br />
Sequence-specific DNA-binding proteins involved in <strong>transcriptional</strong> <strong>regulation</strong><br />
(transcription factors) play a central role in many biological processes. Despite extensive<br />
biochemical and molecular analysis, it remains exceedingly difficult to predict where on<br />
the genome a given factor will bind. Transcription factors bind to degenerate families <strong>of</strong><br />
short (6-20 base-pairs (bp)) sequences that occur frequently in the genome, yet only a<br />
small fraction <strong>of</strong> these sequences are actually bona fide targets <strong>of</strong> the transcription factor<br />
[9]. A major challenge in understanding the <strong>regulation</strong> <strong>of</strong> transcription is to be able to<br />
distinguish real transcription factor binding sites (TFBSs) from sequences that simply<br />
match a factor's binding specificity. Because the <strong>evolution</strong>ary properties <strong>of</strong> TFBSs are<br />
82
expected to be different from their nonfunctional counterparts, comparative analyses hold<br />
great promise in helping to address this challenge.<br />
In the past few years, several methods have been introduced to identify conserved<br />
(and presumably functional) TFBSs for a factor <strong>of</strong> known specificity (in contrast to the<br />
larger set <strong>of</strong> methods that use comparative data in motif discovery or to otherwise<br />
identify sequences likely to be involved in cis-<strong>regulation</strong>). Each <strong>of</strong> these methods<br />
explicitly or implicitly adopts one <strong>of</strong> several distinct definition <strong>of</strong> a conserved TFBS.<br />
These include a binding site in a reference genome that is perfectly or highly conserved<br />
[8,10-12]; a binding site in a reference genome that lies in a highly conserved region [4];<br />
or a position at which the binding model predicts a binding site in all species [13-18].<br />
In a previous study we characterized the <strong>evolution</strong> <strong>of</strong> experimentally validated<br />
TFBSs in the Saccharomyces cerevisiae genome, finding that functional TFBSs evolve<br />
more slowly than flanking intergenic regions, and more strikingly, that there is<br />
considerable position-specific variation in <strong>evolution</strong>ary rates within TFBSs [19]. We<br />
further showed that <strong>evolution</strong>ary rate at each position is a function <strong>of</strong> the selectivity <strong>of</strong><br />
the factor for bases at that position. Our goal here is to incorporate these specific<br />
<strong>evolution</strong>ary properties <strong>of</strong> TFBSs into the search for conserved TFBSs. Or, more<br />
precisely, to develop a method that, given the specificity <strong>of</strong> a transcription factor,<br />
identifies conserved binding sites in multiple alignments <strong>by</strong> taking into account the<br />
sequence specificity and patterns <strong>of</strong> <strong>evolution</strong> expected for TFBSs, while still fully<br />
exploiting the phylogenetic relationships <strong>of</strong> the species being compared.<br />
In addition to developing new methods, there are several hypotheses regarding the<br />
comparative annotation <strong>of</strong> TFBSs that we are interested in testing. It has been noted that<br />
83
the effectiveness <strong>of</strong> such analyses will depend critically on the <strong>evolution</strong>ary distance<br />
separating the species used. At very close distances TFBSs will appear conserved<br />
because there has been insufficient time for substitutions to occur. As distance increases,<br />
and substitutions occur most rapidly at nonfunctional positions, our ability to detect<br />
constrained binding sites should improve until we are no longer able to reliably assign<br />
orthology based on sequence alignment. To overcome this problem <strong>of</strong> divergence<br />
distances exceeding what can be aligned, the sequences <strong>of</strong> multiple closely related<br />
species can be used to span the same <strong>evolution</strong>ary distances (and presumably provide the<br />
same discriminatory power) as fewer more distantly related ones. However, aside from<br />
these qualitative expectations, the dependence <strong>of</strong> the ability to identify conserved TFBSs<br />
on <strong>evolution</strong>ary distance and tree topology has not been rigorously investigated. Because<br />
the s<strong>of</strong>tware MONKEY can be applied to multiple alignments <strong>of</strong> varying numbers <strong>of</strong><br />
species and produces scores that can be meaningfully compared across different sets <strong>of</strong><br />
species, we are now able to address these issues.<br />
Results<br />
Overview<br />
We developed an approach to identify conserved TFBSs that combines probabilistic<br />
models <strong>of</strong> binding-site specificity [20- 22] with probabilistic models <strong>of</strong> <strong>evolution</strong> [23,24].<br />
Starting with an alignment <strong>of</strong> sequences from multiple related species, we use the known<br />
sequence specificity for a transcription factor to compare the likelihood <strong>of</strong> the sequences<br />
under two <strong>evolution</strong>ary models - one for background and one for TFBSs. The central<br />
feature <strong>of</strong> this method that underlies its ability to identify conserved TFBSs is that it uses<br />
a specific probabilistic <strong>evolution</strong>ary model for the binding sites <strong>of</strong> each transcription<br />
84
factor. The <strong>evolution</strong>ary model we use for TFBSs [25] assumes that sites were under<br />
selection to remain binding sites throughout the <strong>evolution</strong>ary history <strong>of</strong> the species being<br />
studied. This model uses the sequence specificity <strong>of</strong> the factor to predict patterns and<br />
rates <strong>of</strong> <strong>evolution</strong> that recapitulate the patterns and rates observed in real TFBSs [19].<br />
MONKEY: scanning alignments to identify conserved transcription factor binding sites<br />
MONKEY, our tool for identifying conserved TFBSs, takes as input a multiple sequence<br />
alignment, a tree describing the relationship <strong>of</strong> the aligned species, a model <strong>of</strong> a<br />
transcription factor's binding specificity and a model for background noncoding DNA. It<br />
returns, for each position in the alignment, a likelihood ratio comparing the probability<br />
that the position is a conserved binding site for the selected factor compared to the<br />
probability that the position is background.<br />
Extending matrix searches to multiple sequence alignments<br />
For the model <strong>of</strong> binding specificity, we use a traditional frequency matrix [20-22]. The<br />
values in the matrix - fib - represent the probability <strong>of</strong> observing the base b (A, C, G or T)<br />
at the ith position in a binding site <strong>of</strong> width w. For the model <strong>of</strong> the background, we use a<br />
single set <strong>of</strong> base frequencies gb. A widely used statistic for scoring the similarity <strong>of</strong> a<br />
single sequence to a frequency matrix is the log likelihood ratio comparing the<br />
probability <strong>of</strong> having observed a sequence X <strong>of</strong> width w under the motif model (a<br />
frequency matrix, designated as motif) to the probability <strong>of</strong> having observed X under the<br />
background model (designated <strong>by</strong> bg), which can be easily reduced to:<br />
p(<br />
X | motif )<br />
f<br />
S(<br />
X ) = log<br />
=<br />
,<br />
p(<br />
X | bg)<br />
85<br />
∑∑<br />
= i w<br />
ib X ib log<br />
i= 1 b gb
where Xib is an indicator variable which equals 1 if base b is observed at position i, and<br />
zero otherwise. This classifier can be motivated <strong>by</strong> the approximation that the data are<br />
distributed as a two-component mixture <strong>of</strong> sequences matching the frequency matrix and<br />
sequences drawn from a uniform background. In practice, we compute this score using a<br />
position-specific scoring matrix (PSSM) with entries, Mib = log(fib/gb), and find S for a<br />
particular wmer <strong>by</strong> adding up the entries that correspond to the bases in the query<br />
sequence. In extending this to a pair <strong>of</strong> aligned sequences X and Y, we want to perform<br />
the same calculation on their common ancestor A. Since A is not observed, we consider<br />
all possible ancestral sequences <strong>by</strong> summing over them, weighting each <strong>by</strong> their<br />
probability given the data (X and Y), the phylogenetic tree (T) that relates the sequences,<br />
and a probabilistic <strong>evolution</strong>ary model [23].<br />
We can write a new score representing the log-likelihood ratio that compares the<br />
hypothesis that X and Y are a conserved example <strong>of</strong> the binding site represented <strong>by</strong> the<br />
frequency matrix to the hypothesis that they have been drawn from the background:<br />
ˆ<br />
p(<br />
X , Y | motif , T,<br />
Rmotif<br />
)<br />
S ( X , Y ) = log<br />
,<br />
p(<br />
X , Y | bg,<br />
T,<br />
R )<br />
bg<br />
where Rmotif and Rbg are rate matrices describing the substitution process <strong>of</strong> the binding<br />
site and background respectively. Using the conditional independence <strong>of</strong> the sequences X<br />
and Y on the ancestor, A, and writing TAX for the <strong>evolution</strong>ary distance separating<br />
sequence X from A, this becomes:<br />
∑ p(<br />
A<br />
∑<br />
X | A,<br />
TAX<br />
, Rmotif<br />
) p(<br />
Y | A,<br />
TAY<br />
, Rmotif<br />
) p(<br />
A | motif )<br />
Sˆ<br />
( X , Y ) = log<br />
.<br />
p(<br />
X | A,<br />
T , R ) p(<br />
Y | A,<br />
T , R ) p(<br />
A | bg)<br />
A<br />
AX<br />
86<br />
bg<br />
AY<br />
bg
The class <strong>of</strong> <strong>evolution</strong>ary models used <strong>by</strong> MONKEY define a substitution matrix,<br />
p(Xi|Ai,t) = e Rt , that represents the probability <strong>of</strong> observing each base at position i in the<br />
extant sequence (X) given each base in the ancestral sequence (A) after t units <strong>of</strong><br />
<strong>evolution</strong>ary time or distance, given some rate matrix, R [23]. Since these models retain<br />
positional independence, we can rewrite this as:<br />
i=<br />
1<br />
∑ p<br />
b<br />
∑<br />
( X =<br />
=<br />
w<br />
i | Aib<br />
1,<br />
TAX<br />
, Rmotif<br />
) p(<br />
Yi<br />
| Aib<br />
1,<br />
TAY<br />
, Rmotif<br />
) fib<br />
Sˆ<br />
( X , Y ) = ∑ log<br />
.<br />
p(<br />
X | A = 1,<br />
T , R ) p(<br />
Y | A = 1,<br />
T , R ) g<br />
b<br />
i<br />
ib<br />
This can be extended to more than two sequences, that is, S ˆ( X , Y,...,<br />
Z)<br />
, <strong>by</strong> replacing the<br />
probabilities <strong>of</strong> X and Y with the probability with the left and right branches <strong>of</strong> the tree<br />
below, and performing the calculation at the root. The probabilities <strong>of</strong> the left and right<br />
branches <strong>of</strong> the tree can be calculated recursively as has been described previously [23].<br />
Once again, for practical purposes we can convert these scores to a PSSM, whose entries<br />
are given for the pairwise case <strong>by</strong>:<br />
ˆ<br />
p(<br />
X ia = 1,<br />
Yib<br />
= 1|<br />
motif , T,<br />
Rmotif<br />
)<br />
M iab = log<br />
p(<br />
X ia = 1,<br />
Yib<br />
= 1|<br />
bg,<br />
T,<br />
Rbg<br />
)<br />
where at each position we now index <strong>by</strong> the bases a and b in the two sequences. For<br />
multiple alignments <strong>of</strong> n species, each position requires 4 n entries.<br />
Evolutionary models<br />
The use <strong>of</strong> <strong>evolution</strong>ary models is critical to the function <strong>of</strong> MONKEY. Myriad <strong>of</strong> such<br />
models exist, and in principle all can be used in MONKEY. For the background, it is<br />
natural to use a model appropriate for sites with no particular constraint, such as the<br />
average intergenic or synonymous rates. MONKEY allows the use <strong>of</strong> the JC [26] or HKY<br />
87<br />
AX<br />
bg<br />
i<br />
ib<br />
AY<br />
bg<br />
b
[27] models, and here we use the latter with the base frequencies, rates and transition-<br />
transversion rate-ratio estimated from noncoding alignments assuming a single model <strong>of</strong><br />
<strong>evolution</strong> over the noncoding regions (see details in Materials and methods). It is also<br />
possible to estimate the <strong>evolution</strong>ary model separately for each intergenic alignment,<br />
although the small size <strong>of</strong> yeast intergenic regions leads to variable estimates. In<br />
principle, the JC and HKY models can also be used for the motif, with rates set according<br />
to our expectation <strong>of</strong> the overall rate <strong>of</strong> <strong>evolution</strong> in functional binding sites, which has<br />
been estimated as two to three times slower than the average intergenic rate [19].<br />
However, we have previously shown that there is position-specific variation in<br />
<strong>evolution</strong>ary rates within functional transcription factor binding sites [19] and that<br />
positions in a motif with low degeneracy in the bindingsite model evolve more slowly<br />
than positions with high degeneracy; this relationship between the equilibrium<br />
frequencies and the position-specific <strong>evolution</strong>ary rates is accurately predicted <strong>by</strong> an<br />
<strong>evolution</strong>ary model from Halpern and Bruno (HB model) [25].<br />
In using this model, we assume that sequences evolve under constant purifying<br />
selection to maintain a particular set <strong>of</strong> equilibrium base frequencies. The use <strong>of</strong> this<br />
model corresponds to a definition <strong>of</strong> a conserved TFBS as a sequence position where<br />
there has always been a binding site for the transcription factor. Although the model does<br />
not strictly require that a binding site be present in each <strong>of</strong> the observed species, positions<br />
lacking such sites will have lower probabilities as they require the use <strong>of</strong> less probable<br />
substitutions. The rate <strong>of</strong> change from residue a to b at position i in the motif is given <strong>by</strong>:<br />
88
R<br />
⎛ fibQ<br />
⎞ ba ln ⎜<br />
f Q ⎟<br />
⎝ ⎠<br />
ia ab<br />
( i)<br />
ab = Qab<br />
× ,<br />
fiaQab<br />
where Q is the (position independent) underlying mutation matrix, which we set equal to<br />
the background model (Q = Rbg), and f is the frequency matrix describing the specificity<br />
<strong>of</strong> the factor. Thus, for each position in the motif, the HB model predicts the rates <strong>of</strong> each<br />
type <strong>of</strong> substitution as a function <strong>of</strong> the frequency matrix, and the background model.<br />
Comparing hits for different factors and <strong>evolution</strong>ary distances: computing the null<br />
distribution<br />
To compare scores from different <strong>evolution</strong>ary distances and different factors, it is<br />
critical that we are able to assign significance to a particular value <strong>of</strong> the score. To do so,<br />
we need to compute the distribution <strong>of</strong> the score under the null hypothesis that the<br />
sequence is part <strong>of</strong> the background. Calculating a p-value for a score S in a single<br />
sequence requires the enumeration <strong>of</strong> all possible w-mers that have a score S or greater<br />
under the background model. For n aligned sequences this requires the enumeration all<br />
4 wn possible sets <strong>of</strong> aligned w-mers with scores S or greater under the background model.<br />
While the number <strong>of</strong> possible alignments <strong>of</strong> n w-mers can be unmanageably large for<br />
even small values <strong>of</strong> n and w, because we treat each position independently we can<br />
enumerate these possibilities efficiently using an algorithm developed for matrix searches<br />
<strong>of</strong> single sequences [28,29]. Every observed score is a sum <strong>of</strong> w numbers, one from each<br />
column <strong>of</strong> the matrix. The probability <strong>of</strong> observing exactly score S is the number <strong>of</strong> paths<br />
through the matrix whose entries add up to S, weighted <strong>by</strong> the probability <strong>of</strong> the path. By<br />
converting the matrix to integers, we can compute this probability for all values <strong>of</strong> S<br />
89<br />
1−<br />
f<br />
ib<br />
Q<br />
ba
ecursively. We initialize Pi(S) (the probability <strong>of</strong> observing score S after i columns in the<br />
matrix) <strong>by</strong> setting P0(S) = 1 for S = 0, and P0(S) = 0 for S ≠ 0. We then compute the<br />
values <strong>of</strong> the function for i = [1, w] as follows:<br />
∑<br />
P S)<br />
P ( S − Mˆ<br />
) p(<br />
c | bg,<br />
T,<br />
R )<br />
i(<br />
= i−1<br />
c<br />
For aligned sequences, c represents a column in the alignment, and the sum is over all 4 n<br />
possible columns an alignment <strong>of</strong> n sequences. The probability distribution function<br />
(PDF) <strong>of</strong> scores is Pw(S), and from this the cumulative distribution function (CDF), the<br />
probability <strong>of</strong> observing a score <strong>of</strong> S or greater, can be directly computed. Although in<br />
principle we can compute the probabilities to arbitrary precision, because the time<br />
complexity increases with the number <strong>of</strong> possible scores, we limit the precision to within<br />
approximately 0.01 bits.<br />
90<br />
ic<br />
Figure 1 compares empirical p-values from 5,000 pairs <strong>of</strong> sequences evolved in a<br />
simulation (see Materials and methods) with those computed <strong>by</strong> this method, and shows<br />
that they agree closely. We have used this method to compute the CDFs for alignments <strong>of</strong><br />
up to six species, and therefore can apply our method to most comparative genomics<br />
applications. We note, in addition, that the likelihood ratio scores are approximately<br />
Gaussian (data not shown). As the means and variance <strong>of</strong> the scores under each model<br />
can be computed efficiently (see Materials and methods) we can estimate p-values using<br />
a Gaussian approximation (Figure 1) when the number <strong>of</strong> sequences in the alignment is<br />
large.<br />
bg
Figure 1.<br />
Accuracy <strong>of</strong> p-value estimations.<br />
To examine the accuracy <strong>of</strong> our p-value estimates, we compared the empirical p-value (computed from the<br />
observed distribution <strong>of</strong> scores) to p-values computed using either the exact method described above (black<br />
points) or Gaussian approximation (gray points). The scores represent the simple score at a distance <strong>of</strong> 0.1<br />
substitutions per site calculated using the Gcn4p matrix from SCPD [33]. Other models and matrices<br />
produce similar results.<br />
Heuristics for alignments with gaps<br />
The treatment <strong>of</strong> alignment gaps in identifying conserved TFBSs is somewhat<br />
problematic. One the one hand, nonfunctional sequences may be inserted and deleted<br />
over <strong>evolution</strong> more rapidly than functional elements [30-32], and thus the presence <strong>of</strong> a<br />
gap aligned to a predicted binding site could indicate that it is nonfunctional. On the other<br />
91
hand, alignment algorithms are imperfect, and must <strong>of</strong>ten make arbitrary decisions about<br />
the placement <strong>of</strong> gaps. We sought to design a heuristic that accommodated both these<br />
aspects <strong>of</strong> genomic sequence data <strong>by</strong> locally optimizing alignments for the purpose <strong>of</strong><br />
comparative annotation <strong>of</strong> regulatory elements.<br />
The idea is to assign a poor score to regions <strong>of</strong> the alignment with a large number<br />
<strong>of</strong> gaps, but to locally realign regions with a small number <strong>of</strong> gaps to identify conserved<br />
but misaligned binding sites. To do this, we scan along the ungapped version <strong>of</strong> one <strong>of</strong><br />
the aligned sequences - the 'reference' sequence. For each position in the reference<br />
sequence pr, we define a window in each other sequence around ps, the position in<br />
sequence s aligned to position p r . The window runs from ps - (a + b) to p s + w + (a +<br />
b), where a and b are the number <strong>of</strong> gaps in the aligned versions <strong>of</strong> sequences r and s in<br />
position p to p + w, where p is the position in the alignment <strong>of</strong> pr. For each subsequence<br />
<strong>of</strong> length w in the window, we calculate the percent identity to the reference sequence,<br />
and create an alignment <strong>of</strong> pr to pr + w (in the reference sequence) to the most similar<br />
word in the window <strong>of</strong> each other sequence. This locally optimized alignment is then<br />
scored. Note that if a and b are zero (meaning there are no gaps in the aligned sequence),<br />
no optimization is done. If a is too large (in most contexts greater than five) we exclude<br />
that region <strong>of</strong> the alignment from further. This heuristic encapsulates the idea that too<br />
many gaps are indicative <strong>of</strong> lack <strong>of</strong> constraint, but conservatively allows for a few gaps<br />
due to alignment or sequence imperfections.<br />
Application to Saccharomyces<br />
92
The genome sequences <strong>of</strong> several species closely related to the budding yeast<br />
Saccharomyces cerevisiae have recently been published and become models for the<br />
comparative identification <strong>of</strong> transcription factor binding sites [8,11]. We aligned the<br />
intergenic regions <strong>of</strong> S. cerevisiae genes to their orthologs in S. paradoxus, S. mikatae, S.<br />
bayanus and S. kudriavzevii genomes using CLUSTALW (see Materials and methods)<br />
and sought to evaluate the effectiveness <strong>of</strong> MONKEY under different <strong>evolution</strong>ary<br />
models and distances. Ideally, we would use several diverse transcription factors with<br />
known binding specificity, where the set <strong>of</strong> matches to the factor's matrix in the S.<br />
cerevisiae genome could be divided into two reasonably sized sets: those known to be<br />
bound <strong>by</strong> the factor (positives) and those known not to be bound <strong>by</strong> the factor<br />
(negatives). Unfortunately, even in yeast, the number <strong>of</strong> such cases is limited. For many<br />
factors we can identify true positives <strong>by</strong> combining high- and low-throughput<br />
experimental data that supports the hypothesis that a particular position in the genome is<br />
bound <strong>by</strong> a given factor. A true negative set, however, must be constructed on the basis<br />
<strong>of</strong> lack <strong>of</strong> evidence that a sequence is functional, as the interpretation <strong>of</strong> negative results<br />
almost always is ambiguous. In the case <strong>of</strong> transcription factor binding sites this is<br />
particularly problematic, because DNA-binding proteins have overlapping specificity,<br />
and we may therefore observe conservation <strong>of</strong> a binding site because it is bound <strong>by</strong><br />
another factor with similar specificity. After evaluating all factors with binding<br />
specificity in Saccharomyces cerevisiae Promoter Database (SCPD) [33], we focus on<br />
Gal4p and Rpn4p for further analysis (see Table 1 for properties <strong>of</strong> these factors, and<br />
Materials and methods for a description <strong>of</strong> the selection <strong>of</strong> positive and negative sets).<br />
93
Table 1.<br />
Definition <strong>of</strong> positive and negative sets <strong>of</strong> matrix matches<br />
Criteria used to define positive and negative sets to use in this study. It is important to avoid factors whose<br />
specificity overlaps with other factors, because binding sites that are not occupied <strong>by</strong> one factor may be<br />
constrained because <strong>of</strong> binding <strong>by</strong> another, and to choose factors with characterized specificity because our<br />
methods rely on the assumption that the specificity is known.<br />
The effects <strong>of</strong> <strong>evolution</strong>ary models on the discrimination <strong>of</strong> functional binding sites<br />
To evaluate the performance <strong>of</strong> our <strong>evolution</strong>ary method in correctly identifying bona<br />
fide binding sites, we calculated the p-values <strong>of</strong> the positive and negative sites for each<br />
factor, using MONKEY on alignments <strong>of</strong> all five genomes for Rpn4p and four species<br />
(with S. kudriavzevii excluded because too few sequences were available) for Gal4p. We<br />
compared the performance <strong>of</strong> MONKEY with the HB model to scores from S. cerevisiae<br />
alone and to a 'simple' score (equal to the average <strong>of</strong> the single sequence log likelihood<br />
ratios) that utilizes all the comparative data without an <strong>evolution</strong>ary model. The results<br />
are summarized in Table 2. An ideal scoring method would assign low p-values to real<br />
sites (positives) and high p-values to spurious sites (negatives), and we therefore<br />
compared the p-values assigned <strong>by</strong> monkey based on the HB model to those based on the<br />
'simple' score. Not surprisingly, both methods were a great improvement over searching<br />
in S. cerevisiae alone. Overall, when compared to each other, the HB score assigned<br />
lower p-values to the binding sites more <strong>of</strong>ten in the positive sets (90% for Gal4p and<br />
94
80% for Rpn4p) and less <strong>of</strong>ten in the negative sets (20% for Gal4p and 25% for Rpn4p)<br />
than did the simple score. We note that some <strong>of</strong> the supposedly functional Rpn4p sites<br />
were assigned higher pvalues in S. cerevisiae alone, suggesting that they are not in fact<br />
conserved; these will be discussed below.<br />
Table 2.<br />
Performance <strong>of</strong> different scores in recognizing functional and nonfunctional sites<br />
The score based on the Halpern-Bruno (HB) model assigns lower p-values to functional binding sites and higher p-<br />
values to nonfunctional binding sites than the simple score, defined as the average <strong>of</strong> the single species scores in at that<br />
position in the alignment. Both methods are far superior to p-values from S. cerevisiae alone. See text for details.<br />
The effect <strong>of</strong> <strong>evolution</strong>ary distance on the discrimination <strong>of</strong> functional binding sites<br />
As <strong>evolution</strong>ary distance increases, we expect fewer matches to the matrix to be<br />
conserved <strong>by</strong> chance, which implies that the probability <strong>of</strong> observing matches as highly<br />
conserved as the functional sites should decrease. Similarly, we expect the nonfunctional<br />
sites to show many substitutions and their p-values to increase over <strong>evolution</strong>. To explore<br />
the change in p-values over <strong>evolution</strong>ary distance, we scored the functional and<br />
nonfunctional sets <strong>of</strong> binding sites at a variety <strong>of</strong> <strong>evolution</strong>ary distances <strong>by</strong> creating<br />
alignments <strong>of</strong> different combinations <strong>of</strong> species (see Materials and methods). The median<br />
p-value <strong>of</strong> the positive set <strong>of</strong> TFBSs decreases monotonically with <strong>evolution</strong>ary distance,<br />
with the rate <strong>of</strong> decrease an approximately constant function <strong>of</strong> <strong>evolution</strong>ary distance (see<br />
95
Figure 2). The median p-value for the binding sites in the negative set increases with<br />
<strong>evolution</strong>ary distance, although somewhat erratically. This demonstrates that MONKEY<br />
effectively exploits <strong>evolution</strong>ary distance, and confirms our intuition that as <strong>evolution</strong>ary<br />
distance increases, functional elements should be increasingly easy to distinguish from<br />
spurious predictions.<br />
Figure 2.<br />
Significance <strong>of</strong> matches increases with <strong>evolution</strong>ary distance.<br />
Median p-values for the positive (black squares) and negative (white triangles or white triangle points) sets<br />
<strong>of</strong> binding sites for (a) Gal4p and (b) Rpn4p at different <strong>evolution</strong>ary distances represented <strong>by</strong> comparing<br />
S. cerevisiae to different subsets <strong>of</strong> the available species. For both factors, as <strong>evolution</strong>ary distance<br />
increases, the median p-value <strong>of</strong> the functional matches decreases, indicating that they are less likely to<br />
have appeared <strong>by</strong> chance. Conversely, the median p-value <strong>of</strong> the nonfunctional matches (negative set, white<br />
symbols) increases. These observations agree with our predictions for the behavior <strong>of</strong> the p-values (solid<br />
traces) under either the HB <strong>evolution</strong> for the motif or HKY <strong>evolution</strong> for the background. There is little<br />
difference between these predictions and similar ones that assume that all the comparisons were pairwise<br />
(dotted traces).<br />
96
To test this hypothesis on a more quantitative level we sought to compare the observed<br />
scores with the expected scores assuming that binding sites evolved precisely according<br />
to the <strong>evolution</strong>ary models used <strong>by</strong> MONKEY. Briefly, given a binding- site model and a<br />
phylogenetic tree, we assume we have observed a binding site in the reference genome,<br />
and that this site evolves along the tree under either the motif model (HB) or background<br />
model (HKY), representing functional and nonfunctional binding sites, respectively (see<br />
Materials and methods for details). The expected p-values associated with the functional<br />
binding sites (Figure 2, solid lines) showed reasonable agreement with the models,<br />
consistent with previous observations that they are evolving under constraint that is well<br />
modeled <strong>by</strong> the purifying selection on the base frequencies in the specificity matrix [19].<br />
Pairwise versus multi-species comparisons<br />
The comparisons at the different <strong>evolution</strong>ary distances used in Figure 2 employed<br />
variable numbers <strong>of</strong> species, with the shorter distances representing primarily pairwise<br />
comparisons and the longer distances comparisons <strong>of</strong> three or more species. While we<br />
expect the variation in p-values with different combinations <strong>of</strong> species to be primarily a<br />
function <strong>of</strong> the <strong>evolution</strong>ary distance spanned <strong>by</strong> these species, there will also be effects<br />
related to the number <strong>of</strong> species and the topology <strong>of</strong> the three. For example, in the limit<br />
<strong>of</strong> very long branch lengths, the <strong>evolution</strong>ary p-values are on the order <strong>of</strong> the power <strong>of</strong><br />
the number <strong>of</strong> species and are independent <strong>of</strong> <strong>evolution</strong>ary distance. In contrast, in the<br />
limit <strong>of</strong> very short branch lengths, the <strong>evolution</strong>ary p-values depend only on the distance<br />
spanned <strong>by</strong> the comparison, as most <strong>of</strong> the information provided <strong>by</strong> additional species is<br />
redundant. However, because most comparisons that are actually carried out are far from<br />
97
either <strong>of</strong> these extremes, we sought to evaluate the effects <strong>of</strong> species numbers and tree<br />
topology for the Saccharomyces species analyzed here. First, we recomputed the<br />
expected p-values for all the distances analyzed in Figure 2, except that instead <strong>of</strong> using<br />
the real tree topology, we used a single pairwise comparison at the same <strong>evolution</strong>ary<br />
distance (Figure 2, dotted lines). For example, for the Rpn4p analyses using all five<br />
species we assumed a pairwise comparison at an <strong>evolution</strong>ary distance <strong>of</strong> around 1.1<br />
substitutions per site. Note that this is considerably more distant than any <strong>of</strong> the pairwise<br />
comparisons available among these species. The predictions for the pairwise and multi-<br />
species comparisons are very similar, suggesting that at the <strong>evolution</strong>ary distances<br />
spanned <strong>by</strong> these species there is little difference in using multiple species alignments<br />
relative to a pairwise alignment that spans the same <strong>evolution</strong>ary distance. Only at the<br />
longest distances considered (greater than 0.8 substitutions per site) does the power <strong>of</strong> the<br />
pairwise comparison begin to level <strong>of</strong>f, although there are other reasons that multiple<br />
species comparisons might still be preferred (see Discussion). To complement this<br />
theoretical analysis, we were interested in using empirical data to compare pairwise and<br />
multi-species analyses. Fortuitously, the <strong>evolution</strong>ary distance between S. cerevisiae and<br />
S. kudriavzevii is almost exactly equal to the <strong>evolution</strong>ary distance spanned <strong>by</strong> S.<br />
cerevisiae, S. paradoxus and S. mikatae (median tree length approximately 0.5<br />
substitutions per site; see Figure 3a). Because our models predict that we are in a regime<br />
where <strong>evolution</strong>ary distance is the primary determinant <strong>of</strong> the p-values, we expect<br />
searches using these different sets <strong>of</strong> species to yield similar results. We tested this<br />
hypothesis <strong>by</strong> calculating the p-values associated with the Rpn4p-binding sites using the<br />
sequences from these two comparisons. The median p-values in both the positive and<br />
98
negative sets are very similar (Figure 3b), confirming that at these relatively short<br />
<strong>evolution</strong>ary distances, the power <strong>of</strong> the comparative method is independent <strong>of</strong> the<br />
number <strong>of</strong> species considered (see Discussion).<br />
Figure 3.<br />
Significance <strong>of</strong> binding sites in pairwise or three-way comparisons at similar<br />
<strong>evolution</strong>ary distance.<br />
(a) Histogram <strong>of</strong> the percent identities <strong>of</strong> all aligned noncoding regions <strong>of</strong> S. cerevisiae and S. kudriavzevii<br />
(open squares) and S. cerevisiae, S. paradoxus and S. mikatae (filled squares). (b) Median p-values <strong>of</strong><br />
functional matches (positive set, gray bars) and the nonfunctional matches (negative set, open bars) for S.<br />
cerevisiae and S. kudriavzevii alignments (left) and S. cerevisiae, S. paradoxus and S. mikatae alignments<br />
(right). The similarity <strong>of</strong> these p-values supports the idea that multiple similar genomes can be used to span<br />
longer <strong>evolution</strong>ary distances, but at these close <strong>evolution</strong>ary distances provide little additional power.<br />
Taken together, these results strongly support the idea that when appropriate methods are<br />
used, data from multiple species can be combined effectively to span larger <strong>evolution</strong>ary<br />
distances. Note that this in no way implies that the addition <strong>of</strong> extra species to an existing<br />
99
pairwise comparisons is not useful - such additions will always increase the <strong>evolution</strong>ary<br />
distance spanned <strong>by</strong> the species and thus will increase the power <strong>of</strong> the comparison.<br />
Testing the power <strong>of</strong> comparative annotation <strong>of</strong>transcription factor binding sites<br />
At the distances spanned <strong>by</strong> all available sequence data, the p-values are so small that we<br />
no longer expect to find matches <strong>of</strong> the quality <strong>of</strong> those in the positive set <strong>by</strong> chance,<br />
especially for Rpn4p. To test this further, we scanned both strands <strong>of</strong> all the available<br />
alignments <strong>of</strong> all five sensu stricto species (around 2.7 Mb) to identify our most confident<br />
predictions <strong>of</strong> conserved matches to the Rpn4p matrix. We chose the pvalue cut<strong>of</strong>f <strong>of</strong><br />
1.85 × 10-8, which corresponds to a probability <strong>of</strong> 0.05 <strong>of</strong> observing one match at that<br />
level over the entire search (using a Bonferroni correction for multiple testing). After<br />
excluding divergently transcribed genes, there were 56 genes that contained putative<br />
binding sites at that p-value. Of 32 genes in our positive set that had sequence available<br />
for all five species, 30 had binding sites below this p-value. Of the 28 genes in the<br />
negative set for which sequences were available, only three had binding sites below this<br />
cut<strong>of</strong>f. In this (nearly ideal) case we have ruled out nearly 90% <strong>of</strong> the negative set at the<br />
expense <strong>of</strong> less than 10% <strong>of</strong> the positives. Examining the expression patterns <strong>of</strong> these<br />
genes (Figure 4a) allows them to be divided into three major classes. The first is a group<br />
(indicated <strong>by</strong> a blue bar) containing 30 genes (28 <strong>of</strong> which were in our original positive<br />
set and two other genes) that show a very similar pattern over the entire set <strong>of</strong> conditions.<br />
The second group (indicated <strong>by</strong> a green bar) contains 11 genes (<strong>of</strong> which only one was in<br />
our original positive set) that show uncoordinated gene expression changes in some<br />
conditions in addition to the stereotypical Rpn4p expression pattern. It is possible that<br />
these genes' <strong>regulation</strong> is controlled <strong>by</strong> multiple mechanisms under different conditions<br />
100
[34], and <strong>regulation</strong> <strong>by</strong> Rpn4p is one contribution to their overall pattern <strong>of</strong> expression.<br />
Further supporting this hypothesis, only one <strong>of</strong> these genes (UFD1) is annotated as<br />
involved in protein degradation, and three (YBR062C, YOR052C and YER163C) have<br />
unknown functions.<br />
Finally, and most surprising from the perspective <strong>of</strong> comparative annotation, is a<br />
third set <strong>of</strong> 14 genes, including one from our original positive set and three from our<br />
negative set, most <strong>of</strong> which show no evidence <strong>of</strong> the proteasomal expression pattern<br />
associated with Rpn4p (Figure 4b). It is extremely unlikely that these sequences have<br />
been conserved <strong>by</strong> chance, and we suggest that they represent matches that are conserved<br />
for reasons other than binding <strong>by</strong> Rpn4p (see Discussion).<br />
Figure 4.<br />
Relationship between conserved Rpn4p-binding sites and expression.<br />
101
(a) We identified 56 Rpn4p-binding sites with p-values below 1.85 × 10-8 using all five species and the<br />
HB model. The expression patterns <strong>of</strong> these genes (clustered and displayed as in [44]) fall into two major<br />
groups: the 'stereotypical' proteasomal pattern (indicated <strong>by</strong> a blue bar at the right), and a second group<br />
expressed in these and additional conditions (indicated <strong>by</strong> the green bar). The orange bars above the<br />
expression data correspond to (left to right) temperature changes, treatment with H2O2, treatment with the<br />
superoxide generating drug menadione, treatment with the sulfhydryl oxidant diamide, deletions <strong>of</strong> YAP1<br />
and MSN2/4, treatment with the DNA damaging agent methylmethanesulfonate (MMS), and heat shock in<br />
deletions <strong>of</strong> MEC1 and DUN1 [48,49]. (b) Examples <strong>of</strong> conserved Rpn4p sites (boxed) that do not fall in<br />
either expression group (neither blue nor green bar).<br />
Non-conserved binding sites in regulated genes<br />
Having identified examples <strong>of</strong> conserved binding sites whose near<strong>by</strong> genes showed no<br />
evidence <strong>of</strong> function, we decided to examine the converse: binding sites near regulated<br />
genes, and therefore presumably functional, that are not conserved. Figure 5 shows the p-<br />
values <strong>of</strong> individual positive Rpn4p sites at different <strong>evolution</strong>ary distances. While most<br />
<strong>of</strong> the sites follow the trajectory predicted for sites evolving under the HB model, the p-<br />
values for four <strong>of</strong> the positive sites seem to be well-modeled <strong>by</strong> the 'background' or<br />
unconstrained model. This is surprising because we expect these binding sites to be<br />
functional, and therefore under purifying selection. One explanation is that some <strong>of</strong> these<br />
sites may have been misannotated as functional. For example, in addition to a<br />
nonconserved positive site, the upstream region <strong>of</strong> REH1 contains another binding site<br />
that is a weaker match to the Rpn4p matrix (Figure 5b) and did not pass our threshold for<br />
inclusion in the positive set (see Materials and methods). This weaker match is more<br />
highly conserved and may represent the functional site in this promoter. In the case <strong>of</strong><br />
PTC3, however, we can find no other candidate binding sites near<strong>by</strong> (Figure 5c). This<br />
102
epresents a possible example <strong>of</strong> binding site gain, a proposed mechanism <strong>of</strong> regulatory<br />
<strong>evolution</strong> at the molecular level (see Discussion).<br />
Figure 5.<br />
Some apparently functional Rpn4p-binding sites are not conserved.<br />
(a) The MONKEY p-values (points) <strong>of</strong> all putatively functional Rpn4p-binding sites at varying<br />
<strong>evolution</strong>ary distances, along with the expected values under the HB and HKY models (solid traces). The<br />
majority <strong>of</strong> sites behave as expected for conserved binding sites (lower trace). Several, however, behave as<br />
expected for unconstrained sites (upper trace). (b) The predicted binding site (indicated <strong>by</strong> a box) in REH1,<br />
which encodes a protein <strong>of</strong> unknown function in S. cerevisiae, is not conserved, whereas a binding site with<br />
a lower score is conserved (indicated <strong>by</strong> a black bar). (c) A very poorly conserved match upstream <strong>of</strong><br />
PTC3; in this case no other sites can be found in the region.<br />
Different factors have different relationships between significance and <strong>evolution</strong>ary<br />
distance<br />
103
The optimal selection <strong>of</strong> species for comparative sequence analysis remains an open<br />
question. To analyze this question for transcription factor binding sites, we examined the<br />
relationship between <strong>evolution</strong>ary distance and the MONKEY pvalues for several S.<br />
cerevisiae transcription factors (Figure 6) for which sufficient characterized binding sites<br />
were available in SCPD [33]. We find that while all factors show the tendency for p-<br />
values to decrease with <strong>evolution</strong>ary distance, the p-values for each factor remain very<br />
different. For example, with alignments <strong>of</strong> four species spanning about 0.8 substitutions<br />
per site, we expect a conserved match to the Gcn4p matrix as good as the median<br />
functional binding site (Figure 6a, red triangles) approximately every million bases <strong>of</strong><br />
aligned sequence. This in contrast to Rpn4p, for which in the same alignments we expect<br />
such a match (Figure 6a, violet crosses) only once in about 1 billion base pairs. Thus, the<br />
<strong>evolution</strong>ary distance required to achieve a desired p-value is different for different<br />
factors. Understanding the relationship between a frequency matrix and the behavior <strong>of</strong><br />
its p-values is an area for further theoretical exploration. We note that, once again, we can<br />
predict the behavior <strong>of</strong> these p-values (Figure 6b), and that while our predictions agree<br />
qualitatively, there is considerable variability.<br />
S<strong>of</strong>tware<br />
MONKEY is implemented in C++. It is available for download under the GPL and can<br />
be accessed over the web at [35].<br />
Discussion<br />
By formulating the problem <strong>of</strong> identifying conserved TFBSs in a probabilistic<br />
<strong>evolution</strong>ary framework, we have both created a useful tool (MONKEY) for comparative<br />
104
sequence analysis capable <strong>of</strong> functioning on relatively large numbers <strong>of</strong> related species,<br />
and enabled the examination <strong>of</strong> several important questions in comparative genomics.<br />
While most previous approaches to this problem have used heuristics to define conserved<br />
and nonconserved TFBSs, with the probabilistic scores and p-value estimates presented<br />
here the assumptions underlying our approach can be made explicit, and where those<br />
assumptions hold we can be assured the reliability <strong>of</strong> our method. In addition, the<br />
probabilistic framework allows us to estimate the amount <strong>of</strong> <strong>evolution</strong>ary distance<br />
required to achieve a certain level <strong>of</strong> significance.<br />
Evolutionary models<br />
The score based on the <strong>evolution</strong>ary model proposed <strong>by</strong> Halpern and Bruno [25]<br />
effectively discriminated the functional and nonfunctional Gal4p- and Rpn4p-binding<br />
sites in S. cerevisiae (Table 2). We believe the success <strong>of</strong> the HB model in predicting<br />
position-specific rates <strong>of</strong> <strong>evolution</strong> [19] and identifying conserved TFBSs reflects its<br />
encapsulation <strong>of</strong> a model <strong>of</strong> binding sites evolving under constant purifying selection.<br />
Although not every functional binding site will remain under purifying selection, as a<br />
result <strong>of</strong> either functional change or binding-site turnover (see below), a large subset <strong>of</strong><br />
functional binding sites do remain under purifying selection, and for these, the 'HB' score<br />
performs better than the 'simple' score. It is interesting to note, however, that the simple<br />
score, which is not based on an <strong>evolution</strong>ary model and does not take into account the<br />
relationships <strong>of</strong> the species used in the comparison, still shows great improvement over<br />
one genome alone, highlighting the value <strong>of</strong> comparative sequence data even when used<br />
suboptimally.<br />
105
Effects <strong>of</strong> <strong>evolution</strong>ary distance<br />
An important hypothesis <strong>of</strong> the comparative genomics paradigm is that as <strong>evolution</strong>ary<br />
distance increases, observing a match with a given level <strong>of</strong> conservation should become<br />
less and less likely <strong>by</strong> chance - the p-values for functional sites that are conserved are<br />
expected to decrease. We confirm this hypothesis for a small number <strong>of</strong> factors from S.<br />
cerevisiae. In addition, our probabilistic models allow us to quantify this relationship. We<br />
can directly measure the confidence that a specific site is a conserved binding site, and<br />
we can predict the <strong>evolution</strong>ary distance needed to achieve a desired level <strong>of</strong> significance.<br />
Typical p-values for functional binding sites scored <strong>by</strong> matching a matrix to a single<br />
genome are on the order <strong>of</strong> 10 -4 to 10 -6 . Even in a relatively small genome like yeast, with<br />
roughly 12 million bases, we expect many matches at this significance level to occur <strong>by</strong><br />
chance. Adding four closely related species that span a total <strong>evolution</strong>ary distance <strong>of</strong><br />
approximately one substitution per site reduces these p-values <strong>by</strong> approximately three<br />
orders <strong>of</strong> magnitude to the range 10 -7 to 10 -9 . In the yeast genome we expect few, if any,<br />
matches to occur at this level <strong>of</strong> significance <strong>by</strong> chance. When we search the alignments<br />
<strong>of</strong> these species with the Rpn4p matrix with a low enough p-value that we expect a match<br />
at that significance to occur only once in a random 50 Mb genome, we recover nearly the<br />
entire positive set <strong>of</strong> Rpn4p-binding sites while excluding most <strong>of</strong> the negative set,<br />
highlighting the utility <strong>of</strong> MONKEY and the statistics we have developed. As a measure<br />
<strong>of</strong> the improvement over searching a single genome alone, we note that even the best<br />
possible match to the Rpn4p matrix in one genome does not meet this significance<br />
criterion. The expected relationship between <strong>evolution</strong>ary distance and p-value can, in<br />
principle, be used to guide to choice <strong>of</strong> species to be sequenced for comparative analyses.<br />
106
However, the dependence <strong>of</strong> p-values on <strong>evolution</strong>ary distance is not the same for all<br />
factors (Figure 6). This suggests that our ability to annotate functional sequences <strong>by</strong><br />
comparative methods will depend on the type <strong>of</strong> sequences that we are trying to annotate,<br />
and that there is no single <strong>evolution</strong>ary distance sweet-spot for identifying TFBSs.<br />
The <strong>evolution</strong>ary distance required to confidently identify conserved binding sites varies<br />
Figure 6.<br />
The <strong>evolution</strong>ary distance required to confidently identify conserved binding sites<br />
varies among transcription factors.<br />
(a) Median p-values for functional binding sites for various factors at different <strong>evolution</strong>ary distances. The<br />
<strong>evolution</strong>ary distance needed to obtain a desired significance varies between factors. (b) Predicted<br />
dependence <strong>of</strong> the p-values on <strong>evolution</strong>ary distance. Specificity data and functional binding sites were<br />
obtained from the SCPD.<br />
Pairwise versus multiple species comparisons<br />
In theory, for a given reference genome it should be possible to pick a single comparison<br />
species at an <strong>evolution</strong>ary distance sufficient to identify any conserved feature <strong>of</strong> interest.<br />
Our results suggest that at distances <strong>of</strong> up to approximately 0.6 substitutions per site,<br />
107
pairwise alignments provide essentially the same amount <strong>of</strong> resolving power as multiple<br />
comparisons spanning the same <strong>evolution</strong>ary distance. We showed that S. cerevisiae and<br />
S. kudriavzevii span almost exactly the same <strong>evolution</strong>ary distance as S. cerevisiae, S.<br />
paradoxus and S. mikatae, and that that distance is well below 0.6 substitutions per site.<br />
Consistent with this, MONKEY produces nearly identical p-values for conserved binding<br />
sites from these two sets <strong>of</strong> species. Thus, our results suggest that from a theoretical<br />
perspective, if the goal <strong>of</strong> comparative analysis is to identify conserved binding sites for<br />
factors like the ones considered here, it is not necessary to sequence species much more<br />
closely related than this limit. We note, however, that there are myriad practical reasons<br />
other than <strong>evolution</strong>ary resolving power (the only factor considered in our models) for<br />
sequencing multiple closely related sequences. First, there may simply be no extant<br />
species at the exact <strong>evolution</strong>ary distance desired. Second, the quality <strong>of</strong> DNA alignments<br />
is expected to be much higher for multiple closely related species than for more distant<br />
pairwise alignments - if alignment errors prevent correct assignment <strong>of</strong> orthology,<br />
conserved binding sites will not be identified. For the factors considered here, the<br />
pairwise comparison performed nearly as well as the multiple species comparison well<br />
beyond the <strong>evolution</strong>ary distances at which pairwise alignments are reliable [36],<br />
suggesting that the necessity <strong>of</strong> alignment will limit the maximum distance between<br />
species. Finally, and perhaps most important, is the assumption that our models make<br />
about constant functional constraint over <strong>evolution</strong>. To illustrate this, consider the<br />
binding sites for Gal4p used in the analysis in Figure 2a. These binding sites could not be<br />
included in Figure 3 because S. kudriavzevii orthologs for these genes were not available<br />
in SGD, apparently because <strong>of</strong> the degeneration <strong>of</strong> the galactose-utilization pathway in<br />
108
this species [37]. Sequencing multiple closely related species provides insurance against<br />
such functional changes, because they are less likely to have occurred in all the lineages.<br />
Conserved sites and binding-site turnover<br />
MONKEY was very effective in identifying functional Rpn4p binding sites from the<br />
alignment <strong>of</strong> five Saccharomyces species. In our search, 41 <strong>of</strong> 56 (73%) predicted sites<br />
were found near genes showing the expected expression pattern, and are therefore likely<br />
to be functional. Even at this level <strong>of</strong> stringency, however, there are highly conserved<br />
sequences that match the matrix, but do not appear to be near genes that are regulated <strong>by</strong><br />
Rpn4p. It is very unlikely that these sites are conserved <strong>by</strong> chance. One possible<br />
explanation for this high degree <strong>of</strong> conservation is that these are functional sites, but that<br />
the expression <strong>of</strong> these genes is not accurately detected in high-throughput assays, or<br />
their function has not been accurately determined. A more likely possibility is that these<br />
sites are conserved because they perform other, unknown functions. Consistent with this<br />
hypothesis is the fact that many <strong>of</strong> these matches fall near other highly conserved<br />
sequences (Figure 4b), suggesting that they may be parts <strong>of</strong> larger conserved features.<br />
In addition to the conserved sequences that are unlikely to represent bona fide<br />
binding sites, we also found examples <strong>of</strong> binding sites associated with properly regulated<br />
genes that do not seem to be conserved (Figure 5). Once again there are several possible<br />
explanations for this observation. First, these binding sites may not actually be functional<br />
and may have been included in our positive set erroneously. While this is a possible<br />
explanation for the case <strong>of</strong> the Rpn4p-binding sites shown in Figure 5 (and may be likely<br />
in the case <strong>of</strong> REH1, where we could identify another apparently conserved binding site<br />
109
in the region) we have also found nonconserved examples among the TFBSs in the SCPD<br />
database (approximately 20% <strong>of</strong> TFBSs we examined, see figure 7), all <strong>of</strong> which have at<br />
least some direct experimental support. Another potential explanation is that these<br />
binding sites are actually conserved, but were not aligned correctly. While this is difficult<br />
to rule out in general, in the few nonconserved cases for Rpn4p at least we could not find<br />
(<strong>by</strong> eye) errors in the alignments. Most interesting, <strong>of</strong> course, would be the situation<br />
where these nonconserved binding sites are not due to some error on our part, but rather<br />
represent a biological change in the functional constraints on these sequences, possibly<br />
resulting in a change in the <strong>regulation</strong> <strong>of</strong> the expression <strong>of</strong> these genes. Our results<br />
represent an upper bound on the number <strong>of</strong> TFBSs for which this has occurred. Cis-<br />
regulatory changes have been proposed to be an important source <strong>of</strong> genetic variation<br />
[32]. Gains and losses <strong>of</strong> functional binding sites represent an important class <strong>of</strong> these<br />
changes [38,39], and an important area for future computational and experimental<br />
analysis, particularly as the genome sequences <strong>of</strong> closely related metazoans become<br />
available. We expect MONKEY to be a useful tool in the comparative analysis <strong>of</strong> these<br />
genomes, and we have found comparable increases in the significance <strong>of</strong> functional<br />
binding sites in alignments <strong>of</strong> Drosophila melanogster and D. pseudoobscura (see figure<br />
8)<br />
110
Figure 7.<br />
Many characterized binding sites do not seem to be evolving under constraint.<br />
We defined a binding site as not conserved if its p-value in the four way alignment <strong>of</strong> S. cerevisiae, S.<br />
paradoxus, S. mikatae, and S. bayanus was higher (less significant) than in S. cerevisiae alone.<br />
Figure 8.<br />
Applying MONKEY to alignments <strong>of</strong> Drosophila<br />
Putative binding sites (D. melanogaster p
Conclusions<br />
We have developed a method to identify conserved TFBSs in sequence alignments from<br />
multiple related species that provides a quantitative framework for evaluating results. The<br />
method - implemented in the open-source s<strong>of</strong>tware MONKEY - extends probabilistic<br />
models <strong>of</strong> binding specificity to multiple species with probabilistic models <strong>of</strong> <strong>evolution</strong>.<br />
We have found that a probabilistic <strong>evolution</strong>ary model [25] that assumes binding sites are<br />
under constant purifying selection performs effectively in discriminating functional<br />
binding sites. We have developed methods to assess the significance <strong>of</strong> hits, and have<br />
shown that the significance <strong>of</strong> functional matches increases while the significance <strong>of</strong><br />
spurious matches decreases over increasing <strong>evolution</strong>ary distance. We can explicitly<br />
model the relationship between the significance <strong>of</strong> a hit and <strong>evolution</strong>ary distance,<br />
allowing the assessment <strong>of</strong> the potential <strong>of</strong> any collection <strong>of</strong> genomes for identifying<br />
conserved binding sites. Applying MONKEY to a collection <strong>of</strong> related yeast species we<br />
find that most functional binding sites are highly significantly conserved, but also find<br />
evidence for conserved sites that are not functional and vice versa. Our results suggest<br />
that development <strong>of</strong> methods that model the <strong>evolution</strong>ary relationships between species<br />
and the <strong>evolution</strong> <strong>of</strong> the genomic features <strong>of</strong> interest yield insight into the challenges for<br />
comparative genomics.<br />
Materials and methods<br />
Simulating pairs <strong>of</strong> sequences<br />
To generate the empirical p-values shown in Figure 1, random sequences <strong>of</strong> length w<br />
were generated according to the average intergenic base frequencies <strong>of</strong> the S. cerevisiae<br />
112
genome. These were then evolved according to the Jukes-Cantor substitution model, to a<br />
specified <strong>evolution</strong>ary distance. Likelihood ratio scores and p-values were then calculated<br />
for each <strong>of</strong> the pairs <strong>of</strong> sequences using the method implemented in MONKEY. Finally,<br />
all pairs <strong>of</strong> sequences were ranked <strong>by</strong> their scores, and the rank divided <strong>by</strong> the total<br />
number <strong>of</strong> pairs was taken as the empirical p-value.<br />
Preparation <strong>of</strong> alignments for different groups <strong>of</strong> species<br />
We aligned the upstream regions <strong>of</strong> all S. cerevisiae genes to their orthologs in S.<br />
paradoxus, S. mikatae, S. bayanus and S. kudriavzevii <strong>by</strong> taking the 1,000 bp upstream <strong>of</strong><br />
each gene, identifying the corresponding region from the other species using data in the<br />
Saccharomyces Genome Database [40], aligning them with CLUSTAL W [41] and<br />
trimming them to remove regions corresponding to S. cerevisiae coding sequence. We<br />
used this strategy rather than simply aligning intergenic regions to control for differences<br />
in alignments that might arise from the use <strong>of</strong> variably sized regions. To obtain estimates<br />
<strong>of</strong> the <strong>evolution</strong>ary distance spanned <strong>by</strong> each comparison, we ran PAML [24] on the<br />
entire set <strong>of</strong> intergenic alignments, using the HKY model [27], with constant rates across<br />
sites. We used the median PAML estimate <strong>of</strong> kappa (the transition-transversion rate ratio)<br />
<strong>of</strong> 3.8, the S. cerevisiae background frequencies (ACGT) = (0.3, 0.2, 0.2, 0.3) and the<br />
median <strong>of</strong> the branch lengths estimates as the 'background' <strong>evolution</strong>ary model. The trees<br />
with these branch lengths were used as input to MONKEY to calculate p-values. The<br />
distances in Figure 4 represent the sum <strong>of</strong> the median branch lengths in each comparison.<br />
The subsets (with <strong>evolution</strong>ary distances in parentheses) were as follows: S. cerevisiae<br />
and S. paradoxus (0.194); S. cerevisiae and S. mikatae (0.403); S. cerevisiae, S.<br />
113
paradoxus S. mikatae (0.477); S. cerevisiae and S. bayanus (0.559); S. cerevisiae, S.<br />
paradoxus, S. mikatae and S. bayanus (0.816); S. cerevisiae, S. paradoxus, S. mikatae, S.<br />
bayanus and S. kudriavzevii (1.090).<br />
Definition <strong>of</strong> Rpn4p and Gal4p matrices and positive and negative sets<br />
Rpn4p: we used Rpn4p sites in proteasomal genes [42,43] to build an Rpn4p specificity<br />
matrix (using a pseudocount <strong>of</strong> 1 per base per position). To identify additional likely<br />
targets, we obtained expression data from public sources [30,31] and compared the<br />
expression patterns <strong>of</strong> all genes to the average expression pattern <strong>of</strong> proteasomal genes<br />
using the following metric:<br />
n − 2<br />
t = 2<br />
1−<br />
θ<br />
where θ is the 'uncentered correlation', a commonly used distance metric for gene-<br />
expression data [44]. Our score adds a correction for the number <strong>of</strong> datapoints, n, that are<br />
available for each gene. All matches to the Rpn4p matrix (S. cerevisiae likelihood ratio<br />
score > 9) in the upstream region <strong>of</strong> a gene that matched the proteasomal expression<br />
pattern (t > 8) were considered to be true Rnp4p sites. The negative set consists <strong>of</strong> all<br />
sites that matched the Rpn4p matrix with a score greater than 9, and excluded sites in<br />
genes with even weak similarity to the proteasomal expression pattern (t > 0) or that were<br />
annotated [40] as involved in protein processing or degradation.<br />
Gal4p: we used the matrix from SCPD [33] (with a pseudo count <strong>of</strong> 1 per base per<br />
position). To define a positive set we used the binding sites in SCPD and systematic<br />
studies <strong>of</strong> this Gal4p regulatory system [45,46], and used matches near additional genes<br />
114
that we identified in these studies with scores above the lowest score in the SCPD set. To<br />
define a negative set, we again scanned the S. cerevisiae genome with a cut<strong>of</strong>f equal to<br />
the lowest score in the positive set and then eliminated any binding sites near genes that<br />
showed evidence for <strong>regulation</strong> in the systematic studies.<br />
It is important to note that our categorization <strong>of</strong> sequences as positive and<br />
negative is done independently <strong>of</strong> the comparative sequence data, thus avoiding potential<br />
circularity.<br />
Calculations <strong>of</strong> expected scores<br />
Because our methods employ explicit probabilistic models for the <strong>evolution</strong> <strong>of</strong> noncoding<br />
DNA, it is possible to compute the expected scores under various assumptions. The<br />
expectation <strong>of</strong> the log likelihood ratio for examples <strong>of</strong> the motif is the 'information<br />
content' and its calculation has been addressed [47]. We can extend this to calculation to<br />
our <strong>evolution</strong>ary scores, as follows. Using the fact that all the scores treat the positions <strong>of</strong><br />
the matrix independently, and the linearity <strong>of</strong> the expectation, we write:<br />
E[<br />
Sˆ<br />
( X , Y ) | m]<br />
=<br />
w<br />
∑<br />
i=<br />
1<br />
E[<br />
Sˆ<br />
( X , Y ) | m]<br />
=<br />
i<br />
i<br />
i<br />
115<br />
w<br />
∑∑<br />
i= 1 X , Y<br />
i<br />
i<br />
p(<br />
X , Y | m,<br />
T ) Sˆ<br />
( X , Y )<br />
where E [x] denotes the expectation <strong>of</strong> the random variable x, m denotes a frequency<br />
matrix and a corresponding <strong>evolution</strong>ary model, either {motif, Rmotif} or {bg, Rbg}.<br />
p(Xi, Yi|m, T) is calculated as above, and we define:<br />
ˆ<br />
p(<br />
X i,<br />
Yi<br />
| motif , T,<br />
R<br />
Si ( X i , Yi<br />
) ≡ log<br />
p(<br />
X i , Yi<br />
| bg,<br />
T,<br />
R<br />
We can write a similar expression for the variance, V:<br />
V[<br />
Sˆ<br />
( X , Y ) | m]<br />
=<br />
w<br />
∑∑<br />
i= 1 X , Y<br />
i<br />
i<br />
i<br />
i<br />
i<br />
i<br />
i<br />
i<br />
bg<br />
)<br />
i<br />
motif<br />
2<br />
p(<br />
X , Y | m,<br />
T )( Sˆ<br />
( X , Y ) − E[<br />
Sˆ<br />
( X , Y ) | m])<br />
)<br />
i<br />
i<br />
i<br />
i<br />
i<br />
i
In order to predict the scores for the genes in our positive and negative sets, we are<br />
interested in the case were we have observed a match to the motif in one species, but the<br />
constraints on its <strong>evolution</strong> are either those <strong>of</strong> the background or the motif. We can<br />
compute the expected scores under these assumptions as follows:<br />
E[<br />
Sˆ<br />
( X , Y ) | X = match,<br />
m]<br />
=<br />
w<br />
∑∑ p(<br />
X i | motif ) ∑<br />
i= 1 X Y<br />
i i<br />
( Y | X , m,<br />
T ) Sˆ<br />
( X , Y )<br />
where p(Xi|motif) is the single species probability <strong>of</strong> observing the base Xi at position i in<br />
the specificity matrix (f), and using Bayes' theorem:<br />
p(<br />
X i,<br />
Yi<br />
| m,<br />
T ) p(<br />
X i,<br />
Yi<br />
| m,<br />
T )<br />
p(<br />
Yi<br />
| X i,<br />
m,<br />
T ) =<br />
=<br />
.<br />
p(<br />
X | m,<br />
T ) p(<br />
X , Y | m,<br />
T )<br />
i<br />
∑<br />
Yi<br />
This calculation can be extended to the multiple species case, <strong>by</strong> replacing the<br />
4. Wasserman WW, Palumbo M, Thompson W, Fickett JW, Lawrence CE: Human-mouse genome<br />
comparisons to locate regulatory sites. Nat Genet 2000, 26:225-228.<br />
116<br />
i<br />
distributions p(Xi, Yi) and p(Yi|Xi) with p(Xi, Yi, ..., Zi) and p(Yi, ..., Zi|Xi) and changing<br />
the sum over Yi to a sum over all the other leaves in the tree except the reference, in this<br />
i<br />
case, Xi. For the functional set, we assumed the binding sites were evolving under the HB<br />
model [25], and for the nonfunctional set we assumed <strong>evolution</strong> under the HKY<br />
background model described above. To model the sequencespecificity matrices most<br />
accurately, we reduced the pseudocount (equal to the background probability <strong>of</strong><br />
observing each base).<br />
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119
11. Applications to alignments <strong>of</strong> multiple primates and human-fugu conserved elements<br />
While the <strong>evolution</strong>ary models and statistical techniques on which the methods described<br />
above are based are general, and should in principle be applicable to any set <strong>of</strong> closely<br />
related species, it remains to be seen if the assumptions and approximations made are<br />
actually valid in other cases. In order to test these methods on other taxonomic groups I<br />
have applied both EMnEM and MONKEY to comparisons <strong>of</strong> human sequences in<br />
collaboration with Eddy Rubin’s group.<br />
Identifying conserved LXR binding sites in alignments <strong>of</strong> multiple primates.<br />
LXRa is a nuclear hormone receptor involved in the <strong>regulation</strong> <strong>of</strong> many genes in response<br />
to oxysterol levels. This is a medically important system because the targets <strong>of</strong> LXR<br />
include CYP7A1, APOE and potentially many other important genes involved in<br />
arteriosclerosis (Tontonoz and Mangelsdorf 2003). Because the large loci and complex<br />
structure <strong>of</strong> many <strong>of</strong> these genes, most <strong>of</strong> the characterized binding sites are in the<br />
proximal promoters <strong>of</strong> these genes. By making multiple species alignments, we can<br />
search for conserved binding sites, thus greatly improving the specificity <strong>of</strong> the<br />
predictions, <strong>by</strong> ruling out matches to the matrix that are not conserved.<br />
LXR binds DNA as a DR4 heterodimer with RXR (Repa and Mangelsdorf 2000).<br />
Using LXREs (the binding sites for the LXRa-RXR heterodimer) curated from the<br />
literature (Zhang and Magelsdorf 2002), I have constructed a specificity matrix (Figure<br />
1).<br />
120
LXRa RXR<br />
Figure 1.<br />
Sequence logo representation <strong>of</strong> the LXRE<br />
Ovals above the letters are a schematic <strong>of</strong> positions <strong>of</strong> the DR4 nuclear hormone receptor heterodimer.<br />
Note that despite the high variability, each half-site seems to contain the AGGTCA hexameric consensus.<br />
Using this matrix I have searched the loci <strong>of</strong> genes known to be regulated <strong>by</strong> LXR in<br />
order to identify conserved binding sites. One example <strong>of</strong> a potentially interesting<br />
prediction is in the SREBF1 locus. SREBF1 encodes the SREBP1 membrane bound<br />
transcription factor involved in lipid biosynthesis (Horton et al 2002). While two<br />
functional LXREs have already been characterized in the proximal promoter <strong>of</strong> this gene,<br />
and both <strong>of</strong> these elements are conserved in alignments <strong>of</strong> human and mouse, (Monkey p-<br />
values for human and mouse, 5.31E-11 and 5.54E-07), there is a predicted conserved<br />
binding site in the primate alignments (monkey p-value, primates 3.31E-10)<br />
approximately 12.5 Kb away, which is gapped in the alignments <strong>of</strong> human and mouse.<br />
This conserved binding site may point to an LXR regulated enhancer, as has been found<br />
for the APOE gene (Laffitte et. al. 2001). Identifying primate specific binding sites is <strong>of</strong><br />
particular interest in the case <strong>of</strong> cholesterol <strong>regulation</strong> because there are important<br />
differences in the <strong>regulation</strong> <strong>of</strong> these genes between rodents and humans, and it is<br />
121
possible that in this case primate sequences will prove more informative. I shall return to<br />
this case in part IV, as an example <strong>of</strong> regulatory <strong>evolution</strong>.<br />
Identifying clusters <strong>of</strong> conserved binding sites<br />
In addition to LXR, there are other transcription factors that are known to be important<br />
for the <strong>regulation</strong> <strong>of</strong> genes involved in cholesterol transport and <strong>regulation</strong>. Although<br />
there are few characterized binding sites for these factors, there have been selex<br />
experiments done on a number <strong>of</strong> them, and matrices representing the specificity <strong>of</strong><br />
several are available (Sandelin et al 2004). Using these matrices I searched for regions in<br />
the alignments were there were binding sites for more than one <strong>of</strong> these factors in close<br />
proximity. Approaches that search for multiple binding sites have proven effective in<br />
identifying regulatory regions computationally (Wasserman and Fickett 1998) because<br />
eukaryotic transcription factors <strong>of</strong>ten work in combination. Using the alignments <strong>of</strong><br />
closely related sequences, and MONKEY, we can extend this approach to search for<br />
clusters <strong>of</strong> conserved binding sites.<br />
An interesting example <strong>of</strong> a group <strong>of</strong> conserved binding sites is found in the<br />
CYP7A1 locus. CYP7A1 catalyzes the rate-limiting step in bile formation, and is already<br />
known to be regulated <strong>by</strong> a number <strong>of</strong> factors, including HNF1, HNF4, LXR and PPAR<br />
at the proximal promoter (Marrapodi and Chiang 2000, Stroup and Chiang 2000, Chen et<br />
al 1999, Chiang et al 2001). Scanning the alignment <strong>of</strong> the CYP7A1 locus, we identified<br />
a region <strong>of</strong> 110 bp that contained conserved matches to the matrices for HNF3, HNF4<br />
and a weak match to the LXR matrix (Figure 2). This putative enhancer is located nearly<br />
122
12 Kb downstream <strong>of</strong> the 3’ end <strong>of</strong> the CYP7A1 transcript, and nearly 20 Kb downstream<br />
<strong>of</strong> the proximal promoter.<br />
A<br />
B<br />
27454 27564<br />
39551<br />
TGCAGGGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTGACCTTGGGCTAAAATAGGCAGTTCTCAAATGTATTATTTCTGAAGGGCAAAGTTAAGAGATT Baboon<br />
TGCAGAGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTGACCTTGGGCTAAAATGGGCAGTGCTCCAATATATTATTTCTGAAGGTCAAAGTTAAGAGATT Colobus<br />
TGCAGGGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTGACCTTGGCCTAAAATGGACAGTGATCAAATATATTATTTCTGAAGGGCAAAGTTAAGAGATT Human<br />
TGCAGAGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTGACCTTGGGCTAAAATAGACAGTGCTCAAATATATTATTTCTGAAGGGCAAAGTTAAGAGATT Marmoset<br />
TGCAGAGATATGTTTACTTTGACTTTAGTACTATTGGAATTCTGTAACCTTGGGCTAAAATAGGCAGTTCTCAAATATACTATTTCTGAAGGGCAAAGTTAAGAGATT Owl monkey<br />
TGCAGAGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTAACCTTGAGCTAAAATAGGCAGTTCTCAAATATATTATTTCTGAAGGGCAAAGTTAAGAGATT Squirrel monkey<br />
TGCAAGGATATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTGACCTTGGGCTAAAATGAACAGTTCTCAAACATATTCTTTCTGAAGGGCAAAGTTAAGCGGTT Lemur<br />
TGAGGTGGCATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTAACCTTGGACTAAAATGGACAGTTCTCAAATATAGTCCTTCTAAAGGGCAAAGTTAAGAGGTT Mouse<br />
TGAGGTGGCATGTTTACTTTGACTTTAGTCCTATTGGAATTCTGTAACCTTGGACTAAAATGGACAGTTCTCAAATATAGTCCTTCTAAAGGGCAAAGTTACGAGGTT Rat<br />
** * ******************** *************** ****** ******* **** ** ** ** * **** **** ******** * * **<br />
HNF3<br />
Figure 2.<br />
LXR<br />
(weak match)<br />
HNF4<br />
Closely spaced, highly conserved matches suggest the presence <strong>of</strong> a downstream<br />
CYP7A enhancer<br />
A schematic <strong>of</strong> the CYP7A1 locus showing the transcribed portion (blue bar) the characterized proximal<br />
promoter (red bar) and the predicted enhancer (green bar). B Multiple sequence alignment with boxes<br />
indicating the conserved binding sites as predicted <strong>by</strong> MONKEY.<br />
Discovering motifs in human-fugu conserved sequences using EMnEM<br />
Although the vast non-coding regions in the human genome make regulatory element<br />
detection exceedingly difficult, it is possible to take advantage <strong>of</strong> more compact<br />
vertebrate genomes, such as the puffer fish, Fugu rubripes (Aparicio et al 1995, Elgar et<br />
al 1996). Because <strong>of</strong> the long <strong>evolution</strong>ary distance that separates them, shared non-<br />
coding sequences between mammals and fish are very likely to be under strong<br />
functional constraint, and may be representative <strong>of</strong> the features <strong>of</strong> the ancestral<br />
123<br />
CYP7A1<br />
49521<br />
Proximal<br />
promoter
vertebrate. These highly conserved non-coding sequences that have been discovered in<br />
vertebrate genomes represent and ideal data set for <strong>evolution</strong>ary motif-finders. They are<br />
a set <strong>of</strong> relatively short sequences, they are very likely to be functional, and very little is<br />
known about their functions. I have applied EMnEM to a set <strong>of</strong> highly conserved regions<br />
that were shown to drive embryonic expression patterns, in vivo in a reporter construct<br />
(Len Pennacchio, personal communication).<br />
In order to test the hypothesis that there are shared sequence featured between all<br />
these highly conserved enhancers, I ran EMnEM on the entire set, using alignments <strong>of</strong><br />
what ever species were available for that region on the UCSC genome browser (Kent et<br />
al. 2002). The resulting motif is shown in Figure 3A.<br />
A B<br />
Figure 3.<br />
Discovery <strong>of</strong> a enriched, conserved homodomain-like motif using EMnEM<br />
A: graphical representation <strong>of</strong> the motif discovered using EMnEM. EMnEM was run assuming the motif<br />
evolved at 0.5 the average rate in these highly conserved sequences, suggesting that this motif is<br />
preferentially conserved. B TAAT is specifically enriched in these conserved enhancers relative to<br />
surrounding regions. The density <strong>of</strong> the core TAAT sequence in the enhancers is much greater than in the<br />
flanking sequences, further supporting the hypothesis that this element is functionally important in these<br />
enhancers.<br />
TAAT per bp<br />
124<br />
0.02<br />
0.015<br />
0.01<br />
0.005<br />
0<br />
-5000 0 5000<br />
position from left or right edge<br />
<strong>of</strong> enhance r<br />
average in<br />
overlapping 500<br />
bp windows<br />
average in<br />
enhancers<br />
genomic<br />
background<br />
(10000 random<br />
windows)
The discovery <strong>of</strong> a highly conserved, significantly enriched motif that contains the<br />
ATTA/TAAT homeodomain core in these highly conserved enhancers that have been<br />
shown to drive specific spatial embryonic expression patterns and fall near<br />
developmental genes (Sandelin et al. 2004, Wolfe et al. 2005) is interesting for the<br />
following reasons. First, although we cannot say which proteins are binding these<br />
sequences, the hox and other homeodomain proteins are well known to regulate spatial<br />
and temporal patterns <strong>of</strong> expression during vertebrate development (Hunt and Krumlauf<br />
1992, Cavodeassi 2001) as well as nervous system patterning in particular (Boyl et al.<br />
2001, McMahon 2000, Cecchi and Boncinelli 2000). That members <strong>of</strong> that family are<br />
regulating these enhancers is consistent with their spatial patterns and putative<br />
developmental patterning functions. Second, and perhaps more interestingly, the<br />
discovery <strong>of</strong> high densities <strong>of</strong> binding sites in these enhancers supports the hypothesis<br />
that they conserved at the nucleotide level because <strong>of</strong> the constraints these binding sites<br />
place on their <strong>evolution</strong>. However, the binding sites for most developmental regulatory<br />
homeodomain proteins are known to be highly degenerate, and it is therefore surprising<br />
that they would be conserved so highly at the nucleotide level. In passing I note that I<br />
also ran MEME (Bailey et al. 1994) on these sequences and did not recover this motif.<br />
Predicting gene expression using conserved binding sites<br />
Because the homeodomain core that was discovered <strong>by</strong> EMnEM could represent the<br />
overlapping specificity <strong>of</strong> many proteins, and the models underlying EMnEM do not<br />
account for the possibility <strong>of</strong> multiple motifs <strong>of</strong> overlapping specificity, I decided to<br />
search the enhancers for other previously characterized motifs. I searched the human-<br />
125
mouse-fugu alignments <strong>of</strong> the tested enhancers with 78 matrices from the JASPAR<br />
database (Sandelin et al. 2004) to find conserved matches to these matrices. Many <strong>of</strong><br />
these motifs area derived from selex, or in vitro footprinting studies, and therefore<br />
represent the specificity <strong>of</strong> only one protein. Indeed, there are multiple matrices in the<br />
database that represent the specificities <strong>of</strong> different homeodomain containing proteins<br />
(e.g., Figure 4B), and using these matrices is may be possible to distinguish between<br />
various members <strong>of</strong> this large protein family.<br />
Because the expression patterns <strong>of</strong> the tested fragments could be divided into<br />
those that showed expression in different spatial and morphological patterns, we were<br />
interested in identifying binding sites that were specifically associated with particular<br />
expression patterns. In testing this hypothesis, I found that several <strong>of</strong> these matrices were<br />
significantly enriched in the enhancers that showed forebrain expression patterns relative<br />
to the fragments that showed no pattern, and those that showed other patterns (Figure 4A)<br />
In order to test the predictive power <strong>of</strong> these conserved matches to the matrices, I<br />
performed a stepwise logistic regression analysis (Wasserman and Fickett 1998).<br />
Although several <strong>of</strong> the motifs were significantly associated with the forebrain expression<br />
pattern, only Nkx was highly significant when a model using more than one motif was<br />
constructed. Furthermore, in order to verify that these weakly significant additional<br />
motifs were not the result <strong>of</strong> the model being overfit, I performed leave one out cross-<br />
validation, and found that the error rate (~20%) using only the hits to Nkx was<br />
comparable to that using all 5 selected motifs (~18%). This suggested that most <strong>of</strong> the<br />
predictive power was coming from Nkx.<br />
126
Although I have presented no specific evidence that these enriched binding sites<br />
are actually responsible for the forebrain expression pattern, or that these enhancers are<br />
indeed regulated <strong>by</strong> Nkx proteins, these observations are certainly consistent with Nkx2.1<br />
and Nkx2.2 (verebrate homologues <strong>of</strong> Drosphila vnd) being involved in forebrain<br />
patterning and neural fate determination (Briscoe et al 1999, Corbin et al 2003). That<br />
these binding sites are conserved since the common ancestor <strong>of</strong> the vertebrates is<br />
consistent with the suggestion <strong>of</strong> an ancient role for Nkx2.1 in the patterning and perhaps<br />
origin <strong>of</strong> the vertebrate forebrain (Venkatesh et al 1999).<br />
Conserved hits in negatives<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
A B<br />
Sox5<br />
chop cEBP<br />
0 10 20 30 40<br />
Conserved hits in forebrain<br />
Figure 4.<br />
Identification <strong>of</strong> motifs specifically associated with enhancers that showed forebrain<br />
expression.<br />
A conserved hits, defined as matches to the matrix in human (p
unusual for homeodomain proteins, as they bind sequences with a CAAG core as well as the canonical<br />
TAAT (Damante et al 1994).<br />
Discussion<br />
While detailed experimental tests <strong>of</strong> the predictions presented here will be necessary to<br />
confirm the power <strong>of</strong> these methods in analysis <strong>of</strong> mammalian vertebrate regulatory<br />
sequences, preliminary results (J. Wang personal communication) suggest that at least the<br />
downstream CYP7A1 enhancer can drive expression <strong>of</strong> a reporter in cell culture. These<br />
results suggest that methods such as those described here that take advantage <strong>of</strong> the<br />
<strong>evolution</strong>ary properties <strong>of</strong> binding sites, principally their preferential conservation in<br />
multiple sequence alignments, will prove effective tools in the efforts to understand even<br />
the most complex regulatory sequences, such as those involved in patterning <strong>of</strong> the<br />
vertebrate nervous system. Further, the methods developed here, which employ a<br />
probabilistic model <strong>of</strong> binding site <strong>evolution</strong> seem not only to be justified in their<br />
conception, but practically useful in the cases studied here.<br />
References<br />
Aparicio S, Morrison A, Gould A, Gilthorpe J, Chaudhuri C, Rig<strong>by</strong> P, Krumlauf R, Brenner S. Detecting<br />
conserved regulatory elements with the model genome <strong>of</strong> the Japanese puffer fish, Fugu rubripes. Proc Natl<br />
Acad Sci U S A. 1995 Feb 28;92(5):1684-8.<br />
Boyl PP, Signore M, Annino A, Barbera JP, Acampora D, Simeone A. Otx genes in the development and<br />
<strong>evolution</strong> <strong>of</strong> the vertebrate brain. Int J Dev Neurosci. 2001 Jul;19(4):353-63.<br />
Briscoe J, Sussel L, Serup P, Hartigan-O'Connor D, Jessell TM, Rubenstein JL, Ericson J. Homeobox gene<br />
Nkx2.2 and specification <strong>of</strong> neuronal identity <strong>by</strong> graded Sonic hedgehog signalling. Nature. 1999 Apr<br />
15;398(6728):622-7.<br />
Cavodeassi F, Modolell J, Gomez-Skarmeta JL. The Iroquois family <strong>of</strong> genes: from body building to neural<br />
patterning. Development. 2001 Aug;128(15):2847-55.<br />
Cecchi C, Boncinelli E. Emx homeogenes and mouse brain development. Trends Neurosci. 2000<br />
Aug;23(8):347-52. Review.<br />
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Chen J, Cooper AD, Levy-Wilson B. Hepatocyte nuclear factor 1 binds to and transactivates the human but<br />
not the rat CYP7A1 promoter. Biochem Biophys Res Commun. 1999 Jul 14;260(3):829-34.<br />
Chiang JY, Kimmel R, Stroup D. Regulation <strong>of</strong> cholesterol 7alpha-hydroxylase gene (CYP7A1)<br />
transcription <strong>by</strong> the liver orphan receptor (LXRalpha). Gene. 2001 Jan 10;26<br />
Corbin JG, Rutlin M, Gaiano N, Fishell G. Combinatorial function <strong>of</strong> the homeodomain proteins Nkx2.1<br />
and Gsh2 in ventral telencephalic patterning. Development. 2003 Oct;130(20):4895-906. Epub 2003 Aug<br />
20.<br />
Damante G, Fabbro D, Pellizzari L, Civitareale D, Guazzi S, Polycarpou-Schwartz M, Cauci S,<br />
Quadrifoglio F, Formisano S, Di Lauro R. Sequence-specific DNA recognition <strong>by</strong> the thyroid transcription<br />
factor-1 homeodomain. Nucleic Acids Res. 1994 Aug 11;22(15):3075-83.<br />
Elgar G, Sandford R, Aparicio S, Macrae A, Venkatesh B, Brenner S. Small is beautiful: comparative<br />
genomics with the pufferfish (Fugu rubripes). Trends Genet. 1996 Apr;12(4):145-50.<br />
Horton JD, Goldstein JL, Brown MS. SREBPs: activators <strong>of</strong> the complete program <strong>of</strong> cholesterol and fatty<br />
acid synthesis in the liver. J Clin Invest. 2002 May;109(9):1125-31.<br />
Hunt P, Krumlauf R. Hox codes and positional specification in vertebrate embryonic axes. Annu Rev Cell<br />
Biol. 1992;8:227-56.<br />
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome<br />
browser at UCSC. Genome Res. 2002 Jun;12(6):996-1006.<br />
Laffitte BA, Repa JJ, Joseph SB, Wilpitz DC, Kast HR, Mangelsdorf DJ, Tontonoz P. LXRs control lipidinducible<br />
expression <strong>of</strong> the apolipoprotein E gene in macrophages and adipocytes. Proc Natl Acad Sci U S<br />
A. 2001 Jan 16;98(2):507-12.<br />
Marrapodi M, Chiang JY. Peroxisome proliferator-activated receptor alpha (PPARalpha) and agonist<br />
inhibit cholesterol 7alpha-hydroxylase gene (CYP7A1) transcription. J Lipid Res. 2000 Apr;41(4):514-20.<br />
McMahon AP. Neural patterning: the role <strong>of</strong> Nkx genes in the ventral spinal cord. Genes Dev. 2000 Sep<br />
15;14(18):2261-4.<br />
Repa JJ, Mangelsdorf DJ. The role <strong>of</strong> orphan nuclear receptors in the <strong>regulation</strong> <strong>of</strong> cholesterol homeostasis.<br />
Annu Rev Cell Dev Biol. 2000;16:459-81.<br />
Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B. JASPAR: an open-access database for<br />
eukaryotic transcription factor binding pr<strong>of</strong>iles. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D91-4.<br />
Sandelin A, Bailey P, Bruce S, Engstrom PG, Klos JM, Wasserman WW, Ericson J, Lenhard B. Arrays <strong>of</strong><br />
ultraconserved non-coding regions span the loci <strong>of</strong> key developmental genes in vertebrate genomes. BMC<br />
Genomics. 2004 Dec 21;5(1):99<br />
Stroup D, Chiang JY. HNF4 and COUP-TFII interact to modulate transcription <strong>of</strong> the cholesterol 7alphahydroxylase<br />
gene (CYP7A1). J Lipid Res. 2000 Jan;41(1):1-11.<br />
Tontonoz P, Mangelsdorf DJ. Liver X receptor signaling pathways in cardiovascular disease. Mol<br />
Endocrinol. 2003 Jun;17(6):985-93. Epub 2003 Apr 10. Review.<br />
Venkatesh TV, Holland ND, Holland LZ, Su MT, Bodmer R. Sequence and developmental expression <strong>of</strong><br />
amphioxus AmphiNk2-1: insights into the <strong>evolution</strong>ary origin <strong>of</strong> the vertebrate thyroid gland and forebrain.<br />
Dev Genes Evol. 1999 Apr;209(4):254-9.<br />
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Wasserman WW, Fickett JW. Identification <strong>of</strong> regulatory regions which confer muscle-specific gene<br />
expression. J Mol Biol. 1998 Apr 24;278(1):167-81.<br />
Zhang Y, Mangelsdorf DJ. LuXuRies <strong>of</strong> lipid homeostasis: the unity <strong>of</strong> nuclear hormone receptors,<br />
transcription <strong>regulation</strong>, and cholesterol sensing. Mol Interv. 2002 Apr;2(2):78-87.<br />
130
12. Future challenges<br />
All <strong>of</strong> the future challenges that face the attempts to utilize sequence data from single<br />
genomes for motif finding and binding site prediction can be extended to the <strong>evolution</strong>ary<br />
situation using the probabilistic framework that we have described. In the <strong>evolution</strong>ary<br />
case, however, there are a great number <strong>of</strong> additional challenges. Of particular<br />
importance is to characterize the <strong>evolution</strong>ary constraints on promoter organization and<br />
context dependencies between binding sites. In order to do this, reasonably large sets <strong>of</strong><br />
promoters will be required where the binding sites have been characterized in detail, and<br />
we can be reasonably confident that the <strong>regulation</strong> has not changed over <strong>evolution</strong>.<br />
Needless to say, assembling such datasets will require considerable experimental efforts.<br />
Another major area <strong>of</strong> interest is to understand the situation where function is not<br />
strictly conserved at the level <strong>of</strong> the binding sites; this will be the focus <strong>of</strong> the majority <strong>of</strong><br />
the remainder <strong>of</strong> this dissertation. From the perspective <strong>of</strong> tools to detect motifs and<br />
identify binding sites, methods that do not assume binding sites to be strictly conserved<br />
since the common ancestor will be particularly useful were the number <strong>of</strong> species<br />
available is large – in these cases binding sites need not be required to be conserved in<br />
the entire tree. Once we try to allow for the <strong>evolution</strong> <strong>of</strong> functional constraints the<br />
problem <strong>of</strong> binding site <strong>evolution</strong> becomes much more complicated. Furthermore,<br />
because there have been relatively few mechanistic studies <strong>of</strong> binding site <strong>evolution</strong>, we<br />
have little empirical data on which to develop our models.<br />
131
Part II<br />
Identifying sequences controlling gene expression using data from a single species<br />
14
4. Searching for statistically enriched sequences<br />
Methods for identifying sequence determinants <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> take<br />
advantage <strong>of</strong> sequence specificity <strong>of</strong> transcription factors, and attempt to recognize<br />
statistically enriched patterns in non-coding DNA. For example, Figure 1 shows the<br />
enrichment <strong>of</strong> zeste binding sites in regions bound in vivo <strong>by</strong> this protein.<br />
-2500 -1500 -500 500 1500 2500<br />
Figure 1.<br />
Enrichment <strong>of</strong> predicted zeste binding sites in bound fragments in a Chip Chip<br />
experiment<br />
The solid line is the density <strong>of</strong> matches to the zeste matrix in non-overlapping 100 bp windows surrounding<br />
peaks in signal intensity from a chip-chip experiment. The dotted traces are 99% confidence intervals for<br />
the binomial distribution. The peak in number <strong>of</strong> hits to the zeste matrix matches corresponds to the peak<br />
in binding as measured in this experiment.<br />
Distance relative to peak<br />
Systematically identifying the binding sites for transcription factors is an important part<br />
<strong>of</strong> understanding how regulatory information is encoded in DNA sequence. We can<br />
break this problem into two parts, first to find the specificity <strong>of</strong> transcription factors, and<br />
second once we know the specificity, to identify the functional examples <strong>of</strong> their binding<br />
15<br />
0.014<br />
0.012<br />
0.01<br />
0.008<br />
0.006<br />
0.004<br />
0.002<br />
0<br />
binding site density (per bp)
site in the genome. Both <strong>of</strong> these problems can be addressed computationally; the first,<br />
identifying the specificity is a pattern discovery problem, while the second, identifying<br />
the ‘instances’ <strong>of</strong> the motif is a pattern matching problem. Much more attention has been<br />
paid to the former, and it shall take most <strong>of</strong> the focus here. I will return to the latter<br />
problem in more detail in the context <strong>of</strong> <strong>evolution</strong>ary binding site identification.<br />
Finding the sequence specificity <strong>of</strong> unknown transcription factors amounts to<br />
looking for statistically enriched patterns or motifs in non-coding DNA, and it is<br />
therefore worth some consideration <strong>of</strong> what definitions <strong>of</strong> statistical enrichment can be<br />
employed.<br />
Statistics for calculating ‘statistical’ enrichment<br />
An important question in these types <strong>of</strong> approaches is how to score the statistical<br />
enrichment <strong>of</strong> the sequence feature <strong>of</strong> interest. The simplest approach is to simply count<br />
the instances <strong>of</strong> the motif in the bound set and compare that to the expected frequency<br />
based on the genome, or some random subset <strong>of</strong> the genome. Statistical significance can<br />
be assessed in these cases using the binomial distribution, where p-values are given <strong>by</strong><br />
N<br />
∑<br />
x=<br />
n<br />
16<br />
n−1<br />
∑<br />
p ( x ≥ n | N,<br />
f ) = p(<br />
x | N,<br />
f ) = 1−<br />
p(<br />
x | N,<br />
f ) ,<br />
where x is the number <strong>of</strong> instances <strong>of</strong> the motif in the subset, N is the number <strong>of</strong> base-<br />
pairs in the subset, f is the genome, or background frequency <strong>of</strong> instances <strong>of</strong> this motif<br />
and<br />
x=<br />
0
p<br />
x<br />
⎛ N ⎞<br />
N − x N!<br />
x N − x<br />
( | , ) = ⎜ ⎟ ( 1−<br />
f ) = f ( 1−<br />
f ) ,<br />
x<br />
N<br />
f<br />
⎝ x ⎠<br />
17<br />
x!<br />
( N − x)!<br />
which is the binomial distribution. The assumption here is that each position in the<br />
sequence can be thought <strong>of</strong> as an independent trial, and that there is some background<br />
rate at which motif instances appear. Then we can ask directly the probability <strong>of</strong><br />
observing as many instances as we have given the background frequency. This statistical<br />
model has limitations, in particular the case were a motif is self-overlapping, or there are<br />
repetitive regions in the subset – in these cases the significance can be either greatly over-<br />
estimated or one sequence or small region <strong>of</strong> a sequence that contains many instances can<br />
contribute disproportionately.<br />
In general, when we are searching for over-represented sequences, we may want<br />
to include some notion <strong>of</strong> the ‘coverage’ in the subset; not only should there be more<br />
instances than some expectation, but that these should be spread over more <strong>of</strong> the subset<br />
than expected. Another way <strong>of</strong> looking at the idea <strong>of</strong> coverage is that we are requiring<br />
the motif to be not only statistically enriched, but also shared amongst the sequences in<br />
the subset. Tests <strong>of</strong> enrichment which take this into account are also possible, and <strong>of</strong><br />
particular utility has been one based on the hypergeometric distribution where the length<br />
<strong>of</strong> the sequences in the subset are all the same. In this case, instead <strong>of</strong> the sample size<br />
being the number <strong>of</strong> basepairs, because the sequences are all the same size, we can use<br />
the number <strong>of</strong> sequences. We now define x to be a ‘coverage statistic’, i.e., the number<br />
<strong>of</strong> sequences in the set that contain the motif. Now the numbers may be too small to<br />
invoke the binomial distribution, but we can use the hypergeometric distribution to<br />
calculate p-values
M<br />
∑<br />
x=<br />
m<br />
18<br />
m−1<br />
p ( x ≥ m | M , n,<br />
N)<br />
= p(<br />
x | M , n,<br />
N)<br />
= 1−<br />
p(<br />
x | M , n,<br />
N)<br />
,<br />
where x is now the number <strong>of</strong> sequences in the subset that contain the motif, M is the<br />
number <strong>of</strong> sequences in the subset, n is the number <strong>of</strong> sequences <strong>of</strong> that size in the<br />
genome that contain the motif, N is the total number <strong>of</strong> sequences <strong>of</strong> that size in the<br />
genome, and<br />
⎛ M ⎞⎛<br />
N − M ⎞<br />
⎜ ⎟⎜<br />
⎟<br />
⎝ x ⎠⎝<br />
n − x<br />
p ( x | M , n,<br />
N)<br />
=<br />
⎠<br />
,<br />
⎛ N ⎞<br />
⎜ ⎟<br />
⎝ n ⎠<br />
which is the hypergeometric distribution. Of course, the requirement that the sequences<br />
be <strong>of</strong> the same length is not always easy to satisfy. We have found this test extremely<br />
robust when using 600 bp upstream <strong>of</strong> each yeast gene, for example. More generally,<br />
when the lengths <strong>of</strong> sequences differ, it is possible to evaluate the significance <strong>of</strong> the<br />
coverage statistic, (the number <strong>of</strong> sequences that contain the motif) <strong>by</strong> sampling<br />
sequences <strong>of</strong> the same size distribution from the genome. For motifs with low specificity<br />
that occur commonly in random sequence, this can be extended <strong>by</strong> defining the coverage<br />
statistic xk to be the number <strong>of</strong> sequences that contain at least k instances <strong>of</strong> the motif.<br />
Ideally, however, we desire some kind <strong>of</strong> hybrid approach that takes into account<br />
both the statistical enrichment (the excess number <strong>of</strong> instances) and the coverage (the<br />
∑<br />
x=<br />
0<br />
excess number <strong>of</strong> sequences that contain an excess <strong>of</strong> instances).<br />
Motif finding based on discrete models <strong>of</strong> transcription factor binding sites<br />
There are two main approaches to the problem <strong>of</strong> discovering enriched patterns in non-<br />
coding DNA. First a set <strong>of</strong> approaches that treat the motif discretely, and then attempt to
enumerate motifs until statistically significant ones are found. Significance can be<br />
assessed <strong>by</strong> comparing the distribution in some subset <strong>of</strong> sequence to a background<br />
model, or explicitly comparing the distribution in the subset to the genomic background,<br />
and using statistical models such as the ones presented above. The availability <strong>of</strong> rigorous<br />
estimates <strong>of</strong> statistical significance, and that the number <strong>of</strong> motifs tested can be controlled<br />
are the main strengths <strong>of</strong> the discrete methods. The main drawback <strong>of</strong> such methods is<br />
that the discrete models they employ cannot always account for the true variability <strong>of</strong> the<br />
binding sites for a given factor.<br />
I have applied methods <strong>of</strong> this kind to a number <strong>of</strong> datasets. ChIP on chip data is<br />
particularly well suited to analysis <strong>of</strong> this kind because the output <strong>of</strong> such experiments is<br />
a list <strong>of</strong> 'bound' fragments that can be tested explicitly against the genome in order to<br />
identify enriched sequences that are candidates for the binding sites <strong>of</strong> these factors. A<br />
simple approach is to start with short words, and then extend them if the extension<br />
increases the significance relative to the background set. An example <strong>of</strong> the output <strong>of</strong><br />
such a motif finder is presented in table 1. There has also been a lot <strong>of</strong> work in<br />
developing sophisticated algorithms to exhaustively search the space <strong>of</strong> discrete sequence<br />
patterns (e.g., Sinha and Tompa 2000).<br />
motif consensus n enrichment p-value<br />
1 YCACTCAR 156 1.89e-83<br />
2 RRGWGAGC 107 1.23e-43<br />
3 GAGWGARW 161 1.48e-37<br />
4 CRCTCRM 227 3.12e-28<br />
5 AGAGMG 137 6.25e-17<br />
Table 1<br />
Running a discrete motif finder on the zeste data.<br />
19
Output <strong>of</strong> a simple discrete motif-finding algorithm that enumerates w-mers, and adds ambiguity characters<br />
to maximize significance <strong>of</strong> the consensus in the bound set (using the simple coverage statistic described<br />
above) relative to a set <strong>of</strong> randomly drawn set. This was run on the zeste ChIP chip data and the top motif<br />
matches the consensus <strong>of</strong> the zeste footprinted sites (underlined). The rest <strong>of</strong> the motifs correspond to<br />
related ‘GAGA’ sequences that may represent binding sites for the GAGA factor (also known as GAF or<br />
trl) which is known to work with zeste in regulating target genes (Laney and Biggin 1996, Mulholland et al.<br />
2003). This example highlights a major challenge for motif finders in this type <strong>of</strong> application – when<br />
multiple factors bind the same regulatory regions it is difficult to interpret the results <strong>of</strong> motif-finding<br />
alogorithms. I shall return to this issue in the future directions section.<br />
Probabilistic approaches to motif finding<br />
The second set <strong>of</strong> motif finding approaches rely on a probabilistic model <strong>of</strong> the motif, a<br />
multinomial for each position (Stormo 2000). Now the parameters <strong>of</strong> the model can be<br />
inferred from the data using a variety <strong>of</strong> optimization techniques and some assumptions<br />
about the distribution <strong>of</strong> the binding sites in the sequence (e.g., Stormo and Hartzell,<br />
1989). These approaches <strong>of</strong>ten suffer from overfitting - the high number <strong>of</strong> parameters<br />
needed for the models require large amounts <strong>of</strong> data to estimate accurately, and because<br />
they rely on a probabilistic model <strong>of</strong> the background, they <strong>of</strong>ten find sequences that are<br />
significantly enriched, but not specific to the set <strong>of</strong> genes <strong>of</strong> interest, such as polyA/T and<br />
other low complexity sequences. An example <strong>of</strong> a probabilistic model for the zeste<br />
motif is presented in Figure 2.<br />
20
A probability B<br />
pos. fA fC fG fT<br />
1 0.159 0.081 0.081 0.678<br />
2 0.011 0.193 0.044 0.752<br />
3 0.011 0.007 0.970 0.011<br />
4 0.974 0.007 0.007 0.011<br />
5 0.011 0.007 0.970 0.011<br />
6 0.048 0.156 0.007 0.789<br />
7 0.011 0.044 0.900 0.048<br />
Figure 2.<br />
Representing families <strong>of</strong> binding sites with probabilistic models<br />
A: a probabilistic representation <strong>of</strong> zeste binding sites. B: a graphical ‘sequence logo’ representation <strong>of</strong> the<br />
binding sites. These were constructed using in vitro footprinted binding sites (David Nix personal<br />
communication), and the ‘sequence logo’ was created using web logo (Crooks et al. 1998). While the<br />
consensus sequence TGAGTG does describe the binding sites, this probabilistic representation<br />
encapsulates the notion that while T is preferred at both the first and second positions, there is more<br />
variability at the first position than the second.<br />
We will return to probabilistic motif finding, both in the context <strong>of</strong> associating motifs<br />
with gene expression data, and in relation to incorporating conservation <strong>of</strong> binding sites<br />
into methods for motif finding and binding site identification.<br />
Methods<br />
Identifying instances <strong>of</strong> a motif<br />
For consensus sequences, we simply enumerate the number <strong>of</strong> times that sequence was<br />
found in the subset or in the background set. For probabilistic models <strong>of</strong> motifs we<br />
compute a likelihood ratio score that compares the probability <strong>of</strong> the w bases under the<br />
motif model to the probability under the background model. In figure <strong>of</strong> zeste<br />
21
enrichement, we used a score cut<strong>of</strong>f <strong>of</strong> 6.0, although the results are similar with other<br />
scores. We shall return to this type <strong>of</strong> score, its <strong>evolution</strong>ary extensions and how to<br />
calculate its distribution in part III.<br />
Building probabilistic models <strong>of</strong> motifs<br />
Frequency matrices <strong>of</strong> the type commonly used to represent the specificity <strong>of</strong><br />
transcription factors are multinomials. The maximum-liklihood estimates for the<br />
parameters are then simply the counts <strong>of</strong> each base over the total number observed.<br />
However, because the sample sizes are small, we add a correction to the ML estimate,<br />
known as a pseusocount, to avoid having parameter estimates <strong>of</strong> 0 in the matrix. This is<br />
<strong>of</strong>ten interpreted as a prior distribution, and we use either the background base<br />
frequencies, or the add-one method.<br />
References<br />
Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: A sequence logo generator. Genome Res<br />
14: 1188-1190.<br />
Laney JD, Biggin MD. Redundant control <strong>of</strong> Ultrabithorax <strong>by</strong> zeste involves functional levels <strong>of</strong> zeste<br />
protein binding at the Ultrabithorax promoter. Development. 1996 Jul;122(7):2303-11.<br />
Mulholland NM, King IF, Kingston RE. Regulation <strong>of</strong> Polycomb group complexes <strong>by</strong> the sequencespecific<br />
DNA binding proteins Zeste and GAGA. Genes Dev. 2003 Nov 15;17(22):2741-6.<br />
Sinha S, Tompa M. A statistical method for finding transcription factor binding sites. Proc Int Conf Intell<br />
Syst Mol Biol. 2000;8:344-54.<br />
Stormo GD, Hartzell GW 3rd. Identifying protein-binding sites from unaligned DNA fragments. Proc Natl<br />
Acad Sci U S A. (1989) Feb;86(4):1183-7.<br />
Stormo GD. DNA binding sites: representation and discovery. Bioinformatics. (2000) Jan;16(1):16-23.<br />
van Helden J, Andre B, Collado-Vides J. Extracting regulatory sites from the upstream region <strong>of</strong> yeast<br />
genes <strong>by</strong> computational analysis <strong>of</strong> oligonucleotide frequencies. J Mol Biol. 1998 Sep 4;281(5):827-42<br />
22
5. Methods that associate sequences with other data<br />
Thus far we have thought <strong>of</strong> the motif-finding problem as the attempt to find patterns that<br />
show statistical differences between a subset and the background model. Another way <strong>of</strong><br />
looking at this to try to find sequence features that explain the variability <strong>of</strong> a certain<br />
quantity, and in the previous examples, this was always 1 or 0, subset or background. In<br />
that case we seek an association between some sequence feature and a discrete variable.<br />
In general, however, we may wish to find associations between sequence features and<br />
continuous or multidimensional variables. All manner <strong>of</strong> tests <strong>of</strong> association can be used,<br />
and several have been tried in this context, such as linear (Bussemaker and Siggia, 2001)<br />
or logistic (Keles et al. 2002) regression or z-tests (Chiang et al. 2001). A main<br />
observation from these studies has been that the discovered sequence features usually<br />
have low predictive power when compared to any functional readout, but can be<br />
discovered because <strong>of</strong> the large statistical power afforded <strong>by</strong> genomic datasets. An<br />
example <strong>of</strong> the patterns that such approaches can produce is given <strong>by</strong> figure 1.<br />
Figure 1.<br />
23
Z scores over the cell cycle<br />
Each point on this graph is the z-score (as in Chiang et. al 2001) for the genes that contain a particular<br />
sequence element vs. the entire genome in microarray data from S. cerevisiae over the cell cycle (Spellman<br />
et. al. 1998). The cyclical patterns <strong>of</strong> transcript abundance recapitulate the temporal ordering <strong>of</strong> these<br />
transcription factors in cell cycle control: Mbp1p/Swi6p, Swi4p/Swi6p, Fhk1p/Fkh2p, Swi5p (Breeden<br />
1996, Merrill et al. 1992, Aerne et al. 1998, Zhu et al. 2000). Note that genes with Gal4p sites are induced<br />
in the first 2 experiments, which were conditional alleles induced in gal+ media, and genes with Ste12p<br />
sites are induced at the beginning <strong>of</strong> the first time-course because the cells were synchronized using alpha<br />
factor.<br />
Our approach to this problem has been to apply the KS test to find associations between<br />
sequence features and quantitative measurements <strong>of</strong> gene expression from microarrays.<br />
The KS-statistic explicitly compares the cumulative distributions <strong>of</strong> the two samples, and<br />
the distribution <strong>of</strong> this statistic can be approximated numerically as a function <strong>of</strong> the<br />
sample sizes (Press et al. 1992). Because it operates on the distributions <strong>of</strong> the data<br />
directly, there is no assumption about the form <strong>of</strong> the distribution and it can identify<br />
subtle relationships between distributions when even when the means are not very<br />
different or when the sample size is small. * In general, it produces similar results as other<br />
similar statistical tests (Figure 2), with the main advantage being the relaxation <strong>of</strong> the<br />
need for a large number <strong>of</strong> genes containing a particular sequence feature.<br />
* In passing it is also important to note that I have found a problem with the KS test in the<br />
case where there are many ties and the number <strong>of</strong> points in two distributions differs. To<br />
our knowledge this problem has not been reported, and whether this is an implementation<br />
difficulty, or a fundamental problem with the test has not been explored.<br />
24
Figure 2.<br />
P-values from various statistical tests in the cell cycle dataset<br />
In these data the KS-test performs similarly to the parametric tests. Because the KS-test looks only at the<br />
cumulative distribution, we lose the information about the direction <strong>of</strong> the change; in this case whether the<br />
genes with the binding site upstream were relatively induced or repressed.<br />
This method effectively identifies both words and pairs <strong>of</strong> words that are associated with<br />
expression data. These words can then be clustered and motifs can be inferred (Chiang et<br />
al. 2003).<br />
Thusfar, we have considered binding sites for transcription factors to be simple<br />
consensus sequences. However, transcription factors <strong>of</strong>ten bind sequence families that<br />
are better represented <strong>by</strong> probability distributions that <strong>by</strong> simple consensus sequences.<br />
We have therefore developed an approach to find matrices that maximize the p-values <strong>of</strong><br />
KS-statistics on expression data. Using this type <strong>of</strong> training procedure we were able to<br />
find matrices with much improved predictive power, although even the optimized<br />
matrices still predicted many genes that were not actually regulated. Figure 3 shows an<br />
example <strong>of</strong> the results <strong>of</strong> such an approach applied to perturbations <strong>of</strong> the phosphate<br />
25
egulatory system in S. cereviaisiae (Ogawa et al. 2000).<br />
counts<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
Entire genome in a pho4p consitutively active allele mutant vs. wt<br />
Genes containing pho4p binding site CACGTG<br />
Genes above cut<strong>of</strong>f on KS-optimized 10 bp matrix<br />
Relative expression<br />
Figure 3.<br />
Optimizing a matrix based on the KS-statistic<br />
Starting with an initial w-mer, I performed a greedy optimization procedure where at each step I added a w-<br />
mer from one <strong>of</strong> the regulated sequences to the ‘matrix’ until no more improvement <strong>of</strong> the KS-statistic was<br />
possible. Even though the optimized matrix predicts many fewer <strong>of</strong> the genes that do not actually change in<br />
expression, these ‘false positives’ still out number the genes that are actually upregulated. This means that<br />
although we have found a sequence element that is strongly associated with the change in expression, the<br />
predictive power <strong>of</strong> the sequence motif is extremely poor.<br />
In addition, to these approaches that associate sequence features with quantitative<br />
measurements <strong>of</strong> gene expression, I have developed approaches to find sequences<br />
associated with multivariate discrete expression or other functional data <strong>by</strong> treating it one<br />
variable at a time and performing hypergeometric tests, and then clustering the variables<br />
and sequences. We have found this to be an effective strategy when the categories have<br />
sufficient numbers <strong>of</strong> genes. I have applied these type <strong>of</strong> methods to microarray data that<br />
has been discretized into expression clusters, MIPS or GO categories, but in principle this<br />
26
type <strong>of</strong> approach can be applied to any other functional categorization <strong>of</strong> genes.<br />
Applying this type <strong>of</strong> approach to yeast upstream regions yields several<br />
interesting observations (Figure 4). First, because this is an enumerative approach and I<br />
am using many categories <strong>of</strong> genes, I can expect to find many significant motifs – thus<br />
producing a systematic, global view <strong>of</strong> the yeast transcription network. Furthermore,<br />
because I am displaying all the data, it is easy to identify cases where multiple motifs are<br />
enriched in a single gene group, or when similar motifs are enriched in separate, though<br />
perhaps related gene groups. For example, consider the transcription factors Bas1p and<br />
Gcn4p. Both <strong>of</strong> these factors are known to bind sequences with the consensus TGACTC<br />
(Springer et al. 1996). While it is clear that this consensus is enriched in the groups <strong>of</strong><br />
genes that are targeted <strong>by</strong> Bas1p and Gcn4p, different variants <strong>of</strong> the flanking bases are<br />
statistically enriched in different expression data clusters. This suggests that there may<br />
be some flanking specificity in these proteins that contributes to their ability to regulate<br />
different genes in vivo. Another example <strong>of</strong> this may be the case <strong>of</strong> Pho4p and Cbf1p;<br />
while both bind the consensus CACGTG, a typical palendromic E-box motif, Cbf1p<br />
seems to prefer a flanking T or A, which is reflected in the significance these w-mers in<br />
its target genes (methionine and sulfur biosynthesis genes). This example, however, also<br />
illustrates how our global, enumerative approach can identify multiple binding sites in a<br />
single group <strong>of</strong> regulated genes; these genes also contain binding sites for Met31p, a zinc<br />
finger transcription factor that can work with Cbf1p in <strong>regulation</strong> these genes (Blaiseau<br />
and Thomas 1998). Other examples <strong>of</strong> multiple motifs in a single expression group<br />
include Ste12p and Alpha1 in mating genes, TTTAAA and GATGAG in non-ribosomal<br />
repressed stress genes, Hap1p and Upc2p in respiration genes, and Rap1p and<br />
27
CCGTACA (the latter <strong>of</strong> which may represent a Rap1 site with altered specificity) in<br />
ribosomal proteins. In many cases these are consistent with previous observations (see<br />
Chiang et al 2003).<br />
Figure 4.<br />
Clustering <strong>of</strong> 7-mers significantly associated with gene categories<br />
Each row in this figure represents a single 7mer, and each column represents a (possibly overlapping) set <strong>of</strong><br />
genes. Gene categories are either expression data clusters (<strong>Eisen</strong> et al. 1998, Hughes et al. 2001, Gasch et<br />
al. 2003, Cyert et al. 2003) or MIPS categories (Mewes et al. 2000). Statistical significance was calculated<br />
using the coverage statistic described above for each category, and the resulting p-values were log-<br />
transformed and clustered. Displayed is a heat map, where brighter green intensity corresponds to greater<br />
28<br />
TTTAAA, GATGAG repressed in stress<br />
ACGCG, MCB, cell cycle<br />
GTGGCAA, Rpn4p, proteasome<br />
TGAAAC, CATGT, ste12p, alpha2p, mating<br />
CACGTG, Pho4p, phosphate metabolism<br />
TGACTCC, Bas1p, adenine biosynthesis<br />
TGACTCA, Gcn4p, amino acid biosynthesis<br />
CAGCAA, SCB, cell cycle<br />
CCGATA, TAAACG, Hap1p, Upc2p, ergos. biosyn.<br />
CCAAT, Hap2/3/4p, respiration<br />
GATAAG, GATA factors, nitrogen<br />
ACTGTG, TCACGTG, Met31p, Cbf1p met. biosyn.<br />
AGGC, Crz1p, calcium signaling<br />
CCCCT, STRE, induced in stress<br />
CACCC, Aft1p, metal transport<br />
ACCCA, CCGTACA, Rap1p, ribosome
statistical significance.<br />
References<br />
Aerne BL, Johnson AL, Toyn JH, Johnston LH. Swi5 controls a novel wave <strong>of</strong> cyclin synthesis in late<br />
mitosis. Mol Biol Cell. 1998 Apr;9(4):945-56.<br />
Blaiseau PL, Thomas D: Multiple <strong>transcriptional</strong> activation complexestether the yeast activator Met4 to<br />
DNA. EMBO J 1998, 17:6327-6336.<br />
Breeden L. Start-specific transcription in yeast. Curr Top Microbiol Immunol. 1996;208:95-127.<br />
Bussemaker HJ, Li H, Siggia ED. Regulatory element detection using correlation with expression. Nat<br />
Genet. 2001 Feb;27(2):167-71.<br />
Chiang DY, Brown PO, <strong>Eisen</strong> MB. Visualizing associations between genome sequences and gene<br />
expression data using genome-mean expression pr<strong>of</strong>iles. Bioinformatics. 2001;17 Suppl 1:S49-55.<br />
Keles S, van der Laan M, <strong>Eisen</strong> MB. Identification <strong>of</strong> regulatory elements using a feature selection method.<br />
Bioinformatics. 2002 Sep;18(9):1167-75.<br />
Merrill GF, Morgan BA, Lowndes NF, Johnston LH. DNA synthesis control in yeast: an <strong>evolution</strong>arily<br />
conserved mechanism for regulating DNA synthesis genes? Bioessays. 1992 Dec;14(12):823-30.<br />
Mewes, H. W., D. Frishman, C. Gruber, B. Geier, D. Haase, A. Kaps, K. Lemcke, G. Mannhaupt, F.<br />
Pfeiffer, C. ller, S. Stocker, and B. Weil. MIPS: a database for genomes and protein sequences. Nucleic<br />
Acids Res. 28: 37-40, 2000.<br />
Ogawa N, DeRisi J, Brown PO. New components <strong>of</strong> a system for phosphate accumulation and<br />
polyphosphate metabolism in Saccharomyces cerevisiae revealed <strong>by</strong> genomic expression analysis. Mol<br />
Biol Cell. 2000 Dec;11(12):4309-21<br />
Press WH, Teukolsky SA, Vertterling WT, Flannery BP: Numerical Recipes in C 2nd edition. Cambridge:<br />
Cambridge University Press; 1992.<br />
Springer C, Kunzler M, Balmelli T, Braus GH. Amino acid and adenine cross-pathway <strong>regulation</strong> act<br />
through the same 5'-TGACTC-3' motif in the yeast HIS7 promoter. J Biol Chem. 1996 Nov<br />
22;271(47):29637-43.<br />
Zhu G, Spellman PT, Volpe T, Brown PO, Botstein D, Davis TN, Futcher B. Two yeast forkhead genes<br />
regulate the cell cycle and pseudohyphal growth. Nature. 2000 Jul 6;406(6791):90-4.<br />
29
6. Improved models <strong>of</strong> regulatory regions<br />
Thus far we have thought <strong>of</strong> the sequence features <strong>of</strong> interest as simple sequences. It is<br />
possible to take into account other features <strong>of</strong> regulatory regions such as the position <strong>of</strong><br />
binding sites in promoters, the total number <strong>of</strong> sites, or the spacing between particular<br />
groups <strong>of</strong> sites that are thought to work together.<br />
Positional biases in transcription factor binding sites<br />
I studied the positional distributions <strong>of</strong> consensus sequences in S. cerevisiae promoters.<br />
Many known transcription factor binding sites show skewed distributions in the<br />
promoters, and genes with binding sites in the favoured regions are more likely to show<br />
expression patterns that are consistent with <strong>regulation</strong> <strong>by</strong> that factor. Figure 1 gives an<br />
example <strong>of</strong> such a positional bias for the MCB element.<br />
Correlation<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-0.2<br />
-0.4<br />
-0.6<br />
-0.8<br />
-1<br />
-800<br />
mbp1 (ACGCGT)<br />
Position <strong>of</strong> site (relative to ATG)<br />
30<br />
0
Figure 1.<br />
Positional biases in binding sites associated with regulated genes<br />
Genes with Mbp1p binding sites in a particular region <strong>of</strong> the promoter are much more likely to show a<br />
strong correlation with the mean expression pattern for this motif. Each point in this figure represents a<br />
single instance <strong>of</strong> the MCB element, and the x coordinate is the position in the promoter relative to the<br />
ATG for the adjacent gene.<br />
Incorporating the positional distribution in the promoter could improve predictive power<br />
and increase the sensitivity <strong>of</strong> motif finding. The significance (<strong>of</strong> a coverage statistic) <strong>of</strong><br />
binding motifs in their target genes is much stronger when the search is restricted to the<br />
appropriate region <strong>of</strong> the promoter. Once again, it is possible to imagine optimization<br />
procedures that search for motifs that are associated with gene expression patterns and<br />
tend to be found in particular regions <strong>of</strong> the promoters.<br />
A main drawback <strong>of</strong> this type <strong>of</strong> approach is that the positions <strong>of</strong> motifs in these<br />
cases are all taken relative to the translation start (ATG) because yeast promoter<br />
prediction is very difficult. Another problem is that these methods may be <strong>of</strong> little use<br />
outside <strong>of</strong> yeast: while in yeast the <strong>transcriptional</strong> regulatory information is largely<br />
confined to the proximal promoters, in organisms with large genomes, cis-regulatory<br />
information can be encoded at distances <strong>of</strong> many thousands <strong>of</strong> basepairs from the<br />
translation start. In these cases rules about the spacing relative to the translation start will<br />
be <strong>of</strong> little use.<br />
Modeling combinatorial control<br />
A perhaps more general feature <strong>of</strong> eukaryotic <strong>transcriptional</strong> <strong>regulation</strong> is that it is<br />
combinatorial, such that the action <strong>of</strong> several transcription factors is necessary to specify<br />
31
an expression pattern. Because these factors may physically interact, or cooperatively<br />
recruit additional factors, their binding sites may be positioned non-randomly in the<br />
promoter. This 'promoter architecture' can be taken into account <strong>by</strong> searching for closely<br />
spaced binding sites. Several attempts have been made to model this feature <strong>of</strong> regulatory<br />
sequences. Of course, this adds additional parameters into the model, and makes<br />
probabilistic approaches more difficult. Recent studies have approached this problem<br />
either <strong>by</strong> separating the 'motif-discovery' and the inference <strong>of</strong> spacing and combinations<br />
<strong>of</strong> factors into different steps, or <strong>by</strong> searching for 'homotypic' clusters <strong>of</strong> binding sites,<br />
such that there are multiple sites from the same motif.<br />
We approached this problem <strong>by</strong> searching for pairs <strong>of</strong> closely spaced sequence<br />
features and using the KS-test to score the improvement in the statistical association with<br />
gene expression (Chiang et al. 2003). Overall, there is much work to be done in<br />
developing methods that capture the complexity <strong>of</strong> regulatory region organization.<br />
When binding specificity is known, this problem <strong>of</strong> modeling promoters is less<br />
difficult and some progress has been made. Approaches that attempt to classify genes<br />
based on the presence <strong>of</strong> multiple upstream motifs (Wasserman and Fickett 1998) and<br />
more recently, searches for 'clusters' <strong>of</strong> predefined sets <strong>of</strong> motifs (Markstein and Levine<br />
2002) have had some success. We have suggested a new approach to this problem that<br />
overcomes the need to predefine p-value cut<strong>of</strong>fs for the motifs, the length <strong>of</strong> the cluster,<br />
or a minimum density <strong>of</strong> binding sites in a cluster. Instead, we proposed to look for the<br />
groups <strong>of</strong> matches to a matrix that are the most statistically unlikely, taking into account<br />
both the number <strong>of</strong> hits, their similarity to the matrix, and the number <strong>of</strong> basepairs that<br />
we looked through. This should greatly reduce the parameterization <strong>of</strong> these methods and<br />
32
therefore allow clusters <strong>of</strong> binding site to be discovered when there is a much smaller set<br />
<strong>of</strong> characterized enhancers. In general there has been much less focus on the problem <strong>of</strong><br />
discovering binding sites when the specificity <strong>of</strong> the DNA binding protein is already<br />
known. In fact, this is a very difficult pattern recognition problem because there is very<br />
little information in a motif alone. We have proposed a simple information theoretic<br />
approach to include the spacing between factors and their orientation.<br />
These methods, while useful in terms <strong>of</strong> their ability to predict new targets for<br />
groups <strong>of</strong> transcription factors, still fail to capture the complexity <strong>of</strong> the organization <strong>of</strong><br />
even relatively simple eukaryotic regulatory regions (Figure 2). Thus, despite their<br />
success, they leave us unsure about why they are succeeding, and yield surprisingly little<br />
insight into the underlying logic <strong>of</strong> the cis-regulatory code.<br />
------------1 ----1 ---------1 ------ STE1 4 ______mating_____________________<br />
-------------1 - 1 - 11--1 --1 ------------- STE1 2 ______mating_ and_ pseudohyphal_ gro<br />
------------1 -----1 ---------1 ---1 --- TEC1 _______pseudohyphal_ growth________<br />
---1 ----1 ------------1 ------------- FAR1 _______cell_ cycle_________________<br />
-------------1 -------1 ------------ STE4_______signaling,_pheromone_pathwa<br />
-----------------1 --------------- GPA1 _______signaling,_ pheromone_ pathwa<br />
-------------------1 ----1 --1 -----0 -- AGA1 _______mating_____________________<br />
-------------------------1 - 11------ FUS1 _______mating;_ cell_ fusion________<br />
---------------0 -------1 - 1 --------- SST2_______mating_____________________<br />
-------------------------------- unknown__________________________unkno<br />
---1 --------------1 ---1 ----0 -------- MFA1 _______mating_____________________<br />
-----------------1 ----0 - 0 ---1 ------- AGA2_______mating_____________________<br />
------------------1 -----0 - 0 - 1 --1 ----- MFA2_______mating_____________________<br />
----------1 ---------1 ---1 0 - 0 --1 ------- BAR1 _______mating_____________________<br />
-------------------------00- 1 - 1 ----- STE6_______mating_____________________<br />
----------------1 ----1 ----0 - 0 1 ------- STE2_______mating_____________________<br />
------1 ----------------1 -----1 ----- FUS3_______mating_(cell_cycle_arrest)_<br />
-------1 --------------1 --1 --------- CDC20 ______mitosis____________________<br />
-800 -1<br />
Figure 2.<br />
Upstream regions <strong>of</strong> the alpha cell type and other pheromone responsive genes<br />
In this schematic representation <strong>of</strong> the upstream regions <strong>of</strong> these genes, a 1 corresponds to a binding site for<br />
ste12p and 0 corresponds to a halfsite for mcm1p/alpha2p complex. While these genes show similar<br />
expression patterns (they all fall into one ‘cluster’ (<strong>Eisen</strong> et al 1998) in response to pheromone, it is clear<br />
33
ased on the organization <strong>of</strong> binding sites in their promoters that they are regulated differently. This<br />
suggests that models incorporating spacing, number and order <strong>of</strong> binding sites will be necessary to accurate<br />
model the sequences that encode <strong>transcriptional</strong> <strong>regulation</strong>.<br />
References<br />
Chiang DY, Moses AM, Kellis M, Lander ES, <strong>Eisen</strong> MB. Phylogenetically and spatially conserved word<br />
pairs associated with gene-expression changes in yeasts. Genome Biol. 2003;4(7):R43. Epub 2003 Jun 26.<br />
Markstein M, Levine M. Decoding cis-regulatory DNAs in the Drosophila genome.<br />
Curr Opin Genet Dev. 2002 Oct;12(5):601-6.<br />
Wasserman WW, Fickett JW. Identification <strong>of</strong> regulatory regions which confer muscle-specific gene<br />
expression. J Mol Biol. 1998 Apr 24;278(1):167-81.<br />
34
7. Future challenges<br />
In addition to many currently unsolved problems in the field, with the availability <strong>of</strong><br />
complete genome sequences and genome wide functional data, motif finding and pattern<br />
matching to identify transcription factor specificity faces many new challenges. I will try<br />
to highlight those that seem the most important.<br />
More realistic cost functions for probabilistic methods<br />
Probabilistic methods are the most popular de novo motif finding methods. Current<br />
methods, however, nearly all optimize similar likelihood functions. While there has been<br />
some progress in developing methods that include some prior knowledge about the<br />
patterns <strong>of</strong> information in the motif (Sandelin and Wasserman 2004, Xing and Karp<br />
2004, Kechris et al. 2004), and incorporating spatial clustering <strong>of</strong> motifs (Zhou and Wong<br />
2004), most methods still rely on a simple set <strong>of</strong> models for the distribution <strong>of</strong> motifs in<br />
the regulated sequence set. These are usually models <strong>of</strong> the form ‘one occurrence per<br />
sequence’, ‘zero or one occurance per sequence’ or ‘don’t use information about which<br />
sequence they come from’. While these cost functions have been somewhat successful,<br />
and are implemented in most popular motif-finders, in most applications, ‘one or more<br />
occurrences per sequence’ or ‘zero or one or more occurrences per sequence’ models<br />
would actually be preferable, particularly for application to transcription factor binding<br />
sites that occur multiple times in a promoter. Further, while some progress has been<br />
made (Narisimhan et al. 2003) most probabilistic motif-finders do not actually find<br />
optimal motifs relative to a real background set, instead they rely on some background<br />
35
markov model. There are many situations where experimental data produces a set <strong>of</strong><br />
functional and non-functional sequences, and it is <strong>of</strong> interest to find motifs that are<br />
associate with function. As discussed above, there are many ways to think about the<br />
statistical enrichment <strong>of</strong> a motif in a group <strong>of</strong> genes, and each <strong>of</strong> these implies a different<br />
likelihood function to optimize.<br />
Overfitting <strong>of</strong> motif models<br />
There has been little work on overfitting, although it is clearly a rampant problem for<br />
current motif finding methods. One <strong>of</strong> the main difficulties here is that it is always<br />
possible to find a longer, more variable motif that is more specific to a subset. However,<br />
Given the number <strong>of</strong> sequences used to make up a 'motif' it is possible to calculate the<br />
probability <strong>of</strong> observing a certain number <strong>of</strong> bases more than once, and thus assess<br />
whether a discovered pattern represents a real binding site or the result <strong>of</strong> a greedy<br />
learning algorithm fitting the noise. It is easy to illustrate this in the discrete case. In the<br />
case <strong>of</strong> two sequences, if ambiguity characters are allowed, one can construct a motif that<br />
will match the entirety <strong>of</strong> the two sequences, and it will contain roughly ¾ two-way<br />
ambiguity characters, and ¼ exact characters. Similar calculations or simulations could<br />
be done as a function <strong>of</strong> motif size and dataset size.<br />
In addition, another approach to avoiding overfitting may come from viewing<br />
motif-finding as a model selection problem, where we hope to show that the model which<br />
includes the discovered motif fits the data significantly better than a model without the<br />
motif. There are techniques that are widely used in molecular <strong>evolution</strong> and other fields<br />
36
to test whether the fit <strong>of</strong> a model is significantly better than the fit <strong>of</strong> another model,<br />
usually <strong>by</strong> adding a penalty for the addition <strong>of</strong> extra parameters. This type <strong>of</strong> nested<br />
model test may be applicable to the probabilistic learning methods employed in motif<br />
finding and can be used to decide whether the found motifs might have been discovered<br />
<strong>by</strong> chance. Unfortunately, model selection problems to not lend themselves naturally to<br />
hypothesis testing, particularly when the number <strong>of</strong> models is large and none <strong>of</strong> them is a<br />
particularly good representation <strong>of</strong> the data. In these cases Bayesian approaches may be<br />
more appropriate.<br />
Motifs with overlapping specificities<br />
A major challenge to which there is no obvious solution within current probabilistic motif<br />
finding frameworks, is that these approaches treat features as unique. Because<br />
transcription factors are members <strong>of</strong> large protein families there are <strong>of</strong>ten several proteins<br />
with very similar binding specificities. This leads to a violation <strong>of</strong> the assumptions <strong>of</strong><br />
almost all these models, and poses a very difficult theoretical problem. Given a family <strong>of</strong><br />
sequences that are all somewhat related, how does one decide that they are the binding<br />
sites for one DNA-binding protein, or two separate proteins with similar specificity? It<br />
seems that much more empirical data about the patterns <strong>of</strong> degeneracy that are observed<br />
in proteins with related DNA binding domains, will be necessary before this can be<br />
adequately addressed.<br />
Truly scalable methods<br />
37
Finally, although some progress has been made with discrete methods (Kellis et al. 2003,<br />
Xie et al. 2005), there are really no probabilistic methods that can practically utilize<br />
genomic data to find motifs de novo in large complex genomes. As genomic functional<br />
data becomes increasingly available, discrete methods that can be applied to large<br />
datasets have already proven useful (e.g., as described above) and these methods should<br />
be extended to the probabilistic case.<br />
References<br />
Kechris KJ, van Zwet E, Bickel PJ, <strong>Eisen</strong> MB. Detecting DNA regulatory motifs <strong>by</strong> incorporating<br />
positional trends in information content. Genome Biol. 2004;5(7):R50. Epub 2004 Jun 24.<br />
Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES. Sequencing and comparison <strong>of</strong> yeast species to<br />
identify genes and regulatory elements. Nature. 2003 May 15;423(6937):241-54.<br />
Narasimhan C, LoCascio P, Uberbacher E. Background rareness-based iterative multiple sequence<br />
alignment algorithm for regulatory element detection. Bioinformatics. 2003 Oct 12;19(15):1952-63.<br />
Sandelin A, Wasserman WW. Constrained binding site diversity within families <strong>of</strong> transcription factors<br />
enhances pattern discovery bioinformatics. J Mol Biol. 2004 Apr 23;338(2):207-15.<br />
Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M. Systematic<br />
discovery <strong>of</strong> regulatory motifs in human promoters and 3' UTRs <strong>by</strong> comparison <strong>of</strong> several mammals.<br />
Nature. 2005 Mar 17;434(7031):338-45. Epub 2005 Feb 27.<br />
Xing EP, Karp RM. MotifPrototyper: a Bayesian pr<strong>of</strong>ile model for motif families. Proc Natl Acad Sci U S<br />
A. 2004 Jul 20;101(29):10523-8. Epub 2004 Jul 13.<br />
Zhou Q, Wong WH. CisModule: de novo discovery <strong>of</strong> cis-regulatory modules <strong>by</strong> hierarchical mixture<br />
modeling. Proc Natl Acad Sci U S A. 2004 Aug 17;101(33):12114-9. Epub 2004 Aug 5<br />
38
Part IV<br />
Evolution <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong><br />
131
13. Evidence for <strong>evolution</strong> <strong>of</strong> transcription networks on a genome-wide scale<br />
Thus far, we have considered the problem <strong>of</strong> detecting motifs and identifying binding<br />
sites in single genomes, and we have attempted to include <strong>evolution</strong>ary models <strong>of</strong> these<br />
binding sites. In all these cases we have been assuming that gene <strong>regulation</strong> is conserved<br />
over <strong>evolution</strong>. Of course, we know this not to be the case – that <strong>evolution</strong> <strong>of</strong> gene<br />
expression is a major source <strong>of</strong> the underlying molecular diversity that leads to<br />
organismal diversity – this in one <strong>of</strong> the motivations for studying gene <strong>regulation</strong> in the<br />
first place.<br />
Therefore we now begin to address the <strong>evolution</strong> <strong>of</strong> the mechanisms controlling<br />
gene expression in earnest, and focus less on the development <strong>of</strong> methods for annotation<br />
and prediction. As a first step in this direction, we have examined the <strong>evolution</strong> <strong>of</strong> gene<br />
<strong>regulation</strong> over longer <strong>evolution</strong>ary timescales than have been previously discussed.<br />
Considering more divergent species affords two major advantages. First, practically,<br />
because these species are sufficiently diverged at the level <strong>of</strong> non-coding DNA that there<br />
is no similarity between regulatory regions due to descent, and they can be treated<br />
independently. This is <strong>of</strong> utility because we can simply apply the methods that we have<br />
developed for analysis <strong>of</strong> single genomes to these multiple related genomes<br />
independently and gain <strong>evolution</strong>ary insight. Secondly, and perhaps more important, is<br />
that at these longer <strong>evolution</strong>ary time scales we are much more likely to see dramatic<br />
changes in the mechanisms <strong>of</strong> <strong>regulation</strong>. This work was published as Gasch et al. 2004.<br />
Conservation and <strong>evolution</strong> <strong>of</strong> cis-regulatory systems in ascomycete fungi<br />
132
Much <strong>of</strong> a gene’s expression pattern is dictated <strong>by</strong> flanking noncoding sequences that<br />
contain, among other things, binding sites recognized <strong>by</strong> sequence-specific nucleotide<br />
binding proteins that modulate transcript abundance. A number <strong>of</strong> recent studies have<br />
examined the <strong>evolution</strong> <strong>of</strong> cis-regulatory elements in alignments <strong>of</strong> orthologous<br />
regulatory regions, consistently showing that these elements evolve at a slower rate than<br />
the nonfunctional DNA that surrounds them (Hardison et al. 1997; Loots et al. 2000;<br />
McGuire et al. 2000; Bergman and Kreitman 2001; Dermitzakis and Clark 2002;<br />
Rajewsky et al. 2002; Moses et al. 2003). Most <strong>of</strong> these studies have been limited to<br />
closely related species whose orthologous noncoding sequences can be aligned, such that<br />
the putative cis-regulatory elements can be identified and compared. Cis-regulatory<br />
elements can be conserved in more distantly related species, even when the orthologous<br />
regulatory regions are too divergent to be accurately aligned (Piano et al. 1999; Cliften et<br />
al. 2003; Romano and Wray 2003). However, without the guidance <strong>of</strong> multiple<br />
alignments, little has been gleaned about the patterns <strong>of</strong> <strong>evolution</strong> or the functional<br />
constraints that act on cis-regulatory elements over longer <strong>evolution</strong>ary timescales.<br />
Recently, several methods have been developed to dissect the regulatory networks<br />
that function within an individual species. Myriad studies have shown that functional<br />
regulatory sequences can be identified in a set <strong>of</strong> coregulated genes on the basis <strong>of</strong> the<br />
enriched fraction <strong>of</strong> those genes that contain the sequence within their flanking regions<br />
(van Helden et al. 1998; Tavazoie et al. 1999; McGuire et al. 2000; Bussemaker et al.<br />
2001; Sinha and Tompa 2002). Gene co<strong>regulation</strong> can be conserved in related species,<br />
and this conservation has been exploited for the computational prediction <strong>of</strong> cis-<br />
regulatory elements that are highly conserved (Gelfand et al. 2000; Qin et al. 2003; Wang<br />
133
and Stormo, 2003; Pritsker et al. 2004; Yu et al. 2004). We reasoned that we could extend<br />
this approach to examine the <strong>evolution</strong> <strong>of</strong> cis-regulatory networks across species, <strong>by</strong><br />
analyzing the orthologs <strong>of</strong> genes coregulated in S. cerevisiae. As a first step toward this<br />
goal, we have examined the simplest model <strong>of</strong> regulatory networks: the connection<br />
between groups <strong>of</strong> coregulated genes and the flanking cis-regulatory sequences that<br />
coordinate their expression. We characterized groups <strong>of</strong> coregulated S. cerevisiae genes<br />
and their orthologs in 13 additional ascomycete fungi (Figure 1) and assessed the<br />
enriched fraction <strong>of</strong> those genes that contain known and novel cis-regulatory sequences.<br />
Our results strongly suggest that many <strong>of</strong> the known cis-regulatory systems from S.<br />
cerevisiae have been conserved over hundreds <strong>of</strong> millions <strong>of</strong> years <strong>of</strong> <strong>evolution</strong> (Berbee<br />
and Taylor 1993; Heckman et al. 2001). Based on these observations, we present a<br />
number <strong>of</strong> models for the mechanisms <strong>of</strong> cis-regulatory <strong>evolution</strong>.<br />
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Figure 1.<br />
Fungal Phylogeny<br />
The phylogenetic tree shows the 14 different fungi analyzed in this study. The topology <strong>of</strong> the tree was<br />
based on Kurtzman and Robnett (2003), and the branch lengths represent the average <strong>of</strong> maximum-<br />
likelihood estimates <strong>of</strong> amino acid substitutions (obtained using the PAML package [Yang 1997]) for the<br />
303 proteins that had orthologs assigned in all 14 <strong>of</strong> these genomes. The closely related saccharomycete<br />
species for which the orthologous upstream regions can be aligned are labeled in orange. The source <strong>of</strong><br />
each genome sequence is also indicated to the right <strong>of</strong> each species.<br />
Results<br />
We began <strong>by</strong> systematically characterizing known cis-regulatory elements and their gene<br />
targets in the well-studied yeast S. cerevisiae. We compiled a catalog <strong>of</strong> known and<br />
predicted S. cerevisiae cis-regulatory elements (Dataset S1) in two ways. First, we<br />
retrieved 80 known consensus transcription factor-binding sites from the literature, based<br />
in part on information summarized on the Yeast Proteasome Database (Costanzo et al.<br />
2001) and the Saccharomyces Genome Database (Weng et al. 2003). The majority <strong>of</strong><br />
these sequences have been experimentally defined. Six others were identified <strong>by</strong> virtue <strong>of</strong><br />
their conservation in the 39 untranslated regions <strong>of</strong> closely related Saccharomyces<br />
species (Kellis et al. 2003), and five downstream elements were computationally<br />
predicted from mRNA immunoprecipitation experiments (Gerber et al. 2004). In addition<br />
to these known consensus sequences, we used the program MEME (Bailey and Elkan<br />
1994) to identify 597 upstream sequence motifs common to groups <strong>of</strong> predicted<br />
coregulated genes (see below). Genes that contained one or more instance <strong>of</strong> each <strong>of</strong><br />
these sequences in the 1,000-bp upstream or 500-bp downstream regions were identified<br />
as described in Materials and Methods.<br />
135
We next identified and manually annotated 264 partially redundant groups <strong>of</strong><br />
genes that are predicted to be coregulated in S. cerevisiae, based on the genes’ similarity<br />
in expression, physical association with the same transcription factor, or functional<br />
relationships (Dataset S2; see Materials and Methods for details). For each gene group,<br />
we systematically scored the enrichment <strong>of</strong> genes that contained each <strong>of</strong> the putative<br />
regulatory elements identified above, compared to all genes in the S. cerevisiae genome<br />
that contained that flanking sequence. Of the 80 consensus sequences, 41 were identified<br />
as significant <strong>by</strong> this criterion. Of these significant sequences, 34 were identified in the<br />
gene group known to be regulated <strong>by</strong> that element (Dataset S3), suggesting an upper limit<br />
<strong>of</strong> 17% false-positive identifications. Of the 597 MEME matrices we identified, only 43<br />
were significantly enriched in the gene group that they were identified in (see matrices in<br />
Dataset S4). All but four <strong>of</strong> these matrices were very similar to the consensus element<br />
known to regulate those genes (see Materials and Methods for details). Therefore, out <strong>of</strong><br />
19,239 motif-gene group comparisons, we recovered 34 consensus sequences and four<br />
additional MEME matrices representing known cis-regulatory elements (thus 38 <strong>of</strong> 80<br />
known elements) and four unannotated MEME matrices that may represent novel S.<br />
cerevisiae regulatory sequences, for a total <strong>of</strong> 42 S. cerevisiae cis-elements in 35 unique<br />
gene groups. Many <strong>of</strong> these S. cerevisiae regulatory elements were shown to be<br />
conserved in orthologous regulatory regions from four closely related saccharomyces<br />
species (Figure 1, orange species) (Cliften et al. 2003; Kellis et al. 2003). However, it<br />
was not known whether these elements are conserved in more distantly related species for<br />
which the intergenic regions cannot be aligned. To explore this possibility, we reasoned<br />
that many genes that are coregulated in S. cerevisiae should also be coregulated in other<br />
136
fungal species, and that functional cis-regulatory elements could be identified with the<br />
same methods applied to coregulated S. cerevisiae genes. Therefore, for each group <strong>of</strong><br />
coregulated S. cerevisiae genes, we identified orthologs in each <strong>of</strong> 13 other fungal<br />
genomes using the method <strong>of</strong> Wall et al. (2003). This method identifies reciprocal<br />
BLAST hits between two genomes that span more than 80% <strong>of</strong> the protein lengths,<br />
there<strong>by</strong> providing a more conservative list <strong>of</strong> putative orthologs than a simple BLAST<br />
method. The complete set <strong>of</strong> orthologs is available in Datasets S5–S13.<br />
For each species-specific gene group, we scored the enrichment <strong>of</strong> genes that<br />
contain each <strong>of</strong> the 80 consensus sequences or examples <strong>of</strong> the MEME matrices<br />
discovered in the orthologous S. cerevisiae genes, as described above. This procedure<br />
was performed separately on each species, so that the identification <strong>of</strong> an enriched<br />
sequence in one species was independent <strong>of</strong> its identification in the other species.<br />
Therefore, when a given sequence was enriched in the orthologous gene groups from<br />
multiple genomes, we interpreted this to reflect the conservation <strong>of</strong> the cis-regulatory<br />
system represented <strong>by</strong> that element in the corresponding species. It is important to note<br />
that we have characterized this conservation at the level <strong>of</strong> regulatory networks, which<br />
does not necessarily imply that the individual elements upstream <strong>of</strong> each gene have been<br />
perfectly conserved (see Discussion).<br />
Many S. cerevisiae Cis-Regulatory Systems Are Conservedin Other Fungi<br />
The patterns <strong>of</strong> cis-sequence enrichment in gene groups from each species strongly<br />
suggest that many <strong>of</strong> the genes coregulated in S. cerevisiae are also coregulated in the<br />
other fungal species. Furthermore, these patterns suggest that the expression <strong>of</strong> those<br />
137
genes is likely to be governed <strong>by</strong> the same cis-regulatory systems. Figure 2 shows the<br />
enrichment measured for each S. cerevisiae cis-regulatory element in the gene group it is<br />
proposed to regulate (represented <strong>by</strong> each row <strong>of</strong> the figure) in the 14 fungal species<br />
(shown in each column in the figure). (All p-values are available in Datasets S14–S46.)<br />
All <strong>of</strong> the 42 elements were identified in the same gene groups from at least three <strong>of</strong> the<br />
four closely related saccharomycete species. The majority <strong>of</strong> these elements were<br />
identified in the orthologous genes from other hemiascomycete species as well: 31 (74%)<br />
were identified in S. castellii, 23 (56%) and 27 (64%) were found in the related species S.<br />
kluyveri and Kluyveromyces waltii, respectively, and 21 (50%) and 14 (33%) were found<br />
in Ash<strong>by</strong>a gossypii and Candida albicans, respectively. Outside <strong>of</strong> the hemiascomycete<br />
group, we identified three to four (7%–10%) <strong>of</strong> these elements in the euascomycete fungi<br />
and two (5%) in Schizosaccharomyces pombe. Notably, when an identical procedure was<br />
performed using randomized consensus sequences, zero sequences were enriched with p<br />
< 0.0002 in their respective gene group from any species (Figures S1 and S2). The<br />
number <strong>of</strong> regulatory systems that could be found in each species roughly correlates with<br />
the species tree, in that more cis-regulatory elements were identified in species closely<br />
related to S. cerevisiae compared to the more distantly related fungi. This result could<br />
arise from the decreased accuracy <strong>of</strong> ortholog assignment in the distantly related species,<br />
which would hinder the identification <strong>of</strong> conserved regulatory systems. However, control<br />
experiments indicate that our ability to identify each regulatory element <strong>by</strong> enrichment is<br />
largely insensitive to noise in each gene group and to the ortholog assignment parameters<br />
(Figure S3 and unpublished data). These results therefore suggest that the number <strong>of</strong><br />
regulatory systems conserved across species correlates with their divergence times. A<br />
138
handful <strong>of</strong> these cis-regulatory systems are conserved in all or nearly all <strong>of</strong> the fungal<br />
genomes. For example, the group <strong>of</strong> G1-phase cell-cycle genes from all species was<br />
significantly enriched for genes containing the upstream Mlu1-cell cycle box (MCB)<br />
(McIntosh 1993). This sequence regulates the expression <strong>of</strong> the G1-phase genes from S.<br />
cerevisiae (Moll et al. 1992) as well as its distant relative Sch. pombe (Lowndes et al.<br />
1992; Malhotra et al. 1993), strongly suggesting that the element has a similar role in the<br />
other fungi. Likewise, the Gcn4p binding site was identified in the amino acid-<br />
biosynthesis genes from all but Sch. pombe, consistent with the known involvement <strong>of</strong><br />
Gcn4p-like transcription factors in the amino acid-starvation responses <strong>of</strong> S. cerevisiae,<br />
C. albicans, Neurospora crassa, and Aspergillus nidulans (Hinnebusch 1986; Ebbole et<br />
al. 1991; Tazebay et al. 1997; Tripathi et al. 2002). The expression <strong>of</strong> nitrogen-<br />
catabolism genes in C. albicans, N. crassa, and As. nidulans is thought to be governed <strong>by</strong><br />
GATA-like factors (Kudla et al. 1990; Chiang et al. 1994; Marzluf 1997; Limjindaporn et<br />
al. 2003), as it is in S. cerevisiae (Magasanik and Kaiser 2002), consistent with our ability<br />
to detect upstream GATA-binding elements in the group <strong>of</strong> nitrogen catabolism genes<br />
from these species. In the majority <strong>of</strong> cases (approximately 80%) in which a given cis-<br />
regulatory element was identified <strong>by</strong> enrichment, we could also identify in that species an<br />
ortholog <strong>of</strong> its binding protein from S. cerevisiae. Therefore, the most parsimonious<br />
model is that gene-expression <strong>regulation</strong> through the identified cis-regulatory sequence is<br />
governed <strong>by</strong> the orthologous transcription factor in each species.<br />
139
Figure 2.<br />
Conservation <strong>of</strong> Cis-Sequence Enrichment in Specific Gene Groups<br />
Gene groups from each <strong>of</strong> the 14 species that are enriched for genes whose flanking regions contain known<br />
or novel cis-sequences are represented <strong>by</strong> orange boxes. Each row represents a group <strong>of</strong> coexpressed S.<br />
140
cerevisiae genes and a single cis-regulatory element known or predicted to control the genes’ expression, as<br />
indicated to the left <strong>of</strong> the figure. Each column in the figure represents the orthologous gene groups in 14<br />
different fungal species. An orange box indicates that the S. cerevisiae cis-regulatory sequence listed to the<br />
left <strong>of</strong> the diagram is enriched in the denoted S. cerevisiae genes or their orthologs in each fungal genome,<br />
according to the key at the bottom <strong>of</strong> the figure. The p-values for each group are available in Datasets S14–<br />
S46, and the number <strong>of</strong> orthologs in each gene group is available in Dataset S49. Some cis-regulatory<br />
elements did not meet our significance cut<strong>of</strong>f for enrichment but had been previously identified as<br />
conserved in related gene groups from the closely related saccharomycete species (Kellis et al. 2003), and<br />
these are denoted with a yellow box. A gray box indicates that the denoted sequence was not significantly<br />
enriched in that gene group, while a white box indicates that fewer than four orthologs were identified in<br />
the species. The rows are organized in decreasing order <strong>of</strong> the number <strong>of</strong> species in which the element was<br />
enriched.<br />
Novel Sequences Are Enriched in Coregulated Gene Groups from Other Fungi<br />
In many cases, we were unable to detect significant enrichment <strong>of</strong> the S. cerevisiae<br />
upstream elements in the orthologous gene groups from other species, particularly in the<br />
more distantly related fungi. One possible explanation for this observation is that,<br />
although the genes are still coregulated in these species, the cis-regulatory mechanisms<br />
that control their expression have evolved. We therefore searched the upstream regions<br />
from each group <strong>of</strong> orthologous genes for novel sequence motifs, using the program<br />
MEME (Bailey and Elkan 1994) and selected matrices that were significantly enriched in<br />
the gene group in which they were identified (see Materials and Methods for details). As<br />
has been previously noted for this type <strong>of</strong> motif discovery (Tavazoie et al. 1999; McGuire<br />
et al. 2000), the majority <strong>of</strong> the identified motifs were not significantly enriched in the<br />
141
appropriate gene group and may represent background sequences that are not functional.<br />
Thus, a total <strong>of</strong> 53 matrices were identified as significant in at least one species based on<br />
this criterion (the complete list <strong>of</strong> matrices and enrichment p values are available in<br />
Datasets S47 and S48). Over half <strong>of</strong> these were similar to known S. cerevisiae elements<br />
shown in Figure 2 and were enriched in the orthologous S. cerevisiae genes. Of the<br />
remaining motifs, two recognizably similar matrices were identified in the same gene<br />
group from multiple species, suggesting that they represent conserved regulatory systems<br />
not present in S. cerevisiae. To further examine this possibility, we scored the enrichment<br />
<strong>of</strong> genes containing examples <strong>of</strong> the 53 matrices in the orthologous gene groups from all<br />
species. This procedure identified 19 unique MEME matrices that were not identified in<br />
the S. cerevisiae genes and therefore may represent novel cis-regulatory elements in these<br />
fungi (Figure 3). More than a third <strong>of</strong> these elements were also enriched in the same gene<br />
group from other species, providing additional support for their functional relevance. For<br />
example, a number <strong>of</strong> upstream sequences identified in ribosomal protein genes were<br />
enriched in the same gene group from four or five other species, but not from S.<br />
cerevisiae. Similarly, sequences identified upstream <strong>of</strong> tRNA synthetase genes and<br />
upstream <strong>of</strong> the proteasome genes were identified in the same genes from all <strong>of</strong> the<br />
euascomycete fungi (N. crassa, Magnaporthe grisea, and As. nidulans). In the case <strong>of</strong> the<br />
proteasome genes, MEME identified the same motif upstream <strong>of</strong> orthologous genes from<br />
the related euascomycete Histoplasma capsulatum, for which partial genome sequence is<br />
available ( http://www.genome.wustl.edu/projects/hcapsulatum/) (unpublished data). That<br />
these sequences were identified in the same gene groups from multiple euascomycetes<br />
(but not the other species) implies that they are clade specific. Although future<br />
142
experiments will be required to elucidate the exact roles <strong>of</strong> these sequences, our<br />
observations suggest that the identified cis-sequences are functionally relevant and<br />
conserved across species.<br />
Figure 3.<br />
Enrichment <strong>of</strong> Novel Sequences in Coregulated Genes from Other Species<br />
Gene groups from each <strong>of</strong> the 14 species that are enriched for genes containing novel upstream sequences<br />
identified <strong>by</strong> MEME (see Materials and Methods for details) are shown, as described in Figure 2.<br />
Enrichment <strong>of</strong> genes that contain the cis-sequence listed to the left <strong>of</strong> the diagram is indicated <strong>by</strong> a purple<br />
box, according to the key at the bottom <strong>of</strong> the figure.<br />
Cis-Regulatory Element Positions and Spacing Are Also Conserved across Species<br />
143
The physical locations <strong>of</strong> many characterized S. cerevisiae cis-regulatory elements are<br />
restricted to a narrow region upstream <strong>of</strong> their target genes (Mannhaupt et al. 1999;<br />
Tavazoie et al. 1999; McGuire et al. 2000; Lieb et al. 2001; Natarajan et al. 2001). This<br />
suggests that these elements must be positioned in the appropriate window <strong>of</strong> the<br />
upstream sequences, perhaps to promote proper interactions between the element’s<br />
binding protein and other factors (such as nucleosomes or RNA polymerase subunits)<br />
(Workman and Kingston 1992; Vashee and Kodadek 1995; Fry et al. 1997; Fry and<br />
Farnham 1999; GuhaThakurta and Stormo 2001). To characterize the upstream positions<br />
<strong>of</strong> cis-regulatory elements in S. cerevisiae, we compared the fraction <strong>of</strong> elements in 50-bp<br />
windows upstream <strong>of</strong> their target genes to the fraction <strong>of</strong> elements in the same 50-bp<br />
window upstream <strong>of</strong> all genes in the S. cerevisiae genome. (This model is required to<br />
overcome the nonrandom nucleotide distribution immediately upstream <strong>of</strong> genes in this<br />
and other species, as described in Materials and Methods.) We found that many <strong>of</strong> the S.<br />
cerevisiae cis-regulatory elements are non-randomly distributed upstream <strong>of</strong> their target<br />
genes (Figure 4, blue boxes). Each element shows a different window <strong>of</strong> peak enrichment<br />
in S. cerevisiae. This likely reflects mechanistic differences between the regulatory<br />
systems that control the expression <strong>of</strong> each set <strong>of</strong> genes.<br />
In the majority <strong>of</strong> cases, when a cis-regulatory system was conserved in another<br />
species, the corresponding element had a similar upstream distribution to that seen in S.<br />
cerevisiae, in that the distributions had the same window <strong>of</strong> peak enrichment (Figure 4).<br />
This is significant, as the underlying genomic distribution <strong>of</strong> many <strong>of</strong> these sequences is<br />
substantially different in each species, due in part to the different GC content <strong>of</strong> some <strong>of</strong><br />
the genomes (unpublished data). For many regulatory systems, there was no correlation<br />
144
etween the positions <strong>of</strong> individual elements in orthologous upstream regions from<br />
multiple species (although there were some exceptions; Figures S4 and S5). This<br />
indicates that the distributions <strong>of</strong> these elements have been conserved, even though the<br />
precise positions <strong>of</strong> individual elements have not (see Discussion). In addition to the<br />
conserved S. cerevisiae elements, many <strong>of</strong> the novel cis-sequences presented in Figure 3<br />
also showed nonrandom distributions in the species in which they were identified (Figure<br />
4, purple boxes). Thus, the positional distribution <strong>of</strong> cis-regulatory elements appears to be<br />
a general feature <strong>of</strong> cis-<strong>regulation</strong> in multiple ascomycete species.<br />
145
Figure 4.<br />
Distribution <strong>of</strong> Cis-Regulatory Elements Upstream <strong>of</strong> Coregulated Genes<br />
146
The distribution <strong>of</strong> nine different sequences motifs (represented to the left <strong>of</strong> the figure <strong>by</strong> the consensus<br />
sequences and their known binding proteins) was measured in 50-bp windows within 1,000 bp upstream <strong>of</strong><br />
the putative target genes (denoted to the right <strong>of</strong> the figure). Each colored box represents the frequency <strong>of</strong><br />
an element in a 50-bp window upstream <strong>of</strong> the target genes compared to the element’s frequency in the<br />
corresponding window <strong>of</strong> all upstream regions in each genome. Blue boxes represent sequences that<br />
matched the S. cerevisiae MEME matrices, while purple boxes represent sequences that matched the<br />
designated species-specific MEME matrices. Distributions that were significantly different from<br />
background in at least one 50-bp window (p , 0.01) were identified using the hypergeometric distribution<br />
(as described in Materials and Methods) and are denoted <strong>by</strong> an asterisk.<br />
In one case, the close spacing between two cis-regulatory elements was conserved across<br />
species. Chiang et al. (2003) previously reported that the distance between the Cbf1p-<br />
and Met31/32p-binding sites upstream <strong>of</strong> the methionine biosynthesis genes is closer than<br />
expected <strong>by</strong> chance. We found this feature to be conserved in other species as well. The<br />
Cbf1p and Met31/32p elements were independently identified upstream <strong>of</strong> the<br />
methionine genes from almost all <strong>of</strong> the hemiascomycetes (see Figure 2). In addition, the<br />
closer-than-expected spacing between these sequences was also conserved in these<br />
species (Figure 5). The spacing between elements was independent <strong>of</strong> the exact positions<br />
<strong>of</strong> the Cbf1p or Met31/32p sites in the saccharomycete species, indicated <strong>by</strong> permutation<br />
tests performed as previously described (p < 0.05; Chiang et al. 2003). Thus, the close<br />
spacing between these sites is not simply due to the conserved positioning <strong>of</strong> the<br />
individual elements in each orthologous upstream region, but likely resulted from an<br />
<strong>evolution</strong>ary constraint on the distance between these sequences (see Discussion).<br />
147
Figure 5.<br />
Spatial Relationships between Cis-Regulatory Elements<br />
The mean spacing between the Cbf1p- and Met31/32p- binding sites within 500 bp upstream <strong>of</strong> the<br />
methionine biosynthesis genes (m) and <strong>of</strong> all <strong>of</strong> the genes in each genome (g) was calculated for the species<br />
indicated. The error bars represent twice the standard error, indicating the range <strong>of</strong> the estimated means<br />
with 95% confidence. The values below each plot indicate the number <strong>of</strong> binding-site pairs used in each<br />
calculation<br />
Evolution <strong>of</strong> the Proteasome Cis-Regulatory Element in S. cerevisiae and C. albicans<br />
We were particularly interested in exploring patterns <strong>of</strong> cis element <strong>evolution</strong> across<br />
fungi. One interesting example is the case <strong>of</strong> Rpn4p, a non-classical Cys2-His2 zinc-<br />
finger protein known to regulate proteasome gene expression in S. cerevisiae (Mannhaupt<br />
et al. 1999; Xie and Varshavsky 2001). For the group <strong>of</strong> S. cerevisiae proteasome genes,<br />
148
the enrichment <strong>of</strong> genes containing the known Rpn4p binding site was highly significant<br />
(GGTGGCAA; p = 6.3 x 10 –41 ). The same consensus sequence was also enriched in the<br />
orthologous upstream regions <strong>of</strong> all <strong>of</strong> the hemiascomycete fungi, but not in the upstream<br />
regions retrieved from fungi outside <strong>of</strong> the hemiascomycete group. We noticed that, in<br />
addition to the Rpn4p consensus site, a number <strong>of</strong> related hexameric sequences were also<br />
highly enriched in the orthologous upstream regions from C. albicans (unpublished data).<br />
This hinted at the possibility that a slightly different set <strong>of</strong> regulatory sequences governs<br />
the expression <strong>of</strong> the C. albicans proteasome genes.<br />
To further explore this possibility, we compared sequences found upstream <strong>of</strong> the<br />
proteasome genes from S. cerevisiae and C. albicans. To identify these sequences in an<br />
unbiased way, we first generated a species-independent ‘‘meta-matrix’’ based on a<br />
limited subset <strong>of</strong> the proteasome upstream regions from both species (see Materials and<br />
Methods for details). We then identified all examples <strong>of</strong> the meta-matrix upstream <strong>of</strong> the<br />
proteasome genes from S. cerevisiae and C. albicans, partitioned the sequences according<br />
to their species, and calculated two species-specific position-weight matrices (Figure 6).<br />
These matrices were statistically different at the second, third, and ninth positions (p ,<br />
0.01; see Materials and Methods for details) and indicated that the C. albicans matrix had<br />
less basepair specificity at these positions.<br />
The matrices are useful because they summarize the set <strong>of</strong> related sequences that<br />
are common to the upstream regions in each group, but a more direct assessment <strong>of</strong> these<br />
elements is to inspect the sequences directly. Sequences upstream <strong>of</strong> the S. cerevisiae and<br />
C. albicans proteasome genes that matched the ‘‘meta-matrix’’ described above were<br />
combined and organized <strong>by</strong> sequence similarity, using a hierarchical clustering method<br />
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described in Materials and Methods. The sequences could be classified into three general<br />
categories (Figure S6). The first category consisted <strong>of</strong> related sequences that were found<br />
in both S. cerevisiae and C. albicans proteasome upstream regions, the second was<br />
composed <strong>of</strong> sequences found almost exclusively upstream <strong>of</strong> S. cerevisiae genes, and the<br />
third was composed <strong>of</strong> elements found only upstream <strong>of</strong> the C. albicans proteasome<br />
genes. Manual inspection <strong>of</strong> the proteasome-gene upstream regions supported these<br />
classi- fications: There were zero instances <strong>of</strong> the S. cerevisiae-specific 10-mer<br />
GGTGGCAAAW upstream <strong>of</strong> any C. albicans proteasome genes, although nearly 75%<br />
<strong>of</strong> the S. cerevisiae proteasome genes contained this upstream sequence. Similarly, zero<br />
instances <strong>of</strong> the C. albicans-specific 10-mer GRAGGCAAAA were found upstream <strong>of</strong> S.<br />
cerevisiae proteasome genes, whereas 25% <strong>of</strong> the C. albicans genes contained the<br />
element. These observations suggest that S. cerevisiae and C. albicans use different<br />
sequences to govern the expression <strong>of</strong> the proteasome genes.<br />
Figure 6.<br />
Position-Weight Matrices Representing Proteasome Cis-Regulatory Element<br />
Sequences within 500 bp upstream <strong>of</strong> the S. cerevisiae or C. albicans proteasome genes that matched the<br />
species-independent metamatrix were identified as described. The identified sequences were used to<br />
generate sequence logos (Crooks et al. 2004) to represent the set <strong>of</strong> cis-sequences from S. cerevisiae (left)<br />
or from C. albicans (right). The height <strong>of</strong> each letter represents the frequency <strong>of</strong> that base in that position <strong>of</strong><br />
the matrix. Positions in the matrices that are statistically different (see Materials and Methods for details)<br />
are indicated with an asterisk.<br />
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Discussion<br />
The ascomycete fungi represent nearly 75% <strong>of</strong> all fungal species, and their diversity is<br />
evident <strong>by</strong> their unique morphologies, life styles, environmental interactions, and niches<br />
(Ainsworth et al. 2001). This diversity has been shaped <strong>by</strong> over a billion years <strong>of</strong><br />
<strong>evolution</strong> (Berbee and Taylor 1993; Heckman et al. 2001) and has almost certainly been<br />
affected <strong>by</strong> variation in gene expression. To explore the <strong>evolution</strong> <strong>of</strong> gene-expression<br />
<strong>regulation</strong> in these fungi, we have examined the cis-regulatory networks <strong>of</strong> 14<br />
ascomycete species whose genomes have been sequenced, using a framework that is not<br />
dependent on multiple alignments <strong>of</strong> orthologous regulatory regions. We have identified<br />
probable cis-acting sequences in each <strong>of</strong> these species <strong>by</strong> applying motif search and<br />
discovery methods to the flanking regions <strong>of</strong> orthologs <strong>of</strong> coregulated S. cerevisiae genes.<br />
Our ability to identify such sequences in the same gene groups from multiple species<br />
strongly suggests that the co<strong>regulation</strong> <strong>of</strong> those genes has been conserved. Examples<br />
from our analysis indicate that in many cases the genes’ co<strong>regulation</strong> is governed <strong>by</strong> a<br />
conserved regulatory system, while other examples suggest that some regulatory<br />
networks have evolved. These examples provide insights into the functional constraints<br />
that underlie the <strong>evolution</strong> <strong>of</strong> geneexpression <strong>regulation</strong>, as summarized below.<br />
Conservation <strong>of</strong> Cis-Regulatory Systems<br />
Our results indicate that a large number <strong>of</strong> cis-regulatory networks that function in S.<br />
cerevisiae are conserved in other ascomycete species. This is expected for the closely<br />
related species, since conserved regulatory elements can be readily identified in<br />
alignments <strong>of</strong> orthologous regulatory regions (Cliften et al. 2003; Kellis et al. 2003).<br />
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However, we show here that many <strong>of</strong> the cis-regulatory systems represented <strong>by</strong> these<br />
elements are conserved over much longer <strong>evolution</strong>ary time frames, beyond those for<br />
which orthologous noncoding regions can be aligned. For example, 50%–75% <strong>of</strong> the<br />
regulatory systems identified in S. cerevisiae are also found in S. kluyveri and S. castellii,<br />
which are diverged enough from S. cerevisiae that much <strong>of</strong> the gene synteny is lost and<br />
most orthologous intergenic regions cannot be aligned (Cliften et al. 2003). Over a third<br />
<strong>of</strong> these regulatory systems were identified in C. albicans, which is estimated to have<br />
diverged from S. cerevisiae over 200 million years ago, and a small number <strong>of</strong> regulatory<br />
networks have been conserved since the origin <strong>of</strong> the Ascomycetes some 500 million to a<br />
billion years ago (Berbee and Taylor 1993; Heckman et al. 2001). It is likely that we have<br />
underestimated the number <strong>of</strong> conserved regulatory networks, partly because <strong>of</strong> statistical<br />
limitations <strong>of</strong> our method. Nonetheless, these data indicate that regulatory networks can<br />
be conserved over very long periods <strong>of</strong> <strong>evolution</strong>. Despite the widespread conservation <strong>of</strong><br />
cis-regulatory networks, it is important to note that this does not necessarily imply that<br />
the individual cis-elements have remained perfectly conserved. For example, while we<br />
could identify the same cis-sequences in orthologous gene groups, the positions <strong>of</strong> the<br />
individual elements in orthologous upstream regions in many cases appear to have<br />
changed (see Figure S4). Evolution <strong>of</strong> cis-element position has been observed in closely<br />
related drosophilids, mammals, and other species (Ludwig and Kreitman 1995; Ludwig et<br />
al. 1998; Piano et al. 1999; Dermitzakis and Clark 2002; Scemama et al. 2002;<br />
Dermitzakis et al. 2003) and is proposed to occur <strong>by</strong> two general mechanisms (reviewed<br />
in Wray et al. 2003). The first is binding-site turnover, where<strong>by</strong> the appearance <strong>of</strong> a new<br />
cis-element elsewhere in a promoter can compensate for the loss <strong>of</strong> a functional element<br />
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in the same regulatory region. Simulation studies show that cis-element turnover occurs<br />
frequently over short <strong>evolution</strong>ary time scales and is likely to play an important role in<br />
gene-expression <strong>regulation</strong> (Stone and Wray 2001; Dermitzakis et al. 2003).<br />
Alternatively, small insertions and deletions in a regulatory region can permute the cis-<br />
element’s position without changing the element’s sequence (Ludwig and Kreitman<br />
1995; Piano et al. 1999; Ruvinsky and Ruvkun 2003). Thus, regulatory regions appear to<br />
be relatively plastic in their organization. Despite this plasticity, however, a gene’s<br />
expression pattern and the regulatory system governing its expression can remain intact<br />
even though the gene’s flanking regulatory region has undergone reorganization (Piano et<br />
al. 1999; Ludwig et al. 2000; Scemama et al. 2002; Hinman et al. 2003; Romano and<br />
Wray 2003; Ruvinsky and Ruvkun 2003). This indicates that some combination <strong>of</strong><br />
purifying selection and drift (Ludwig et al. 2000) can act to maintain the appropriate<br />
regulatory connections to conserve the gene’s expression pattern. Although the positions<br />
<strong>of</strong> many <strong>of</strong> the individual cis-elements have evolved in these species, we found that the<br />
distribution <strong>of</strong> elements upstream <strong>of</strong> their gene targets was <strong>of</strong>ten similar across species.<br />
This suggests that there has been constraint on the region in which the elements are<br />
positioned, without pressure to maintain the exact positions <strong>of</strong> individual elements. One<br />
explanation for this model is that mechanistic features <strong>of</strong> these regulatory systems are<br />
also conserved across species (Wray et al. 2003). For example, the restricted location <strong>of</strong><br />
cis-regulatory elements may promote interactions between the cognate binding protein<br />
and other regulatory proteins. Therefore, selective pressure may act to maintain these<br />
interactions through the relative positions <strong>of</strong> the underlying binding sites. This model<br />
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may also explain the conserved close spacing between Cbf1p and Met31/32p elements in<br />
methionine biosynthesis genes from the hemiascomycete fungi. These transcription<br />
factors are proposed to act cooperatively in S. cerevisiae to recruit additional<br />
<strong>transcriptional</strong> regulators (Blaiseau and Thomas 1998). That the spacing between the<br />
Cbf1p and Met31/32p elements is closer than expected in other species as well suggests<br />
that the cooperative interaction between the factors has been conserved across the<br />
Hemiascomycetes.<br />
Evolution <strong>of</strong> Cis-Regulatory Networks<br />
In addition to the clear cases <strong>of</strong> network conservation discussed above, we also found<br />
evidence for the <strong>evolution</strong> <strong>of</strong> cis-regulatory systems. Our ability to identify novel<br />
sequences enriched in orthologs <strong>of</strong> coregulated S. cerevisiae genes implies that, although<br />
the genes are still coregulated in those species, the systems governing their expression<br />
have changed. This indicates that the regulatory regions <strong>of</strong> those genes coevolved to<br />
contain the same cis-sequences. We were interested in identifying global predictors <strong>of</strong> the<br />
relative rates <strong>of</strong> cis-regulatory network <strong>evolution</strong>, but these factors remain enigmatic.<br />
Unlike the <strong>evolution</strong>ary rates <strong>of</strong> protein coding regions, for which essential proteins<br />
typically evolve at a slower rate (Wilson et al. 1977; Hirsh and Fraser 2001; Krylov et al.<br />
2003; H. B. F., personal communication), we found no evidence for a retarded rate <strong>of</strong><br />
<strong>evolution</strong>/loss <strong>of</strong> the cis-regulatory systems <strong>of</strong> essential genes (unpublished data). For<br />
example, the proteasome subunits and the ribosomal proteins are among the most highly<br />
conserved proteins, and the genes that encode them are expressed with similar patterns in<br />
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S. cerevisiae, C. albicans, and Sch. pombe (Gasch et al. 2000; Chen et al. 2003; Enjalbert<br />
et al. 2003). Nonetheless, we identified different upstream sequences for these groups in<br />
the different species we analyzed, suggesting that the <strong>regulation</strong> <strong>of</strong> the genes’ expression<br />
has evolved even though their expression patterns have not. This is consistent with<br />
previous observations <strong>of</strong> developmentally regulated genes in higher organisms, whose<br />
temporal and spatial expression can be conserved across taxa despite divergence in their<br />
<strong>regulation</strong> (Takahashi et al. 1999; True and Haag 2001; Scemama et al. 2002; Hinman et<br />
al. 2003; Romano and Wray 2003; Ruvinsky and Ruvkun 2003; Wang et al. 2004). In<br />
contrast, we observed that proteins involved in mating have a high rate <strong>of</strong> <strong>evolution</strong>, yet<br />
we could identify the Ste12p binding site (Fields and Herskowitz 1985) upstream <strong>of</strong><br />
mating genes in nearly all <strong>of</strong> the hemiascomycetes. Consistently, orthologs <strong>of</strong> Ste12p are<br />
known to be required for mating in distantly related fungi that mate through significantly<br />
different processes (Lengeler et al. 2000; Vallim et al. 2000; Young et al. 2000; Chang et<br />
al. 2001). Since mating may be triggered <strong>by</strong> similar environmental cues (Lengeler et al.<br />
2000), <strong>evolution</strong>ary pressure may have conserved the regulatory system that mediates this<br />
process (to the extent <strong>of</strong> our observations), even though the mating proteins have<br />
evolved. Although we could not find global correlates with the patterns <strong>of</strong> cis-regulatory<br />
network <strong>evolution</strong>, a number <strong>of</strong> individual examples from our analysis are consistent with<br />
specific models <strong>of</strong> network <strong>evolution</strong>. These examples are discussed below.<br />
Addition <strong>of</strong> Gene Targets into an Existing Regulatory Network<br />
Sequences that match cis-regulatory elements can readily appear in noncoding DNA<br />
through drift. In the same way that this process can promote binding site turnover within<br />
155
a given regulatory region, it can create de novo elements in the regulatory regions <strong>of</strong><br />
random genes, giving rise to novel targets <strong>of</strong> that regulatory system (Stone and Wray<br />
2001; Rockman and Wray 2002). The addition <strong>of</strong> novel targets into cis-regulatory<br />
systems may have occurred in the case <strong>of</strong> E2Flike transcription factors. In S. cerevisiae,<br />
the related MCB (ACGCG) and Swi4-Swi6 cell-cycle box, or SCB (CGCGAAA)<br />
regulatory elements are found upstream <strong>of</strong> G1-phase cell cycle genes, similar to the E2F<br />
element found in these genes in worms, flies, humans, and plants (Lowndes et al. 1992;<br />
Malhotra et al. 1993; DeGregori 2002; Ren et al. 2002; De Veylder et al. 2003; Rustici et<br />
al. 2004). What is striking about the conservation <strong>of</strong> this network is that cell-cycle<br />
progression is markedly different in these organisms: The hemiascomycete fungi<br />
replicate <strong>by</strong> budding, unlike the filamentous fungi in the euascomycete group, the fission<br />
yeast Sch. pombe, and the other higher eukaryotes. While some <strong>of</strong> the genes regulated <strong>by</strong><br />
these elements are well conserved across organisms (namely, the DNA replication<br />
proteins), genes whose products are involved in budding are also expressed in G1 phase<br />
and regulated <strong>by</strong> these elements in S. cerevisiae (Spellman et al. 1998; Iyer et al. 2001)<br />
and likely in its budding cousins as well. Because these genes are not conserved outside<br />
the hemiascomycete clade, and since it is unlikely that budding represents the ancestral<br />
mode <strong>of</strong> replication, this suggests that genes involved in budding were assumed into an<br />
existing cis-regulatory network in these yeasts.<br />
Co<strong>evolution</strong> <strong>of</strong> an Existing Regulatory Network<br />
Mutation <strong>of</strong> a cis-regulatory element can be compensated <strong>by</strong> the stabilizing effects <strong>of</strong><br />
binding site turnover (Ludwig et al. 2000), as discussed above, but it could also be<br />
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overcome <strong>by</strong> corresponding changes in its DNA-binding protein, such that the interaction<br />
between the two is maintained. Parallel changes in DNA element and protein sequence<br />
can occur to conserve the overall regulatory network (i.e., the same binding protein<br />
regulating the same set <strong>of</strong> genes), despite <strong>evolution</strong> <strong>of</strong> their molecular interaction. We<br />
found slightly different sets <strong>of</strong> sequences enriched upstream <strong>of</strong> the proteasome genes<br />
from S. cerevisiae versus C. albicans, and these differences corresponded with the<br />
different binding specificities <strong>of</strong> Sc_Rpn4p and Ca_Rpn4p in vitro. This result is<br />
consistent with the model that the binding specificity <strong>of</strong> Sc_Rpn4p and Ca_Rpn4p<br />
coevolved with the elements found upstream <strong>of</strong> the proteasome genes in each species.<br />
[Neither Ca_Rpn4p nor the hybrid protein functioned in an in vivo reporter system<br />
(unpublished data); however, Sc_Rpn4p could transcribe a reporter gene to higher levels<br />
if Sequence A was present in its promoter compared to when Sequence B or a minimal<br />
promoter was placed upstream <strong>of</strong> the reporter gene (see Figure S7). These results are<br />
consistent with the hypothesis that Sc_Rpn4p ineffectively initiates transcription from the<br />
C. albicans-specific element. Since Ca_Rpn4p and Nc_Rpn4p both bind significantly to<br />
Sequence B, it is likely that this was also true <strong>of</strong> the proteins’ common ancestor and that<br />
Sc_Rpn4p largely lost the ability to bind productively to this sequence. The altered<br />
specificity <strong>of</strong> Sc_Rpn4p is due to amino acid differences in its DNA-binding domain,<br />
since the hybrid Rpn4p (containing the Ca_Rpn4p DNA binding domain) bound to<br />
Sequence B as well as it did to Sequence C (see Figure 7C). Determining which residues<br />
are responsible for the altered activity is a difficult task, however, since all <strong>of</strong> the residues<br />
known to participate in zinc coordination and DNA contact (Rhodes et al. 1996; Wolfe et<br />
al. 1999; Wolfe et al. 2000; Pabo et al. 2001; Benos et al. 2002) are perfectly conserved<br />
157
etween these orthologs (Figure 9). One obvious difference in the orthologous proteins is<br />
the spacing between the cysteine and histidine pair in the second zinc finger, which is<br />
proposed to contact the first half <strong>of</strong> the DNAbinding site (Wolfe et al. 2000; Pabo et al.<br />
2001) wherein the base-specificity differences reside. Sc_Rpn4p, Ca_Rpn4p, and the<br />
euascomycete Rpn4p orthologs all vary in amino acid length and identity in this region,<br />
which implicated the region as relevant to the specificity differences. However, a mutant<br />
Sc_Rpn4p that contained the Nc_Rpn4p sequence in this region (see Figure 9) had the<br />
same binding specificity as the wild-type Sc_Rpn4p (albeit with less activity;<br />
unpublished data), indicating that this region alone is not sufficient to explain the<br />
differences in binding pr<strong>of</strong>iles.]<br />
Figure 9.<br />
Sequence Alignment <strong>of</strong> the DNA-Binding Domain <strong>of</strong> Rpn4p and Its Orthologs<br />
Clustal W was used to identify a multiple alignment between S. cerevisiae Rpn4p and its orthologs in the<br />
other fungi; the alignment over the DNA binding domain is shown. No ortholog was identified <strong>by</strong> our<br />
method in S. kluyveri, apparently due to poor sequence coverage in that region (unpublished data). The<br />
conserved cysteine and histidine residues <strong>of</strong> the two C2H2 zinc-finger domains are highlighted in yellow,<br />
and the domain in each finger that is predicted to contact the DNA is indicated with a gray bar. The region<br />
<strong>of</strong> sequence variation between the hemiascomycete and euascomycete Rpn4p proteins is indicated with a<br />
box.<br />
158
Cooption <strong>of</strong> a Regulatory System to Govern a Different Set <strong>of</strong> Genes<br />
An extreme example <strong>of</strong> the previously discussed modes <strong>of</strong> <strong>evolution</strong> is the complete<br />
alteration <strong>of</strong> a regulatory system’s target genes (True and Carroll 2002). This may have<br />
occurred for the Rpn4p regulatory system sometime after the divergence <strong>of</strong> the<br />
euascomycete and hemiascomycete fungi. Our data suggest that, while Sc_Rpn4p and<br />
Ca_Rpn4p control proteasome-gene expression in these species, the euascomycete<br />
orthologs <strong>of</strong> this transcription factor probably do not. Nc_Rpn4p did not bind the novel<br />
sequence we identified upstream <strong>of</strong> euascomycete proteasome genes, and reciprocally the<br />
majority <strong>of</strong> these genes did not contain examples <strong>of</strong> the Rpn4p binding site. One<br />
possibility is that Nc_Rpn4p and its orthologs regulate a different set <strong>of</strong> genes in the<br />
euascomycete clade. Preliminary investigation <strong>of</strong> orthologous euascomycete genes that<br />
contain examples <strong>of</strong> the Ca_Rpn4p matrix (used as a surrogate for the Nc_Rpn4p binding<br />
matrix) did not reveal any obvious relationships in the genes’ functional annotations or<br />
striking similarities in their patterns <strong>of</strong> expression (T. Kasuga, personal communication).<br />
Interestingly, however, the orthologs <strong>of</strong> RPN4 in all three euascomycete species<br />
contained upstream Rpn4p elements, raising the possibility that this gene is autoregulated<br />
at the level <strong>of</strong> expression in these fungi. Future experiments will test the function <strong>of</strong> this<br />
factor in N. crassa as well as the role <strong>of</strong> the novel sequence in mediating proteasome<br />
gene expression. The converse <strong>of</strong> this situation is that the regulatory regions <strong>of</strong><br />
coregulated genes must coevolve, such that they all contain the same regulatory elements<br />
recognized <strong>by</strong> the new system. This apparently occurs despite strong constraint on the<br />
genes’ expression patterns. For example, most proteasome subunits are essential and<br />
159
equired in proper stoichiometric amounts (Russell et al. 1999; Kruger et al. 2001).<br />
Nonetheless, we found different cis-sequences upstream <strong>of</strong> the proteasome genes from<br />
the hemiascomycete and euascomycete fungi. Another example can been seen in the<br />
ribosomal protein genes, which must also be expressed to the same relative levels<br />
(Warner 1999; Zhao et al. 2003). In all species, we could find elements upstream <strong>of</strong> the<br />
ribosomal proteins, but different cis-sequences were identified in subsets <strong>of</strong> these species<br />
(see Figures 2 and 3). How the regulatory systems that control the genes’ expression<br />
evolve is unclear. This process may involve an intermediate stage in which the genes’<br />
expression is controlled <strong>by</strong> two distinct, but partially redundant, regulatory systems (True<br />
and Haag 2001; True and Carroll 2002). Differential loss <strong>of</strong> one system in two diverged<br />
species would render the orthologous genes coregulated <strong>by</strong> different regulatory systems.<br />
This model for regulatory system ‘‘turnover’’ is in direct analogy to the case <strong>of</strong> binding<br />
site turnover, in which partially redundant cis-elements that are created <strong>by</strong> drift coexist in<br />
a regulatory region before they are differentially lost in the diverged species (Ludwig et<br />
al. 2000; Stone and Wray 2001).<br />
Conclusions and Future Directions<br />
We have provided a framework for studying cis-regulatory <strong>evolution</strong> without relying on<br />
alignments <strong>of</strong> intergenic regions. The <strong>evolution</strong>ary dynamics <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong><br />
is evident from the examples we have presented. We expect that as more complete fungal<br />
genomes emerge, particularly for fungi with intermediate <strong>evolution</strong>ary relationships,<br />
important gaps in the existing phylogeny will be filled. These key species may provide a<br />
160
window into intermediate stages <strong>of</strong> cis-element <strong>evolution</strong>, allowing us to further delineate<br />
the patterns <strong>of</strong> and constraints on the <strong>evolution</strong> <strong>of</strong> cis-<strong>regulation</strong>.<br />
Materials and Methods<br />
Genome sequences.<br />
Genome sequence and open reading frame (ORF) annotations for the saccharomycete<br />
species were obtained from P. Cliften, M. Kellis, and the Saccharomyces Genome<br />
Database (G<strong>of</strong>feau et al. 1996; Cliften et al. 2003; Kellis et al. 2003). Sequences for other<br />
genomes were downloaded from the published or listed Web sites as follows. K. waltii<br />
(Kellis et al. 2004), A. gossypii (Dietrich et al. 2004), C. albicans (Assembly 6;<br />
http://www-sequence.stanford.edu/ group/candida/) (Jones et al. 2004), N. crassa<br />
(Release 3; Galagan et al. 2003), M. grisea (Release 2; http://www-<br />
genome.wi.mit.edu/annotation/ fungi/magnaporthe/) , As. nidulans (Release 3.1;<br />
http://www.broad. mit.edu/annotation/fungi/aspergillus/) , and Sch. pombe (Wood et al.<br />
2002). A conservative list <strong>of</strong> putative ORFs from S. kudriavzevii, S. castellii, and S.<br />
kluyveri was generated, taking all ORFs <strong>of</strong> more than 100 amino acids as putative genes.<br />
ORFs orthologous to S. cerevisiae genes were identified as described below; some intron-<br />
containing S. cerevisiae genes that may also contain introns in these species (namely<br />
ribosomal protein genes) were identified <strong>by</strong> tBLASTn and manually added to the list <strong>of</strong><br />
orthologs for these species. Orthologs between S. cerevisiae and S. paradoxus, S.<br />
mikatae, and S. bayanus (Kellis et al. 2003) were downloaded from the Saccharomyces<br />
Genome Database ( http://www.yeastgenome.org/) . All other orthologs to S. cerevisiae<br />
genes were assigned using the method <strong>of</strong> Wall et al. (Wall et al. 2003) using a BLAST e-<br />
161
value cut<strong>of</strong>f <strong>of</strong> 10-5 and the requirement for fewer than 20% gapped positions in the<br />
Clustal W alignments. The number <strong>of</strong> orthologs assigned in each species is listed in Table<br />
1, and the complete results are available in Datasets S5–S12.<br />
S. cerevisiae gene clusters.<br />
Groups <strong>of</strong> known or putatively coregulated genes were identified in three ways. First, we<br />
used hierarchical (<strong>Eisen</strong> et al. 1998) and fuzzy k-means (Gasch and <strong>Eisen</strong> 2002)<br />
clustering to organize publicly available yeast gene expression data (DeRisi et al. 1997;<br />
Spellman et al. 1998; Gasch et al. 2000; Lyons et al. 2000; Ogawa et al. 2000; Primig et<br />
al. 2000; Gasch et al. 2001; Yoshimoto et al. 2002), taking gene clusters that were<br />
correlated <strong>by</strong> more than about 0.7 or with a membership <strong>of</strong> 0.08 or greater (Gasch and<br />
<strong>Eisen</strong> 2002). Second, we identified genes or transcripts whose flanking regions are<br />
physically bound <strong>by</strong> the same DNA or RNA binding proteins, as indicated <strong>by</strong><br />
immunoprecipitation experiments (Simon et al. 1993; Iyer et al. 2001; Lieb et al. 2001;<br />
Simon et al. 2001; Lee et al. 2002; Gerber et al. 2004): For the DNA<br />
immunoprecipitation experiments, genes were ranked according to the published binding<br />
p-values, and a sliding p value (between 10-2 and 10-4) was applied such that at least 20<br />
genes were selected in each group. Transcripts that are bound <strong>by</strong> RNA binding proteins<br />
were taken from (Gerber et al. 2004). Finally, genes with the same functional annotations<br />
(Weng et al. 2003), and genes known to be coregulated <strong>by</strong> various transcription factors<br />
(Gasch et al. 2000; Lyons et al. 2000; Ogawa et al. 2000; Shakoury-Elizeh et al. 2004),<br />
were grouped together. In all, we identified 264 partially redundant groups <strong>of</strong> S.<br />
cerevisiae genes that are likely to be coregulated. These gene groups ranged in size from<br />
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four to 570 genes, with a median size <strong>of</strong> 17 genes per group. The complete gene groups<br />
are available in Dataset S2.<br />
Motif identification and enrichment.<br />
We compiled from the literature a list <strong>of</strong> 80 known transcription factor-binding sites,<br />
represented <strong>by</strong> IUPAC consensus sequences (Dataset S1) (Costanzo et al. 2001; Weng et<br />
al. 2003). Unless otherwise noted, we searched 1,000 bp upstream or 500 bp downstream<br />
<strong>of</strong> the genes from each group in each fungal genome for sequences that matched the<br />
consensus binding sites, <strong>by</strong> doing string comparisons on both strands using PERL scripts.<br />
For each group <strong>of</strong> genes identified above, we scored the enrichment <strong>of</strong> genes whose<br />
163
flanking regions (either 500 bp upstream, 1,000 bp upstream, or 500 bp downstream)<br />
contain one or more example <strong>of</strong> each cis-regulatory element, using the hypergeometric<br />
distribution<br />
⎛ M ⎞⎛<br />
N − M ⎞<br />
⎜ ⎟⎜<br />
⎟<br />
l<br />
⎝ i ⎠⎝<br />
l − i ⎠<br />
∑ ,<br />
i=<br />
q ⎛ N ⎞<br />
⎜ ⎟<br />
⎝ l ⎠<br />
where M is the number <strong>of</strong> genes that contain the motif in a group <strong>of</strong> i selected genes,<br />
relative to N genes that contain the motif in a genome <strong>of</strong> l genes. A p < 0.0002<br />
(approximately 0.01/80 tests) was deemed statistically significant for the consensus<br />
sequences, although if the sequence was enriched in the known group <strong>of</strong> target genes, we<br />
relaxed the cut<strong>of</strong>f to p < 0.01. A cut<strong>of</strong>f <strong>of</strong> p < 2.3x10 -5 was applied to sequences that<br />
matched the MEME matrices. For the Mig1p and GATA binding sequences, which are<br />
sufficiently short and occur frequently in each genome, we also scored the enrichment <strong>of</strong><br />
genes whose upstream region contained two or more examples <strong>of</strong> the known binding<br />
sites. For each group <strong>of</strong> genes, we also ran the motif-finding algorithm MEME (Bailey<br />
and Elkan 1994) on the upstream regions <strong>of</strong> S. cerevisiae genes or their orthologs in each<br />
species, using a two-component mixture model both with and without a motif-width<br />
specification <strong>of</strong> 8 bp. Unless otherwise noted, we used 500 bp upstream (for the<br />
hemiascomycetes) or 1,000 bp upstream (for the euascomycetes and Sch. pombe) <strong>of</strong> the<br />
genes in each group. Thus, for each group <strong>of</strong> coregulated genes, we performed 14 MEME<br />
analyses (each identifying three matrices) on the upstream regions <strong>of</strong> the genes from a<br />
given species. Matrices that matched known S. cerevisiae regulatory elements were<br />
identified <strong>by</strong> manual and automated comparisons, similar to that previous described<br />
164
(Hughes et al. 2000). A position-weight matrix was calculated for each motif on the basis<br />
<strong>of</strong> n motif examples MEME identified <strong>by</strong> counting the number <strong>of</strong> occurrences <strong>of</strong> each<br />
base at each position in n motifs, adding one pseudocount, and dividing <strong>by</strong> n + 4. A log-<br />
likelihood score S was calculated for each motif example as follows.<br />
∑∑ ⎟ motif ⎛ f ⎞ pb<br />
S = X ⎜<br />
pb log<br />
⎜ background<br />
p b ⎝ f pb ⎠<br />
In this formula, p is each position in the motif, b is the base (GACT) and X is a matrix <strong>of</strong><br />
indicator variables representing the sequence, where Xpb = 1 if the sequence has base b at<br />
position p, and zero otherwise. The probabilities <strong>of</strong> bases in the motif according to the<br />
position-weight matrix are represented <strong>by</strong> f motif , and the probabilities <strong>of</strong> bases in the<br />
genomic background are represented <strong>by</strong> f background (see below). The score S’ was assigned<br />
to each matrix, equal to 0.75 the average S <strong>of</strong> the motif examples, using the base<br />
frequency from each genome as the background model (G/C = 0.2 and A/T = 0.3 for all<br />
species except N. crassa, where G/C/A/T = 0.25). This score was used as a cut<strong>of</strong>f to<br />
identify genomic examples <strong>of</strong> the matrix. To identify genes whose upstream regions<br />
contained examples <strong>of</strong> each motif, we calculated the log-likelihood S <strong>of</strong> each 8-bp<br />
sequence within the 1,000 bp upstream region <strong>of</strong> each gene. The background model was<br />
based on the genomic nucleotide frequency in the 50 bp upstream window corresponding<br />
to the position <strong>of</strong> the sequence being assessed. We used this model to overcome the<br />
species-specific positional nucleotide biases immediately upstream <strong>of</strong> coding sequences<br />
(A. M. M., A. P. G., D. Y. C., and M. B. E., unpublished data). A sequence was<br />
considered a match to the matrix if S > S’. The enrichment <strong>of</strong> genes that contained each<br />
motif was scored using the hypergeometric distribution, as described above. A p < 3x10 -5<br />
165
(0.01 divided <strong>by</strong> the number <strong>of</strong> matrices tested in each species) was considered<br />
statistically significant. Out <strong>of</strong> the MEME matrices trained on the non-S. cerevisiae<br />
species, 53 were enriched in the gene group in which they were identified. Of these<br />
elements, 28 were similar to S. cerevisiae elements shown in Figure 2 and were enriched<br />
in the S. cerevisiae genes. An additional six matrices were redundantly identified in<br />
nearly identical gene groups (namely, Fhl1p targets and ribosomal protein genes) from<br />
the same species, and two elements were very similar and identified in the same gene<br />
group from As. nidulans and M. grisea. Thus, in all, 19 novel elements were identified.<br />
The complete list <strong>of</strong> matrices is available in Dataset S47.<br />
Positional distribution and spacing <strong>of</strong> cis-sequences.<br />
Genes that contained sequences that matched the S. cerevisiae position-weight matrices<br />
were identified as described above. We then calculated the frequency <strong>of</strong> each sequence in<br />
50-bp windows upstream <strong>of</strong> the potential target genes and compared it to the frequency <strong>of</strong><br />
that element in the corresponding upstream window for all <strong>of</strong> the genes in that genome.<br />
To identify distributions that were statistically different from the background, we<br />
identified 50-bp windows that contained a disproportionate number <strong>of</strong> the cis-sequences<br />
in the target upstream regions compared to the background, using the hypergeometric<br />
distribution presented above, where i was the total number <strong>of</strong> elements identified<br />
upstream <strong>of</strong> the genes in each group, M was the number <strong>of</strong> those elements that fell within<br />
a given 50-bp window, l was the total number <strong>of</strong> elements upstream <strong>of</strong> all <strong>of</strong> the genes in<br />
that genome and N was the number <strong>of</strong> those elements that fell within the same 50-bp<br />
window. We considered an element’s distribution to be significant if there was at least<br />
166
one 50-bp window with p < 0.01; only 5%–10% <strong>of</strong> the elements had distributions that<br />
met this criterion in gene groups other than their putative target genes. We calculated the<br />
correlation between element positions in S. cerevisiae and each <strong>of</strong> the other species <strong>by</strong><br />
taking all possible pairwise combinations <strong>of</strong> a cis-element’s positions in a given S.<br />
cerevisiae upstream region and in the orthologous region from other species and plotting<br />
these values for each group <strong>of</strong> coregulated genes (example scatter plots shown in Figures<br />
S4 and S5).<br />
Genes that contained sequences that matched the S. cerevisiae Cbf1p and<br />
Met31/32p position-weight matrices were identified in each species as described above.<br />
The average spacing between Cbf1p and Met31/32p binding sites within the 500 bp-<br />
upstream regions <strong>of</strong> the methionine biosynthesis genes and <strong>of</strong> all <strong>of</strong> the genes in each<br />
genome was measured <strong>by</strong> calculating the distance between all pairwise combinations <strong>of</strong><br />
the two motifs in each upstream region and taking the average spacing for the respective<br />
group <strong>of</strong> genes.<br />
Rpn4p matrix comparisons.<br />
To compare the upstream sequences identified in proteasome genes from S. cerevisiae<br />
and C. albicans, and to ensure that the identified sequences were not obtained <strong>by</strong><br />
sampling bias, we performed the following permutation analysis. We ran MEME on the<br />
entire set <strong>of</strong> upstream regions <strong>of</strong> 26 proteasome genes with orthologs in both species,<br />
using the conservative one-per sequence model. This produced a ‘‘meta-matrix’’ that<br />
identified exactly one putative binding site from each gene, leaving us with a set <strong>of</strong><br />
exactly 52. We calculated the likelihood-ratio statistic, testing the hypothesis that the<br />
167
sequences were drawn from a single multinomial, or from multinomials estimated<br />
separately for each species. In order to test the significance <strong>of</strong> this statistic, we randomly<br />
divided the data into two equal-sized groups 10,000 times, recalculated the statistic, and<br />
found that matrix positions 2, 3, and 9 had values <strong>of</strong> p
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14. Evidence for losses and gains <strong>of</strong> functional binding sites<br />
Given the dramatic <strong>evolution</strong>ary dynamics that we observed over longer <strong>evolution</strong>ary<br />
distances I sought to characterize the micro<strong>evolution</strong>ary processes at the level <strong>of</strong> the<br />
DNA sequence that are responsible for the network level changes. As I noted in the<br />
context <strong>of</strong> the monkey results, there are examples even at short distances there are<br />
examples <strong>of</strong> binding sites that are functional and not conserved. In order to address<br />
whether gains and losses <strong>of</strong> individual binding sites could underlie the higher level<br />
changes, I decided to study these changes systematically. Thus having discussed at<br />
length the conservation <strong>of</strong> binding sites and methods to exploit this conservation, we now<br />
turn to <strong>evolution</strong>ary changes in binding sites and the part these changes play in the<br />
<strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> regulatory networks.<br />
As I noted previously, we must remember that while conservation over <strong>evolution</strong><br />
implies function (perhaps not necessarily an understandable function) the lack <strong>of</strong><br />
conservation does not imply the lack <strong>of</strong> function. The absence <strong>of</strong> conservation in<br />
sequence alignment can be indicative <strong>of</strong> both the lack <strong>of</strong> constraint but also changes in<br />
function over <strong>evolution</strong>. Thus the observation that binding sites that are functional in one<br />
species are not always conserved is consistent with several scenarios. Either the gene<br />
expression has changed and that change has become fixed in the population (e.g.,<br />
Shasikan et al. 1998), or gene expression has remained constant because a compensatory<br />
mutation added a new binding site or because the binding site was functionally redundant<br />
and its loss had no effect (Ludwig et al. 2000, Piano et al. 1999, Hersh & Carroll 2005).<br />
Regardless <strong>of</strong> the interpretation, a requirement for any such analysis is that we<br />
176
must first identify examples <strong>of</strong> these non-conserved functional binding sites. This is a<br />
challenge because non-functional sequences that are not binding sites, yet match the<br />
specificity <strong>of</strong> transcription factors, are frequent in the genome and these are not generally<br />
conserved. Further, we must account for the possibility that the binding sites are actually<br />
conserved but we have failed to identify them as such, because <strong>of</strong> the necessarily<br />
imperfect inferences we must make about their <strong>evolution</strong>ary history.<br />
Specific Examples<br />
The <strong>regulation</strong> <strong>of</strong> the cholesterol biosynthesis genes in mammals is an excellent example<br />
<strong>of</strong> a specific regulatory network that has evolved over relatively short time scales. This<br />
has led to very different responses to high cholesterol diets between, for example,<br />
humans and rodents. Aside from its clinical relevance, this system is attractive because<br />
<strong>of</strong> the relatively well-characterized regulatory proteins and binding sites that are<br />
responsible for the <strong>regulation</strong>. This system provides several examples <strong>of</strong> regulatory<br />
elements that have evolved since the divergence <strong>of</strong> the common ancestor <strong>of</strong> the rodents<br />
and humans. In order to study this <strong>evolution</strong> in more detail, we have obtained non-<br />
coding sequences from several primates, including lemur, for several <strong>of</strong> the important<br />
genes in the pathway.<br />
An excellent case study is the LXRE in the promoter <strong>of</strong> the CYP7A1 gene. In this<br />
case, in vivo and in vitro studies have show that that different transcription factors bind to<br />
this sequence in human and in mouse, leading to different patterns <strong>of</strong> activation and<br />
repression <strong>of</strong> the CYP7A1 gene under similar environmental conditions (Chen et al.<br />
1999). Comparison <strong>of</strong> the human and mouse sequences in this region <strong>of</strong> the promoter<br />
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eveals few nucleotide substitutions, but these changes are responsible for the differential<br />
binding to the promoter. The pattern <strong>of</strong> <strong>evolution</strong> we observe is consistent with rapid<br />
<strong>evolution</strong> along the branch leading to all primates but lemur, suggesting that there was<br />
either positive selection or a relaxation <strong>of</strong> constraint in the common ancestor: while there<br />
are no changes between the mouse and lemur, there are 2 substitutions and one indel<br />
event along the lineage leading to humans, which under the assumption <strong>of</strong> neutral<br />
<strong>evolution</strong>, we expect to have the same branchlength (See figure 1).<br />
TGAGCTTGGTTGA-CAA 4.47493 Human<br />
TGAGCTTG-TTGACCAA -0.30334 Baboon<br />
TGAACTTGGTTGACCA 8.17303 Colobus<br />
TGAGCTTGGTTGACCA 7.26454 Marmoset<br />
TGAGCTTGGTTGACCA 7.26454 Owl Monkey<br />
TGAGCTTTGTTGACCA 7.12248 Squirrel Monkey<br />
TGAACTTGGGTGACCA 10.6822 Lemur<br />
TGAACTTGGGTGACCA<br />
*** *** * *** **<br />
10.6822 Mouse<br />
Figure 1<br />
The CYP7A1 LXRE in alignment <strong>of</strong> primates<br />
The LXRE in the CYP7A1 promoter that is functional in rodents, but not in human. There is an apparent<br />
reduction in constraint since the common ancestor <strong>of</strong> the Lemur and the other primates. Substitution or<br />
deletion events inferred <strong>by</strong> parsimony are indicated <strong>by</strong> circles on the appropriate branches. The score <strong>of</strong> the<br />
sequence on the LXRE matrix is included to the right <strong>of</strong> the sequence for each species.<br />
While the LXRE in the CYP7A1 promoter exemplifies an <strong>evolution</strong>ary change in<br />
<strong>transcriptional</strong> <strong>regulation</strong> due to a cis-regulatory change, it has also been suggested that<br />
functional cis-regulatory changes may occur with no corresponding change in function<br />
(Ludwig et al. 2000, Piano et al. 1999).<br />
An example <strong>of</strong> this type <strong>of</strong> binding site <strong>evolution</strong> in expression pattern is found in<br />
the PPRE in the LXRa promoter. Here both the human and mouse promoters are<br />
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egulated <strong>by</strong> the PPARa RXR heterodimer (Laffitte et al. 2001), but the binding site (the<br />
PPRE) is not composed <strong>of</strong> orthologous positions in the DNA sequence. This is consistent<br />
with so-called 'binding site turnover’, where a new site has arisen to take the function <strong>of</strong><br />
the old. We can examine the <strong>evolution</strong> <strong>of</strong> this binding site over the primates in order to<br />
get a higher resolution view <strong>of</strong> this <strong>evolution</strong>ary change.<br />
A<br />
B<br />
GTACAAAGTTCA 6.53315<br />
GTACAAAGTTCA 6.53315<br />
GTACAAAGTTCA 6.53315<br />
GTATAAAGTTCA 2.89941<br />
GTACAAAGTTCA 6.53315<br />
GTACAAAGTTCA 6.53315<br />
GCACAAAGTTCA 5.43068<br />
TCAGAAAGTTGA -3.93158<br />
* ****** *<br />
GGTTTAGGATTT -4.56755 Human<br />
AGTTTAGGATTT -8.30522 Baboon<br />
GGTTTAGGATTT -4.56755 Colobus<br />
GGTTTAGGATTT -4.56755 Dusky<br />
GGTTTAGGATTT -4.56755 Owl Monkey<br />
GGTTTAGGATTT -4.56755 Squirrel Monkey<br />
GAGTCAGGATTT -11.9453 Lemur<br />
GGGCAAAGTTCA 8.98<br />
* * *<br />
Mouse<br />
Figure 2.<br />
LXRa PPRE sites in an alignment <strong>of</strong> primates and mouse<br />
A: the functional human binding site. B: the functional mouse binding site. Inferred substitutions are<br />
indicated <strong>by</strong> circles and matrix scores are indicated beside the sequence for each species. Boxes indicate<br />
the Direct repeat with a single spacer (DR1) structure <strong>of</strong> the PPAR alpha RXR heterodimer<br />
Once again, the pattern <strong>of</strong> substitutions is consistent with the functional observations.<br />
The PPRE that is functional in human shows few substitutions within the primate<br />
179<br />
Human<br />
Baboon<br />
Colobus<br />
Dusky<br />
Owl Monkey<br />
Squirrel Monkey<br />
Lemur<br />
Mouse
lineages, and many changes since the common ancestor <strong>of</strong> the primate ancestor and the<br />
mouse (Figure 2A). Similarly, the PPRE that is functional in mouse shows many changes<br />
along that lineage (Figure 2B). Thus the number <strong>of</strong> changes is consistent with an<br />
<strong>evolution</strong>ary change, but the number <strong>of</strong> changes does not imply anything about the<br />
direction <strong>of</strong> the functional change. However, this information combined with which<br />
species have strong similarity to the matrix allows the inference <strong>of</strong> the <strong>evolution</strong>ary<br />
changes. Interestingly, in this case the <strong>evolution</strong> seems to have occurred since the<br />
divergence <strong>of</strong> the common ancestor <strong>of</strong> rodent and primates, whereas in the case <strong>of</strong> the<br />
CYP7A1 LXRE the changes seem to have occurred after the divergence <strong>of</strong> the rest <strong>of</strong> the<br />
primates from the shared ancestor with lemur.<br />
These examples illustrate the two major types <strong>of</strong> <strong>evolution</strong>ary changes that we are<br />
interested in detecting. The former exemplifies a change at the level <strong>of</strong> the binding site<br />
that is accompanied <strong>by</strong> a corresponding change in <strong>transcriptional</strong> <strong>regulation</strong>, while the<br />
latter demonstrates that changes in functional binding sites may occur even while<br />
function remains constant. Further, these examples demonstrate that the number and<br />
phylogenetic position <strong>of</strong> changes is not sufficient to infer the functional scenario, rather<br />
we must also consider what types <strong>of</strong> changes have occurred leading to binding sites on<br />
particular lineages.<br />
Statistics to detect binding site loss and gain in alignments <strong>of</strong> non-coding sequences<br />
In beginning to study functional changes in binding sites over <strong>evolution</strong>, we must first<br />
rule out the hypothesis that the binding site was present in the common ancestor, and has<br />
simply been conserved ever since (Dermitzakis et al. 2003). We must therefore develop<br />
180
statistical methods that classify between binding sites that are conserved or non-<br />
conserved. This is in contrast to the situation in the previous sections where the goal was<br />
to distinguish between binding sites and background sequences. The key point in<br />
developing these statistics is that the decision about whether the binding site is conserved<br />
should be made based on the inferred <strong>evolution</strong>ary history <strong>of</strong> the sequences, rather than a<br />
matrix similarity cut<strong>of</strong>f. Unless we have confidence that we have a representative <strong>of</strong><br />
every possible target site for a transcription factor, we cannot how similar a sequence<br />
must be to a specificity matrix to be functional; the similarity cut<strong>of</strong>fs used are almost<br />
always arbitrary. When selecting binding sites the choice <strong>of</strong> a cut<strong>of</strong>f is not critical:<br />
including some spurious matches may add noise, or excluding some bona fide sites may<br />
throw out some data. However, if an arbitrary similarity cut<strong>of</strong>f is used in deciding<br />
whether a binding site has evolved or not, the interpretation <strong>of</strong> results based thereupon<br />
will be ambiguous – in that case, for example, it is not possible to know if binding sites<br />
are evolving into background sequences, or remaining binding sites, but slipping below<br />
the arbitrary threshold.<br />
A simple way to accomplish the classification based on <strong>evolution</strong>ary history is to<br />
first identify all binding sites with a p-value below some threshold in a single genome, to<br />
infer the <strong>evolution</strong>ary history <strong>of</strong> each position in the binding site (e.g., using parsimony),<br />
and then to compare the likelihood each history under the model <strong>of</strong> binding site <strong>evolution</strong><br />
(e.g., HB) to the model <strong>of</strong> background or neutral <strong>evolution</strong> (e.g., HKY). For example,<br />
p(<br />
IH | HB)<br />
Tparsimony = log<br />
,<br />
p(<br />
IH | HKY )<br />
where IH represents the inferred history for the alignment <strong>of</strong> that binding site. Note that<br />
181
this statistic depends only on a single most parsimonious history.<br />
It is possible to formulate this in the probabilistic framework for binding site<br />
<strong>evolution</strong> described in part III, <strong>by</strong> conditioning the probabilities on the event that we have<br />
already observed a binding site in one <strong>of</strong> the genomes. We define<br />
p(<br />
Y...,<br />
Z | X , HB)<br />
T = log<br />
.<br />
p(<br />
Y...,<br />
Z | X , HKY )<br />
Here, as above, X,Y…Z represent the sequences in the alignment, and the likelihood <strong>of</strong><br />
the sequences is calculated <strong>by</strong> summing over all the possible ancestral states using<br />
Felsenstein’s algorithm (Felsenstein 1981). Unlike the statistic used above in MONKEY,<br />
we are now conditioning on the observation <strong>of</strong> one <strong>of</strong> the sequences, X, which we will<br />
choose to be the reference sequence, or the sequence that passed the threshold to be<br />
called a binding site. This means that the classification is done based on the pattern <strong>of</strong><br />
<strong>evolution</strong> conditioned on the observation that one <strong>of</strong> the sequences was a good match to<br />
the matrix; the initial similarity cut<strong>of</strong>f, then, should have little impact on the value <strong>of</strong> this<br />
statistic. We note that this can be re-written using Bayes’ theorem as<br />
p(<br />
X , Y...,<br />
Z | HB)<br />
p(<br />
X | HKY )<br />
T = log<br />
p(<br />
X , Y...,<br />
Z | HKY ) p(<br />
X | HB)<br />
p(<br />
X , Y...,<br />
Z | HB)<br />
p(<br />
X | HB)<br />
ˆ p(<br />
X | HB)<br />
= log<br />
− log<br />
= S − log<br />
,<br />
p(<br />
X , Y...,<br />
Z | HKY)<br />
p(<br />
X | HKY ) p(<br />
X | HKY)<br />
where the first term is familiar as the statistic used in MONKEY and the second term is<br />
similar to the single genome likelihood ratio, but takes into account the distance from the<br />
root to the reference node, X. As with the statistics used in MONKEY it is possible to<br />
compute the distribution <strong>of</strong> this statistic under various assumptions. Of particular interest<br />
182
in detecting non-conserved binding sites is the distribution <strong>of</strong> this statistic under the<br />
assumption that there was a conserved binding site, and using this to reject that<br />
hypothesis. This can be calculated exactly using the same methods described for<br />
MONKEY, but using the probabilities under the HB <strong>evolution</strong>ary model to generate the<br />
null (or background) distribution. In addition, we can use this statistic to provide a<br />
conservative test <strong>of</strong> binding site conservation <strong>by</strong> computing the probability <strong>of</strong> observing a<br />
score as large under the hypothesis that there was a match to the matrix, but it was<br />
evolving under the background (HKY) <strong>evolution</strong>ary model.<br />
Figure 3.<br />
P-values for the T-statistic to detect binding site <strong>evolution</strong><br />
As described for statistic used in MONKEY to identify conserved binding sites, it is possible to calculate p-<br />
values for the ‘T’ statistic described above to identify non-conserved binding sites. In this simulation,<br />
10000 binding sites were generated from the bcd matrix based on footprinted binding sites (Dan Pollard,<br />
personal communication, Bergman et al. 2005). They were evolved along the tree ((((dmel:0.01301,<br />
dsim:0.01011):0.04507, dyak:0.05366):0.11171, dpse:0.18951):0.12500, dvir:0.25924), and the 5132<br />
instances with a p-value in the dmel sequence less than 0.001 were assigned p-values using the exact<br />
method, and <strong>by</strong> ranking them and dividing <strong>by</strong> 5132. That these p-values agree, even though a cut<strong>of</strong>f was<br />
183
used to choose the binding sites suggests that the distribution is relatively independent <strong>of</strong> the single genome<br />
p-value cut<strong>of</strong>f chosen.<br />
Identifying evolving binding sites using the T statistic<br />
It is possible to search alignments <strong>of</strong> non-coding DNA to identify putative binding sites<br />
that are evolving rapidly. For example, the CYP7A1 LXRE that is functional in rodents,<br />
and conserved to Lemur has T= -1.85913, using the binding site in Lemur as the<br />
reference sequence. That the statistic is less than zero implies that the binding site<br />
appeared to be evolving more like the background model (HKY) than the model <strong>of</strong><br />
selective constraint (HB). As described above, it is possible to assign statistical<br />
significance to the score <strong>by</strong> calculating the probability <strong>of</strong> having observed a score as<br />
small or smaller assuming the binding site was evolving under the HB model. In this case<br />
the p-value associated was p=0.0149213.<br />
I searched alignments <strong>of</strong> known Drosophila enhancers to identify potential cases<br />
<strong>of</strong> binding site <strong>evolution</strong>. Figure 4 shows two non-conserved hb binding sites in the<br />
Kr730 enhancer (Hoch et al. 1990), which are within a region <strong>of</strong> the enhancer that has<br />
been shown to be bound <strong>by</strong> hunchback in vitro (Hoch et al. 1991). This demonstrates the<br />
utility <strong>of</strong> the T statistic in identifying candidates for cis-regulatory <strong>evolution</strong>.<br />
dmel GCATGATCATAAAAAGCAATTTGCTACAATTTA<br />
dsim GCATGATCATAAAAAGCAATTTGCTACAATTTA<br />
dyak GCATGGTCATAAAAAGCAATTTGCTACAATTTA<br />
dere ACATGATCATAAATTGCAATTTGCTACAATTTA<br />
**** ******* ******************<br />
dmel TATTTTTTT--GCTTTTCCTTCTTTTAAGCAT<br />
dsim TATTTTTTT--GCTTTTACTTCTTTTAAGCAG<br />
dyak TATTTTCCTATGCTTTTACCTCTTTAGAGCAG<br />
dere TATTTTCTTATGCTTTTACCTCTTTAAAGCAG<br />
****** * ****** * ***** ****<br />
184
Figure 4<br />
Non-conserved binding sites in the Kr 730 enhancer<br />
Two hunchback binding sites in D. melanogaster (p
stripe 2 enhancer (Small et al. 1991), aligned using the mlagan alignment algorithm<br />
(Brudno et al. 2003). In this case, there is almost certainly an orthologous binding site in<br />
the D. psuedoobscura sequence, but it has not been aligned to the appropriate sequence in<br />
D. melanogaster. This binding site had a T= –4.11328 (p=0.0017), and would therefore<br />
seem to be a good candidate for cis-regulatory <strong>evolution</strong>. In order to study binding site<br />
<strong>evolution</strong> systematically and describe the patterns <strong>of</strong> <strong>evolution</strong> quantitatively we must<br />
therefore understand the behaviors <strong>of</strong> the alignment algorithms and how <strong>of</strong>ten we can<br />
expect the binding sites to be incorrectly aligned.<br />
Controlling for alignment error<br />
Automated sequence alignment is one <strong>of</strong> the major accomplishments <strong>of</strong> computational<br />
molecular biology. Indeed, as genomic sequence data becomes available, manual<br />
alignments are increasingly impractical, and automated alignments must serve as the<br />
basic orthology map for all <strong>evolution</strong>ary inference. Assessing the impact <strong>of</strong> these<br />
automated alignments is therefore <strong>of</strong> much importance.<br />
Automated alignments are particularly difficult in the case <strong>of</strong> non-coding DNA<br />
where a sizeable fraction <strong>of</strong> bases can be expected to match between random (or non-<br />
homologous) sequences. A recent study showed that the accuracy <strong>of</strong> pairwise alignments<br />
<strong>of</strong> non-coding DNA varies considerably as a function <strong>of</strong> <strong>evolution</strong>ary distance (Pollard et<br />
al. 2004). It is therefore important to assess the impact <strong>of</strong> the alignment quality on<br />
phylogenetic analysis.<br />
Using a simulation <strong>of</strong> molecular <strong>evolution</strong> we showed that two important<br />
problems in phylogenetic analysis <strong>of</strong> non-coding DNA are pr<strong>of</strong>oundly affected <strong>by</strong> the<br />
186
quality <strong>of</strong> the alignments. For example, our estimates <strong>of</strong> non-coding divergence, and<br />
therefore estimates <strong>of</strong> the fraction <strong>of</strong> bases under constraint are alignment limited – at<br />
intermediate <strong>evolution</strong>ary distances (~1 subs. per site) we can accurately estimate these<br />
parameters using the true alignments, but once we use an alignment algorithm, our<br />
estimates become increasingly biased. This is in contrast to coding sequences, where<br />
saturation <strong>of</strong> sites in synonymous positions is thought to be limiting. This implies that<br />
estimates <strong>of</strong> the fraction <strong>of</strong> bases under constraint in non-coding DNA must be re-<br />
evaluated in the context <strong>of</strong> the divergence distance and specific alignment algorithms<br />
used.<br />
More important for the questions considered here is the reliability <strong>of</strong> automated<br />
sequence alignments with respect to transcription factor binding sites. By including<br />
transcription factor binding sites in our simulation, we showed that binding sites evolving<br />
under constant functional constraint are aligned incorrectly at relatively high frequency.<br />
This implies that estimates <strong>of</strong> conservation <strong>of</strong> functional elements must also be<br />
reevaluated in the context <strong>of</strong> the alignments and <strong>evolution</strong>ary distances considered. It has<br />
been suggested that using multiple alignments <strong>of</strong> more closely related species to span<br />
similar <strong>evolution</strong>ary distances can greatly improve alignment accuracy; we showed that if<br />
multiple species are used to span the same distances, we can accurately estimate<br />
divergences and identify conserved binding sites at much greater distances.<br />
In practice, however, we are limited <strong>by</strong> the species that are actually available.<br />
The sequencing <strong>of</strong> several closely related Drosophila species has provided us with<br />
several <strong>evolution</strong>ary comparisons to choose from. Using our simulation, we can show<br />
that in alignments <strong>of</strong> non-coding sequence <strong>of</strong> the four most closely related species (D.<br />
187
melanogaster, D. simulans, D. yakuba and D. erecta) which span a total <strong>of</strong> 0.45<br />
substitutions per synonymous site, we can expect our multiple alignment algorithm<br />
mlagan (Brudno et al. 2003) to align binding sites such that they are at least overlapping<br />
<strong>by</strong> one position greater than 99% <strong>of</strong> the time. This is in contrast to comparisons which<br />
include D. psuedoobscura (such as the eve stripe 2 alignment shown above) in which<br />
binding sites can be expected to be aligned incorrectly ~20% <strong>of</strong> the time.<br />
Finally, because that our simulations show that the binding sites will be<br />
overlapping but not necessarily exactly aligned in these sequences, I have developed a<br />
new version <strong>of</strong> the MONKEY heuristic to search for any groups <strong>of</strong> binding sites that<br />
overlap <strong>by</strong> at least 1 base pair. This is done <strong>by</strong> searching for matches in the unaligned<br />
versions <strong>of</strong> the single sequences, finding the best overlapping matches in the other<br />
sequences, and then excluding the region <strong>of</strong> the alignment that is spanned <strong>by</strong> these<br />
binding sites. In practice this can be done recursively and does not significantly change<br />
the performance <strong>of</strong> MONKEY.<br />
Enrichment <strong>of</strong> non-conserved binding sites implies functional <strong>evolution</strong><br />
Equipped with statistical tests to detect binding site <strong>evolution</strong> and confidence that<br />
alignments <strong>of</strong> very closely related species will have extremely low error rates, we can<br />
look for evolving binding sites systematically in genome wide functional data. Figure 6<br />
shows the results <strong>of</strong> such an analysis on the zeste chromatin IP data. As expected, we see<br />
extremely strong enrichment <strong>of</strong> the zeste binding sites in D. melanogaster near the peak<br />
in signal on the array. In this case there are ~3 fold more binding sites immediately<br />
surrounding the peaks, implying a total <strong>of</strong> ~500 excess (functional) binding sites in these<br />
188
egions. Binding sites were defined as matches to the matrix in D. melanogaster with<br />
p
consistent with the action <strong>of</strong> purifying selection on the bound regions. I will return to this<br />
observation in the context <strong>of</strong> inferring selection.<br />
Most interesting from the perspective <strong>of</strong> binding site <strong>evolution</strong>, figure 6C shows<br />
that there is also an enrichment <strong>of</strong> non-conserved binding sites in this region. Once<br />
again, a significance threshold <strong>of</strong> p=0.025 was used, suggesting that fewer than 10<br />
conserved sites will pass the threshold. Regardless <strong>of</strong> the significance threshold chosen,<br />
there was an excess <strong>of</strong> ~30 non-conserved binding sites in the regions immediately<br />
adjacent to the peaks. This indicates that ~6% (30 <strong>of</strong> 500) <strong>of</strong> the functional zeste sites<br />
show patterns <strong>of</strong> <strong>evolution</strong> that are not consistent with constant selection to retain their<br />
function.<br />
A<br />
3000 -2000 -1000 0 1000 2000<br />
0<br />
3000<br />
180<br />
B<br />
3000 -2000 -1000 0 1000 2000<br />
0<br />
3000<br />
190<br />
400<br />
350<br />
300<br />
250<br />
200<br />
150<br />
100<br />
50<br />
160<br />
140<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
Number <strong>of</strong> zeste binding sites
C<br />
-3000 -2000 -1000 0 1000 2000<br />
0<br />
3000<br />
Figure 6<br />
Enrichment <strong>of</strong> conserved and non-conserved binding sites in the regions bound <strong>by</strong><br />
zeste<br />
A shows the number <strong>of</strong> binding sites (p
this allows me to provide statistical evidence for the hypothesis that binding site<br />
<strong>evolution</strong> occurred, it does not allow any me to distinguish between different types <strong>of</strong><br />
<strong>evolution</strong>ary scenarios or test specific models <strong>of</strong> binding site <strong>evolution</strong>. For example, two<br />
more realistic scenarios I am interested in considering are 1) where most <strong>of</strong> the<br />
<strong>evolution</strong>ary history is consistent with <strong>evolution</strong> under functional constraint, but that the<br />
binding site has been lost on some subset <strong>of</strong> the lineages, or 2) most <strong>of</strong> the <strong>evolution</strong>ary<br />
history is consistent with <strong>evolution</strong> in the absence <strong>of</strong> the functional constraint, and that<br />
the binding site has appeared on some subset <strong>of</strong> the lineages.<br />
The T statistic will identify some <strong>of</strong> such cases, but because it considers <strong>evolution</strong> over<br />
the entire tree, it will be extremely conservative in cases where, for example, the binding<br />
site has been lost on the lineage leading to a terminal branch (leaf). Once again, it is<br />
possible to formulate these scenarios in the probabilistic framework for binding site<br />
<strong>evolution</strong> that I have developed. In this case there, however, instead <strong>of</strong> explicitly<br />
rejecting a null hypothesis, we are attempting to choose between a number <strong>of</strong> related<br />
models. From a statistical perspective this is a much more difficult problem particularly<br />
because the number <strong>of</strong> models is relatively large, and they have been selected from a<br />
potentially even larger set.<br />
192
A<br />
B<br />
C<br />
Figure 7<br />
Evolutionary scenarios consistent with the observation <strong>of</strong> a non-conserved binding<br />
in D. melanogaster<br />
A and B show ‘gains’ <strong>of</strong> binding sites along the lineage leading to D. melanogaster. C-F show losses.<br />
Here a ‘1’ represents a binding site at that point on the tree, while a ‘0’ represents background sequence.<br />
Only scenarios consistent with one <strong>evolution</strong>ary change between binding site and background are<br />
considered.<br />
0<br />
1 0 0 0<br />
Dmel Dsim Dyak Dere<br />
1<br />
1 1 0 0<br />
Dmel Dsim Dyak Dere<br />
1<br />
1 1 0 0<br />
Dmel Dsim Dyak Dere<br />
1<br />
0<br />
0<br />
0<br />
0<br />
0<br />
D<br />
E<br />
F<br />
Figure 7 shows the 6 <strong>evolution</strong>ary scenarios that are consistent with a non-conserved<br />
binding site that require only one change from the binding site ‘state’ to the background<br />
‘state.’ Because the <strong>evolution</strong>ary distances spanned here are small, it is reasonable to<br />
assume only one such event has occurred, although the process <strong>of</strong> binding site <strong>evolution</strong><br />
193<br />
1<br />
1 0 1 1<br />
Dmel Dsim Dyak Dere<br />
1<br />
1 1 0 1<br />
Dmel Dsim Dyak Dere<br />
1<br />
1 1 1 0<br />
Dmel Dsim Dyak Dere<br />
1<br />
1<br />
1<br />
1<br />
1<br />
1
on a tree can be modeled explicitly, and we will return to that problem in a later section.<br />
There are an additional 6 scenarios requiring only one change, and these include gains<br />
and losses on the other lineages.<br />
In order to distinguish between these scenarios, we can explicitly calculate the<br />
likelihood <strong>of</strong> each one using our <strong>evolution</strong>ary model. A similar approach has been<br />
applied to the detection <strong>of</strong> pseudogenes (Coin and Durbin 2004), and technically the<br />
method used is very similar. I calculate the likelihood <strong>of</strong> the <strong>evolution</strong> on each branch<br />
according to the state <strong>of</strong> the child node, and use the equilibrium frequencies associated<br />
with the state <strong>of</strong> the root node. For each binding site, I calculate the T-statistic to reject<br />
the hypothesis that is was conserved, and then calculate the likelihood <strong>of</strong> the data given<br />
the 12 <strong>evolution</strong>ary scenarios requiring only one change from binding site to background<br />
or vise versa, and assign the binding site to the most likely. In order to be unbiased, we<br />
now no longer require a binding site to pass a the threshold in D. melanogaster, but rather<br />
consider all binding sites with p
Inferring the action <strong>of</strong> selection from patterns <strong>of</strong> binding site losses and gains<br />
Thus far we have developed methods to classify the <strong>evolution</strong>ary patterns <strong>of</strong> at the level<br />
<strong>of</strong> binding sites in order to provide systematic statistical evidence for the claim that<br />
binding sites are not simply conserved. The tools developed, however, allow us to infer<br />
the action <strong>of</strong> selection at the level <strong>of</strong> binding sites. I have shown previously that the<br />
individual nucleotides in binding sites are evolving slower than the surrounding<br />
sequences, which is consistent with purifying selection on the binding sites. Another<br />
prediction <strong>of</strong> the model that binding sites are evolving under purifying selection is that<br />
the binding sites in functional regions should be more likely to be conserved than the<br />
same binding sites that occur randomly in non-coding DNA.<br />
195
A<br />
3000 -2000 -1000 0 1000 2000 3000 90<br />
gain on D mel lineage<br />
B<br />
80<br />
loss on D mel lineage<br />
Figure 8<br />
Enrichment <strong>of</strong> binding sites classified <strong>by</strong> <strong>evolution</strong>ary scenario<br />
A shows the total number (triangles) <strong>of</strong> non-conserved binding sites (p
melanogaster lineage (diamonds and squares, respectively), or gains or losses on the other lineages<br />
(triangles and x’s respectively).<br />
In the absence <strong>of</strong> selection, sequences randomly drift through sequence space turning<br />
from potential binding sites to background sequence and back according to some model<br />
<strong>of</strong> molecular <strong>evolution</strong>. In the presence <strong>of</strong> selection, purifying selection may act to<br />
preserve the sequences that are acting as binding sites, <strong>by</strong> removing from the population<br />
mutations that disrupt the binding sites, thus slowing the rate at which they convert into<br />
background sequence. We can observe this effect <strong>of</strong> purifying selection <strong>by</strong> looking at the<br />
fraction <strong>of</strong> the total binding sites that are conserved: the stronger the purifying selection,<br />
the greater the fraction <strong>of</strong> total binding sites that should be conserved. As eluded to<br />
earlier, using the T statistic to reject the hypothesis <strong>of</strong> background <strong>evolution</strong> is a<br />
conservative method to classify a binding site as conserved. Figure 9 shows this<br />
signature <strong>of</strong> selection in the zeste binding regions.<br />
-3000 -1000 1000 3000<br />
Figure 9<br />
Evidence for purifying selection on zeste binding sites<br />
197<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
fraction <strong>of</strong> total sites in D. mel
The proportion <strong>of</strong> binding sites in D. melanogaster (p
Further, I have suggested a method to compare the relative frequencies <strong>of</strong> particular<br />
<strong>evolution</strong>ary scenarios, and shown that in these data losses <strong>of</strong> binding sites along the<br />
lineages other than D. melanogaster account for most <strong>of</strong> the functional non-conserved<br />
binding sites. Finally, I have noted that based on the patterns <strong>of</strong> conservation and<br />
<strong>evolution</strong> <strong>of</strong> binding sites detected here it is possible to detect the effects <strong>of</strong> selection on<br />
the functional binding sites relative to the binding sites occurring in surrounding regions.<br />
References<br />
Bergman CM, Carlson JW, Celniker SE. Drosophila DNase I footprint database: a systematic genome<br />
annotation <strong>of</strong> transcription factor binding sites in the fruitfly, Drosophila melanogaster. Bioinformatics.<br />
2005 Apr 15;21(8):1747-9.<br />
Brudno M, Do CB, Cooper GM, Kim MF, Davydov E, Green ED, Sidow A, Batzoglou S; NISC<br />
Comparative Sequencing Program. LAGAN and Multi-LAGAN: efficient tools for large-scale multiple<br />
alignment <strong>of</strong> genomic DNA. Genome Res. 2003 Apr;13(4):721-31.<br />
Chen J, Cooper AD, Levy-Wilson B. Hepatocyte nuclear factor 1 binds to and transactivates the human but<br />
not the rat CYP7A1 promoter. Biochem Biophys Res Commun. 1999 Jul 14;260(3):829-34<br />
Coin L, Durbin R. Improved techniques for the identification <strong>of</strong> pseudogenes. Bioinformatics. 2004 Aug<br />
4;20 Suppl 1:I94-I100.<br />
Dermitzakis ET, Bergman CM, Clark AG. Tracing the <strong>evolution</strong>ary history <strong>of</strong> Drosophila regulatory<br />
regions with models that identify transcription factor binding sites. Mol Biol Evol. 2003 May;20(5):703-<br />
14.<br />
Dermitzakis ET, Clark AG. Evolution <strong>of</strong> transcription factor binding sites in Mammalian gene regulatory<br />
regions: conservation and turnover. Mol Biol Evol. 2002 Jul;19(7):1114-21.<br />
Felsenstein J: Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 1981,<br />
17:368-376.<br />
Hersh BM, Carroll SB. Direct <strong>regulation</strong> <strong>of</strong> knot gene expression <strong>by</strong> Ultrabithorax and the <strong>evolution</strong> <strong>of</strong> cisregulatory<br />
elements in Drosophila. Development. 2005 Apr;132(7):1567-77.<br />
Hoch M, Schroder C, Seifert E, Jackle H. cis-acting control elements for Kruppel expression in the<br />
Drosophila embryo. EMBO J. 1990 Aug;9(8):2587-95<br />
Hoch M, Seifert E, Jackle H. Gene expression mediated <strong>by</strong> cis-acting sequences <strong>of</strong> the Kruppel gene in<br />
response to the Drosophila morphogens bicoid and hunchback. EMBO J. 1991 Aug;10(8):2267-78.<br />
Laffitte BA, Repa JJ, Joseph SB, Wilpitz DC, Kast HR, Mangelsdorf DJ, Tontonoz P. LXRs control lipidinducible<br />
expression <strong>of</strong> the apolipoprotein E gene in macrophages and adipocytes. Proc Natl Acad Sci U S<br />
A. 2001 Jan 16;98(2):507-12.<br />
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Laney JD, Biggin MD. Redundant control <strong>of</strong> Ultrabithorax <strong>by</strong> zeste involves functional levels <strong>of</strong> zeste<br />
protein binding at the Ultrabithorax promoter. Development. 1996 Jul;122(7):2303-11.<br />
Laney JD, Biggin MD. zeste, a nonessential gene, potently activates Ultrabithorax transcription in the<br />
Drosophila embryo. Genes Dev. 1992 Aug;6(8):1531-41.<br />
Ludwig MZ, Bergman C, Patel NH, Kreitman M. Evidence for stabilizing selection in a eukaryotic<br />
enhancer element. Nature. 2000 Feb 3;403(6769):564-7.<br />
Ludwig MZ, Kreitman M. Evolutionary dynamics <strong>of</strong> the enhancer region <strong>of</strong> even-skipped in Drosophila.<br />
Mol Biol Evol. 1995 Nov;12(6):1002-11.<br />
Piano, F., M. J. Parisi, R. Karess, and M. P. Kam<strong>by</strong>sellis. Evidence for redundancy but not trans factor-cis<br />
element co<strong>evolution</strong> in the <strong>regulation</strong> <strong>of</strong> Drosophila Yp genes. Genetics 1999. 152:605-616.<br />
Pollard DA, Bergman CM, Stoye J, Celniker SE, <strong>Eisen</strong> MB. Benchmarking tools for the alignment <strong>of</strong><br />
functional noncoding DNA. BMC Bioinformatics. 2004 Jan 21;5(1):6.<br />
Shasikan C. S., C. B. Kim, M. A. Borbely, W. C. H. Wang, F. H. Ruddle, Comparative studies on<br />
mammalian Hoxc8 early enhancer sequence reveal a baleen whale-specific deletion <strong>of</strong> a cis-acting element<br />
Proc. Natl. Acad. Sci. USA 1998 95:15446-15451<br />
Small S, Kraut R, Hoey T, Warrior R, Levine M. Transcriptional <strong>regulation</strong> <strong>of</strong> a pair-rule stripe in<br />
Drosophila. Genes Dev. 1991 May;5(5):827-39.<br />
200
Part V<br />
Conclusions<br />
208
16. Insight from quantitative <strong>evolution</strong>ary analyses<br />
I have studied the <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> at the molecular level <strong>by</strong><br />
examining the patterns <strong>of</strong> conservation and <strong>evolution</strong> <strong>of</strong> transcription factor binding sites.<br />
Based on models <strong>of</strong> <strong>evolution</strong> for these sequences, I have defined statistics to test for<br />
their conservation and <strong>evolution</strong> that have facilitated systematic analyses at the genome-<br />
scale. Because binding sites are a key component <strong>of</strong> <strong>transcriptional</strong> regulatory networks,<br />
understanding their <strong>evolution</strong> provides a key molecular mechanism for the <strong>evolution</strong> <strong>of</strong><br />
these networks and suggests that the <strong>evolution</strong> <strong>of</strong> binding sites may underlie important<br />
<strong>evolution</strong>ary processes.<br />
More generally, this detailed study <strong>of</strong> the <strong>evolution</strong>ary properties <strong>of</strong> a specific<br />
class <strong>of</strong> functional non-coding sequence is an important step in understanding the<br />
information encoded in non-coding sequences. I suggest that other such detailed<br />
characterizations will greatly improve our ability to detect other functional classes <strong>of</strong><br />
non-coding DNA, and understanding the constraints on their <strong>evolution</strong> will help us<br />
understand the function <strong>of</strong> these important molecules. This highlights the importance <strong>of</strong><br />
an understanding <strong>of</strong> <strong>evolution</strong> at the molecular level, not only for the sake <strong>of</strong><br />
understanding <strong>evolution</strong> - a major challenge in biology - but also because <strong>evolution</strong>ary<br />
information gives a unique perspective on the functions <strong>of</strong> biological molecules.<br />
Because the techniques developed here exploit the relationship between the<br />
structural-functional constraints on biological sequences and the <strong>evolution</strong>ary constraints<br />
on those sequences, where we have some knowledge <strong>of</strong> the former, these types <strong>of</strong><br />
approaches should be generally applicable, particularly as sequence data from closely<br />
209
elated organisms continues to accumulate. Incorporating specific <strong>evolution</strong>ary<br />
information is a general and powerful approach to sequence analysis, regardless <strong>of</strong> the<br />
type <strong>of</strong> biological sequences are considered.<br />
While analysis <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> nucleotide and amino acid substitution<br />
processes has formed the cornerstone <strong>of</strong> molecular phylogenetic and <strong>evolution</strong>ary<br />
analysis since the inception <strong>of</strong> those disciplines, dramatic experimental and<br />
computational progress in molecular biology has lead to a large number <strong>of</strong> motifs,<br />
domains and other elements that can be recognized in these sequences. Combined with<br />
the availability <strong>of</strong> mathematical tools and abundant computer power, we are entering an<br />
age where it will become possible to model the <strong>evolution</strong> <strong>of</strong> higher-order molecular<br />
characters, just as systematists have long studied the <strong>evolution</strong> <strong>of</strong> phenotypic characters.<br />
Because these molecular features can in many cases be predicted from primary sequence,<br />
the availability <strong>of</strong> complete genome sequences means that there are already enormous<br />
amounts <strong>of</strong> data to be analyzed. This dissertation provides an example <strong>of</strong> such an<br />
analysis: it focused on one example <strong>of</strong> such a higher-order molecular property, namely,<br />
the binding sites for sequence-specific transcription factors. It should be possible to<br />
glean the same types <strong>of</strong> <strong>evolution</strong>ary insights that we were able to gain here for all types<br />
<strong>of</strong> molecular features.<br />
This work also exemplifies the utility <strong>of</strong> quantitative modeling <strong>of</strong> biological<br />
phenomena. In order to perform analyses systematically, but still capture the subtlety and<br />
complexity <strong>of</strong> the <strong>evolution</strong> <strong>of</strong> transcription factor binding sites, realistic probabilistic<br />
models <strong>of</strong> their <strong>evolution</strong> have provided an invaluable tool. Once again, I suggest that<br />
the availability <strong>of</strong> such models for other classes <strong>of</strong> sequences, and more generally other<br />
210
molecular features and processes, will be critical if they are to be studied systematically<br />
and at the genomic scale. This seems to be particularly true in analyses that incorporate<br />
<strong>evolution</strong>ary information, where the descent from a common ancestor along a tree<br />
provides a reasonable approximation to the history <strong>of</strong> the molecules or features, but<br />
introduces complicated statistical relationships between the extant species.<br />
IN CLOSING, it is clear that the availability <strong>of</strong> non-coding sequence from many<br />
organisms has enabled the study <strong>of</strong> non-coding DNA and its <strong>evolution</strong>. The<br />
surprises and mysteries that await us present many exciting challenges and<br />
possibilities. Because <strong>of</strong> the incredible importance <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> in<br />
development and <strong>evolution</strong> <strong>of</strong> the animals, analysis <strong>of</strong> these sequences will yield<br />
enormous insight into our own <strong>evolution</strong> and development in the near future.<br />
211
15. Future directions<br />
The methods developed above have already yielded many exciting observations, and<br />
suggest that many fascinating hypotheses regarding binding site <strong>evolution</strong> will be<br />
systematically tested in the near future. Of particular interest will be to test hypotheses<br />
regarding compensation, which should be possible with the tools developed above. In the<br />
zeste data, however, there were few examples <strong>of</strong> binding site gains (<strong>by</strong> the criteria<br />
described above) and no correlation between the patterns <strong>of</strong> gains and losses that would<br />
suggest compensation could be observed. In addition to testing more hypotheses, there is<br />
much improvement possible in the methods described above. I now consider the next<br />
steps in modeling binding site <strong>evolution</strong>.<br />
Probabilistic models for changing functional constraints<br />
The probabilistic models that we have developed thus far make the approximation that<br />
for each branch in a phylogeny positions in multiple alignments are either binding sites or<br />
not. We are now interested in generalizing the models to account for the dynamical<br />
changes functional constraints may change over <strong>evolution</strong>. This is <strong>of</strong> particular interest<br />
given the evidence for gains and losses <strong>of</strong> functional binding sites described above, as<br />
well as the evidence for dramatic changes in <strong>transcriptional</strong> regulatory mechanisms<br />
observed over longer <strong>evolution</strong>ary distances.<br />
We seek to incorporate into our models a process that describes not only the<br />
changes from one base to another (e.g., A->T) but also changes in the functional<br />
constraints on the bases (binding site -> background sequence). Thus, we are considering<br />
201
an <strong>evolution</strong>ary process that switches between two constraint regimes - at any point in<br />
time the sequence may be evolving under a binding site <strong>evolution</strong>ary model or under<br />
some background or neutral model. In particular, a two-state continuous time markov<br />
model, which allows each position in a DNA sequence to be either binding site or a part<br />
<strong>of</strong> background <strong>evolution</strong> seems appropriate. In addition, this seems like a natural<br />
extension <strong>of</strong> the two-component mixture formulation that is widely used in probabilistic<br />
motif finding.<br />
A B<br />
A B C D E F G<br />
Figure 1<br />
Modeling changing <strong>evolution</strong>ary constraints, such as binding site gain or loss<br />
A schematic representation <strong>of</strong> changing constraints along an <strong>evolution</strong>ary tree. A binding site appeared<br />
along the branch leading to the ancestor <strong>of</strong> E,F and G (filled circle). Evolution along those linages (thick<br />
line) proceedes according to a different substitution model than the rest <strong>of</strong> the tree. B two state continuous<br />
time model for binding site loss and gain Over <strong>evolution</strong>, w-mers can go from a background sequence state<br />
(represented as ‘0’) to binding sites (represented as ‘1’). The parameters <strong>of</strong> the model are the rate <strong>of</strong><br />
binding site gain (λ) and rate <strong>of</strong> binding site loss (µ).<br />
Modeling <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> at short <strong>evolution</strong>ary distances<br />
At short <strong>evolution</strong>ary distances, where sequence can be aligned at the level <strong>of</strong> the DNA<br />
202<br />
-λ<br />
µ<br />
0 1<br />
λ<br />
-µ
sequence, we are considering a model where each w-mer can change into any other, and<br />
there exists an unobserved hidden variable that indicates which selective regime the w-<br />
mer currently falls under. For notational simplicity let us consider the likelihood <strong>of</strong> an<br />
ancestral sequence A evolving into a new sequence X. Under the traditional probabilistic<br />
model <strong>of</strong> <strong>evolution</strong> (Felsenstein 1981) this would be written as<br />
N −w<br />
∏∑p( Ai<br />
| dataabove)<br />
∑<br />
L = p(<br />
data)<br />
=<br />
p(<br />
X | A ) p(<br />
data | X ) ,<br />
i= 1 Ai Xi<br />
where i index the positions in the sequence, w is the length <strong>of</strong> the binding site<br />
p(Ai|dataabove) and p(databelow|Xi) are the likelihoods <strong>of</strong> the rest <strong>of</strong> the tree above A and<br />
below X respectively, and p(Xi|Ai) is the substitution matrix that gives the probability<br />
observing the w-mer Xi given that you observed the w-mer Ai as its ancestor.<br />
We seek to extend this model to incorporate a new variable, m, that indicates the<br />
selective regime. We now have<br />
N −w<br />
∏∑∑ p(<br />
Ai<br />
, mAi<br />
| dataabove)<br />
∑∑<br />
L = p(<br />
data)<br />
=<br />
p(<br />
X , m | A , m ) p(<br />
data | X , m ) ,<br />
i= 1 mAi Ai mXi Xi<br />
where the mi, the indicator variables at each position, retain the property that they can<br />
only depend on their parents, and the tree retains the property that the likelihood <strong>of</strong><br />
everything below can only depend on the variables at the parent.<br />
From a technical perspective this model possesses a very unattractive feature: it<br />
no longer treats the positions <strong>of</strong> the motif independently – the state space we must<br />
consider will now be on the order <strong>of</strong> 4 w . In fact the substitution matrix defined <strong>by</strong><br />
p(X,m|A,m) is a 2 x 4 w <strong>by</strong> 2 x 4 w matrix. Since the probabilities we seek are given <strong>by</strong> P =<br />
e Rt , we must consider how to calculate the matrix exponential <strong>of</strong> such an extremely large<br />
203<br />
i<br />
i<br />
Xi<br />
i<br />
i<br />
Ai<br />
below<br />
i<br />
below<br />
i<br />
Xi
matrix. Even if this were possible, we must consider the time needed to run Felsenstein’s<br />
recursion on a tree with such a large state space.<br />
Despite the technical challenges, developing models <strong>of</strong> this kind would be<br />
extremely useful, not only for this application, but for many problems where it is<br />
reasonable to assume constraints may be changing over the course <strong>of</strong> <strong>evolution</strong>.<br />
Modeling <strong>evolution</strong> <strong>of</strong> transcription <strong>regulation</strong> at longer <strong>evolution</strong>ary distances<br />
As described above, beyond closely related species is not possible to reliably align<br />
binding sites. In order study <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> over longer<br />
<strong>evolution</strong>ary distances we must consider methods that do not rely on alignments. At<br />
these distances, the non-coding portions <strong>of</strong> genomes can be treated as independent and at<br />
equilibrium. Because this model treats positions independently, in the absence <strong>of</strong><br />
selection, at equilibrium there is some constant probability <strong>of</strong> observing a binding site at<br />
that position. This means that the model is a binomial at each position, which implies that<br />
in any finite stretch <strong>of</strong> sequence the expected number <strong>of</strong> binding sites is poisson with<br />
parameter = p*L, as long as the probability <strong>of</strong> observing the binding site (p) is much<br />
smaller than 1.<br />
We can also extend the model to the case <strong>of</strong> unaligned sequences without the<br />
equilibrium assumption <strong>by</strong> instead <strong>of</strong> considering the indicator variable mi at each<br />
position i considering instead the sum, n = Σmi, which is simply the total number <strong>of</strong><br />
binding sites in each sequence. Once again, as long as we assume the number <strong>of</strong> binding<br />
sites will be much smaller than the total length <strong>of</strong> the regulatory region, and that the<br />
204
length <strong>of</strong> the regulatory region does not change over <strong>evolution</strong>, the number, n, follows a<br />
simple birth death process, and the equilibrium distribution <strong>of</strong> n is poisson with parameter<br />
equal to the rate <strong>of</strong> binding site gain divided <strong>by</strong> the rate <strong>of</strong> binding site loss (refs).<br />
Comparing this result to the one above immediately suggests an interesting<br />
property <strong>of</strong> transcription factor binding sites, i.e., the probability that we find them in<br />
non-coding sequence in the absence <strong>of</strong> selection depends only on the ratio <strong>of</strong> rate <strong>of</strong><br />
binding site gain to the rate <strong>of</strong> binding site loss, and not the magnitude <strong>of</strong> these rates.<br />
Further, as I have shown above, purifying selection acts <strong>by</strong> reducing the rate at which<br />
binding sites convert to background sequences. Because the equilibrium number <strong>of</strong><br />
binding sites depends only on the ratio <strong>of</strong> rates between binding site loss and gain,<br />
reducing the rate <strong>of</strong> loss through the action <strong>of</strong> purifying selection is sufficient to produce<br />
the large excess <strong>of</strong> binding sites observed in regulatory sequences.<br />
A<br />
B<br />
µ<br />
- λ<br />
0 1 2 3<br />
∞<br />
λ<br />
2µ 3µ 4µ ∞ µ<br />
205<br />
…<br />
λ λ λ<br />
λ<br />
n = 2<br />
n = 1<br />
n = 2<br />
n = 3<br />
n = 2
Figure 2<br />
Probabilistic <strong>evolution</strong>ary model for binding sites in un-alignable promoters.<br />
A. schematic representation <strong>of</strong> the promoter <strong>of</strong> a single gene, as the number <strong>of</strong> binding sites (n) changes<br />
over <strong>evolution</strong>. B. continuous time markov model for the number <strong>of</strong> binding sites. Once again there are<br />
two parameters, rate <strong>of</strong> binding site gain (λ) and rate <strong>of</strong> binding site loss (µ).<br />
In order to test for selection, we can estimate the equilibrium distribution, based on the<br />
genome frequency <strong>of</strong> binding sites, for example, and try to reject the hypothesis that the<br />
binding sites in a particular stretch <strong>of</strong> sequence are distributed according to this<br />
distribution. Indeed, the methods described in part II that search for sequences that are<br />
statistically enriched are taking advantage <strong>of</strong> precisely this principle. In order to address<br />
<strong>evolution</strong>ary questions, distantly related species whose non-coding regions show no<br />
similarity at the sequences level can also be assumed to be at equilibrium with respect to<br />
this model. Thus, we can test for selection on sequences <strong>of</strong> interest in each genome<br />
independently. Where we find evidence for a constraint in multiple species, we can infer<br />
that the common ancestor had shared the constraint. This is precisely the principle we<br />
have applied in studying <strong>evolution</strong> <strong>of</strong> <strong>transcriptional</strong> <strong>regulation</strong> in the ascomycete fungi.<br />
However, in the context <strong>of</strong> the probabilistic model it is clear that we can test much<br />
more specific hypotheses about binding site <strong>evolution</strong> than simply rejecting the<br />
equilibrium distribution in the absence <strong>of</strong> selection. Indeed, it should be possible to<br />
obtain reconstructions <strong>of</strong> the ancestral states, and test for lineage specific changes as with<br />
any <strong>evolution</strong>ary character.<br />
Models for <strong>evolution</strong> <strong>of</strong> transcription networks.<br />
206
While I have developed methods and tools for understanding the <strong>evolution</strong> <strong>of</strong><br />
transcription factor binding sites and shown that their <strong>evolution</strong> can reflect the <strong>evolution</strong><br />
<strong>of</strong> the network, the problem <strong>of</strong> understanding how selection acts on transcription<br />
networks remains a challenge for the future. Understanding the <strong>evolution</strong> <strong>of</strong> transcription<br />
networks would be <strong>of</strong> great interest for both their role in generating the diversity <strong>of</strong> extant<br />
organisms, but also because understanding how <strong>evolution</strong> has designed these networks<br />
may teach us how to better manipulate and design them for medical and technological<br />
benefit.<br />
207