Genome-wide transcript analysis of early maize leaf development ...

The Plant Journal (2013) 

doi: 10.1111/tpj.12229 

Genome-wide transcript analysis of early maize leaf 

development reveals gene cohorts associated with the 

differentiation of C 4 Kranz anatomy 

Peng Wang † , Steven Kelly † , Jim P. Fouracre † and Jane A. Langdale* 

Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK 

Received 26 March 2013; revised 29 April 2013; accepted 1 May 2013. 

*For correspondence (e-mail jane.langdale@plants.ox.ac.uk). 

† These authors contributed equally to this work. 

SUMMARY 

Photosynthesis underpins the viability of most ecosystems, with C 4 plants that exhibit ‘Kranz’ anatomy 

being the most efficient primary producers. Kranz anatomy is characterized by closely spaced veins that 

are encircled by two morphologically distinct photosynthetic cell types. Although Kranz anatomy evolved 

multiple times, the underlying genetic mechanisms remain largely elusive, with only the maize scarecrow 

gene so far implicated in Kranz patterning. To provide a broader insight into the regulation of Kranz 

differentiation, we performed a genome-wide comparative analysis of developmental trajectories in Kranz 

(foliar leaf blade) and non-Kranz (husk leaf sheath) leaves of the C 4 plant maize. Using profile classification 

of gene expression in early leaf primordia, we identified cohorts of genes associated with procambium 

initiation and vascular patterning. In addition, we used supervised classification criteria inferred 

from anatomical and developmental analyses of five developmental stages to identify candidate regulators 

of cell-type specification. Our analysis supports the suggestion that Kranz anatomy is patterned, at 

least in part, by a SCARECROW/SHORTROOT regulatory network, and suggests likely components of 

that network. Furthermore, the data imply a role for additional pathways in the development of Kranz 

leaves. 

Keywords: Zea mays, accession numbers SRS394616–SRS394626. 

INTRODUCTION 

The development of an anatomical and biochemical framework 

upon which photosynthesis operates is a pre-requisite 

for plant survival, with the specifics of this framework differing 

between plant groups. Most plants fix atmospheric 

carbon dioxide into a three-carbon compound and hence 

are referred to as C 3 plants. In contrast, C 4 plants such as 

maize (Zea mays) have evolved an alternative pathway in 

which the first product of photosynthesis is a four-carbon 

compound. The majority of C 4 plants split the biochemical 

reactions of photosynthesis between two morphologically 

distinct cell types known as bundle sheath (BS) and mesophyll 

(M) (Langdale, 2011). BS cells and M cells are 

arranged in concentric wreaths around veins (V) to form 

‘Kranz’ anatomy (Haberlandt, 1896), in which all veins are 

separated by two BS and two M cells in a repeating V–BS– 

M–M–BS–V pattern. C 4 metabolism in leaves with Kranz 

anatomy is the most productive photosynthetic pathway on 

earth. 

© 2013 The Authors 

The Plant Journal © 2013 John Wiley & Sons Ltd 

Concerns about future food supplies have led to the suggestion 

that C 4 traits should be introduced into C 3 plants 

such as rice (Oryza sativa) to increase crop yields (Hibberd 

et al., 2008; von Caemmerer et al., 2012). Clearly, this 

ambitious goal requires a fundamental understanding of 

how leaf development in C 4 plants differs from that in C 3 

plants. Comparisons between leaf transcriptomes of closely 

related C 3 and C 4 species in the genera Cleome and Flaveria 

showed that at least 500 genes are differentially expressed 

in mature C 3 and C 4 leaves, and suggested that an upper 

limit of 3500 gene changes were associated with the evolutionary 

transition from C 3 to C 4 (Brautigam et al., 2011; 

Gowik et al., 2011). Other studies have used systems 

approaches to analyze developmental transitions along the 

maize leaf blade (Li et al., 2010; Majeran et al., 2010; Pick 

et al., 2011), and to identify similarities and differences 

between differentiated BS and M cells (Li et al., 2010; Chang 

et al., 2012). These latter studies benefit from comparisons 

1

2 Peng Wang et al. 

in the context of a single species, and have provided an 

overview of transcriptome dynamics during the induction 

and maintenance of the C 4 pathway in the leaf. However, in 

all cases, the vein spacing and cellular arrangement associated 

with Kranz anatomy were already established. A more 

recent study examined earlier developmental stages, but 

embryonic leaf primordia of various ages and displaying 

various degrees of Kranz differentiation were pooled for 

analysis (Liu et al., 2013). As such, no overview of changes 

in gene activity that occur during the development of Kranz 

anatomy is currently available. 

In maize, the morphological trajectory associated with 

the development of Kranz anatomy has been well documented 

through histological and cell lineage analyses 

(Sharman, 1942; Esau, 1943; Langdale et al., 1989). Importantly, 

veins develop with associated BS cells, and the 

positioning of veins determines the number of M cells separating 

them. The timing of vein formation is such that the 

leaf mid-vein is initiated first, and then lateral veins begin 

to appear at plastochron 2 (P2; a plastochron is the time 

interval between initiation of primordia, and P1 is always 

the most recently initiated; Esau, 1943; Sharman, 1942). A 

previous transcriptome study of maize shoot development 

identified gene cohorts in P1 primordia and in the associated 

shoot meristem (Takacs et al., 2012). However, it is 

the development of intermediate veins during P5 that 

leads to the vein spacing pattern that is characteristic of 

Kranz anatomy (Sharman, 1942). Intermediate veins are 

initiated at the tip of P5 primordia, and, by the end of the 

plastochron, have extended from the leaf blade, through 

the developing ligule region, into the leaf sheath. However, 

anastomoses (fusions) at the ligule result in fewer 

intermediate veins in the sheath than in the blade. Kranz 

anatomy is thus a feature of the leaf blade but not of the 

leaf sheath. This difference is seen most dramatically in 

the husk leaves that surround the ear, where most if not 

all of the leaf is sheath tissue. Veins in husk leaf sheaths 

are surrounded by a wreath of BS cells, but then each vascular 

bundle is separated by up to 20 M cells rather than 

the two seen in Kranz anatomy (Langdale et al., 1988). In 

combination, these temporal and anatomical differences 

suggest that positive regulators of Kranz anatomy act 

between P2 and P5 in foliar leaf primordia, and are 

repressed or absent in equivalent husk leaf primordia. 

Conversely, negative regulators act in husk primordia but 

not in foliar primordia. 

Although the histological manifestation of Kranz anatomy 

in maize is understood, very little is known about the 

genetic regulation of Kranz development. Importantly, a 

recent reverse genetics study reported that the maize 

scarecrow1 (Zmscr1) mutant exhibits perturbed leaf venation 

patterns and ectopic BS cells (Slewinski et al., 2012). 

On the basis of this phenotype, it was proposed that the 

SCARECROW (SCR)/SHORTROOT (SHR) pathway, which 

regulates radial patterning in the stem and roots of C 3 

plants, was recruited into the leaf during evolution of Kranz 

anatomy (Slewinski et al., 2012). However, no association 

between other genes in the SCR/SHR pathway and the 

development of Kranz anatomy has yet been demonstrated. 

To obtain a comprehensive overview of changes in 

gene activity during the patterning of Kranz anatomy, we 

have used Illumina sequencing to generate transcriptomes 

of developing foliar (Kranz) and husk (non-Kranz) primordia. 

These transcriptome profiles have been compared 

with later stages of leaf development where the initiation 

or maintenance of C 4 metabolism is superimposed on the 

pre-established anatomical framework. Our results reveal 

cohorts of genes that are active during early leaf development. 

From within these cohorts, we have identified a 

group of transcription factors associated with the patterning 

of Kranz anatomy. The data support a role for the SCR/ 

SHR pathway in Kranz development, but also reveal 

additional candidate regulators. 

RESULTS AND DISCUSSION 

Maize foliar (Kranz) and husk (non-Kranz) leaves exhibit 

distinct developmental trajectories 

To characterize transitions in transcriptome signatures 

throughout maize leaf development, a range of biological 

samples representing various developmental trajectories 

and anatomical traits were harvested for RNA sequencing 

analysis. Figure 1(a) shows the position on the plant and 

the overall morphology of the samples used. Foliar and 

husk leaf samples were both harvested at five stages of 

development covering the range from P1 to P9. To define 

the anatomical status of all ten samples, transverse sections 

were examined by light microscopy (Figure 1b–k). In 

both foliar and husk samples, P1 and P2 primordia comprise 

cytoplasmically dense cells that lack vacuoles, and 

procambium associated with the mid-vein is barely visible 

(Figure 1b,g). By P4, however, the mid-vein and developing 

lateral veins may be distinguished, and the distance 

between them is already smaller in foliar primordia 

(Figure 1c) than in husk primordia (Figure 1h). At P5, foliar 

primordia have initiated intermediate veins and the 

bauplan of Kranz anatomy is apparent: each vascular centre 

is surrounded by a wreath of small BS cells, and is separated 

from the next centre by one or two intervening M 

cells (Figure 1d). In contrast, intermediate veins are not 

formed in husk primordia. Instead, cell division and expansion 

in M cells increases the space and cell number 

between veins from five at P4, to approximately ten at P5 

(Figure 1i), and ultimately to 15 or more in mature husk 

leaves (Figure 1j,k). Vein spacing does not change after P5, 

and, as such, both foliar immature (FI; Figure 1e) and foliar 

expanded (FE) (Figure 1f) leaf blades exhibit the classical 

cellular arrangement associated with Kranz anatomy 


The Plant Journal © 2013 John Wiley & Sons Ltd, The Plant Journal, (2013), doi: 10.1111/tpj.12229

Development of Kranz anatomy 3 

(a) 

(b) 

(c) (d) (e) (f) 

(g) 

(h) (i) (j) (k) 

Figure 1. Biological samples used for transcriptome sequencing. 

(a) Schematic and images of the ten tissues sampled. Foliar primordia (FP) and husk (HP) primordia samples included the vegetative apical and axillary inflorescence 

meristem (M) respectively, plus P1 and P2 leaf primordia. Arrows in the insets for the FP and HP samples show the position of the samples prior to dissection 

from the plant shoot. The prophyll (Pr) was discarded from the HP sample prior to RNA extraction. FP3/4, FP5, HP3/4 and HP5 primordia (arrows) were 

isolated from the meristem and other leaf primordia prior to RNA extraction. Foliar immature (FI) and foliar expanded (FE) leaf blades were harvested above the 

ligule. Arrows in the insets for the FI and FE samples show the position of the leaves on the shoot prior to harvesting. Husk leaf samples were the outermost leaf 

(HE) on the ear, and the third leaf in from the outside (HI). 

(b–k) Transverse sections showing leaf anatomy in FP (b), FP3/4 (c), FP5 (d), FI (e), FE (f), HP (g), HP3/4 (h), HP5 (i), HI (j) and HE (k) samples. Arrows indicate to 

developing or developed veins. 

(V–BS–M–M–BS–V). A summary of the key anatomical differences 

between tissues is shown in Table 1. 

Distinct transcriptome signatures define different stages 

of foliar and husk leaf development 

To determine whether specific transcriptome signatures 

define the five developmental stages represented in both 

foliar and husk samples, we first deduced the number of 

signature genes in each sample. Signature genes were 

defined as those whose relative mRNA abundance was 

significantly higher (P ≤ 0.05) in the named sample than in 

all other samples. mRNA abundance estimates, gene 

annotations and enrichment analyses for all of the 

signature genes in each sample are provided in Tables S1– 

S10. A comparative summary (Table 1) shows that the 

highest number of signature genes (1479) was found in the 

most photosynthetically active sample (FE), but only ten 

signature genes were identified in the foliar P3/4 (FP3/4) 

and husk P3/4 (HP3/4) samples. Generally, there is an 

increase in the number of signature genes in both foliar 

and husk tissue as leaves mature and become more metabolically 

complex. The one exception is seen between the 

foliar P1/2 (FP)/husk P1/2 (HP) and FP3/4/HP3/4 stages, where 

the number decreases from approximately 20 to 10. This 

decrease probably reflects the inclusion of meristematic 

tissue in the FP and HP samples, such that a suite of meristem-specific 

genes is represented in the FP and HP transcriptomes, 

but not in the FP3/4 and HP3/4 transcriptomes. 

To provide a comparative overview of the developmental 

and metabolic differences between foliar and husk leaves, 

gene ontology (GO) terms, MaizeCyc pathway terms, Mapman 

terms and Pfam domains were assessed in the signa- 




Table 1 Key anatomical traits and transcriptome profiles of foliar and husk leaf samples 

Sample 

Veins initiated 

Number of M cells 

between veins 

BS 

cell size 

BS 

plastid size 

Number of signature 

genes in dataset 

Metabolic pathways 

significantly enriched in 

signature gene sets 

FP Mid-vein NA NA NA 20 

FP3/4 Mid-vein Laterals 4-6 NA NA 10 

FP5 Mid-vein Laterals 

Intermediates 

1-2 Small NA 42 Ribonucleotide synthesis DNA 

synthesis 

FI Mid-vein Laterals 

Intermediates 

2 Large Small 1341 Protein synthesis Lipid metabolism 

Tetrapyrrole biosynthesis 

FE Mid-vein Laterals 

Intermediates 

2 Large Large 1479 Light-harvesting reactions C 4 carbon 

assimilation Gluconeogenesis 

Photorespiration 

HP Mid-vein NA NA NA 24 

HP3/4 Mid-vein Laterals 5 Small NA 10 

HP5 Mid-vein Laterals 10 Small NA 26 Lipid biosynthesis 

HI Mid-vein Laterals Approximately 12 Medium Small 344 Cell-wall biosynthesis 

HE Mid-vein Laterals Approximately 15 Medium Small 713 Secondary metabolite biosynthesis 

Amino acid degradation Lipid 

degradation 

Signature genes are those whose transcripts were detected at significantly higher levels in the named sample compared to the other nine 

samples (P ≤ 0.05). 

ture gene sets (Table 1, Figure 2, Figures S1 and S2, and 

Tables S1–S10). Although enriched metabolic pathways 

were not identified in any of the primordia samples, GO 

term enrichment indicated transcriptional activity in both 

FP and HP samples, and also in FP5 samples (Figure S1). 

The shared terms between FP and HP samples, and the lack 

of corresponding terms in FP3/4 and HP3/4 samples, suggest 

that the transcriptional activity represented in FP/HP 

tissues is associated with meristem function, as opposed 

to leaf formation. At P5, enriched terms indicative of transcriptional 

activity are seen in foliar but not husk leaf samples, 

suggesting that foliar leaf-specific differentiation 

events have been initiated. The difference between FP5 and 

HP5 tissues is further evidenced by the accumulation profile 

of transcription factors: transcripts of 22 genes encompassing 

ten transcription factor families are detected in foliar 

samples, but only 13 genes are represented in husk samples 

(Figure 2). In FI/husk inner (HI) and FE/husk exposed (HE) 

tissues, both shared and organ-specific enriched pathways 

are detected (Figure S2). Overall, global transcriptome profiles 

are consistent with a developmental trajectory in foliar 

leaves from proliferating non-photosynthetic leaf primordia 

(P1–P5), through expanding leaves that are initiating photosynthesis 

(FI), to actively photosynthetic FE leaves (Table 1). 

In contrast, husk transcriptome profiles are consistent with 

the development of an inner leaf that is expanding to surround 

the developing ear, and an outer leaf that is photosynthesizing 

at a low level but is also starting to senesce. 

Profile classification analysis distinguishes foliar and husk 

primordia development 

To identify genes associated with the earliest patterning 

processes that operate in maize leaf primordia, we 

assigned all genes that were detected in primordia 

samples to one of seven predetermined profiles (Figure 3). 

Gene lists in each of the seven profiles were generated for 

both foliar and husk leaves (Tables S11–S24). Each profile 

was designed to identify genes associated with specific 

aspects of leaf development. For example, descending 

profiles (D1, D2, D3) should contain genes required for 

meristem function and for very early events in leaf development 

such as mid-vein formation and adaxial/abaxial 

axis formation. In contrast, ascending profiles (A1, A2, A3) 

should contain genes required for the differentiation of lateral 

leaf veins, patterning of intermediate veins, specification 

of cell types and early plastid biogenesis. Importantly, 

genes with known function appeared in the expected profiles 

(summarized in Table 2). For example, knotted1-like 

homeobox (KNOX) genes that are required for both shoot 

and axillary meristem function (Jackson et al., 1994) are 

present in descending profiles, as are most of the genes 

required for adaxial–abaxial patterning in the leaf 

(Husbands et al., 2009). Genes required for patterning 

medio-lateral and proximo-distal leaf axes are also present 

in the expected profiles, with a notable difference in mRNA 

accumulation dynamics for the liguleless1 and 2 genes (Becraft 

et al., 1990; Walsh et al., 1997) in foliar versus husk 

leaves. This difference correlates with the presence and 

absence of ligules in the foliar and husk leaf samples, 

respectively. The golden2-like (glk) regulators of chloroplast 

development (Rossini et al., 2001) also exhibit the expected 

trajectories, in that g2 transcripts show equivalent accumulation 

dynamics in foliar and husk leaves, consistent with 

the loss-of-function mutant phenotype (Langdale and 

Kidner, 1994), whereas Zmglk1 transcripts are specific to 

foliar A2 profiles and thus to the development of active C 4 



metabolism. In combination, these observations validate 

the use of profile-based analysis to identify regulators of 

leaf development. 

Profile D1: meristematic function 


D1 profiles contain transcripts that are detected at significantly 

higher levels in samples that contain both meristematic 

and leaf tissue than those that contain only leaf 

tissue (Tables S11 and S18). As expected, knotted1-like 

homeobox (KNOX) genes that are required for meristem 

maintenance (Jackson et al., 1994) are represented in both 

the FD1 and HD1 profiles, as are homologues of the rice 

DROOPING LEAF (DL) gene that is required for mid-vein 

formation (Yamaguchi et al., 2004), and the DROOPING 

LEAF repressor OsMADS32 (Sang et al., 2012). Two genes 

that are specific to the FD1 profile may play roles in 

meristem maintenance: one (GRMZM2G151223, www. 

maizesequence.org) is related to genes known to be involved 

in cytokinin signal transduction (Muller and Sheen, 2007), 

and has been annotated as maize histidine kinase 1 

(Zmhk1) (Yonekura-Sakakibara et al., 2004); the other 

(GRMZM2G065496) is a member of the reproductive meristem 

(REM) sub-family of B3 domain transcription factors, named 

after the Brassica oleracea gene REM1 (Wang et al., 2012). 

Profile D2: meristematic function and early leaf 

development 

Figure 2. Transcription factor complements in the signature genes of foliar 

and husk leaf samples. 

Numbers represent the total number, and bar length denotes the relative 

proportion, of gene family members represented in each foliar or husk sample. 

FP/HP samples are shown as blue bars; FP3/4 and HP/3/4 samples are 

shown as orange bars, FP5/HP5 samples are shown as yellow bars; FI/HI 

samples are shown as light green bars; FE/HE samples are shown as dark 

green bars. 

As with D1, D2 profiles contain transcripts that accumulate 

at higher levels in meristem-containing samples than leafonly 

samples (Tables S12 and S19). However, whereas D1 

profiles contain transcripts that are detected at significantly 

higher levels in P3/4 primordia than in P5 primordia, transcripts 

in D2 profiles are detected at similar levels in the 

two samples. D2 profile genes are therefore likely to play a 

role in both the meristem and early leaf primordia. Both 

FD2 and HD2 profiles contain genes that are known to be 

expressed within the meristem, for example nam2 (Zimmermann 

and Werr, 2005) and wox9B (Nardmann et al., 

2007), but neither profile contains the KNOX genes that are 

required for meristem identity per se. Other shared genes 

in foliar and husk D2 profiles include homologues of Arabidopsis 

genes that are required for meristem specification 

(BEL-like/AtBLR; Rutjens et al., 2009) and axial patterning 

in the leaf (KAN-like; Kerstetter et al., 2001), plus genes 

involved in auxin signalling (arf4 and arf29; Xing et al., 

2011). Although these examples demonstrate overlap 

between FD2 and HD2 profiles, over 60% of the transcription 

factors in D2 profiles are specific to either foliar or 

husk samples, with specificity manifested in two ways. In 

the first, specificity reflects the presence of distinct gene 

family members. For example, two homologues of OVATE 

genes (that are known to repress KNOX genes in Arabidopsis) 

(Wang et al., 2011) are present in both FD2 and HD2 

profiles, but both profiles contain a unique gene family 




Figure 3. Comparative profile analysis of transcriptomes in developing leaf 

primordia. 

Descending (D1–D3), ascending (A1–A3) and neutral (N) transcriptome profiles. 

The total number of genes in each foliar (F) and husk (H) profile is 

shown, together with the number of genes encoding transcription factors 

(TFs). The specificity of TFs with respect to each profile is shown, as is the 

number of TFs that are shared with other profiles. 

member. The second type of specificity reveals processes 

unique to foliar and husk samples. For example, only the 

FD2 profile contains homologues of Arabidopsis and rice 

genes involved in ethylene signalling (OsERF4 and AtEIN3- 

like; Guo and Ecker, 2004)] In contrast, the HD2 profile contains 

homologues of Arabidopsis genes involved in timing 

of the floral transition (LHY and CO; Putterill et al., 2004), 

plus maize genes that are known to regulate the floral 

phase change (mads1; Heuer et al., 2001). The GO term 

‘trehalose biosynthetic process’ is also uniquely enriched 

in the HD2 profile, consistent with the role of ramosa3 and 

the trehalose metabolic pathway in branching of the maize 

inflorescence (Satoh-Nagasawa et al., 2006). The differences 

between FD2 and HD2 profiles thus reflect distinct 

developmental trajectories towards active vegetative 

growth and the transition to inflorescence development. 

Profile D3: early leaf development 

D3 profiles were designed to identify genes that regulate 

leaf developmental processes prior to P4, in that transcript 

levels may be equivalent in P and P3/4 samples but must 

be significantly lower in P5 samples (Tables S13 and S20). 

Shared genes include homologues of those expressed in 

early primordia (ANT-like; Mizukami and Fischer, 2000) and 

those required for axial patterning (zyb9; Juarez et al., 

2004). The FD3 profile additionally contains homologues of 

genes that are expressed in dividing primordia [PAN-like 

(Chuang et al., 1999), ULT-like (Carles et al., 2005), ig1 

(Evans, 2007), zfl1 (Bomblies et al., 2003), zmm16 (Munster 

et al., 2001)], genes required for axial patterning [kan5 

(Zhang et al., 2009), dl2 (Yamaguchi et al., 2004; Ishikawa 

et al., 2009)], genes involved in epidermal patterning 

[SPCH-like, SCRM-like (Pires and Dolan, 2010), MIXTA-like 

(Dubos et al., 2010)], and genes responsive to auxin (iaa4, 

iaa16 and iaa26; Wang et al., 2010). In contrast to FD3, and 

consistent with the identity of the axillary meristem, the 

HD3 profile contains homologues of genes that play roles in 

light signalling (LAF1-like; Ballesteros et al., 2001), drought/ 

abscisic acid/stress responses [SNAC-like, NAP-like (Zhu 

et al., 2012), MYB (Dubos et al., 2010), WRKY (Wei et al., 

2012), TINY-like (Sun et al., 2008)], flowering time [U2AF35 

(Wang and Brendel, 2006), CO-like (Putterill et al., 2004)], 

and axillary bud outgrowth [gt1 (Whipple et al., 2011), TB1- 

like (Martin-Trillo and Cubas, 2010), OsNAC2-like (Mao 

et al., 2007)]. This profile is consistent with integration of 

signals for the floral transition and with the fact that HP5 

samples were harvested from axillary meristems that 

were clearly differentiated as inflorescences (Figure 1). 

Notably, over 80% of the transcription factors in the D3 

profiles are specific to either FD3 or HD3, indicative of 

organ-specific differences in early leaf patterning mechanisms. 

Many of the identified transcription factors cannot 

be assigned a putative function by homology with characterized 

proteins (Tables S25), and hence are defined as 

factors that distinguish the formation of foliar and husk 

leaf primordia. 

Profile A1: organ identity and metabolic function 

A1 profiles comprise transcripts that accumulate at consecutively 

higher levels in P, P3/4 and P5 samples (Tables S14 

and S21). These profiles were designed to identify genes 

that progressively establish organ identity and function. As 

with the descending profiles, substantially fewer genes are 

present in HA1 than FA1, and the only shared transcription 

factor is a BME1-like GATA protein (Reyes et al., 2004). 

More overlap is found between FA1 and HA2/3 profiles, 

and between HA1 and FA3 profiles. This pattern is consistent 

with shared developmental processes that exhibit variant 

timing in foliar versus husk primordia. Most of the 

genes encoding transcription factors that are shared 

between FA1 and HA2/3 profiles are homologues of genes 

involved in organ outgrowth/differentiation [ATBH16-like 

(Harris et al., 2011), AmDIV-like (Galego and Almeida, 

2002), TSO-like (Hauser et al., 1998), TCP-like (Martin-Trillo 




Table 2 mRNA abundance dynamics for regulatory genes known to be associated with meristem maintenance, axis patterning in the leaf 

and chloroplast biogenesis 

Gene accession Gene name F cluster H cluster FP FP3/4 FP5 F trendline HP HP3/4 HP5 H trendline 

Meristem maintenance 

GRMZM2G017087 kn1 FD1 HD1 109.3 2.9 0.0 128.8 10.8 1.5 

GRMZM2G028041 rs1 FD1 HD1 37.7 2.1 0.0 66.6 9.3 1.6 

GRMZM2G452178 gn1 FD2 HD2 31.4 0.9 0.0 28.6 2.0 0.4 

GRMZM2G087741 lg3 FD1 HD1 52.7 2.7 0.0 67.9 8.2 1.7 

GRMZM2G094241 lg4a FD1 HD1 27.7 2.1 0.1 58.4 7.3 1.4 

GRMZM5G832409 lg4b FD2 HD2 5.3 0.3 0.0 14.7 2.8 2.1 

Lateral organ initiation and adaxial/abaxial patterning 

GRMZM2G088309 dl1 FD1 HD1 85.0 42.0 22.0 104.2 12.5 1.1 

GRMZM2G403620 rs2 FN HN 113.8 76.0 64.5 95.2 74.6 75.3 

GRMZM2G441325 arf3 FD2 HD2 67.4 28.8 26.6 45.5 15.7 22.1 

GRMZM2G074543 zyb9 FD3 HD3 69.7 37.8 0.8 88.2 74.3 22.1 

GRMZM2G056400 kan1 FA2 HA 2.2.4 7.9 7.1 0.0 3.1 3.9 

Mediolateral patterning 

GRMZM5G892991 rgd2 FD1 HN 64.4 29.0 11.7 90.3 45.5 23.4 

GRMZM2G069028 ns1 FN HN 7.4 5.1 1.7 7.9 3.6 1.3 

Proximodistal patterning 

GRMZM2G036297 lg1 FA3 HA1 0.0 0.1 1.9 0.2 6.4 24.2 

GRMZM2G060216 lg2 FA2 HN 3.8 15.3 9.6 40.2 20.9 7.6 

Chloroplast biogenesis 

GRMZM2G026833 glk1 FA2 HN 0.2 4.2 3.1 0.3 0.2 0.0 

GRMZM2G087804 g2 FN HN 25.9 42.9 62.8 44.4 71.7 82.3 

Columns show gene accession number in maizesequence organism, gene name, foliar and husk profile identification (ID), mean transcript 

reads in reads per kilobase per million in FP, FP3/4, FP5, HP, HP3/4 and HP5 samples, and non-normalized trendlines of transcript abundance. 

The discrepancies between profile ID and trendline trajectories (e.g. rs2 is in FN but read data show a descending trajectory) are due 

to the fact that profiles are defined by significant differences between samples as opposed to general trends. 

and Cubas, 2010)], and photomorphogenesis [e.g. >OBP3- 

like (Ward et al., 2005), GATA2-like (Reyes et al., 2004)]. 

The number and range of genes in the FA versus the HA 

profile suggest greater complexity in photomorphogenesis 

and differentiation programs in foliar as opposed to husk 

primordia. 




(a) 

(b) 

Figure 4. Distinct transcription factor cohorts associated with early differentiation events in foliar and husk leaf primordia. 

(a) Transcription factors identified in FA2, FA3 and HA3 profiles that are specific to foliar or husk leaves, and have no previously identified role in early maize leaf 

development. 

(b) Schematic overview of early differentiation events. Transverse sections of foliar and husk leaf primordia are colour-coded to indicate the mid-vein and lateral 

veins (yellow), intermediate veins (green) and inter-veinal mesophyll cells (blue). Transcription factors listed under black headings are common to both foliar 

and husk leaves, those under red headings are husk-specific, and those under blue headings are foliar-specific. Scale bar = 20 lm. 

Profile A2/A3: patterning of leaf venation and cell-type 

differentiation 

The A2 and A3 profiles were designed to identify genes 

required for patterning leaf venation and regulating celltype 

differentiation in foliar leaves. The profiles contain 

transcripts that accumulate to higher levels in P3/4 and 

P5 samples than in P1/2 (A2 profiles; Tables S15 and S22) 

or to higher levels in P5 than the other two primordia 

samples (A3 profiles; Tables S16 and S23). As lateral 

veins are being extended during P4 and P5, whilst intermediate 

veins are being patterned, the presence of genes 

in the FA2/3 profiles that are related to those that regulate 

xylem differentiation (e.g. XYLEM NAC DOMAIN 1; 

Zhao et al., 2008) and phloem differentiation (e.g. 

ALTERED PHLOEM 1; Bonke et al., 2003) was expected 




(Tables S15, S16 and S25). Similarly, the presence of 

foliar-specific. AUXIN RESPONSE FACTOR (ARF) and 

AUX/IAA homologues in the FA3 profile (Figure 4a, 

Figures S3 and S4, Tables S16 and S25) is consistent with 

the known role of auxin in vascular differentiation 

(Mockaitis and Estelle, 2008). However, because very little 

is known about the patterning of leaf venation or the 

regulation of BS and M differentiation, it was expected 

that most of the genes in the FA2 profile would not have 

been previously characterized and/or annotated. 

There are 16 genes of note that are specific to the FA2 

profile (Figure 4a, and Tables S15 and S25). First, there 

are three bHLH genes that may regulate cellular differentiation 

given the role of other bHLH proteins in patterning 

processes (Pires and Dolan, 2010). One of the genes is 

related to a target of the auxin-dependent transcription 

factor MONOPTEROS, which regulates vascular development 

in Arabidopsis (Schlereth et al., 2010). Second, there 

is a bZIP gene (Gibalova et al., 2009), and three DoF zinc 

finger genes that share clades with the Arabidopsis DoF 

gene AFFECTING GERMINATION (DAG)-like (Gualberti 

et al., 2002; Gibalova et al., 2009) and the HIGH CAMBIAL 

ACTIVITY 2 (HCA2) gene (Guo et al., 2009) (Figure S5), 

which are expressed in vascular tissue. Notably, 

transcripts of the DAG-like genes were detected 

specifically in BS cells of expanded maize leaves (Li et al., 

2010). Third, two GRAS family SHORTROOT (SHR)-like 

genes are present, homologues of which play a role in 

radial patterning around vasculature in the Arabidopsis 

root, hypocotyl and stem (Fukaki et al., 1998; Helariutta 

et al., 2000). Fourth and most notable are seven C2H2 zinc 

finger proteins. Two are related to the previously characterized 

Arabidopsis gene DEFECTIVELY ORGANIZED TRIB- 

UTARIES 5 (DOT5) that regulates vascular patterning 

(Petricka et al., 2008), and three are related to SHOOT 

GRAVITROPISM 5 (SGR5) (Morita et al., 2006) and JACK- 

DAW (JKD) (Welch et al., 2007) (Figure S6), which are 

known components of the SHR pathway in Arabidopsis. 

Recently, orthologues of four of these genes (the bZIP 

gene, two DAG-like DoF zinc finger genes and a SGR5-like 

C2H2 zinc finger gene) were shown to be up-regulated in 

Arabidopsis provascular cells compared to other leaf cell 

types, suggesting conserved roles in early vascular development 

(Gandotra et al., 2013). The remaining two C2H2 

ZnF proteins are related to maize MYB-related protein 1- 

interacting proteins (Royo et al., 2009). Intriguingly, MYBrelated 

protein 1-interacting proteins interact with MYBrelated 

protein 1 through a C-terminal domain that is 

shared with SHR target proteins (Levesque et al., 2006; 

Royo et al., 2009; Cui et al., 2011). The phylogenetic 

relationships and expression profiles of all 16 of these 

genes suggest that they are likely regulators of vascular 

patterning and cellular differentiation in maize foliar 

leaves (Figure 4b). 

Consistent with the fact that intermediate veins do not 

form in husk leaves and that cellular differentiation processes 

are less complex than in foliar leaves (Figure 1), the 

HA2/3 profiles are dominated by homologues of genes 

involved in the floral transition and in stress responses, as 

opposed to genes that are likely to be involved in leaf 

development (Tables S22, S23 and S25). The only striking 

feature is the presence of eight R2R3 MYBs in the HA3 profile, 

five of which are husk-specific (Figure 4a, and Tables 

S23 and S25). Two of the shared genes are MIXTA-like, and 

thus are probably involved in epidermal patterning (Perez- 

Rodriguez et al., 2005). The function of the remaining 

shared gene and of the five husk-specific genes is unclear, 

but four of the husk-specific genes share a clade with an 

Arabidopsis gene that is involved in regulating mucilage 

deposition and phenylpropanoid metabolism (Penfield 

et al., 2001; Newman et al., 2004), and the other shares a 

clade with genes that are involved in defence responses 

(Dubos et al., 2010) (Figure S7). This observation, plus the 

presence of SHINE (SHN)1-like homologues that regulate 

epidermal wax deposition in Arabidopsis (Aharoni et al., 

2004), suggests considerable modification of the husk leaf 

epidermis between P4 and P5, possibly as a defence strategy 

to protect the developing inflorescence. 

Supervised classification identifies genes associated with 

the development of Kranz anatomy 

Supervised classification criteria inferred from anatomical 

and developmental analyses (Figure 1) were used to identify 

both positive and negative candidate regulators of 

Kranz anatomy. For positive regulators, it was assumed 

that genes would be expressed at significantly higher 

levels in foliar leaves than in husk leaves, and that Kranz 

patterning genes would be expressed prior to and/or during 

P5. For negative regulators, it was assumed that genes 

would be expressed at significantly higher levels in husk 

leaf primordia than in foliar leaf primordia, and that 

expression levels would be significantly higher in primordia 

than in expanded leaves with established anatomy. 

Table 3 summarizes the classification criteria applied, and 

shows that 283 putative positive and 142 putative negative 

regulators of Kranz anatomy were identified. The corresponding 

gene lists are supplied in Tables S26 and S27, 

and include accession numbers, mRNA abundance estimates, 

and enrichment analyses for GO terms, MaizeCyc 

pathways, MapMan terms and Pfam domains. 

Amongst the 283 putative positive regulators of Kranz, GO 

term enrichment identified a cohort of genes associated with 

the cytoskeleton and with ribosomes (Figure 5a). As the 

classification criteria selected genes that were highly 

expressed in developing foliar primordia, this result is 

expected, as pre-P5 primordia consist of actively dividing 

cells. However, it is unlikely that such genes play a central 

role in regulating Kranz anatomy. The enrichment of genes 




Table 3 Filtration steps to identify putative regulators of Kranz anatomy 

Positive regulators 

Negative regulators 

All genes 

All genes 

n = 35 770 n = 35 770 

Step 1 

FP or FP3/4 or FP5 significantly > FE + HP + HI + HE 

HP or HP3/4 or HP5 significantly > FE + HI + HE 

Genes must pass test in two of three FP samples. 

Genes must pass test in two of three HP samples 

n = 918 n = 2567 

Step 2 

FP3/4 or FP5 significantly > or not different to FP 

FP not significantly > HP 

FP3/4 not significantly < FP 

FP3/4 not significantly > HP3/4 

HP3/4 not significantly > FP3/4 

FP5 not significantly > HP5 

HP5 not significantly > FP5 

FE or HI or HE not significantly > FP or 

HP not significantly > FP 

FP3/4 or 

HP5 significantly < FP3/4 

FP5 

HP significantly < FP3/4 

HP3/4 significantly > FP 

FP5 or FP3/4 > 0 

HP5 significantly > FP 

HI not significantly > FP, FP3/4, FP5, FI or FE n = 160 

HE not significantly > FP, FP3/4, FP5, FI or FE 

FE not significantly > FP, FP3/4, FP5 or FI 

n = 334 

Step 3 

If in leaf gradient dataset: 

If in leaf gradient dataset: 

Base not significantly < 1, 4 cm or tip 

Tip not significantly > 4 cm, 1 cm or base 

1 cm not significantly < 4 cm, tip 4 cm not significantly > 1 cm or base 

4 cm not significantly < tip 1 cm not significantly > base 

n = 283 n = 142 

All tests were performed at a P value cut-off of ≤0.05. 

annotated with the term ‘ice binding’ is also unlikely to 

reflect an association with the regulation of Kranz anatomy. 

The remaining enriched terms in the positive regulator 

dataset and all of those in the negative regulator dataset 

(Figure 5a,b) are of greater interest because they suggest 

roles in the regulation of transcription (e.g. nucleus, DNA 

binding). 

Within the lists of positive and negative regulators of 

Kranz anatomy, 71 genes were identified that are predicted 

to encode transcription factors (Figure 5c). Although only 

some of these transcription factors are likely to play a role 

in Kranz patterning, a number of observations support the 

suggestion that regulatory roles will be confirmed from 

within either the positive or negative dataset. For putative 

negative regulators to be feasible candidates, orthologues 

must be expressed in developing rice leaves. This is the 

case for the majority of transcription factors in the negative 

regulator list, in that transcripts are detected in rice seedlings 

(Table S28). With respect to positive regulators, the 

presence of Zmscr1 within the list is supportive of a role 

for at least a subset of this cohort in Kranz development 

given that Zmscr1 is expressed during early vascular development 

in maize (Lim et al., 2005), and mutant alleles 

show some disruption to Kranz anatomy (Slewinski et al., 

2012). Similar support is provided by the presence of DOT5 

orthologues in the list, given the role of DOT5 in patterning 

leaf venation in Arabidopsis (Petricka et al., 2008), and by 

the presence of orthologues of a further five genes that are 

up-regulated in Arabidopsis provascular cells (Gandotra 

et al., 2013). Finally, of the positive regulators identified as 

transcription factors, approximately 81% of the genes 

(including Zmscr1 plus the SHR and DOT5 orthologues) 

have been classified as members of co-expression modules 

associated with increased Kranz differentiation in a 

pooled embryonic leaf dataset (compared with approximately 

27% of transcription factors in the total embryonic 

leaf dataset: co-expression modules ≥C13; Liu et al., 2013). 

Key regulators of Kranz anatomy are thus likely to be 

present within the list of transcription factors shown in 

Figure 5(c). 

Concluding remarks 

Table S29 summarizes data for all detected genes, and 

shows maize ID, EntrezGene classification (http:// 

www.ncbi.nlm.nih.gov/gene), reads per kilobase per million 

for replicates of all ten samples, presence in the signature 

gene set (if relevant), plus affiliation with specific 

foliar and husk profiles. The co-expression of gene cohorts 

in spatial and temporal patterns that are associated with 

the development of Kranz anatomy in maize supports the 

role of the SCR/SHR pathway in patterning Kranz anatomy, 

and identifies likely additional components of that pathway. 

The data further suggest that other pathways are also 

involved, particularly in the elaboration of venation patterns. 




Figure 5. Putative regulators of Kranz anatomy. 

(a, b) GO term enrichment categories in putative 

positive (a) and negative (b) regulator gene 

lists. 

(c) Genes encoding transcription factors in positive 

and negative regulator gene lists. 

(a) 

(b) 

(c) 

EXPERIMENTAL PROCEDURES 

Plant material and growth conditions 

Maize inbred line B73 was grown in soil in a greenhouse with a 

diurnal light regime of 16 h light (supplemented to 300 lM 

m 2 sec 1 ) and 8 h dark, a mean daytime temperature of 28°C and 

a mean night-time temperature of 20°C. Tissue was harvested 

from 2-, 4- or 8-week-old plants, and placed directly into liquid 

nitrogen. FP comprised the vegetative apical shoot meristem plus 

P1 and P2 leaf primordia, FP3/4 comprised P3 and P4 primordia 

harvested from the same apex and then pooled, FP5 comprised P5 

primordia only, FI comprised expanding 5th leaves at P7, and FE 

comprised fully expanded 3rd leaves at P9. FP5 primordia, FI and 

FE leaves were all harvested above the ligule to ensure that only 

blade (Kranz) tissue was represented. HP samples were harvested 

from the same plants as the foliar samples, from the axils of P9 

and P10 leaves. HP samples included the axillary inflorescence 

meristem plus P1 and P2 husk primordia (the prophyll was discarded). 

HP3/4 and HP5 samples were harvested from the upper, 

ear-forming nodes of 4-week-old plants, and inner (HI) and outermost 

exposed (HE) husk leaves were harvested from the ears of 8- 

week-old plants. Under our growth conditions, husk leaves did not 

form blades, and thus all samples comprised only sheath (non- 

Kranz) tissue. 

Histology 

Leaf samples were fixed overnight in FAA (4% formaldehyde, 5% 

acetic acid, 50% ethanol), and embedded in Paraplast Plus 

(Sigma–Aldrich, http://www.sigmaaldrich.com) as described 

previously (Langdale, 1994). Sections (8 lm) were stained with 

Safranin/Fast Green (Sigma-Aldrich) and viewed using a Leica 

DMRB microscope (Leica, http://www.leica-microsystems.com/). 




RNA extraction and cDNA synthesis 

Multiple individual isolates were pooled prior to RNA extractions. 

Each sample pool comprised tissues for n = 1200 (FP), 300 (FP3/4), 

100 (FP5), 12 (FI), 12 (FE), 2400 (HP), 300 (FP3/4), 100 (FP5), 12 (HI) 

and 12 (HE) plants. Two independent biological replicates were 

constructed according to the pool structure above, and each replicate 

for each sample was subjected to RNA-seq. This pooled replicate 

strategy was adopted to circumvent the need for RNA 

amplification and to minimize biological noise from individual 

samples. RNA was extracted using a mirVana TM 

miRNA isolation 

kit (Applied Biosystems, http://www.invitrogen.com/). RNA integrity 

was analysed by formaldehyde gel electrophoresis, and samples 

were sent to the Beijing Genome Institute (http://www. 

genomics.cn/en/) for further quality control. 

Sequencing 

cDNA library preparation and sequencing were performed at the 

Beijing Genome Institute. Each RNA sample was treated in the same 

manner. Total RNA was treated with DNase I and then purified over 

an oligo(dT) column. Enriched mRNA was sheared and converted 

into cDNA using standard Illumina protocols (http://www.illumina. 

com/). cDNAs were subsequently ligated to Illumina adaptors and 

subjected to standard Illumina paired-end read library preparation. 

Paired end reads of 72 bp were generated for samples FP1, FI1, FE1, 

HP1, HI1 and HE1. All other samples were sequenced using 90 bp 

paired end reads. 

Transcript quantification and differential gene expression 

analysis 

Paired end reads were subject to quality-based trimming using 

the FASTX toolkit (Goecks et al., 2010), setting the PHRED quality 

threshold at 20 and discarding reads 98.5 for 

accurate quantitative analysis. For each sample, two PCR replicates 

were performed for each of two independent cDNA synthesis 

reactions. After manual checking of dissociation curves and C t 

values, the mean value across the replicates was calculated. Fold 

changes for each gene in each sample were then calculated using 

the Pfaffl method (Pfaffl, 2001), and correlated against fold changes 

of the same genes in the same samples as measured by RNA-seq 

(Figure S8). 

Enrichment analysis 

Enrichment analyses were performed for gene ontology (GO) 

terms (http://www.geneontology.org/), MaizeCyc pathways (http:// 

maizecyc.maizegdb.org/), MapMan terms (http://www.mapman. 

gabipd.org/) and Pfam domains (http://pfam.sanger.ac.uk/). 

P values were obtained by approximating Wallenius’ non-central 

hypergeometric distribution (Wallenius, 1963). GOseq (Young 

et al., 2010) was used to compensate for over-detection of differential 

expression for long and highly expressed transcripts. The 

resulting P values were subject to multiple hypothesis test correction 

to correct for type I family-wise error using the Benjamini– 

Hochberg method (Benjamini and Hochberg, 1995). Significantly 

enriched annotation terms were identified as those that showed a 

corrected P value of ≤ 0.05. 

Profile classification analysis 

Profiles were generated such that descending profiles (D1, D2, D3) 

contained only genes whose relative mRNA abundance decreased 

significantly from P1/2 to P5, while ascending profiles (A1, A2, A3) 

included genes whose relative mRNA abundance increased significantly 

from P1/2 to P5, and neutral profiles (N) had non-significantly 

different mRNA abundance estimates in all three (P1/2, P3/4 

and P5) primordia samples (P ≤ 0.05). Genes were assigned to 

expression profiles using custom Perl scripts. The input data for 

these scripts were the Benjamini–Hochberg-corrected P values 

obtained from the pairwise DESeq-based significance tests (see 

above). The 383 automatically annotated transcription factors in 

the descending and ascending profiles were subject to manual 

classification by Reciprocal Best BLAST. In cases where orthology 

was not clear, additional phylogenetic analyses were performed 

to identify the most closely related homologues, and published 

data were used to infer the most likely gene function. 

Classification criteria for selection of candidate Kranz 

regulators 

Filters for the identification of Kranz anatomy regulators were 

based on the following biological criteria. For positive regulators, 

it was assumed that genes would be expressed at significantly 

higher levels in foliar leaves than in husk leaves, and that patterning 

genes would be expressed prior to and/or during P5. It 

was further assumed that if genes were expressed in the expanding 

leaf, transcript levels would be significantly higher in the 

immature basal regions of the leaf than in more distal regions. 

For negative regulators, it was assumed that genes would be 

expressed at significantly higher levels in husk leaf primordia 

than in foliar leaf primordia, and that expression levels would be 

significantly higher in primordia than in expanded leaves with 

established anatomy. It was further assumed that if genes were 

expressed in the expanding leaf, transcript levels would not be 

significantly higher in the distal regions of the leaf than in the 

immature basal regions. The expanding leaf gradient dataset was 

generated by processing the raw reads from Li et al. (2010) and 

re-mapping them to the genome in the same manner as all other 

samples. 




Phylogenetic tree inference 

Iterative hidden Markov model searches were performed as previously 

described (Kelly et al., 2011) using the genomes of Arabidopsis, 

rice, Brachypodium distachyon, Sorghum bicolour and 

maize, and initiating each iterative query using a maize gene from 

the candidate list. For each search, the identified protein 

sequences were aligned using MergeAlign-91 (Collingridge and 

Kelly, 2012), and a maximum-likelihood phylogenetic tree was 

inferred using FastTree (Price et al., 2010) using the JTT model of 

sequence evolution (Jones et al. 1992) and CAT rate heterogeneity 

(Lartillot and Philippe, 2004). In all cases, SH-like support values 

(Shimodaira and Hasegawa, 1999) are shown at branch nodes. 

Pfam domains exceeding a threshold expectation value of 

1 9 10 5 were identified, and used to construct domain cartoons 

beside each sequence shown in each phylogenetic tree. 

ACKNOWLEDGEMENTS 

We are grateful to Julie Bull for technical support and John Baker 

for photography. We thank Mara Schuler, Sayuri Ando, Olga 

Sedelnikova and Tom Hughes for contributions to the C 4 project, 

and all members of the lab at the Department of Plant Sciences, 

University of Oxford, Oxford, for helpful discussions. Andy Plackett 

and Laura Moody provided constructive comments on the 

manuscript. This work was funded by a grant to J.A.L. from the 

Bill and Melinda Gates Foundation Grant through the International 

Rice Research Institute, Los Banos, Philippines, and by the Oxford 

Martin School. S.K. was funded as a Biotechnology & Biological 

Sciences Research Council Systems Biology Fellow, and J.F. was 

funded by a Biotechnology & Biological Sciences Research Council 

Doctoral Training Account. We thank all of our colleagues in 

the C 4 rice consortium for sharing ideas and data ahead of publication. 

P.W. prepared plant material and extracted RNA, S.K. 

designed the bioinformatics methodology and wrote the code, 

and J.F. validated the quality of sequencing data and performed 

the profile analyses. J.A.L. proposed the original experimental 

design and obtained funding. All authors designed the data filtration 

criteria, analysed datasets and wrote the paper. 

ACCESSION NUMBERS 

All read datasets were deposited at the NCBI sequence read 

archive (http://www.ncbi.nlm.nih.gov/sra) under accession 

numbers SRS394616–SRS394626 inclusive. 

SUPPORTING INFORMATION 

Additional Supporting Information may be found in the online version 

of this article. 

Figure S1. GO term enrichment in foliar and husk signature genes. 

Figure S2. Pathway enrichment in foliar and husk signature genes. 

Figure S3. Maximum-likelihood phylogenetic tree of auxin 

response factor proteins. 

Figure S4. Maximum-likelihood phylogenetic tree of AUX/IAA 

family proteins. 

Figure S5. Maximum-likelihood phylogenetic tree of zinc finger 

family proteins. 

Figure S6. Maximum-likelihood phylogenetic tree of C2H2 family 

transcription factors. 

Figure S7. Maximum-likelihood phylogenetic tree of MYB family 

transcription factors. 

Figure S8. Pairwise sample correlations and quantitative PCR 

validation of RNA-seq abundance estimates. 

Table S1. FP signature genes. 

Table S2. FP3/4 signature genes. 

Table S3. FP5 signature genes. 

Table S4. FI signature genes. 

Table S5. FE signature genes. 

Table S6. HP signature genes. 

Table S7. HP3/4 signature genes. 

Table S8. HP5 signature genes. 

Table S9. HI signature genes. 

Table S10. HE signature genes. 

Table S11. FD1 profile genes. 



Table S14. FA1 profile genes. 



Table S17. FN profile genes. 

Table S18. HD1 profile genes. 



Table S21. HA1 profile genes. 



Table S24. HN profile genes. 

Table S25. Manual annotation of transcription factors in all 

descending and ascending profiles. 

Table S26. Putative positive Kranz regulators. 

Table S27. Putative negative Kranz regulators. 

Table S28. Expression levels in rice of putative negative regulators 

of Kranz anatomy. 

Table S29. Summary of all data. 

Table S30. Primer pairs for quantitative RT-PCR. 

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