Uravnavanje izražanja genov, transkriptomika in drugi ... - cfgbc

Podiplomski študij Biomedicine
Genetika: Genetski koncepti (2009-010)
Uravnavanje izražanja genov, transkriptomika in
drugi pristopi po-genomskega obdobja
1. Osnove uravnavanje izražanja genov.
Prof. dr. Damjana Rozman
Damjana.rozman@mf.uni-lj.si
http://cfgbc.mf.uni-lj.si/
2. Študije genoma in transkriptoma z DNA mikromrežami (čipi).
3. Študije genoma in transkriptoma z novo generacijo sekveniranja.
4. Pomen informatike pri študijah “omov”.
5. Primeri uporabe “omov”: kemogenomika, toksikogenomika, farmakogenomika.

2008

Različne ravni uravnavanja

Prenos
signala
razdružitev
nukleosomov
Prostorsko
razporejanje
DNA
Molekularni dogodki pri izražanju genov
30 nm vlakno
Premik nukleosomov s
tkivno-specifičnimi
faktorji
Preiniciacijski
kompleks
Aktivatorji
prepisovanja
polII
10 nm vlakno

The chromosome territory–interchromatin compartment (CT–IC) model
Lanctôt et al. Nature Reviews Genetics 8, 104–115 (February 2007) | doi:10.1038/nrg2041
Chromatin is organized in distinct CTs. Nuclear topography remains a subject
of debate, expecially with regard to the extent of chromatin loops expanding
into the IC and intermingling between neighbouring CTs and chromosomal
subdomains.

Three hypothetical chromosome territories
Chromatin) might become repositioned over time;
Subchromosomal 1 Mb domains (corresponding to
DNA replication units that are labelled during early S
phase32) are depicted as filled circles.
The lighter-green circles indicate gene-poor
chromatin;
Dark-coloured circles indicate gene-dense
chromatin.
Areas that contain transcriptionally active genes
are indicated by dark background shading.
Chromatin mobility is constrained in several different
ways.
The chromatin mobility is constrained by the tendency
of transcriptionally active and transcriptionally inactive
chromatin regions to cluster in three-dimensional
space.
Lanctôt et al. Nature Reviews Genetics 8, 104–115 (February 2007) | doi:10.1038/nrg2041

Chromatin mobility allows dynamic interactions between genomic loci and
between loci and other nuclear structures.
a) Movement of chromatin from one compartment to
another leads to changes in expression of the
corresponding genomic regions.
Activation is triggered by repositioning of the gene loci to
an activating compartment, away from silencing
compartments.
b) Compartments are transient self-organizing entities.
Gene activation leads to dissolution of the silencing
compartments, changes in gene positioning and de novo
assembly of an activating compartment.
Lanctôt et al. Nature Reviews Genetics 8, 104–115 (February 2007) | doi:10.1038/nrg2041

Uravnavanje izražanja genov
•RNA polimeraze, osnovni aparat in cikel transkripcije
•Mehanizmi prepisovanja in aktivacije
•Kontrola prepisovanja preko spremembe strukture kromatina
•Epigenetska kontrola
• Kombinirani dogodki pri prenosu signala
•Metode za študij interakcij DNA-proteini

•Osnovni aparat – RNA polimeraza
Transkripcijski aparat
•Regulatorji (aktivatorji, represorji; trans-delujoči faktorji)
Cis-elementi
•Cis-regulatorni moduli (CRM); vzpodbujevalci (enhacers), utiševalci (silencers)
http://the_brain.bwh.harvard.edu/Lever/Lever.png

Generating a high-resolution atlas of mesodermal CRMs.
RP Zinzen et al. Nature 462, 65-70 (2009) doi:10.1038/nature08531

Bakterije
– 1 RNA polimeraza
Evkarionti
– RNA pol I (45s pre-rRNA )
-RNA pol II (mRNA kodirani geni, miRNA)
-RNA pol III (tRNA, 5S rRNA)

“Tovarna” RNA polimeraze II
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

Komunikacija med CRM in promotorjem
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

Tipični pol II gen ima promotor, ki zavzema področje cca 200 bp navzgor
od mesta pričetka prepisovanja. Lahko vsebuje tudi pospeševalec
(enhancer), ki je oddaljen poljubno (???)
B. Lewin, GenesVII, 2000

Struktura gena razreda pol II
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

Core promoter-selective RNA polymerase II transcription
•Gross P, Oelgeschlager T, Transcription Laboratory, Marie Curie Research Institute, The Chart, Oxted, Surrey RH8
OTL, UK.
The core promoter:
-minimal DNA region that is sufficient to direct low levels of activator-independent (basal)
transcription by RNAP II in vitro;
-typically extends approx. 40 bp up- and downstream of the start site of transcription;
-can contain several distinct core promoter sequence elements;
-contains TATA box or INR (initiator) element.
Computational analysis of metazoan genomes suggests that the prevalence of the
TATA box has been overestimated in the past and that the majority of human genes
are TATA-less.
While TATA-mediated transcription initiation has been studied in great detail and is very
well understood, very little is known about the factors and mechanisms involved in the
function of the INR and other core promoter elements.
Biochem Soc Symp. 2006;(73):225-36.

TATA-less promoter – example – CYP19 (aromatase)
3, 821-831 (November 2003)

TATA-less promoter - example
The human CYP19 (P450arom) gene is located in the chromosome 15q21.2 region and is
comprised of a 30 kb coding region and a 93 kb regulatory region.
The unusually large regulatory region contains 10 tissue-specific promoters that are
alternatively used in various cell types. Each promoter is regulated by a distinct set of
regulatory sequences in DNA and transcription factors that bind to these specific
sequences.
The most recently characterized promoter Is TATA-less and accounts for the transcription
of 29-54% of P450arom mRNAs in breast cancer tissues and contains endothelial-type
cis-acting elements that interact with endothelial-type transcription factors, e.g. GATA-2.
This promoter may upregulate aromatase expression in vascular endothelial cells. The in
vivo cellular distribution and physiologic roles of this promoter in healthy tissues is not
known.
J Steroid Biochem Mol Biol. 2003 Sep;86(3-5):219-24

TATA-less promoters
Molina and Grotewold BMC Genomics 2005 6:25

Transkripcijski cikel
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

Transkripcijski mehurček
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

RNA polimeraza se lahko premika v obe smeri
A.J. Courey, Mechanisms in transcriptional regulation, 2008)

Vezavno mesto za DNA-RNA hibrid
Matrična veriga
Kodirajoča veriga
Cramer et al., Science 288, 640-645 (2000)

Model of the pol II transcription initiation complex with the addition of electron microscope
structures of TFIIE, TFIIF, and TFIIH.
Bushnell et al., Science 303, 983-988 (2004)

Splošni transkripcijski faktorji RNA polimeraze II
•TFIID: velik multiproteinski kompleks, ki prične sestavljanje transkripcijskega aparata.
Vsebuje TBP in TAFe
•TFIIB: interagira z TBP in z DNA (analog bakterijskih σ faktorjev)
•TFIIF: udeležen pri iniciaciji kot pri podaljševanju verige.
•TFIIE: veže TFIIH
•TFIIH ima kinazno aktivnost, je helikaza, sodeluje tudi pri popravljanja DNA.
Predstavlja povezavo med prepisovanjem in popravljanjem DNA in uravnavanjem
celičnega cikla.
•TFIIA - po nekaterih izsledkih ne spada med splošne transkripcijske faktorje temveč
med specializirane TFIID koaktivatorje. V pogojih in vitro veže TBP-DNA kompleks in
povzroči širjenje DNA-protein kontaktov, obstajajo pa tudi dokazi, da ima pomen le pri
kontaktiranju z gensko-specifičnimi transkripcijskimi faktorji.

Kromatin in prepisovanje
http://www.wadsworth.org/images/bio_icons/tn_transchromatin.jpg

N-terminalni repki histonov so gibljivi in obrnjeni navzven.
Modifikacija histonskih repkov vodi do strukturnih
sprememb, potrebnih pri pričetku prepisovanja
GenesVII

www.med.ufl.edu/biochem/ keithr/fig1pt2.html

Three hypothetical chromosome territories
Chromatin) might become repositioned over time;
Subchromosomal 1 Mb domains (corresponding to
DNA replication units that are labelled during early S
phase32) are depicted as filled circles.
The lighter-green circles indicate gene-poor
chromatin;
Dark-coloured circles indicate gene-dense
chromatin.
Areas that contain transcriptionally active genes
are indicated by dark background shading.
Chromatin mobility is constrained in several different
ways.
The chromatin mobility is constrained by the tendency
of transcriptionally active and transcriptionally inactive
chromatin regions to cluster in three-dimensional
space.
Lanctôt et al. Nature Reviews Genetics 8, 104–115 (February 2007) | doi:10.1038/nrg2041

Nukleolarna
tovarna Nukleoplazemska
tovarna
Transkripcijske (nukleolarne)
Tovarne v celicah HeLa
Nascentni transkripti označeni z Br-UTP,
Br-RNA imunobarvana s fluorescin
izotiocianatom (zelena), nukleinske kisline
označene s TO-TO-3 (rdeča)
Millerjeva razporeditev –
rRNA transkripcijska enota
(pol I) s ~125 prepisi
Pol II transkripcijska
enota z enim prepisom
Cook P.R., Science 284, 1790-1795 (1999)

Transkripcijski cikel
RNA II polimeraza ima večjo
potezno moč kot kinezin in miozin.
Energijo hidrolize ATP pretvori v
mehansko in kinetično energijo
Cook P.R., Science 284, 1790-1795 (1999)

RNA polimeraze, osnovni aparat in cikel transkripcije– povzetek
•Cikel transkripcije vsebuje začetek (iniciacijo), podaljševanje (elongacijo) in
zaključek (terminacijo)
•Transkripcijo katalizirajo evolucijsko ohranjene RNA polimeraze. Evkarionti
imajo RNA poI I, II in III. Potrebni pa so še drugi proteini (RNA polimeazni
kompleks)
•Jedro RNA polimeraze vsebuje par “klešč” (clamp), ki tvorijo režo aktivnega
centra encima za sintezo RNA. V stopnji podaljševanja DNA doseže aktivno
mesto skozi kanal med kleščama, se denaturira (mehurček), rastoča RNA
veriga pa tvori heterodupleks z matrično verigo DNA. NTP-ji dosežejo
aktivno mesto skozi sekundarni kanal.
•Če polimeraza naleti na oviro, se lahko premakne nazaj (back-tracking).
•TBP je univerzalni transkripcijski faktot, ki prepozna TATA, potreben pa je
tudi za prepisovanje s promotorjev brez TATA.

CRM in kromatin - povzetek
•Cis-regulatorni modul (CRM) je področje DNA, kamor se vežejo
številni transkripcijski faktorji in uravnavajo izražanje bližnjih genov .
Posamezni gen ima pri živalih lahko do 20 CRM.
•CRM se običajno nahajajo v 5’ neprevedenih regijah gena ali v intronih.
•Prepisovanje genov se dogaja v nukleolarnih tovarnah, ki predstavljajo
transkripcijsko aktivna področja jedra.
•Acetilacija (deacetilacija) uravnava kondenzacijo kromatina. Za
prepisovanje primeren je acetiliran kromatin.

2. Študije transkriptoma z DNA mikromrežami (čipi),
povezave z drugimi “omi”
The suffix “-ome-” originated as a back-formation from “genome”, a word formed
in analogy with “chromosome”.
Because “genome” refers to the complete genetic makeup of an organism, some
people have made the inference that there exists some root, *“-ome-”, of Greek
origin referring to wholeness or to completion.
Because of the success of large-scale quantitative biology projects such as
genome sequencing, the suffix "-ome-" has migrated to a host of other contexts.
Bioinformaticians and molecular biologists figured amongst the first scientists to
start to apply the "-ome" suffix widely.
http://en.wikipedia.org/

employees.csbsju.edu/.../ olbindtransciption.html

The “ome” world
http://en.wikipedia.org/
The transcriptome, the mRNA complement of an entire organism, tissue type, or cell;
with its associated field transcriptomics
The metabolome, the totality of metabolites in an organism; with its associated field
metabolomics
The metallome, the totality of metal and metalloid species; with its associated field
metallomics
The lipidome, the totality of lipids; with its associated field Lipidomics
The glycome, the totality of glycans, carbohydrate structures of an organism, a cell or tissue type. Glycomics:
The associated field of study.
The interactome, the totality of the molecular interactions in an organism a once proposed field of
interactomics has generally become known as systems biology
The spliceome (see spliceosome), the totality of the alternative splicing protein isoforms; with its associated
field spliceomics.
The ORFeome refers to the totality of DNA sequences that begin with the initiation codon ATG, end with a
nonsense codon, and contain no stop codon. Such sequences may therefore encode part or all of a protein.
The Phenome - the organism itself. The Phenome is to the genome what the phenotype is to the genotype.
Also, the complete list of phenotypic mutants available for a species.
The Exposome - the collection of an individual's environmental exposures.

Genomics
http://en.wikipedia.org/
Genomics is the study of an organism's entire genome.
The field includes intensive efforts to determine the entire DNA sequence of
organisms and fine-scale genetic mapping efforts.
http://www.wiley.com/legacy/college/boyer/0470003790/cutting_edge/shotgun_seq/computer.gif

Študije transkriptoma z DNA čipi
• Analiza genoma (genotpizacija enojnih nukleotidnih polimorfizmov SNP,
analiza variance števila kopij CNV oz. CGH, resekvenciranje).
• Analiza transkriptoma (ekspresijsko profiliranje: 3' ekspresijski, eksonski ali
genski čipi, čipi za sledenje izražanja miRNA).
• Študije uravnavanja izražanja genov (kromatinska imunoprecipitacija na čipu
ChIP-on-Chip, mapiranje prepisov).

A DNA microarray
http://en.wikipedia.org/
A DNA microarray (also commonly known as gene chip, DNA chip, or biochip) is a
collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or
silicon chip forming an array for the purpose of monitoring of expression levels for
thousands of genes simultaneously.
The affixed DNA segments are known as probes (although some sources will use different
nomenclature), thousands of which can be used in a single DNA microarray.
Microarray technology evolved from Southern blotting, where fragmented DNA is attached
to a substrate and then probed with a known gene or fragment.
Measuring gene expression using microarrays is relevant to many areas of biology and
medicine, such as studying treatments, disease, and developmental stages. For example,
microarrays can be used to identify disease genes by comparing gene expression in
disease and normal cells.

Robust, simple representation
focusing on the 3' ends.
Different parts of the gene as probes on the array
Genome-wide, exon-level analysis
on a single array —
a survey of alternative splicing and
gene expression.
High-density tiled microarrays for
transcript mapping.
http://www.affymetrix.com/products/arrays/index.affx

Parts of a gene represented in different types of microarrays

Hybridization on the microarray
http://www.ieee.org/portal/cms_docs_sscs/sscs/07Fall/HamFig1s.jpg

Analisys of genome - Genotyping microarrays
DNA microarrays can be used to read the sequence of a genome in particular
positions.
SNP microarrays are a particular type of DNA microarrays that are used to
identify genetic variation in individuals and across populations.
Short oligonucleotide arrays can be used to identify the single nucleotide
polymorphisms (SNPs) that are thought to be responsible for genetic variation
and the source of susceptibility to genetically caused diseases.
Amplifications and deletions can also be detected using comparative genomic
hybridization in conjuction with microarrays.
Resequencing arrays have also been developed to sequence portions of the
genome in individuals. These arrays may be used to evaluate germline
mutations in individuals, or somatic mutations in cancer.
Genome tiling arrays include overlapping oligonucleotides designed to blanket
an entire genomic region of interest. Many companies have successfully
designed tiling arrays that cover whole human chromosomes.

If every human genome is different, what does it mean to sequence
"the" human genome?
The complete human genome sequence announced in June 2000 is a
"representative" genome sequence based on the DNA of just a few
individuals.
Over the longer term, scientists will study DNA from many different people to
identify where and what variations between individual genomes exist.
Sequencing a genome is such a Herculean task that capturing its person-toperson
variability on the first pass would be next to impossible.
Since every person's genome is unique, no one person is any more or less
"representative" than any other and it hardly matters whose genome is
sequenced first.
The vast majority of the genome's sequence is the same from one person to
the next, with the same genes in the same places.
www.genomenewsnetwork.org/.../ Chp4_1.shtml

DNA polymorphisms
• Single Base Pair Differences,
– Single Nucleotide Polymorphisms (SNPs),
• Microsatellites (short sequence repeats),
• Minisatellites (long sequence repeats),
• Deletions,
• Duplications.

High density Afymetrix microarrays
Affymetrix uses a unique combination of photolithography and combinatorial
chemistry to manufacture GeneChip® Arrays.

SNP chips
To determine which alleles are present, genomic DNA from an individual is isolated, fragmented,
tagged with a fluorescent dye, and applied to the chip.
The genomic DNA fragments anneal only to these oligos to which they are perfectly
complementary.
A computer reads the position of the two fluorescent tags and identifies the individual as a C / T
heterozygote.
The single spots in the other three columns indicate that the individual is homozygous at the three
corresponding SNP positions.
http://www.mun.ca/biology/scarr/DNA_VDA_chip_schematic.jpg

1,500 SNPs arrayed
Genotyping

Comparative genomic hybridization
Array comparative genomic hybridization (CGH) is a tool for the assessment of DNA
copy number variation (CNV) within any given DNA sample.
The technique is derived from the concept of conventional CGH where patient and
reference DNA (the latter derived from a normal male/female) is differentially labelled
and co-hybridized to a normal metaphase spread.

Some diseases are associated with sections of chromosomes that are
erroneously replicated or deleted.
The technique of array-based genomic hybridisation (array-comparative
genomic hybridization CGH or copy number variation CNV) allows highthroughput,
rapid identification and mapping of these genomic DNA copy
number changes, with higher resolution than is possible using traditional,
non-array methods.
http://www.nature.com/labinvest/journal/v85/n9/images/3700312f2.jpg

Expression profiling and CGH
http://www.genomictree.com/images/bioimg09.jpg

Array-based Comparative genome hybridization (CGH)
In array-based CGH, large insert clones like BACs , containing human DNA, replace the metaphase
spread as target.
The clones are spotted in three- four- or fivefold on a microscope slide. Depending on the contents of
the array, the collection of clones that is printed on the slide, specific chromosome parts or even the
entire genome can be analyzed in one experiment.
http://www.sanger.ac.uk/HGP/Cytogenetics/gfx/cgh_300.jpg

Resequencing Arrays
Tiling arrays - tools for resequencing, based on the concept of interrogating the
genome in a systematic, unbiased fashion.
Before tiling arrays came along, other methods were used to predict genes that
were unknown, including expressed-sequence tag (EST) sequencing,
comparison with known protein-coding sequences, and ab initio prediction.
Tiling arrays are designed to address this problem by taking an empirical
measurement across the entire genomic DNA. This allows a researcher to probe
for to probe for transcribed sequences without a predetermined concept of thier
location
Using the tiling chips it is possible to perform the most comprehensive analysis of
the whole genome combined with transcript mapping. The probes are designed
(tiled) at an average resolution of 35-bp throughout the whole genome, thus
enabling a more accurate view of the transcription and discovery of novel RNA
transcripts.

Analiza transkriptoma

Ekspresijsko profiliranje z DNA mikromrežami – dvojno označevanje
Principle of expression profiling
Geni zastopani s
cDNA ali z oligonukleotidi
Robot-nanašalec
(spotter)
mikromreže s cDNA in dolgimi oligonukleotidi s 3’-konca
Test
Obratno prepisovanje
označevanje
ko-hibridizacija
Referenca
Laser 1
Računalniška analiza
čitalec
Emisija
Laser 2
Prekrivanje

Exon arrays
GeneChip Exon Arrays provide the most comprehensive coverage of the genome and
enable two complementary levels of analysis: gene expression and alternative splicing.
Multiple probes per exon enable "exon-level" analysis and allow you to distinguish between
different isoforms of a gene. T
his exon-level analysis on a whole-genome scale opens the door to detecting specific
alternative splicing events that may play a central role in disease mechanism and etiology.
You can also perform "gene-level" analysis, in which multiple probes on different exons are
summarized into a single expression value of all transcripts from the same gene, enabling
similar analysis workflows to 3'-gene expression.

Exon arrays – example I
Complex organisation and structure of the ghrelin antisense strand gene GHRLOS, a candidate noncoding
RNA gene
Seim I et al., BMC Molecular Biology 2008, 9:95

Gene arrays
Human Gene Arrays (April 2007), interrogate approximately 29,000 well annotated
genes by using an average of 26 probes per gene.
Probes are designed to cover the entire transcript length, unlike the 3’ focused arrays
which contain probes targeting the transcript’s 3’ end.
Probes on the array are perfect match (PM) only; mismatch probes found on the 3’
focused arrays have been replaced on the Gene Arrays with a set of approximately
20,000 generic background probes.

miRNA Profiling
MicroRNAs (miRNAs) are 21-23 nucleotide long non-coding RNA molecules that have been shown to
regulate the stability or translational efficiency of target messenger RNAs.
They have been shown to regulate diverse processes such as Fragile X , cancer proliferation and fat
metabolism.
At present over 530 different miRNA sequences have been validated in the human (Sanger Institute
mirBase database) although recent research suggests that the number of unique miRNAs could exceed
800.
It is also thought that miRNA genes regulate protein production for 10% or more of all human genes
making them an increasingly important area of research

miRNA isolation
The starting material for Agilent miRNA Arrays is total RNA (not small RNA) by isolated with i.e.
TRIZOL still containing small RNA molecules.
As such, they should not have been cleaned up using columns as this will invariably remove the
small RNAs which the arrays are trying to measure.
Just 100ng of total RNA is required for labelling. Once isolated, samples can be quantified using the
Nanodrop Spectrophotometer.
Because miRNAs are so short, they are less prone to degradation then mRNAs.
Screen capture of Agilent 2100 Bioanalyzer electropherograms of a TRIZOL Reagent isolated total RNA. Note the
pronounced peak of RNA at

Interactome by Chromatine immunoprecipitation on a chip (CHIP-Chip)

The "ChIP to Chip" Assay

Chip-chip – example I
Detection of Nkx2-5 binding to the Actc1 promoter and intron 1 (A) and the Nppa promoter (B)
using the Integrated Genome Browser
www.nimr.mrc.ac.uk/devbiol/mohun/cardiacdiff/

DNA čipi so zbirka mikroskopskih “DNA točk”, cDNA ali oligonukleotidov, pritrjenih
na trdo podlago.
Uporabljajo se za:
Povzetek
• Analizo genomske DNA (genotipizacija, SNP analiza, CHG, sekvenciranje).
• Analizo izražanja genov (ekspresijsko profiliranje) , kjer probe lahko
predstavljajo 3'–konce genov, eksone ali različne dele genov. Posebni čipi pa
so za sledenje izražanja miRNA.
• Študije uravnavanja izražanja genov (določanje vezavnih mest
transkripcijskih faktorjev (Chip-chip), metilacija kromatina), kjer probe
predstavljajo 5’-neprevedene dele in nerepetitivne dele genov.

Nova generacija sekvenciranja
www.gatc.co.uk/cgi-bin/wPrintpreview.cgi?sour...

Thu Nov 5, 2009 7:24pm EST
Want to know your entire DNA sequence? A California company has done it for as little as $1,700.
Privately held Complete Genomics says it can do a better quality, usable genome map for about $4,400 -- compared with the
$100 million the Human Genome Project spent to complete the first sequencing of the human genome in 2000.
"Whole-genome sequencing costs have dropped from the more than $100 million cost of the first human genomes to the point
where individual labs have generated genome sequences in a matter of months for material costs of as low as $48,000," the
company's Radoje Drmanac and colleagues reported in the journal Science.
"This high-quality, cost-effective approach to genome sequencing will allow researchers to study complete genomes from
hundreds of patients with a disease to advance the understanding of the genetic causes of that disease, with an end to
preventing and treating common human ailments," said Cliff Reid, chief executive officer of Complete Genomics.
Two of the people whose DNA was mapped had taken part in an international sequencing project called the International
HapMap project -- a man of European descent and a Yoruban female. The third came from a white man taking part in the
Personal Genome Project, an online registry in which people are asked to donate both their DNA and a little money.
Genome sequencing is still early stage science. While researchers can get the code, figuring out what it means is a different
matter.Genomics pioneer Craig Venter had his own genome sequenced -- at a cost of "several million" dollars -- and found the
analysis could only show he was likely to have blue eyes, for instance. Venter does have blue eyes.
Last month Pauline Ng of the J. Craig Venter Institute in San Diego and Sarah Murray of Scripps Translational Science Institute
in La Jolla, California, tested kits provided by California-based firms Navigenics Inc, a private company, and 23andMe, backed
by Google Inc. They found they varied in predicting disease risk.
Complete Genomics and The Institute for Systems Biology said earlier this week they plan to sequence the genomes of 100
people to try and find insights into Huntington's disease. Scientists also use a technique called genome-wide association to try
to find genes that no one suspected were involved in various diseases.
(Reporting by Maggie Fox; Editing by Eric Wals)

Nova (naslednja) generacija sekvenciranja
- Sekvenciranje človeškega genoma je trajalo več let, z uporabo cca 20 kb BAC klonov, ki so
vsebovali cca 100 kb dolge tarčne fragmente, in 8-kratnega pokrivanja vsakega dela tarče. Analiza s
kapilarno elektroforezo.
-Nadaljnji razvoj sekvenciranja je temeljil na sočasnem sekvenciranju celotnega genoma (WHS, angl.
whole genome sequencing), ki je bil vstavljen v vektorje. Metoda je hitrejša, pušča pa velike praznine
v zelo polimorfnih ali repetitivnih genomih. Analiza je potekala s kapilarno elektroforezo.
-Naslednja generacija sekvenciranja (2004) – visokozmogljivostno paralelno čitanje odsekov DNA na
ravni celega genoma preko PCR pomnoževanja enoverižnih fragmentov genomske knjižnice.

Next-generation of DNA and RNA sequencing methods
- The bead-amplification sequencing (Roche/454FLX)
-Sequencing by synthesis (Illumina/Solexa Genome analyzer)
-Sequencing by ligation (Applied Biosystems SOLID System)
-Helicos Helioscope (2008)
-Pacific Biosciences SMRT (2010)
Common features:
-A compex interplay of enzymology, chemistry, software, hardware, optics engineering…)
-A streamline of sample preparation prior to sequencing (time saving)
-Preparation of fragment libraries of the DNA of interest by annealing for platform-specific linkers
and amplification
-Amplification of single stranded fragment library and performing sequencing on amplified
fragments
-Single molecule sequencing just arrived or is under development

Comparison between capillary sequencers and novel generation sequencers
-A huge difference in the throughput of a single run: 96 capillaries of about 750 bp compared
to several thousand (Roche) to tens of millions (Illumina, ABi) shorter reads.
-Time runs are longer that by next generation sequencers (8h – 10days)
- Longer run times are required to image the massive parallel sequencing reactions
-Due to the streamline preparation and long reading times a single human operator can
operate several machines of next generation at the full capacity.
ABI Solexa

Classical Sanger dideoxy sequencing method
Dideoxynucleotide sequencing represents only one method of
sequencing DNA. It is commonly called Sanger sequencing since
Sanger devised the method. This technique utilizes 2',3'dideoxynucleotide
triphospates (ddNTPs), molecules that differ
from deoxynucleotides by the having a hydrogen atom attached
to the 3' carbon rather than an OH group. These molecules
terminate DNA chain elongation because they cannot form a
phosphodiester bond with the next deoxynucleotide.
campus.queens.edu/faculty/jannr/molecular/

General concepts for clonal-array generation and sequencing
a | Bead-chips. Genomic DNA is fragmented and adaptors are ligated to create an insert library that is
flanked by two universal priming sites. Because of the random fragmentation, the complexity of this
signature sequence library is equivalent to the genome. This library is cloned on beads using emulsion
PCR technology. A water-in-oil emulsion is created from a PCR mix that contains a limiting dilution of
DNA and beads. The emulsion creates micro-compartments with, on average, a single bead and single
DNA template each. After PCR, beads with clones are affinity selected and assembled onto a planar
substrate. A subsequent cycle-sequencing reaction is used to read out the sequence on the clones.
b | Sequencing by synthesis (SBS). A common anchor primer is annealed to a constant sequence
(universal priming site) that is contained within the library clones that are located on the polony (clonal
bead) array (the orientation of the immobilized target might vary depending on the platform that is used).
The sequence is read out by polymerase extension in a base-by-base fashion using either reversible
terminators or sequential nucleotide addition (pyrosequencing). After incorporation of a single base or
base type, the incorporated base is identified by fluorescence (laser) or chemiluminescence (no laser
required).
c | Sequencing by ligation. The polony array set-up is similar to SBS in which a common primer is
annealed to an arrayed polony library and used to read out the sequence through a stepwise ligation of
random oligomers. The labelled oligomers are designed to have random bases inserted at every site
except the query site. The query site has one of four base substitutions, each matched to a particular
fluorescent label on the oligonucleotide. After read-out of each ligation event, the primer and the ligated
oligomer are stripped, a new primer reannealed and the process repeated with an oligomer that contains
a query base at a different position.
Fan et al. Nature Reviews Genetics 7, 632–644 (August 2006) | doi:10.1038/nrg1901

Sequencing by
synthesis
Solexa / illumina
General concepts for clonal-array generation and sequencing
Bead chips
Roche
pyrosequencing
Sequencing by
Ligation
Applied biosystems
Fan et al. Nature Reviews Genetics 7, 632–644 (August 2006) | doi:10.1038/nrg1901

Roche/454 FLX Pyrosequencer
Library fragments are mixed with agarose
beads with oligos complementary to
adapter sequences on the library.
Each bead is associated with a single
fragment.
Each fragment-bead complex is isolated
into individual oil:water micelles with PCR
mixture.
Thermal cycling of this emulsion PCR of
the micelles produces amplified unique
sequences on the bead surface.
“En mass” sequencing of PCR products
on picotiter plates (PTP) with single beads
in each picowell.
Enzyme/substrate containing beads for
the pyrosequencing reaction are added to
wells that act as floww cells for addition of
individual pure nucleotide solutions. The
CCD camera records the light emitted at
each bead.

Principles of Pyrosequencing
pyrogram
A sequencing primer is hybridized to a single-stranded PCR
amplicon that serves as a template.
Mixtures incubated with the enzymes, DNA polymerase,
ATP sulfurylase, luciferase, and apyrase as well as the
substrates, adenosine 5' phosphosulfate (APS), and
luciferin.
ATP sulfurylase converts PPi to ATP in the presence of
adenosine 5' phosphosulfate (APS).
ATP drives the luciferase-mediated conversion of
luciferin to oxyluciferin that generates visible light in
amounts that are proportional to the amount of ATP.
Apyrase, a nucleotide-degrading enzyme, continuously
degrades unincorporated nucleotides and ATP. When
degradation is complete, another nucleotide is added

Watson and Cricks pyrosequencing readout
2008

Timeline of the pyrosequencing development
October 2005 Release of the Genome Sequencer 20, the first next-generation sequencing
system on the market
October 2005 Collaboration agreement signed with Roche Diagnostics .
January 2007 Release of the Genome Sequencer FLX System
March 2007 Roche Diagnostics completes integration with 454 Life Sciences
May 2007 Complete sequence of Jim Watson published in Nature. First genome to be
sequenced for less than $1 million.
November 2007 Announcement of the 100th peer-reviewed publication enabled by 454
Sequencing
June 2008 454 Joins the 1000 Genome Project, an international effort to build the most detailed
map to date of human genetic variation as a tool for medical research
September 2008 Announcement of the 250th peer-reviewed publication enabled by 454
Sequencing
October 2008 Release of Genome Sequencer FLX Titanium Series reagents, featuring 1
million reads at 400 base pairs in length

sequencing technology
•Is based on arrays of randomly assembled glass (silica) beads;
The beads have oligonucleotides covalently attached to the surface;
Each bead has about one million oligos on its surface;
All oligos on each bead have the same sequence.
•Attached DNA fragments are extended and bridge amplified to create an ultra-high density
sequencing flow cell with 80-100 million clusters, each containing ~1,000 copies of the same template.
These templates are sequenced using a robust four-color DNA sequencing-by-synthesis technology
that employs reversible terminators with removable fluorescent dyes. This novel approach ensures
high accuracy and true base-by-base sequencing, eliminating sequence-context specific errors and
enabling sequencing through homopolymers and repetitive sequences.
The beads are randomly assembled on the arrays, and the location of a particular probe is initially
unknown;a process called decoding is used to find the location of each bead;

Illumina sequencing by synthesis

Illumina sequence - decoding

Approaches to create highly parallel genotyping assays by novel generation sequencing
Fan et al. Nature Reviews Genetics 7, 632–644 (August 2006) | doi:10.1038/nrg1901

Fan et al. Nature Reviews Genetics 7, 632–644 (August 2006) | doi:10.1038/nrg1901

Nova generacija sekvenciranja - povzetek
• Nova generacija visokozmogljivostnega sekvenciranje omogoča razpoznavanje zaporedij
DNA na ravni celega genoma, z resolucijo posameznega baznega para.
• Iz vsakega vzorca se pripravi z adaptorji ligirana knjižnica, ki vsebuje vse v vzorcu
prisotne fragmente DNA ali RNA (cDNA).
• Vse platforme bazirajo na ligaciji adaptorjev in pomnoževanju, imajo pa različne pristope
sekvenciranja:
-Pirosekvenciranje (Roche-Nimblegen)
-Sekvenciranje s sintezo (Illumina-Solexa)
-Sekvenciranje z ligacijo (ABI)
•Razvijajo se tudi metode, ki pred sekvenciranjem ne potrebujejo pomnoževanja.
• Aplikacije so enake kot pri klasičnih mikromrežah (ekspresijsko profiliranje oz. SAGE,
genotipizacija SNP in CNV, kroamtinska imunoprecipitacija, metilacija kromatina, itd.).
•Prednost pred klasičnimi mikromrežami je v preprosti pripravi vzorca in zmožnosti
procesiranja velikega števila vzorcev v kratkem času.
•Procesiranje velikega števila vzorcev na eni ali več aparaturah lahko upravlja en človek.

Bioinformatika v raziskavah “omov”
http://bioinformatics.ubc.ca/about/what_is_bioinformatics/images/computer.gif
www.gwumc.edu

Technical Issues Involved in Obtaining Reliable Data from Microarray Experiments
– Standardization and beyond

Bioinformatics
Wikipedia
Making sense of the huge amounts of DNA data produced by gene sequencing
projects.
Bioinformatics and computational biology involve the use of techniques from
applied mathematics, informatics, statistics, and computer science to solve biological
problems.
Research in computational biology often overlaps with systems biology.
Major research efforts in the field include sequence alignment, gene finding, genome
assembly, protein structure alignment, protein structure prediction, prediction of gene
expression and protein-protein interactions, and the modeling.
The terms bioinformatics and computational biology are often used interchangeably,
although the former typically focuses on algorithm development and specific
computational methods, while the latter focuses more on hypothesis testing and
discovery in the biological domain.

Microarrays and bioinformatics
Standardization
The lack of standardization in arrays presents an interoperability problem in
bioinformatics, which hinders the exchange of array data.
Various projects are attempting to facilitate the exchange and analysis of data
produced with non-proprietary chips.
The "Minimum Information About a Microarray Experiment" (MIAME) XML
based standard for describing a microarray experiment is being adopted by
many journals as a requirement for the submission of papers incorporating
microarray results.
http://www.mged.org/Workgroups/MIAME/miame.html

Statistical analysis
The analysis of DNA microarrays poses a large number of statistical problems,
including the normalisation of the data.
From a hypothesis-testing perspective, the large number of genes present on a
single array means that the experimenter must take into account a multiple
testing problem: even if each gene is extremely unlikely to randomly yield a
result of interest, the combination of all the genes is likely to show at least one
or a few occurrences of this result which are false positives.

Steps in microarray experiments
irfgc.irri.org/cropbioportal/index.php?option...

Data mining includes gene ontology
Gene ontology is a controlled vocabulary used to describe the biology of a
gene product in any organism. There are 3 independent sets of vocabularies,
or ontologies, that describe the:
-molecular function of a gene product,
- the biological process in which the gene product participates,
-and the cellular component where the gene product can be found.

Experimental design is crucial for a succesfull “omic” experiment
A proper experimental design is crucial for obtaining useful conclusions from a project. The choice of
design ideally includes an assessment of the biological variation, the technical variation, the cost and
duration of the experiment, and the availability of biological material. The experimental design can
also depend on the methods that will be used to analyse the data afterwards. In certain cases, the
parameters needed to find the optimal design must be obtained by a pilot experiment.
A related problem is the comparison of different competing experimental methods or devices. Here, a
proper test design is crucial as well to be able to make a firm conclusion in favor of one or the other
method.
Dere E. et al., BMC Genomics 2006, 7:80doi
lunabiosciences.com/experimentaldesign.html

Experimenal design – an example
Changes in cholesterol homeostasis and drug metabolism caused by different factors in mouse liver
T. Rezen et al., BMC Genomics 2008, 9:76

Primary data analysis – experimental example
Images of the Sterolgene v0 microarrays were analyzed by Array-Pro Analyzer 4.5 (Media Cybernetics, Bethesda, MD, USA).
The median feature and local background intensities were extracted together with the estimates of their standard deviation.
Only features with foreground to background ratio higher than 1.5 and coefficient of variation (CV, ratio between standard
deviation of the background and the median feature intensity) lower than 0.5 in both channels were used for further analysis.
Log2 ratios were normalized using LOWESS fit to spike in control RNAs according to their average intensity. Two types of
spike in controls were used: custom-made (Firefly luciferase) and commercial ArrayControl Spikes (Ambion, Austin, TX, USA).
In phenobarbital and cholesterol-feeding experiments data were additionally standardized (median-centered and scaled by
median absolute deviation) in order to reduce inter-array variability. All data analysis were done in Orange software [37]
Images of Agilent microarrays were analyzed using Array-Pro Analyzer 4.5 (Media Cybernetics, Bethesda, MD, USA).
Features, with CV>0.39, were filtered out and data were normalized using LOWESS fit to all genes according to their average
intensity. Filtration and normalization was done in BASE softwar].
Affymetrix data were normalized by the Robust Multichip Average (RMA) algorithm. After transformation to non-logarithmic
data, the expression estimates were scaled to the average expression levels in the control group analyzed using GeneSpring
software (Silicon Genetics, Redwood, USA). Classification of the differentially expressed genes was done in Orange using
single-factor ANOVA or two tailed Student’s t-test. For Sterolgene microarrays probability of type I error αS=0.05 or αS=0.1
was used. For Agilent microarrays complementary probability of type I error was calculated according to Bonferroni correction for
multiple testing (αA=αS*nS/nA), but final comparisons were made using more relaxed criteria αA=0.001 and αA=0.01. For
Affymetrix microarrays complementary probability αAf=0.00043 was calculated, but also a more relaxed criterion αAf=0.001 was
used.
Additional data analyses were done only using common genes between platforms, which were matched using unigene, refseq
or gene symbol. On this gene list another classification of differentially expressed genes was done in Orange using ANOVA for
Affymetrix and Agilent platforms. A probability for type I error was selected as in a complementary Sterolgene experiment (α=0.1
in Affymetrix analyses and α=0.05 in Agilent analyses). Pearson’s product moment correlation coefficient and a scatterplot
between log2 ratios from common genes were calculated in SPSS 14.0. All data have been submitted to GEO (Gene expression
omnibus) under accession codes: GSE6271 (Affymetrix data), GSE6317 (Agilent data), GSE6447 (Sterolgene phenobarbital and
high cholesterol diet data), and GSE6423 (Sterolgene fasting and inflammation data).
T. Rezen et al., BMC Genomics 2008, 9:76

Pomen bioinformatike v raziskavh “omov” - povzetek
- Matematično-informatični pristopi v bioloških poskusih pogenomske dobe (bioinformatika)
omogočajo razbiranje biološkega smisla v enormni količini podatkov.
-Bioinformatika (računska biologija, matematična biologija) za reševanje bioloških problemov
uporablja metode uporabne matematike. Informatike, statistike in računalniških znanosti.
-Načrtovanje bioloških poskusov zahteva sodelovanje eksperimentatorjev in informatikov že
od vsega začetka.
-Zasnova poskusa zahteva definicijo biolškega vprašanja, izbiro platforme za analizo,
določitev števila bioloških in tehničnih replik in načrt serije poskusov (hibridizacij ali
sekvenciranj).
-Po tehnični izvedbi poskusa sledita statistična in informatična obdelava ter rudarjenje
podatkov.
- Statistično-informatična obdelava obsega ekstrakcijo intenzitet signala, normalizacijo
podatkov, ter različne statistične teste, da pridobimo listo diferencialno izraženih genov ali
zaporedij DNA.
- Sekundarna informatična analiza in rudarjenje podatkov obsegata gručanje in
razpoznavanje vzorcev, študije seznama genov z genskimi ontologijami, iskanje regulatornih
vzorcev, kot tudi načrtovanje validacijskih eksperimentov.
-Matematično-informatične metode za povezovanje podatkov različnih “omov” (npr.
transkriptom, proteom, metabolom) so še v razvoju.

Kemogenomika, toksikogenomika in farmakogenomika
– primeri uporabe “omskih” tehnologij v praksi

What’s the difference?
• Chemogenomics – screening a compound library to detect drug candidates
• Toxicogenomics – investigates the effect of a chemical substance (drug) on
the genome
• Pharmacogenomics – investigates inherited basis for difference in drug
response among individuals

A blockbuster drug is a drug generating more than $1 billion of revenue for its
owner each year. The search for blockbusters has been the foundation of the R&D
strategy adopted by big pharmaceutical companies, but this looks set to change.
New advances in genomics, and the promise of personalized medicine, are likely to
fragment the pharmaceutical market.

Chemogenomics: structuring the drug discovery process to gene
families
C.J. Harris and A. P. Stevens,Drug Discov Today., 11, 880-888, 2006.
In the post-genomic era, if all proteins in a gene family can putatively be
identified, how can drug discovery effectively tackle so many novel
targets that might lack structural and small-molecule inhibitory data?
In response, chemogenomics, a new approach that guides drug
discovery based on gene families, has been developed.
By integrating all information available within a protein family (sequence,
SAR data, protein structure), chemogenomics can efficiently enable
cross-SAR exploitation, directed compound selection and early
identification of optimum selectivity panel members.

Aims of chemogenomics - the “druggable” genome
Chemogenomics aims to identify systematically all ligands and modulators for all
the gene products expressed and allows the accelerated exploration of their
biological function.
The subject brings together diverse disciplines including chemistry, genetics,
chemo- and bioinformatics, structural biology, and biological screening in
phenotypic and target-based assays.
http://www.nature.com/nbt/journal/v21/n1/images/nbt0103-31-F1.gif

Expert Review of Proteomics
June 2007, Vol. 4, No. 3, Pages 411-419
Chemogenomics approaches to novel target discovery
L Alex Gaither
Chemogenomics involves the combination of a compound’s effect on biological targets together with
modern genomics technologies. The merger of these two methodologies is creating a new way to
screen for compound–target interactions, as well as map chemical and biological space in a parallel
fashion. The challenge associated with mining complex databases has initiated the development of
many novel in silico tools to profile and analyze data in a systematic way. The ability to analyze the
combinatorial effects of chemical libraries on biological systems will aid the discovery of new
therapeutic entities.
Chemogenomics provides a tool for the rapid validation of novel targeted therapeutics, where a
specific molecular target is modulated by a small molecule. Along with targeted therapies comes the
ability to discovery pathway nodes where a single molecular target might be an essential component
of more than one disease.
Several disease areas will benefit directly from the chemogenomics approach, the most advanced
being cancer. A genetic loss-of-function screen can be modulated in the presence of a compound to
search for genes or pathways involved in the compound’s activity. Several recent papers highlight how
chemogenomics is changing with RNA interference-based screening and shaping the discovery of
new targeted therapies. Together, chemical and RNA interference-based screens open the door for a
new way to discovery disease-associated genes and novel targeted therapies.

Steps of chemogenomics
1. Formation/existance of a compound library
2. Chosen representative biological system
3. A reliable readout (i.e. gene expression, protein expression, HTP binding or
functional assays, etc.)
Rognan D. Chemogenomic approaches to rational drug design.Br J Pharmacol. 2007, 152:38-52. Epub 2007 May 29. Review.

Analysing chemogenomic data is a never ending learning process aimed at
completing a 2D-matrix, where columns represent targets/genes and rows
chemical compounds. Reported values are binding constants or functional
effects.
Assumptions of chemogenomic-based approach:
1. compounds sharing some chemical similarity should also share targets,
2. targets sharing similar ligands should share similar patterns (binding sites).
Defining the ligand space by descriptors (molecular weight, atom and bound counts,
topological fingerprints, shape, field, spectra, etc.)
Defining the target space by descriptors (databases for sequence, patterns
secondary structure fold, atomic coordinates, binding site, etc.)

Ligand space: molecular descriptors
Rognan D. J Pharmacol. 2007, 152:38-52. Epub 2007 May 29. Review.

Representation of ligand and target space correlations - example

Correlation matrices - networks

In silico target fishing approaches
Machine learning algorithm using Bayesian statistics to predict target profiles from connectivity
fingerprints of compounds from the biologically annotated database.

The key to improving drug discovery is to manage the whole value chain of research.
Information needs to flow from fundamental analysis of genomes, proteins and
chemistry through to managing clinical trials and drug submissions.
Integrating so many fields of life sciences presents a massive task of integrating
knowledge and information.
Needed are bridges which help researchers in one area to communicate with others.

http://www.nature.com/embor/journal/v5/n9/images/7400236-f2.jpg

The TIP Database - an example
The Target Informatics Platform's content includes more than 80,000 high resolution protein
structures with reliably annotated small molecule binding sites, including >44,000 comparative
models of human proteins, covering every major drug target family including proteases, kinases,
phosphatases, phosphodiesterases, nuclear receptors, and GPCRs.
In addition to TIP's high quality structural annotation, it is the only database of its kind that stores all
similarity relationships between every sequence, structure, and binding site, making it an
incredibly powerful system for structural and comparative proteomics.

Challenges in Chemogenomics
• “Conceptual” challenges
– Multidisciplinary project teams
– Data from chemistry, biology, genomics, …
• Data challenges
– Disparate data sources, formats and identifiers
– Multiple data types – gene expression, chemical structures, clinical
chemistry, histopathology, receptor binding assays, …
– Incomplete and missing data
• Analysis challenges
– Should the data be normalized?
– Does it make sense to cluster the data?
– How to relate chemical structures and gene expression data?
Identify correlations between chemical properties and biological function
Brian Prather, Ph.D., Application Specialist, Spotfire, Third Virtual Conference on Genomics and Bioinformatics

“Conceptual” challenges
Multidisciplinary Drug Compound Candidate Project Team
Chemistry Genomics
CAS
ISIS
ABase
Shared Analysis
Information Technology
Affymetrix
Biology
Toxicology
CodeLink

Toxicogenomics
http://en.wikipedia.org/
is a form of analysis by which the activity of a particular toxin or chemical
substance on living tissue can be identified based upon a profiling of its known
effects on genetic material.
Toxicogenomics may also be of use as a preventative measure to predict adverse
"side", i.e. toxic, effects, of pharmaceutical drugs on susceptible individuals. This
involves using genomic techniques such as gene expression level profiling and
single-nucleotide polymorphism analysis of the genetic variation of individuals.
Studies of those types are then correlated to adverse toxicological effects in clinical
trials so that suitable diagnostic markers (measurable signs) for these adverse
effects can be developed.
There are many well-publicized cases in which popular drugs were pulled from the
market because of toxic effects experienced by a small percentage of patients, with
a cost of many billions of dollars to the companies responsible.

Some drugs recently withdrawn form the market
Rofecoxib developed by Merck & Co. to treat osteoarthritis, acute pain conditions, and
dysmenorrhoea. Rofecoxib was approved as safe and effective by the FDA in 1999 and was marketed
under the brand name Vioxx, Ceoxx and Ceeoxx. Worldwide, over 80 million people were prescribed
rofecoxib at some time. In 2004, Merck voluntarily withdrew rofecoxib from the market because of
concerns about increased risk of heart attack and stroke associated with long-term, high-dosage use.
Rofecoxib was one of the most widely used drugs ever to be withdrawn from the market.
Fen-phen was an anti-obesity medication which consisted of two drugs fenfluramine and
phentermine. After reports of valvular heart disease and pulmonary hypertension, primarily in women who
had been undergoing treatment with Fen-phen, the FDA requested its withdrawal from the market in
September 1997. As of 2004 Fen-phen is no longer widely available. In More than 50,000 product liability
lawsuits had been filed by alleged Fen-phen victims. Estimates of total liability run as high as $14 billion.
Cerivastatin (Baycol®, Lipobay®) is a synthetic member of the class of statins, used to lower
cholesterol and prevent cardiovascular disease. It was withdrawn from the market in 2001 because of the
high rate of serious side-effects.Cerivastatin was marketed by the Bayer A.G. During post-marketing
surveillance, 52 deaths were reported in patients using cerivastatin, mainly from rhabdomyolysis and its
resultant renal failure. Risks were higher in patients using fibrates and in patients using the high (0.8
mg/day) dose of cerivastatin. Another 385 nonfatal cases of rhabdomyolysis were reported. This put the
risk of this (rare) complication at 5-10 times that of the other statins.

Toxicogenomic approaches
http://fig.cox.miami.edu/~cmallery/150/gene/toxicogenomics.gif

http://www.kitox.re.kr/eng/images/study/img014.gif

Pharmacogenomics
http://en.wikipedia.org/
is the branch of pharmacology which deals with the influence of genetic variation on
drug response in patients by correlating gene expression or single-nucleotide
polymorphisms with a drug's efficacy or toxicity.
Pharmacogenomics aims to develop rational means to optimise drug therapy, with
respect to the patients' genotype, to ensure maximum efficacy with minimal adverse
effects.
Such approaches promise the advent of "personalized medicine", in which drugs
and drug combinations are optimised for each individual's unique genetic makeup.
Pharmacogenomics is the whole genome application of pharmacogenetics, which
examines the single gene interactions with drugs.

Adverse drug reactions
An adverse drug reaction (abbreviated ADR) is an expression that describes the
unwanted, negative consequences associated with the use of given medications.
An ADR is a particular type of adverse effect. The meaning of this expression
differs from the meaning of "side effect", as this last expression might also imply
that the effects can be beneficial. The study of ADRs is the concern of the field
known as pharmacovigilance.
In 1994, an estimated 2,216,000 hospital patients in the US, suffered a serious
ADR, leading to approximately 106,000 patient deaths, making ADR fatalities the
fourth to sixth leading cause of death in the US in 1994.
The current regime of “one dose fits all” is not ideal for patients and is not costeffective
for the health service.

Why is every human genome different?
Where are genome variations found?
What kinds of genome variations are there?

Polymorphisms
• Single Base Pair Differences,
– Single Nucleotide Polymorphisms (SNPs),
• Microsatellites (short sequence repeats),
• Minisatellites (long sequence repeats),
• Deletions,
• Duplications.

Pharmacogenomic principle
Pharmacogenomics principle: To identify patients at risk for toxicity or reduced response
to therapy for optimal medication and/or dose selection

Pharmacogenomic approaches

SNP chips
To determine which alleles are present, genomic DNA from an individual is isolated, fragmented,
tagged with a fluorescent dye, and applied to the chip.
The genomic DNA fragments anneal only to those oligos to which they are perfectly
complementary.
A computer reads the position of the two fluorescent tags and identifies the individual as a C / T
heterozygote.
The single spots in the other three columns indicate that the individual is homozygous at the three
corresponding SNP positions.
http://www.mun.ca/biology/scarr/DNA_VDA_chip_schematic.jpg

Kemogenomika, toksikogenomika, farmakogenomika - povzetek
Kemogenomika– išče kandidatna zdravila z rešetanjem knjižnic kemičnih
spojin
Toksikogenomika– preiskuje potencialni toksični vpliv kemičnih spojin
(potencialnih zdravil) na genom
Farmakogenomika – preiskuje vpliv genetskega ozadja na različen odziv
posameznika na zdravila.
Določitev korelacije med kemičnimi lastnostmi spojine in biološko
funkcijo predtavlja še vedno velik izziv. Zahteva multidisciplinarne
raziskovalne skupine, ki lahko pridobivajo, analizirajo in povežejo
podatke s področja kemije, biologije in “omik”.