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Uncoupling evolutionary changes in DNA sequence, transcription factor occupancy and enhancer activity

  1. Pierre Khoueiry
  2. Charles Girardot
  3. Lucia Ciglar
  4. Pei-Chen Peng
  5. E Hilary Gustafson
  6. Saurabh Sinha
  7. Eileen EM Furlong Is a corresponding author
  1. European Molecular Biology Laboratory, Genome Biology Unit, Germany
  2. University of Illinois, United States
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Cite as: eLife 2017;6:e28440 doi: 10.7554/eLife.28440

Abstract

Sequence variation within enhancers plays a major role in both evolution and disease, yet its functional impact on transcription factor (TF) occupancy and enhancer activity remains poorly understood. Here, we assayed the binding of five essential TFs over multiple stages of embryogenesis in two distant Drosophila species (with 1.4 substitutions per neutral site), identifying thousands of orthologous enhancers with conserved or diverged combinatorial occupancy. We used these binding signatures to dissect two properties of developmental enhancers: (1) potential TF cooperativity, using signatures of co-associations and co-divergence in TF occupancy. This revealed conserved combinatorial binding despite sequence divergence, suggesting protein-protein interactions sustain conserved collective occupancy. (2) Enhancer in-vivo activity, revealing orthologous enhancers with conserved activity despite divergence in TF occupancy. Taken together, we identify enhancers with diverged motifs yet conserved occupancy and others with diverged occupancy yet conserved activity, emphasising the need to functionally measure the effect of divergence on enhancer activity.

https://doi.org/10.7554/eLife.28440.001

Introduction

Transcription factors (TFs) act largely through enhancer elements - modular sequences that instruct genes when and where to be expressed (Levine and Davidson, 2005; Slattery et al., 2014; Spitz and Furlong, 2012) and provide robustness and precision to developmental programs (Cannavò et al., 2016; Frankel et al., 2010; Perry et al., 2010). Changes in TF binding sites within an enhancer can lead to phenotypic differences between individuals (Prud'homme et al., 2007; Rogers et al., 2013), and between species (Levine and Davidson, 2005; Wray, 2007). In sticklebacks, for example, the recurrent deletion of an entire enhancer driving pelvic pitx expression leads to the lack of spines in several freshwater populations (Chan et al., 2010; Shapiro et al., 2004). In other cases, however, changes in enhancer sequence appear to have limited impact on phenotype. The loss or gain of TF binding sites in the otx enhancer in ascidians (Oda-Ishii et al., 2005), the Endo 16 enhancer in sea urchins (Balhoff and Wray, 2005), and the eve enhancer in Drosophila (Ludwig et al., 1998), for example, have only marginal effects on enhancer activity. This often-surprising gap between changes in genotype and phenotype in developmental contexts highlights the current challenge in understanding both enhancer function and evolutionary constraints.

Several models have been proposed to explain enhancer function. The enhanceosome describes elements that are bound by many TFs through cooperative interactions facilitated by the very precise relative orientation, spacing and helical phasing of the TF’s binding sites (TFBSs) within the enhancer. Mutations or sequence variants that affect either the motif quality or the motif arrangement results in loss of enhancer activity. The best studied, and perhaps only true enhancesome, is the interferon beta enhancer, a 57 bp core element with a very strict arrangement of TFBS (Panne et al., 2007; Thanos and Maniatis, 1995). At the other extreme the ‘billboard’ model (Arnosti and Kulkarni, 2005; Kulkarni and Arnosti, 2003) proposes that TFs bind additively or in subgroups to an enhancer with relatively independent effects on gene expression, and therefore have flexibility in the relative positions of their TFBSs (Khoueiry et al., 2010). We recently proposed the TF collective model, where TFs bind to enhancers cooperatively through a combination of protein::DNA and protein::protein interactions allowing for a flexible combination of TFBSs. Unlike billboard enhancers, a TF collective does not require the presence of motifs for every TF to be occupied by the full collective, as observed in cardioblast (Junion et al., 2012), leg precursor (Uhl et al., 2016) and dopaminergic neuron (Doitsidou et al., 2013) enhancers. One prediction from this model is that such enhancers should tolerate some evolutionary changes in TFBS content, while having little or no effect on TF occupancy across species, as recently observed for the distal-less enhancer occupied by a Hox TF collective (Uhl et al., 2016). If the TF collective model holds true more globally, it provides one explanation for why it is currently not possible to accurately predict which genetic variants will affect enhancer function based on DNA sequence alone.

As a step towards linking sequence variation to enhancer function, several studies have coupled sequence analyses with in vivo measurements of TF occupancy between species, in either developing embryos (Bradley et al., 2010; He et al., 2011; Ni et al., 2012; Paris et al., 2013) or differentiated tissues (Ballester et al., 2014; Odom et al., 2007; Schmidt et al., 2010; Wilson et al., 2008). The results revealed marked differences in the extent to which TF binding is conserved, highlighting a complex interplay between tissue type, cell state (unspecified versus differentiated), the TF analyzed and evolutionary distance, as well as potential technical issues such as antibody specificity across species and the difficulty to assign orthologous non-coding regions (reviewed by [Sakabe and Nobrega, 2013; Villar et al., 2014]). For example,~60% of sites bound by the developmental master regulator Twist (Twi) have conserved occupancy between Drosophila melanogaster and pseudoobscura (He et al., 2011), an evolutionary distance comparable to that between human and chicken (estimated 1.1 substitutions per neutral site (Stark et al., 2007)). However, the level of conserved binding dropped significantly when considering more distant species, D. melanogaster and D. virilis (1.4 substitutions per neutral site), for factors involved in anterior-posterior patterning in the early Drosophila embryo (Paris et al., 2013). In vertebrates, extensive divergence in TF occupancy was observed in differentiated tissues. For instance, in the adult liver, only 14% of CEBPA binding are conserved between human and mouse (Schmidt et al., 2010), species separated by ~60 million years (0.5 substitutions per neutral sites [Stark et al., 2007]). The conservation for CEBPA binding drops to ~2% when the authors compared between very distant species, human and chicken, separated by ~300 million years. In contrast, the binding of CTCF is highly conserved (Schmidt et al., 2012). In each study the authors used different methods to define conserved TF binding. When the datasets were re-analyzed using a unifying statistical framework, which alleviates differences in analytical methods (Carvunis et al., 2015), the rate of evolution or divergence in TF binding are similar between vertebrates and flies, suggesting that transcriptional networks evolve at a common rate. While these studies have greatly improved our understanding of the rate at which TF occupancy events evolve, the functional consequences of these changes to enhancer activity, and therefore developmental programs, remain poorly understood.

Here, we used cross-species TF occupancy to assess inherent properties of enhancers, including testing the evolutionary flexibility predicted by the TF collective model. To initiate the study, we performed ChIP-seq on five essential developmental TFs across multiple stages of embryonic development in D. virilis and compared their binding signatures to orthologous stages in D. melanogaster. These two species are highly diverged (neutral substitutions per base of 1.4) with an estimated molecular distance ~3 times that between human and mouse (Stark et al., 2007).The five factors examined, Twi, Mef2, Tinman (Tin), Bagpipe (Bap) and Biniou (Bin), are the major drivers of the subdivision of the mesoderm into different muscle primordia and form part of a highly interconnected gene regulatory network (Figure 1a) (Bonn and Furlong, 2008; Jakobsen et al., 2007; Liu et al., 2009; Sandmann et al., 2007, 2006; Zinzen et al., 2009). This thereby provides an ideal system to investigate divergence and conservation along different layers of a developmental network (Figure 1a,b). Using these signatures of TF occupancy, we identified a conservative set of ~2800 orthologous cis-regulatory elements and examined their combinatorial and dynamic occupancy across five embryonic time-points. We first examined how the loss of a given TF binding event affects the binding of other TFs that co-occupy the same element using evolutionary divergence as a tool to identify potential cooperative TF interactions. We found inter-species divergence in TF binding associated with either the loss of that TF’s motif or the loss of binding of other TFs, as also observed in studies in closely related Drosophila (Bradley et al., 2010; He et al., 2011) and mouse (Odom et al., 2007; Schmidt et al., 2010; Stefflova et al., 2013) species. In addition, we also observed the converse, conserved TF binding associated with reduced sequence dependency, when the TF is combinatorially bound with other factors. This is consistent with the inherent flexibility predicted by the TF collective model, and provides the first large-scale insights into the extent to which this can occur.

Figure 1 with 2 supplements see all
Conservation in TF occupancy across developmental and evolutionary time-scales.

(a) The regulatory network linking 5 mesodermal transcription factors essential for mesoderm specification. (b) D. melanogaster and D. virilis ChIP were performed using species-specific antibodies for Twi, Tin, Bap and Bin. Colored boxes illustrate TFs and time points analyzed, with developmental stages on the x-axis. (c) The scale illustrates equivalent time points for corresponding stages between D. melanogaster and D. virilis. Axis labels correspond to hours of development, after egg laying (AEL) and numbers below axis correspond to developmental stages and Time Points (TP 1–5). Confocal images of embryos with Mef2 immuno-stains at the most representative stage for each time-point, orientated anterior-left, dorsal-up. Barplots show percentage of embryos at each stage (x-axis) for each TP, averaged over two replicates. (d) Number of significant ChIP peaks in each condition in both species. TP1, TP2, etc, correspond to Time point 1, Time point 2 etc. D. melanogaster peaks are from (Zinzen et al., 2009). (e) Percentage of conserved peaks between D. melanogaster and D. virilis. (f) Peak summits in close proximity were used to define cis-regulatory modules (CRMs, see Materials and methods). (g) Venn diagram of orthologous CRMs conserved between both species; 3,497 CRMs (D. mel CRMs overlapping more than one D. vir CRMs are counted once).

https://doi.org/10.7554/eLife.28440.002

Second, we directly assessed the impact of evolutionary changes in TF occupancy on enhancer function by examining spatio-temporal activity of orthologous enhancer pairs. This cross-species in-vivo enhancer testing revealed conserved activity despite extensive divergence in TF occupancy. Taken together, our results indicate many instances where evolutionary changes in enhancer motifs, or in enhancer TF occupancy, have little effect on enhancer activity, highlighting the inherent difficulty in predicting the functional effects of cis-regulatory variants on enhancer activity and therefore gene expression.

Results

Interspecies ChIP for five developmental TFs across five stages of embryogenesis

To accurately compare cross-species signatures of TF binding during embryogenesis, it is essential to compare equivalent developmental stages. The life cycle from egg lay to adult between D. virilis and D. melanogaster is temporally different, with embryogenesis taking ~10 hr longer in D. virilis compared to D. melanogaster (Markow et al., 2009). We therefore first determined equivalent embryo collection windows by comparing developmental stages of timed collections between both species using the expression of Mef2 protein (Figure 1c). In D. melanogaster, Mef2 is very dynamically expressed throughout the mesoderm during all stages of its specification and subsequent differentiation and thereby serves as a very informative molecular indicator of developmental stage. By counting the distribution of stages between multiple, overlapping collections, we identified the optimal time intervals to obtain an equivalent distributions of embryonic stages across the five developmental time windows of interest (Figure 1c). These time-intervals were recently confirmed in an independent study (Kuntz and Eisen, 2014).

To circumvent potential technical inconsistencies due to reduced antibody affinity across species, we generated new antibodies for four TFs directed against the full-length D. virilis proteins: Tin, Twi, Bin and Bap. For these TFs, ChIP was therefore performed with species-specific antibodies. For Mef2, the D. melanogaster antibody performed equally well using D. virilis or D. melanogaster embryos for both ChIP-qPCR (Materials and methods) and immunostaining (Figure 1—figure supplement 1). Importantly, all five antibodies gave the expected, highly specific and dynamic patterns of expression in D. virilis embryos (Figure 1—figure supplement 1), demonstrating the specificity of the antibodies and the conservation in the spatio-temporal expression patterns of these TFs.

ChIP-seq was performed in D. virilis embryos across five time windows of embryogenesis, equivalent to those we previously used to analyze these transcription factors occupancy in D. melanogaster (Zinzen et al., 2009) (Figure 1b,c). The number of time-points examined for each TF reflects the developmental stages that the TF is expressed, totaling 14 conditions (TF and time point). For all TFs across all corresponding time-points, two independent biological replicates were generated, using two independent sera when possible (Twi, Mef2), leading to a total of 28 D. virilis ChIP-seq datasets and 10 stage-matched genomic input controls (Figure 1b). Replicates were highly reproducible for both raw reads (median Pearson correlation Rho = 0.83) and significant TF peaks (median Pearson correlation Rho = 0.82). In addition, de novo motif discovery systematically identified the expected motifs enriched under the TF peak summits, attesting to the quality of the D. virilis ChIP-data (Figure 1—figure supplement 2). The number of high confidence peaks, called at a 1% Irreproducible Discovery Rate (IDR, a measure ensuring equivalent reproducibility between replicas) threshold, corresponding to peak calling q-value <0.001 in all conditions (Materials and methods). The number of peaks was generally higher for each TF in the D. virilis ChIP-seq compared to their corresponding D. melanogaster ChIP-chip data (Zinzen et al., 2009) (Figure 1d, Supplementary file 1), potentially reflecting the larger size of the D. virilis non-coding genome (which we estimate is approximately 50% larger) or the higher sensitivity of ChIP-seq versus ChIP-chip. Importantly, the general trend in the relative number of peaks per TF across time-points appears largely conserved between both species (Figure 1d).

Conservation of TF binding is factor-dependent

Evolutionary changes in non-coding DNA, including TF binding site turnover and high rates of insertions and deletions can complicate the identification of orthologous TF binding events (Kalay and Wittkopp, 2010; Moses et al., 2006). Given the evolutionary distance between D. melanogaster and D. virilis, we determined ChIP peaks in each species separately, defining species-specific significantly bound regions. The peaks identified in D. virilis were then translated onto D. melanogaster coordinates using pslMap (Zhu et al., 2007), resulting in 89% of bound peaks translated (Materials and methods, Supplementary file 1).

To evaluate the conservation and divergence of TF occupancy, we first examined cross-species TF occupancy in the 14 TF-conditions separately (Figure 1b), by calculating the fraction of D. melanogaster peaks that overlap with D. virilis peaks for a specific TF at the same developmental time-point. This revealed no obvious relationship between binding conservation and the underlying biological function of the different TFs, with the interesting exception of Mef2. Early in embryogenesis Mef2 binds to a small number of regions and the binding is highly diverged amongst species. This could represent a shift (or delay) in the initiation of Mef2 binding between species (Liotta et al., 2007), rather than in Mef2 expression (which is conserved as seen in Figure 1c and Figure 1—figure supplement 1). In contrast, by mid- and late- embryogenesis both the number and conservation in Mef2 binding is substantially higher (Figure 1d,e; compare TP1, 2 to TP3-5), matching the essential role of Mef2 in muscle differentiation at these later embryonic stages. In contrast, the occupancy of Twi and Tin are highly conserved over all time-windows examined, with on average ~50% conservation in binding. These results for Twi are in line with previous comparisons between D. melanogaster and D. pseudoobscura (He et al., 2011). Both Twi and Tin are essential for cell fate specification of the trunk and dorsal mesoderm, respectively, and both factors regulate extensive downstream transcriptional cascades (Bonn and Furlong, 2008; Liu et al., 2009; Sandmann et al., 2007). In contrast, only ~19% of Bap’s and 26% of Bin’s occupancy is conserved (Figure 1e, Supplementary file 1), yet both TFs are essential for the specification of the visceral muscle primordial, and Bin for its subsequent differentiation (Jakobsen et al., 2007; Zaffran et al., 2001); loss-of-function bap or bin mutant embryos develop no gut muscle and are therefore embryonic lethal (Azpiazu and Frasch, 1993; Zaffran et al., 2001). It is interesting to note that the orthologs of Bin, FoxF TFs, are also essential for gut muscle development in both mice (Ormestad et al., 2006) and Xenopus (Tseng et al., 2004). Therefore, although these TFs have an ancient essential role in visceral muscle development, their enhancer occupancy appears to be rapidly evolving.

Conserved enhancer occupancy is associated with enhancer function

TFs rarely act alone, but rather bind to developmental enhancers in a highly combinatorial, and often cooperative manner (Spitz and Furlong, 2012). Evolutionary constraints, therefore, likely act at the level of enhancers, selecting for global enhancer composition rather than on the positioning of individual binding sites (Lusk and Eisen, 2010). The five TFs assessed here are known to combinatorially regulate enhancers during mesoderm and muscle development (Jakobsen et al., 2007; Liu et al., 2009; Sandmann et al., 2007, 2006; Zinzen et al., 2009). We therefore next examined evolutionary changes in TF occupancy at the level of combinatorial occupancy, rather than at individual bound peaks.

We previously defined cis-regulatory modules (CRMs) targeted by these five TFs in D. melanogaster by clustering ChIP peak summits within 100 bp of each other, collapsing the 19,522 significant ChIP-peaks into 8008 ChIP defined putative CRMs (ChIP-CRMs, [Zinzen et al., 2009]), henceforth called CRMs. Importantly, 95% of the elements tested (with an average size of 271 bases), function as developmental enhancers in vivo in stable transgenic embryos, as seen from a number of studies (Cannavò et al., 2016; Ciglar et al., 2014; Junion et al., 2012; Zinzen et al., 2009). Here, we grouped the set of 33,285 significant D. virilis ChIP peaks, spanning all TFs and all time-points, into a high-confidence set of 14,385 non-overlapping CRMs, with an average size of 273 bases (Materials and methods, Supplementary file 2); 11,073 (77%) of which could be translated to D. melanogaster coordinates using pslMap (Figure 1f, Supplementary file 3, Materials and methods).

Fewer than half of D. melanogaster CRMs (44%; 3497/8008, Figure 1g), overlap a D. virilis CRM (median overlap of 170 bp, corresponding to 62% median CRM length): 2846 CRMs are in the orthologous region in a one-to-one relationship (Figure 1f), while an additional 651 D. melanogaster CRMs overlap two or more adjacent D. virilis CRMs. Interestingly, this overlap is very similar to that observed in humans, where less than half of CRMs were found in an orthologous position in another vertebrate species (Ballester et al., 2014). Although additional non-overlapping orthologous enhancer pairs surely exist, we focused all subsequent analysis on this conservative set of 2846 orthologous elements. Given the large evolutionary distance between the species, we reasoned this should avoid inflating estimates of divergence in TF occupancy (enhancer input) due to mis-matched non-orthologous enhancer pairs.

Of the 2846 orthologous CRMs, only 14% (404/2846 elements) are bound by a single TF at a single time-point in both species, with the vast majority (86%, 2442 elements) being bound by multiple TFs and/or at multiple developmental stages in one or the other species. To quantify the overall conservation in combinatorial binding of each orthologous CRM pair at each time-point, we use Jaccard similarity coefficient (J), which ranges from 0 (none of the significant TF binding events are shared) to 1 (all TF binding across all time-points (14 conditions) are shared) (Figure 2a). Based on this systematic indexing, the 2442 cross-species multi-ChIP peak CRM pairs were distributed into three binding conservation categories; those with strong (J >= 0.5; 812 CRMs, Supplementary file 4), intermediate (0 < J < 0.5; 1130 CRMs, Supplementary file 5) and no binding conservation (J = 0; 500 CRMs, Supplementary file 6) (Figure 2a). Strong and intermediate categories thereby reflect CRMs with greater or fewer than 50% of binding events conservation, respectively.

Enhancers with conserved binding are associated with functional elements.

(a) Jaccard similarity index was used to categorize CRMs into three groups based on their TF binding conservation. The schematic represents CRM pairs for each of the three groups with hypothetical combinatorial binding and the associated Jaccard Index. (b) Barplot representing the complexity of D. melanogaster CRMs, in term of number of binding events (x-axis), for each CRM binding conservation category. (c) Genome browser visualization of strongly conserved (left) and non-conserved (right) CRM pairs. D. virilis (D. vir) tracks display RPM normalized, input subtracted ChIP-seq signal. D. melanogaster (D. mel) tracks show log2(IP/mock) ChIP-chip signal. In D. mel, both D. mel and translated D. vir CRMs are shown along with D. mel gene models. In D. vir, D. vir CRMs and genes are shown with D. mel orthologous gene name depicted between parenthesis. The same genomic window size was used for both species, indicated in the lower left corner. (d) Fraction of mesodermal (‘M’, coloured) and non-mesodermal (‘O’ for ‘Other tissues’, gray) enhancers overlapping CRMs from the three categories defined in (a). Overall p-value corresponds to a two-sided proportions test, p-values within categories correspond to Fisher exact test. (e) CRMs with strong or intermediate conservation in their TF binding events have stronger sequence constrains than those with no conserved binding, based on their average PhastCons conservation scores. Solid lines represent the mean and shading delineates standard error.

https://doi.org/10.7554/eLife.28440.005

Enhancers with conserved TF binding (both strong and intermediate levels) have a higher number of total TF binding events in D. melanogaster, i.e. more combinatorial occupancy compared to ones with no conservation (Figure 2b, Chi-square test p-value=5e−04 for each of the three pairwise comparisons), as expected (Paris et al., 2013). D. mel CRM-3805, for example, is bound by all five factors at multiple stages and is therefore occupied in all 14 developmental conditions examined in D. melanogaster (Figure 2c). This extensive occupancy is highly conserved in D. virilis (D. vir CRM-11767) for all five factors across almost all time-points examined, with the exception of Bap (Figure 2c). In contrast, D. mel CRM-6503, is bound only by Mef2 at four stages of embryogenesis in D. melanogaster, but has no significant Mef2 binding in D. virilis (D. vir CRM-14210) (Figure 2c). Interestingly, it is occupied by Twi at the three early stages in D. virilis, but not in D. melanogaster, suggesting either a change in the activity of the enhancer itself or a change in the functional TF required to achieve the same enhancer activity (e.g. a TF with similar expression).

To assess the likely functional consequences of diverged versus conserved TF binding, we took advantage of a large collection of developmental enhancers with characterized spatio-temporal activity in vivo, using transgenic D. melanogaster embryos (Bonn et al., 2012; Gallo et al., 2011; Kvon et al., 2014). Enhancers were divided into two categories, those active in the same cell type as the TFs (mesoderm/muscle: 1254 enhancers) and enhancers active in any other tissue, excluding the mesoderm (2970 enhancers, Materials and methods, Supplementary file 7). Overlapping our inter-species CRMs bound by mesoderm/muscle TFs with these enhancers indicates that 494 (20.3%; 494/2442) CRMs in an orthologous pair have been experimentally tested in D. melanogaster and shown to function as developmental enhancers in vivo. Importantly, CRMs with strong and intermediate conservation in their occupancy (based on their Jaccard similarity coefficient) have significantly higher overlap with characterized mesodermal and muscle enhancers compared to CRMs with non conserved binding (Figure 2d, two-sided proportions test p-value=5.3e−05). Conserved enhancer binding is therefore globally associated with enhancer function. It is interesting to note that CRMs with no conserved binding (J = 0) are still enriched for mesoderm/muscle activity, although to a much lesser extent than the two conserved categories (odds ratio of 1.43 vs 5.26 and 4.04 for the strong and intermediate conservation classes, respectively, Figure 2d). In addition, CRMs with strong and intermediate conserved binding show stronger sequence conservation (Figure 2e), suggesting that much of the conservation and divergence in occupancy is directed by changes in the DNA sequence within the regulatory element itself, which we examine in more detail below.

Developmental enhancers preserve dynamic combinatorial patterns of TF occupancy over a large evolutionary time-span

We previously showed that TFs occupy enhancers in much more dynamic temporal windows than their expression patterns suggest (Wilczyński and Furlong, 2010). Twi, Tin, Mef2 and Bin, for example, each occupy their enhancers during D. melanogaster development in three temporal classes (Jakobsen et al., 2007; Liu et al., 2009; Sandmann et al., 2007, 2006): continuously at all stages of development where they are expressed, and two interesting transient classes occupied only at early or late stages of development, despite the TFs being expressed at broader developmental windows. The occupancy of the two latter classes cannot be readily explained by the sequence motifs of the TFs themselves (Wilczyński and Furlong, 2010) and suggests cooperative binding with other TFs, which is supported by motif enrichment in each temporal class (Wilczyński and Furlong, 2010; Yáñez-Cuna et al., 2012). Taking advantage of the 2846 cross-species orthologous CRMs identified in this study, we can now assess if these dynamic patterns of TF occupancy are conserved, and explore how they might be regulated.

To more directly compare dynamic patterns of TF occupancy across this evolutionary time-scale, we used hierarchical clustering based on the quantitative ChIP signal from both species (Materials and methods). This minimizes thresholding issues and better reflects the dynamic range of TF occupancy as a quantitative continuum (Biggin, 2011). Although the D. melanogaster data is ChIP-chip and D. virilis ChIP-seq, the TF occupancy patterns from the chromatin immunoprecipitation are highly correlated (Figure 3—figure supplement 1).

The level of conserved occupancy for these orthologous enhancers is striking; CRM pairs have similar coordinated changes of the same TF coming either ‘on’ or ‘off’ certain elements during developmental progression in species that span over 40 million years of evolution (Figure 3a). For instance, one group of enhancers is coordinately bound by Twi only at the first two developmental stages in both species (early-only CRMs), while another group is bound by Mef2 or Mef2 and Bin at late stages (late-only) or bound by all factors at almost all stages in both species (continuous) (Figure 3a). In D. melanogaster, these temporal patterns in TF occupancy are highly correlated with both the timing of enhancer activity (Jakobsen et al., 2007) and the expression and developmental function of the neighboring gene (Wilczyński and Furlong, 2010). We now show that these dynamic combinatorial patterns are also maintained across large evolutionary distances, suggesting that these temporal patterns of TF occupancy are functionally important.

Figure 3 with 1 supplement see all
Dynamic patterns of developmental enhancer occupancy across distantly related species.

(a) Hierarchical clustering of ChIP signal of CRMs with high, intermediate and low binding conservation, as classified using Jaccard distance (Figure 2). Each row corresponds to an orthologous CRM, and each column to a condition, with 14 D. melanogaster conditions followed by their equivalent 14 D. virilis conditions. The vertical dashed line delimits data from both species, while the horizontal colored bars indicate the TF. All three heatmaps are visualized using the quantitative ChIP-signal shown in the scale in lower right. (b) Differential motif analysis on early vs late Twi bound enhancers indicates that the regulation of these elements is largely conserved. Heatmap shows fold enrichment (early vs late bound enhancers, log2) of all motifs with >2 fold significant enrichment in one of the two species. *p<0.05; **p<0.01; ***p<0.001 indicates significance in that species. Color indicates motif enrichment, which is generally in the same direction in both species, even when significant in only one species.

https://doi.org/10.7554/eLife.28440.006

To determine how these temporal patterns of TF occupancy are regulated, we used sequence analysis to search for motifs for additional potential partner TFs. For each species, we selected elements that are bound at only early or late stages, in either D. virilis or D. melanogaster (Materials and methods) and then performed motif analysis on each species separately. Previously we, and Yanez-Cuna et al., found many motifs specifically enriched in D. melanogaster Twist early- versus late-bound enhancers (Wilczyński and Furlong, 2010; Yáñez-Cuna et al., 2012). Here, using a more expanded set of PWMs, we rediscover many of these motifs in the D. melanogaster elements and now determine if these motifs are evolutionarily conserved within the D. virilis Twist early- versus late-bound elements. Almost all motifs specifically enriched in the early-bound CRMs are present in both species (Figure 3b), suggesting a role, together with Twist, in the regulation of early-bound CRMs. This includes motifs for Zelda and Dorsal, two TFs functionally required for Twi binding to a number of early blastoderm enhancers (Jiang and Levine, 1993; Yáñez-Cuna et al., 2012), in addition to many other motifs that may indicate new partner TFs. Interestingly, the Twist-late bound enhancers have more divergence in the additional motifs enriched in each species (Figure 3b), which may reflect the cellular heterogeneity that is being specified during this developmental stage. This motif analysis on CRMs with evolutionarily conserved patterns of TF occupancy can therefore help discriminate between functional and likely non-functional motifs, and provides a useful tool to identify additional potential partner TFs that can form the basis of future studies. It also indicates that the underlying biology of how these two groups of enhancers are regulated goes beyond Twist, the factor that we have experimentally measured here.

The non-conserved set (J = 0) contains interesting orthologous enhancers where the identity of the significantly bound TF(s) has changed during evolution (Figure 3a). A set of enhancers bound by Bin in D. virilis, for example, is bound by Mef2 at these stages of development in D. melanogaster (Figure 3a, cluster C1). Another cluster of orthologous CRMs is bound exclusively by Mef2 in D. melanogaster, but by Twi and Tin in D. virilis (Figure 3a, cluster C2). A third set is bound by Mef2 in D. melanogaster and Twi in D. virilis (Figure 3a, cluster C3). These switches in TF occupancy may be the result of adaptive changes, as reported in yeast (Hogues et al., 2008) and suggests that either (1) the swapped TFs can perform the same function and thereby compensate for the TF loss resulting in a conserved enhancer activity or (2) this change in TF occupancy will lead to a dramatic change in enhancer function between these two species. Both scenarios are assessed below, and may be missed in studies examining the occupancy of a single factor in isolation.

TF binding site divergence is tolerated in the context of collective TF binding

Loss of TF binding in inter- and intra-species analysis is often linked to sequence changes in the motif for the bound TFs (He et al., 2011; Kasowski et al., 2010; Schmidt et al., 2010; Zheng et al., 2010). To evaluate this for the occupancy of these five developmental factors, we compared overall motif density and conservation between CRMs with conserved versus non-conserved TF binding. As regulatory elements bound by more TFs tend to be more conserved (Ballester et al., 2014; Hemberg and Kreiman, 2011), we assessed motif properties across three classes of orthologous CRMs: (1) globally across all CRMs bound by four or five TFs in D. melanogaster (267 CRMs; called High Bound), (2) CRMs bound by two or three TFs in D. melanogaster (1004 CRMs; called Low Bound) and (3) CRMs with high-confidence binding by only one TF in D. melanogaster (1475 CRMs, called Singletons)(Materials and methods). For each category, CRMs where a TF binding was absent in D. virilis but present in D. melanogaster (lost binding) were compared to those where the binding was present in both species (conserved binding).

In all cases, the loss of TF binding is associated with a significantly lower density of that TF’s motif (p-values < 1e-7, binomial test) for each of the five TFs in D. virilis (Figure 4a, compare circles to triangles). Notably, when TF binding is lost, the motif densities drop to background averages (Figure 4a, compare triangles to the bars), indicating that loss of TF occupancy is generally concomitant with loss of functional motifs for that TF (Figure 4a). This trend generally holds true for all three enhancer categories (High bound, low bound or singletons, Figure 4a), with the interesting exception of Tin, a TF previously shown to act in a TF collective at cardioblast enhancers (Junion et al., 2012). The number of Tin, and to a lesser extent Mef2 and Bin, motifs (overall motif density) in enhancers with conserved binding (Figure 4a, circles) tends to decrease as TF binding complexity increases (for example 7.96 versus 10.41 Tin motifs/kb (p-value < 5e-7, binomial test) in high-bound versus singleton enhancers, respectively) suggesting looser sequence requirements when the TF binds to regulatory elements collectively with other factors.

The relationship between TF binding conservation and motif conservation depends on enhancer context.

(a) Orthologous CRMs between D. melanogaster (D. mel) and D. virilis (D. vir) were divided into 3 categories based on their occupancy in D. mel; CRMs bound by 1 TF (Singletons), 2 or 3 TFs (Low Bound) or by 4 or 5 TFs (High Bound). CRMs in each category were divided into CRMs with conserved occupancy (circle) and non-conserved occupancy (triangle) in D. vir and the density of transcription factor binding sites (TFBS) for the corresponding factor in D. vir was calculated (Materials and methods). Asterisks depict the differences in TFBS density between CRMs with conserved and non-conserved binding, while horizontal lines indicate average densities across the genome for comparison. The lack of conserved binding for Bap singleton CRMs results in the missing value for conserved Bap TFBS (also in [b]). (b) Spearman correlation between interspecies variation in motif presence (ΔSTAP) and variation in TF occupancy (ΔChIP) at orthologous CRMs for each TF, in the different occupancy classes. A high Spearman correlation indicates inter-species changes in TF binding due to inter-species changes in the presence of the TF’s motif (i.e. binding changes are highly sequence-dependent). (c) Relationship between scaled ΔSTAP and ΔChIP for Singletons (left, 150 peaks) and High Bound (right, 488 peaks) orthologous enhancer pairs bound by Tin in D. melanogaster. Highlighted in red (0 and 28 peaks for Singletons and High Bound, respectively) are enhancer pairs with little changes in Tin occupancy (low ΔChIP) but high changes in Tin motifs (ΔSTAP) and in green (2 and 21 peaks for Singletons and High Bound, respectively) are enhancer pairs with high changes in Tin occupancy (high ΔChIP) with little changes in their Tin motifs (ΔSTAP). The former (red) are consistent with TF collective occupancy, while the latter (green) are consistent with Tin cooperative recruitment by other partner TFs. Dashed lines delineate the thresholds used. Note, many orthologous enhancers fulfill these criteria in the high-bound enhancers while very few or none do in the singletons class (p=0.06 and 0.0005 comparing (High Bound greater than Singletons) green and red populations (one sided Fisher test), respectively).

https://doi.org/10.7554/eLife.28440.008

We confirmed this relationship between TF binding and sequence fitness with a threshold-free approach using STAP (sequence to affinity prediction) (Cheng et al., 2013), which integrates one or more strong or weak sites present in a sequence to produce a numeric prediction of TF occupancy in that sequence. STAP models were trained to discriminate between TF bound regions (significant ChIP peaks) and random sequences for each motif and in each species (Materials and methods). We then used the STAP-predicted TF occupancy to score (‘STAP score’) motif presence within each enhancer (Materials and methods, Supplementary file 8). For each orthologous enhancer pair, we calculated the interspecies differences between STAP score for each TF (ΔSTAP, indicating a change in predictive motifs) and the interspecies ChIP score (ΔChIP, indicating an observed evolutionary change in TF occupancy). This identified a positive and significant correlation between global ΔSTAP and ΔChIP for each TF (Supplementary file 9) indicating that divergence in TF binding is generally associated with a loss in that TF’s motif, as expected. Repeating the ΔSTAP-ΔChIP analysis for each enhancer category separately (high-bound, low-bound and singletons, Figure 4b) showed a significantly lower correlation between TF occupancy predicted from sequence (ΔSTAP) and that observed (ΔChIP) in highly-bound CRMs compared to singleton CRMs for Tin (Fisher z-transformation, p=0.05, Figure 4b). Therefore, although Tin enhancer occupancy is generally deeply conserved in both cases (high or low bound CRMs), its underlying requirements for the Tin motif is reduced when the TF is bound with other factors. To investigate this further, we directly compared the observed evolutionary changes in Tin binding (ΔChIP) to changes in predictive Tin motifs (ΔSTAP) for orthologous enhancer pairs bound by Tin alone (singletons) or with many other TFs (High bound) (Figure 4c, Materials and methods). The scatter plot (Figure 4c) reveals two types of ‘partner’ relationships: (1) orthologous enhancers in which Tin binding has diverged (high ΔChIP), while the predictive motifs for Tin binding are conserved (low ΔSTAP) (Figure 4c, green dots) and (2) orthologous enhancers in which Tin binding is conserved (low ΔChIP), while the sequence fitness for Tin binding has diverged (high ΔSTAP) (Figure 4c, red dots). The former is consistent with a second partner TF being required to co-operatively recruit Tin to those enhancers, while the latter is consistent with the ‘TF collective’ model of enhancer occupancy (Junion et al., 2012), which proposed that enhancers bound by many functionally related TFs should be able to withstand some motif divergence while having little effect on the occupancy of the TFs themselves. Our cross-species occupancy data provides the first evidence that this prediction holds true for a collection of elements: in the context of enhancers bound by many TFs (4–5 factors), Tin occupies the enhancer in both species despite divergence in its motif, which is not the case when Tin binds alone (Figure 4a–c).

Co-divergence in TF occupancy identifies potential cooperative binding of TFs

To identify potential mesodermal partner TF(s) that either preserve Tin binding across species despite divergence in its motif and/or are required to cooperatively recruit Tin, we assessed co-association between the occupancy of all TFs across all 14 conditions in both species. For this we determined if TF co-occupancy deviates from random expectations by deriving z-scores based on a background distribution generated by permuting the TF binding matrix 500 times (Figure 5a, Materials and methods). Indeed, Tin co-associates significantly with three other factors, Twi, Bap and Mef2, and to a lesser extent with Bin. Importantly, these co-associations in binding are present in both species, and therefore maintained across a large evolutionary distance, suggesting that the general properties by which these TFs regulate enhancer activity, and by extension the overall topology of the mesodermal gene regulatory network, is largely conserved.

Co-divergence in TF occupancy across evolution can reveal potential cooperative interactions.

(a) Pair-wise co-association of TF/time point in D. melanogaster (left) and D. virilis (right). Co-association significances correspond to Z-Score denoting deviation from random expectation ranging from red (lower than random expectation denoting lack of co-association) to blue (higher than random expectation denoting strong co-association). (b) Above table, schematic representation of the 4 categories of binding events affecting orthologous CRM pairs. Below, table indicates the number of CRM pairs in each category with p-values (Fisher exact tests). Significant cases (p<=0.05) are highlighted in red. (c) Association test between the loss of TF1 occupancy (TF1 ChIP, rows) and the loss of TF2 motifs (TF2 STAP, column). Significant associations between pairs are indicated (Fisher exact tests). (d) Occupancy of orthologous enhancers with co-divergent binding. Binding of both Tin and Bap are lost in D. vir (right) compared to D. mel (left). Track descriptions are as in Figure 2c. Orthologous CRM region is highlighted in gray.

https://doi.org/10.7554/eLife.28440.009

If Tin binding depends on the binding of other TFs (Figure 5a), either directly or indirectly, then the loss of binding of this ‘partner’ TF during evolution should lead to loss of Tin binding at those orthologous enhancers. To assess this, we examined co-divergence in TF occupancy across species for all factors. Previous studies assessed similar TF partner (or combinatorial) relationships to explain loss of TF binding by searching for motif divergence for other TFs (Borneman et al., 2007; He et al., 2011; Zheng et al., 2010), or TF occupancy divergence (Stefflova et al., 2013). Here, we can use our measured co-divergence in TF occupancy to directly assess if the observed co-associations of Tin with other TFs are functionally required for TF occupancy.

Orthologous CRM pairs bound by two (or more) TFs in D. melanogaster were divided into all four possible binding categories: (1) CRMs with conserved binding of both TFs in D. virilis, (2) CRMs that have lost the binding of one TF (factor 1), (3) CRMs that have lost the binding of the second TF (factor 2) and (4) CRMs that have lost the binding of both TFs (Figure 5b). For each of the 10 possible co-association pairs (combinations of any two factors from the set of 5), we asked if the loss of both TFs in D. virilis is more frequent than expected, under the hypothesis of binding independence of the two TFs. Four TF pairs show such a significant binding dependency, which we refer to as co-divergence (Figure 5b, highlighted cells). In concordance with the association analysis (Figure 5a), three of the four cases involve Tin, and include Tin/Bap, Tin/Twi, and Tin/Mef2 (corrected p-values of 6 × 10−11, 0.007, and 0.0006 respectively, Figure 5b,d). We next assessed if the loss of TF1 occupancy (Figure 5c, rows) is associated with sequence divergence (change in TFBSs) for TF2 (Figure 5c, columns) using STAP to score for presence of the TF2 motif. Consistent with the findings above, Tin-Bap emerged as the strongest associations (p=2.09E−04, Figure 5c). Tin and Bap were suggested to bind as a heterodimer to one enhancer in D. melanogaster (Zaffran and Frasch, 2002). Our results indicate that both TFs have conserved co-binding to hundreds of elements (327 CRMs) during visceral mesoderm specification. This analysis also indicates the direction of dependency: Loss of Tin and Bap occupancy is associated with the loss of Bap and Tin motifs, respectively (Figure 5c, p=2.09E−04 and p=8.44E−04, respectively), a bidirectional dependency consistent with cooperative recruitment rather than via a heterodimer motif. As Tin directly regulates bap expression, this represents a conserved feed forward loop on these 327 developmental enhancers. Our results also suggest a bidirectional dependency between Tin and Twi occupancy (Figure 5b,c), suggesting that they act cooperatively to regulate early mesoderm enhancers. Mef2 and Tin exhibit a unidirectional association where the loss of Mef2 occupancy is associated with the loss of Tin motifs (p=1.96E−04) but not vice-versa (p=8.05E−01)(Figure 5c). The last case of suggested dependent binding is Bap recruitment being dependent on Bin motifs (p=3.71E−02, Figure 5c), two TFs essential for trunk visceral mesoderm development (Jakobsen et al., 2007; Zaffran et al., 2001).

Taken together, the co-association and co-divergence analysis indicate that the TFs in each of these pairs co-bind to enhancers in both species more frequently than expected, and that this co-binding appears to be required at some enhancers to preserve occupancy as when the binding of one factor is lost during evolution it is associated with the loss of binding of the second TF. The presence of Tin in the majority of co-divergent pairs suggests that the occupancy of Tin is highly cooperative, which could involve direct cooperativity that is dependent on the interplay between protein::DNA and protein::protein interactions, although we note this could also involve indirect co-operativity through co-factor recruitment or nucleosome displacement.

Linking evolutionary changes in TF occupancy to their effects on enhancer activity

The relationship between TF occupancy and enhancer activity is not straightforward. Enhancers occupied by similar combinations of TFs can regulate very similar patterns of expression (Frankel et al., 2010; Hong et al., 2008; Perry et al., 2010, 2011). However, different combinations of TFs can also regulate very similar patterns of enhancer activity (Brown et al., 2007; Liberman and Stathopoulos, 2009; Zinzen et al., 2009). Over evolutionary time-scales, it is therefore not clear what effect changes in TF occupancy (enhancer input) will have on enhancer activity (enhancer output). A number of studies have indicated very good correlation between highly conserved binding and the expression of the closest proximal gene (Ballester et al., 2014; Paris et al., 2013). However, partial conservation of binding events within enhancers is much more common when distantly related species are compared, as observed here: almost 50% (1130/2442) of our orthologous multi-peak CRMs have partial conservation in the combination of TFs they recruit (Figure 2a and Figure 3). In these cases, it is difficult to predict what impact a moderate change in combinatorial TF binding (partial gain or loss in enhancer occupancy) will have on enhancer activity. Moreover, using the expression of the closest proximal gene as a proxy for enhancer activity is problematic for two reasons: First, a gene’s expression represents the combined activity of multiple enhancers (often five or more), making it difficult to disentangle the functional consequences of evolutionary changes in TF occupancy at each individual element. Second, many enhancers do not regulate the closest proximal gene, but rather skip one or more genes, sometimes spanning large distances even in Drosophila (Ghavi-Helm et al., 2014).

To directly assess if the activity of orthologous CRMs is robust to changes in TF occupancy, we examined the extent to which conservation or divergence in TF occupancy, a criteria widely used to infer functional importance of TF binding events, are consistent with conserved or diverged enhancer activity. For this, we took advantage of our previous predictions of the activity of the 8008 D. melanogaster CRMs used here (Zinzen et al., 2009), in which we demonstrated that Support Vector Machines (SVMs) trained with the binding signatures of TFs on characterized enhancers could qualitatively predict enhancer activity with high accuracy in four tissue classes (Zinzen et al., 2009). When the predictions were tested in-vivo using transgenic embryos, the enhancers were active in the predicted tissue in ~85% of cases (Cannavò et al., 2016; Zinzen et al., 2009). Here we compared the spatio-temporal expression patterns of both D. melanogaster and D. virilis orthologous CRM pairs (Figure 6). For this, we selected pairs with medium to high divergence in TF binding to orthologous CRMs (i.e. a medium to low Jaccard index (J <= 0.5); Supplementary file 10) in addition to having a high SVM specificity (spec. >0.98) of predicted expression for the D. melanogaster CRM. Of the 161 orthologous pairs passing these criteria, we selected seven pairs after manual checking for a good alignment between orthologous loci.

Figure 6 with 1 supplement see all
Relationship between divergence in TF occupancy and enhancer activity.

(a) Table summarizing the orthologous D. melanogaster (D. mel) and D. virilis (D. vir) CRMs tested for in-vivo activity in D. mel embryos. The enhancer’s predicted tissue activity, and SVM specificity score, for the D. mel enhancer is indicated (from Zinzen et al., 2009). The observed enhancer activity for both the D. mel and orthologous D. vir enhancers are indicated. SM = somatic muscle, VM = visceral muscle, Meso = mesoderm. (b–c) Upper panel, TF occupancy (ChIP signal) of two orthologous CRM pairs representing the quantitative binding profiles along the 14 conditions in both species. D. mel data corresponds to log2(IP/mock) ChIP-chip signal. D. vir data corresponds to RPM normalized, input subtracted ChIP-Seq signal. In D. mel, both D. mel and translated D. vir CRMs are shown along with D. mel gene models. In D. vir, D. vir CRMs and D. mel orthologous gene models are shown. Middle panel shows TF binding logos, where blue represents significant ChIP peaks at the indicated time point (columns = Time Point (TP) and rows = factor). Lower panel shows double in-situ hybridization of transgenic embryos for each orthologous enhancer (lacZ reporter driven by the enhancer, green channel) and a mesodermal marker (Mef2, red). White arrow indicates the somatic muscle (b) and visceral muscle (c) of an embryo in a lateral or dorsal view, respectively.

https://doi.org/10.7554/eLife.28440.010

All 14 CRMs (7 D. melanogaster and their 7 D. virilis orthologs) were assayed in transgenic reporter assays in D. melanogaster (Materials and methods, Supplementary file 10). Five CRM pairs (71%) showed conserved tissue activity between both species (Figure 6a), of which four are fully concordant with the SVM tissue predictions and one has partial concordance predicting one tissue correctly (Figure 6a). The fifth pair (D. mel CRM-4682/D. vir CRM-12291) has conserved activity in the mesoderm with additional activity in D. virilis in two mesodermally derived tissues, the visceral muscle (VM) and somatic muscle (SM) (Figure 6a, Figure 6—figure supplement 1). We note that three CRM pairs with conserved overlapping activity have activity in other non-overlapping tissues indicating that these elements are also bound by other TFs not assessed here (D. mel CRM-6087/D. vir CRM-459; D. mel CRM-4682/D. vir CRM-12291; D. mel CRM-4725/D. vir CRM-12156). The last pair (D. mel CRM-3330/D. vir CRM-1344), with a D. melanogaster SVM prediction in the VM, showed no activity in either species despite conserved Bin binding.

Overall, the five orthologous enhancers with conserved tissue activity (output) have extensive divergence in their TF occupancy, with less than 40% of their TF binding conserved (Figure 6a, Median Jaccard index = 0.37). For example, D. mel CRM-3215 is bound by all five TFs in D. melanogaster while in D. virilis it is only bound by Mef2 (Figure 6b). Even more striking is D. mel CRM-1560, which is bound by Tin, Bap and Bin in melanogaster (Figure 6c). All significant binding for these factors is lost in D. virilis (CRM 2469), but the enhancer has gained Mef2 binding – yet the very specific VM activity is conserved (Jaccard index = 0, Figure 6a,c and Figure 6—figure supplement 1).

These data indicate that divergence in TF binding does not necessarily equate to divergence in enhancer activity, as generally assumed. The strong conservation in orthologous enhancer output, despite a low conservation in TF binding may reflect stabilizing selection acting to maintain the correct spatio-temporal activity of orthologous developmental enhancers.

Discussion

The evolutionary properties of TF function can be considered at two levels; (1) the relationship between sequence divergence and TF occupancy and (2) the relationship between TF occupancy divergence and enhancer activity. While many studies have addressed the first, few have tackled the second, and when they have, they focused on an individual well-characterized enhancer. This study touches on both aspects, where we look at global properties of TF occupancy within the context of orthologous developmental cis-regulatory elements.

We initiated this study by measuring the cross-species occupancy of 5 developmental TFs, which function within the same gene regulatory network to specify the subdivision of the mesoderm into different muscle primordia. We used this data to assess the impact of divergence in DNA sequence and TF occupancy at the level of cis-regulatory elements, rather than individual binding events, as these are the functional units through which selection most likely acts.

The relationship between sequence divergence and TF occupancy

Divergence in TF occupancy is often related to either (1) loss of the binding site for the TF itself (He et al., 2011; Kasowski et al., 2010; Schmidt et al., 2010; Zheng et al., 2010) or (2) the loss of TFBSs for additional partner TF (Paris et al., 2013; Stefflova et al., 2013), as depicted in Figure 7 (upper panels). Our study confirms these findings, showing that loss of TF binding is associated with either the loss of the TF’s motif (Figure 4) or the loss in TF binding of an associated ‘partner’ TF (due to the loss of its motif) that combinatorially binds to the same element (Figure 5). We also observed the converse – conserved TF binding despite sequence changes, that is, divergence of that TF’s binding site (Figure 4c and schematized in Figure 7, lower panels). This property depends on the degree to which the TF is combinatorially bound to an enhancer with other TFs (Figure 4b,c). When a TF co-occupies an enhancer with many other functionally related TFs (i.e. as a TF collective [Junion et al., 2012; Spitz and Furlong, 2012]) its occupancy is less sequence dependent. For example, when Tin is bound to enhancers with few other mesodermal TFs, the conservation in its binding is correlated with conservation in its TFBS. However, when Tin is bound to enhancers that are highly bound by other mesodermal TFs, for whom Tin is known to function with, its conservation in binding is less dependent on its DNA motif (Figure 7). In other words, the collective binding of Tin with other mesodermal TFs can compensate for divergence in its motif, in keeping with the predicted increased flexibility proposed by the TF collective model (Junion et al., 2012; Spitz and Furlong, 2012; Uhl et al., 2016) (Figure 7).

Schematic representation of the relationship between sequence divergence and TF occupancy on enhancer elements.

Upper panel, indicates an enhancer bound directly by two factors, A and B. In some enhancers the loss of TF binding (e.g. TF B) is associated with the loss of that TF’s binding site (motif b - middle). In other enhancers (right), the loss of a partner TF’s motif (motif a) will result in the loss of binding of TF B, indicating that TF B occupies the enhancer cooperatively (either directly or indirectly) with TF A. Both cases have been observed in inter- and intra- species analysis of TF occupancy, and are also observed here with these mesodermal developmental TFs. Lower panel, represents an enhancer bound by a TF collective. Here, the loss of motif b does not result in loss of TF B binding (middle panel), unlike the situation in enhancers with purely additive occupancy (depicted above). Rather the divergence in the TF binding site is tolerated as the TF’s binding is stabilized through protein-protein interactions.

https://doi.org/10.7554/eLife.28440.012

The relationship between divergence in TF occupancy and enhancer activity

The ‘input – output’ relationship of enhancers, a trait on which selection directly acts, has been largely unexplored in global comparative cross-species studies of TF binding. The large evolutionary distance between Drosophila melanogaster and virilis (equivalent to that between humans and lizards based on neutral mutation rate [Stark et al., 2007]), has led to extensive motif changes within these orthologous enhancers. We used this high sequence divergence to tease apart functional associations between TFs, identifying potentially cooperative pairs of TFs whose co-associations are essential for TF recruitment at enhancers. Importantly for evolutionary analyses, these cooperative interactions often allow a TF to occupy an enhancer (conserved binding) despite a loss of its functional motif (diverged sequence), as measured using STAP, a threshold free thermodynamics-based model of quantitative occupancy (Figure 5c and Supplementary file 9). This suggests that combinatorial binding may offer evolution a greater neutral neighborhood, and thus can increase the robustness of developmental systems.

When associating TF occupancy with enhancer activity it is clear that not all TF binding events behave equally; the loss or gain of TF binding at an enhancer during evolution can have diverse effects. By examining the activity of 7 orthologous enhancer pairs with high divergence in their TF occupancy in transgenic embryos, we uncovered extensive ‘plasticity’ of enhancers to evolutionary changes in TF occupancy. Despite highly diverged TF occupancy (enhancer input), their tissue activity (output) was often highly conserved. Therefore, a loss in TF binding does not necessarily lead to a loss (or even a tissue change) in an enhancer’s activity, rather it is an inherent property of the enhancer due to the combination of regulatory inputs that define its activity.

Conservation in TF occupancy in relation to the TF’s function and position within a developmental GRN

Our results suggest a potential relationship between the extent of conservation in TF binding and the position of the TF within its developmental gene regulatory network. Four of these TFs directly regulate each-others expression in a linear cascade (Figure 1a), providing an excellent system to examine this: Twi directly regulates tin expression (Yin et al., 1997), Tin regulates bap (Lee and Frasch, 2005) and Bap in turn regulates bin expression (Zaffran et al., 2001). The TFs higher up in the network (Twi and Tin) generally have more conserved occupancy (~54%) compared to TFs lower down in the network (Bap and Bin; 19% and 25% respectively) (Figure 1a, Supplementary file 1). Interestingly, studies, in diverse developmental processes and species have observed both cases; higher variation in key regulators towards the bottom of the GRN in line with our findings (Abzhanov et al., 2006; Garfield et al., 2013), or conversely at the top of their respective GRN (Hinman et al., 2007, 2003). The extent of divergence and conservation of TF activity may therefore depend on the function of the GRN itself (Erwin and Davidson, 2009).

The TFs examined in this study have a conserved role in mesoderm specification and muscle development from flies to man, and form part of an evolutionarily conserved GRN. Our results indicate that the evolutionary patterns acting on this network are shaped by the collective interactions between these TFs, as much as the functions of any individual factor themselves. Evolutionary divergence of an individual TF should therefore not be assessed in isolation. It is rather the context in which developmental TFs act that determines their evolution, both at the enhancer (i.e. the presence of other TFs at each individual enhancer) and network (i.e. the position of a TF within a GRN and its role in feed forward and feedback regulation) levels.

Materials and methods

D. virilis embryo staging

Drosophila virilis (w[-]) was reared on standard bottles, identical to D. melanogaster. To establish a large population of D. virilis, we found extra feeding at day 3 if reared at 25 degrees and day 5 if reared at 18 degrees critical to efficiently amplify the stock. To determine equivalent stages between the two species, rounds of small-scale timed embryo collections were obtained at 25°C for D. virilis embryos to define time intervals with the desired stages. Following each collection, embryos were fixed and immunostained using anti-D. melanogaster Mef2 to precisely stage each embryo. The distribution of stages was manually counted for each sample’s embryo collection using a Zeiss LSM 510 META confocal microscope.

ChIP-seq on D. virilis embryos

Staged D. virilis embryo collections, at the defined time intervals (Figure 1a), were obtained at 25°C and fixed in 1.8% formaldehyde for 15 min. Extracted chromatin was sonicated on a Bioruptor for 18 cycles of 30 s ON, 30 s OFF at high frequency, yielding an average shearing size of 250–300 bp. Rabbit polyclonal antibodies targeting the full-length protein for D. virilis Twi (2 independent sera), Tin, Bap and Bin were generated specifically for this study to increase target specificity. For this, the protein was expressed in, and purified from, E. coli as a SUMO fusion protein and used as antigen to raise the antibodies. The SUMO tag was used for protein purification. D. melanogaster anti-Mef2 was highly specific when tested on D. virilis embryos (both by immunostaining (Figure 1—figure supplement 1) and ChIP) and thus used for Mef2 D. virilis ChIP.

Chromatin from D. virilis staged embryos was extracted as described previously for D. melanogaster (Sandmann et al., 2006) and ChIP conditions were optimized for each TF by ChIP-qPCR using the recovery of positive and negative regions (Supplementary file 12). All antibodies were first pre-absorbed using chromatin from 0 to 2 hr D. virilis embryos, a time window when none of the TFs are expressed. Two independent biological replicates were generated for each transcription factor at each time-point, using two independent antibodies when possible (for Twi and Mef2, targeting the full-length and DNA binding domains, respectively). Although we have no guarantee that both sera target different portions of the protein, these polyclonals were generated in different rabbits and therefore have a different background level of IgG antibodies in the sera. Immunoprecipitated DNA was used for library preparation, and sequenced by multiplexing 10 samples on one Illumina HiSeq lane. Two genomic input samples were sequenced for each time point.

ChIP-seq sample processing

Reads were aligned using Bowtie 1 (-n 2 -e 70 l 28 --maxbts 800 -y -m 1 --best --strata -S --phred33-quals) (Langmead et al., 2009) on Flybase-R1.2 assembly version of the D. virilis genome (corresponds to droVir3 in UCSC) and filtered for PCR and optical duplicates using ‘rmdup’ function of Samtools (Li et al., 2009). Peak calling was performed using MACS2 (-g 1.2e8 --to-large and -p 1e-3 as requested for the IDR analysis, see below) against stage-matched input. MACS2 peaks were selected using the Irreproducibility Discovery Rate (IDR) measure with the workflow (Landt et al., 2012) using a 1% IDR threshold leading to a unique, highly confident and consistent peak sets for biological replicates. More precisely, we selected the top Np peaks from MACS2 ran on merged replicates (where Np is the number of peaks returned at a 1% IDR threshold on the pooled pseudo-replicates analysis, see Figure 7 of [Landt et al., 2012]). Note that the selected peaks have MACS2 q-values < 0.001 in all conditions. For visualization, BAM file reads were extended to the average length of the genomic fragments for the corresponding experiment, merged and scaled to Read Per Million (RPM) followed by input subtraction of the control from the corresponding time point using deeptools (Ramírez et al., 2014).

Motif discovery on ChIP-seq samples was done using the console version of MEME-ChIP with the parameters ‘-meme-mod zoops -meme-nmotifs 5 -meme-minw 6 -meme-maxw 15’ (Machanick and Bailey, 2011). For each sample, fasta sequences of IDR defined peak summits were extended by 100 bp on 3’ and 5’and used as input. Discovered Position Weight Matrices (PWM) are shown in Figure 1—figure supplement 2.

Defining ChIP CRMs across two distantly related species

D. virilis ChIP-seq peak summits across all conditions were clustered using Bedtools (Quinlan and Hall) with a maximum distance of 400 bp between adjacent summits. Clusters were then extended by 100 bp to account for possible uncertainty in defining peak summits with high resolution. Coordinates of defined CRMs are provided in Supplementary file 2.

To identify orthologous TF binding events D. virilis IDR based MACS2 Peaks and CRMs coordinates were translated to D. melanogaster using the pslMap, an implementation of the TransMap alignment algorithm (Zhu et al., 2007). pslMap uses a precomputed alignment and assigns orthologous information based on alignment scores in a base-by-base manner, taking rearrangements including insertions and deletions into consideration, thereby allowing expansion or contraction of block sizes. pslMap is therefore more suitable for translation between species, while liftover is more suitable for translation between assemblies. Upon translation, 10,532 D. virilis CRMs (73% of the total 14,385 CRMs) were uniquely mapped. 275 CRMs mapped to more than one locus in D. melanogaster, yielding 541 CRMs (10,532 + 541 = 11,073).

The 10,532 D. virilis CRMs were overlapped with the 8,008 D. melanogaster CRMs to define a high confidence set of orthologous putative developmental enhancers. Clusters of overlapping CRMs involving >1 CRMs of either species were filtered out, resulting in a conservative set of 2846 orthologous CRM pairs. For TFBS density analysis (Figure 4a), each orthologous CRM of a pair was extended to an identical size S, equal to the sum of the size of both pair of CRMs (S/2 bases was added on each side of the CRM center). TFBS density analysis for each TF over all TFBS densities (Figure 4a) were computed using TFBSs located within (1) a 100 bp region around TF binding peak summit(s) when the CRM is bound by the considered TF (conserved binding) or (2) a 200 bp window randomly selected within the CRM boundaries when the CRM is not bound by the considered TF (lost binding).

Enrichments of characterized enhancers

To assess if our conserved set of CRMs are enriched for known mesodermal enhancers, we extended our previously built CRM Activity Database (CAD [Zinzen et al., 2009]) by adding the activity information of active tiles from Kvon and colleagues (Kvon et al., 2014) and a set of new entries from RedFly (Gallo et al., 2011). This led to a final set of 8876 enhancers of which 1254 are active in the mesoderm and 2970 are active in other tissues, excluding the mesoderm (Supplementary file 7). The remaining 3105 regions show no or ubiquitous staining. Fisher exact test was used to assess the significance of overlaps between different categories.

Hierarchical clustering of ChIP quantitative signal

Heatmaps for clustering of CRM pairs in conservation categories were constructed using the log2 (IP/control) for both D. melanogaster ChIP-chip and D. virilis ChIP-seq data. The Ward algorithm was used to cluster ChIP signals across orthologous CRMs using Pearson correlation to calculate distance. The R package ‘pheatmap’ was used to draw the heatmaps at a fixed scale for comparison.

Differential motif analysis on temporally bound interspecies enhancers

Early and late bound enhancers were determined in each species separately. First, enhancers only bound early (‘early enhancers’) were required to have a Twi ChIP peak at 2–4 hr (2–5 hr in D. virilis) but not at later stages while enhancers only bound late (‘late enhancers’) were required to have a Twi ChIP peak at 6–8 hr (7–10 hr in D. virilis) but not at earlier stages. Second, enhancers selected in the first step were post filtered using the quantitative ChIP signal, being required to have a ChIP signal ratio greater than two early/late or late/early for early and late enhancers, respectively. The final numbers of temporally bound elements were: 638 for Twi early, 439 for Twi late in D. melanogaster and 490 for Twi early and 254 for Twi late in D. virilis.

Motifs from the FlyFactorSurvey database (downloaded from the MEME (Bailey et al., 2015) web site) preferentially enriched in Twi early vs Twi late enhancers (and conversely) were identified in D. melanogaster and D. virilis separately using the following procedure. First, AME (McLeay and Bailey, 2010) was run with --scoring totalhits --method fisher --pvalue-threshold 0.0001 --pvalue-report-threshold 0.05 options on late bound enhancers with early bound enhancers as background, and conversely. Second, motif matches (for motifs reported by the AME analysis) were identified in enhancers using FIMO (Grant et al., 2011) ran with a similar p-value threshold (--thresh 0.0001) and 0-order background files generated using the genome fasta files. For each motif reported by AME, the overall ratio R (match number per Kb of sequence in early bound CRM/match number per Kb of sequence in late bound CRM) was computed and only motifs exhibiting an absolute log2(R) greater than 1 (i.e. 2 fold difference between early and late set) were considered in the heatmap presented in Figure 3b (redundant motifs were removed for clarity). The heat map therefore presents the (de-duplicated) motifs (1) found significant in either D. melanogaster or D. virilis AME analysis (or both) and (2) having an abs(log2(R))>1 in the organism where the motif was reported significant by AME.

Transcription factor binding site predictions and thresholds

Transcription factor binding sites (TFBS) predictions for Twi, Mef2, Tin, Bap and Bin were generated using patser (D. virilis options: -A a:t 0.28 c:g 0.18 -d2 -c -ls 0 s; D. melanogaster options -A a:t 0.28 c:g 0.20 -d2 -c -ls 0 s) (Hertz and Stormo, 1999) in both D. melanogaster and D. virilis genomes. For each TF, we selected the best performing PWM between PWMs discovered using D. virilis ChIP-seq peaks (this study, see ‘Motif discovery’ section) and the D. melanogaster PWM derived from ChIP-chip (Zinzen et al., 2009). For each PWM, patser was run with very low stringency (-ls 0) to predict TFBSs genome-wide in both D. melanogaster and D. virilis genomes. Each PWM was then evaluated using ROC plots representing true positive and false positive results gained from the whole range of patser scores (0 and higher). For each species, ChIP CRMs were split into two groups: a group containing ChIP CRMs bound by the considered transcription factor and a group containing ChIP CRMs not bound by that transcription factor. The latter was used as ‘background’ regions to control for general sequence biases in ChIP CRMs, such as GC content. These CRM groups were then used to assemble the positive and negative sets as 200 bp regions centered on ChIP peak summits. Note that summits were as (1) predicted by MACS2 for ChIP-seq or (2) obtained from Zinzen and colleagues (Zinzen et al., 2009) and translated from dm2 to dm3 using UCSC lift-over for the positive set; while a random summit position was sampled within each unbound CRM for the negative sequence sets (‘background’). Peaks from the positive and negative sets with a TFBS having a patser score ≥x were identified for x ranging from 0 to the maximum observed score, and used to build ROC plots using ROCR (Sing et al., 2005) presented on Figure 1—figure supplement 2. These plots indicate D. melanogaster Tin and Bin PWMs and D. virilis Mef2, Bap and Twi PWMs as the best performing models and are therefore used here. We finally determined the final thresholds as (1) the patser score giving a TPR of 70–80% and a FPR of 40% maximum on the best performing organism (as evaluated by the ROC) and (2) the patser score giving the same TPR for the other organism.

STAP analysis

STAP (Sequence To Affinity Prediction) is a thermodynamics-based method for predicting TF occupancy in a DNA segment, given the TF’s binding motif (He et al., 2009). We trained STAP models for each of the 14 conditions (TF, time point pairs) and in each species separately, following the method we used previously in Cheng et al. (2013). Each training set contained the top 1000 ChIP peaks and 1000 random windows of the same length, and their respective ChIP scores (suitably normalized, see below). Once the single free parameter in STAP was trained on these data, we used the trained model to score each CRM for motif presence. For each TF, the binding motif (PWM) was designated based on the best performing PWMs discovered from D. melanogaster and D. virilis ChIP data sets (this study, see ‘ChIP-seq sample processing’ section). To make the STAP scores comparable across species, we performed the following normalization on the ChIP scores in the training set: we set μ+3σ as the maximum ChIP score, where μ and σ are the mean and standard deviation of ChIP scores in the training set, replacing all ChIP scores greater than this maximum with μ+3σ, and finally applied min-max normalization to set all ChIP scores in a 0–1 range. We also normalized STAP scores computed by the trained model, using the same normalization procedure as above, separately for each data set.

To assess the accuracy of STAP on each of the 28 data sets (combinations of TF, time point, species), we applied 4-fold cross validation on the 2000 DNA segments mentioned above (each fold used 1500 segments to train the single free parameter in STAP and to score the remaining 500 segments). The resulting set of 2000 STAP scores were compared to respective ChIP scores, using Pearson correlation coefficient (PCC). The models fit the data very well, with PCC = 0.51 being the average over the 28 data sets (Supplementary file 8). For comparison, the average PCC in our previous analysis of 45 different ChIP data sets (also using 2000 segments) was ~0.30 (Cheng et al., 2013), and that the p-value of a PCC of 0.51 calculated on 2000 points is <1e−133. Figure 4b shows the Spearman correlation between differences in the normalized interspecies ChIP scores, ΔChIP = ChIPDmel - ChIPDvir (indicating an observed evolutionary change in TF occupancy) and the differences in the normalized STAP scores, ΔSTAP = STAPDmel - STAPDvir (indicating a change in predictive motifs) for each orthologous enhancer pair.

Figure 4c shows this correlation (interspecies differences between the normalized ΔSTAP and ΔChIP scores) for the subset of elements that represents two orthologous enhancer pairs bound by Tin in D. melanogaster. ΔChIP and ΔSTAP scores were converted to Z scores using the scale() function in R. Scatter plots presented in Figure 4c show the ΔSTAP-ΔChIP correlation of Tin Singletons (150 elements bound only by Tin in D. melanogaster) and High Bound enhancers (488 elements bound by Tin plus 3 or 4 additional TFs in D. melanogaster). Highlighted enhancer pairs were selected using the following criteria: (1) low absolute ΔSTAP (abs(ΔSTAP)<0.7) and high ΔChIP (ΔChIP > 1.5) correspond to orthologous enhancers in which Tin binding in D. melanogaster is largely reduced (or lost) in D. virilis without noticeable variation in the sequence fitness for Tin binding; and (2) high ΔSTAP (ΔSTAP > 1.5) and low absolute ΔChIP (abs(ΔChIP)<0.7) correspond to orthologous enhancers in which Tin binding in D. melanogaster is not affected in D. virilis while the sequence fitness for Tin binding is largely reduced.

Co-association and co-divergence analysis

Pairwise TF/time point co-association (Figure 5a) was performed following the method described by Cheng et al. with minor changes (Cheng et al., 2014). Briefly, we generated binary matrices with CRMs in rows and conditions in columns with cells containing 0 or 1 whether the CRM is bound at that condition or not, respectively. A real 14 × 14 association similarity matrix was generated first containing the number of times a certain association of conditions exists. We then permuted the content of each column and each row in the binary matrix by keeping the counts per column and row unchanged, and re-calculated a ‘pseudo’ association similarity matrix each time, for 500 times. This corresponds to the background distribution. The real association numbers for each pair of conditions were then compared to the ‘pseudo’ association numbers to derive z-scores.

To assess co-divergence in TF occupancy (Figure 5b), orthologous CRM pairs bound by two (or more) TFs in D. melanogaster were divided into all four possible binding categories: (1) CRMs with conserved binding of both TFs in D. virilis (‘TF1 and TF2 conserved’), (2) CRMs that have lost the binding of one TF (factor 1, ‘TF1 lost and TF2 conserved’), (3) CRMs that have lost the binding of the second TF (factor 2, ‘TF1 conserved and TF2 lost’) and (4) CRMs that have lost the binding of both TFs (‘TF1 lost and TF2 lost’). For each of the 10 possible co-association pairs (combinations of any two factors from the set of 5), the table in Figure 5b presents the CRM numbers found in each of the four categories together with the p-value of the Fisher exact test (with Benjamini & Hochberg correction for multiple testing) evaluating the binding independence of the two TFs. The expected number of D. virilis CRMs that would have lost the binding of both TFs under the null hypothesis (‘Expected TF1 lost and TF2 lost’ column of Figure 5b) is computed as: Expected1,2 = S1,2 * P1 * P2 where S1,2 is the number of orthologous CRMs co-bound by TF1 and TF2 in D. melanogaster (sum of the ‘TF1 and TF2 conserved’, ‘TF1 lost and TF2 conserved’, ‘TF1 conserved and TF2 lost’ and ‘TF1 lost and TF2 lost’ columns), P1 is the observed probability of a D. melanogaster CRM to lose TF1 binding in its D. virilis orthologous CRM (sum of ‘TF1 lost and TF2 conserved’ and ‘TF1 lost and TF2 lost’ divided by S1,2) and P2 is the observed probability of a D. melanogaster CRM to lose TF2 binding in its D. virilis orthologous CRM (sum of ‘TF1 conserved and TF2 lost’ and ‘TF1 lost and TF2 lost’ divided by S1,2).

Transgenic reporter assays

To assay ChIP-CRMs for their enhancer activity, corresponding genomic regions were amplified by PCR (primers list in Supplementary file 11) and placed upstream of minimal eve-promoter driving a lacZ reporter gene. All constructs were injected according to standard methods (Rubin, G.M. and Spradling, A.C, Science, 1982) into the landing site line M{3xP3-RFP.attP'}ZH-51C (Basler lab, Kosman, D et al., Science, 2004), yielding integration at chromosomal position 51C. Transgenic lines were generated by BestGene Inc.

Subsequently, formaldehyde-fixed embryos were stained by double fluorescent in situ hybridization (Furlong et al., Science, 2001) using anti-sense probes against lacZ and Mef2. All images were taken with a Zeiss LSM 510 META confocal microscope.

NGS data availability

Raw sequence data has been deposited in ArrayExpress under accession number E-MTAB-3798 (D. virilis Twi, Mef2, Tin, Bap and Bin ChIP-seq developmental time courses) and processed files for visualization are available at: http://furlonglab.embl.de/data/

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Decision letter

  1. Patricia J Wittkopp
    Reviewing Editor; University of Michigan, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Evolutionary changes in transcription factor occupancy and its impact on orthologous developmental enhancers' activity" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.

Our decision has been reached after extensive discussion among the reviewers and editors. Based on these discussions and the individual reviews below, we regret to inform you that your work (at least in its current form) will not be considered further for publication in eLife. We would, however, consider a resubmission that addresses the concerns raised by the reviewers. There was a consensus that the data collected were interesting and had the potential to move the field forward, but also that the specific analyses of these data and concluding points made in the manuscript were not sufficiently novel to justify publication in this journal. Many references were identified that we felt should have been cited and it is in part with these references in mind that the novelty of the submitted manuscript was lowered (see below). We also discussed how this work could be modified to increase impact and one well-received suggestion was that focusing more on comparing the developmental time courses might provide new insights. For example, can you identify novel functionality based on divergence of TF clusters? If not, then why? We envision that such a revision would likely involve novel analyses that more carefully connect the regulatory networks of matched stages between the species, which was largely untapped as an analysis strategy. Other ideas for reframing this work are suggested by individual reviewers below.

To more accurately discuss how your TF binding analyses confirms TF interaction concepts that have been discussed for some time, see the following recent high impact research studies and reviews:

doi: 10.1016/j.cell.2013.07.007.

doi: 10.1016/j.devcel.2011.09.008.

http://dx.doi.org/10.1016/j.tibs.2014.07.002

These two reviews cite many recent publications that analyse at TF interactions and co-binding in multiple eukaryotes, a selection of which should be smoothly incorporated into a new Discussion, as should the 2013 Cell paper.

In summary, we felt that the data underlying this work could be used to write a paper more suitable for eLife in terms of impact, but that performing new analyses and substantially rewriting the manuscript would take more than the two month revision window typically expected by eLife. The revised manuscript would also need to be subjected to a full review, thus we decided to reject it in its current form and encourage a resubmission.

Reviewer #1:

Khoueiry et al. provide ChIP-seq in vivo DNA binding of five transcription factors in D. virilis embryos at several stages of embryogenesis. They compare these data to existing ChIP-chip data of the same TFs at equivalent stages in D. melanogaster. As expected many regions bound strongly in both species are identified, including a set of ~3,400 likely cis regulatory modules (CRMs). The temporal patterns of DNA binding are well conserved over a large evolutionary distance. The most interesting results are the analysis of changes in DNA binding and the presence of recognition motifs between species. Consistent with earlier work, there is a clear correspondence between loss of recognition site motifs and loss of measured DNA binding in vivo for a subset of regions. For a subset of pair-wise combinations of transcription factors, there appear to be modest preferences for DNA binding to co-evolve. Unlike many prior studies, changes in the transcriptional output of enhancers is also examined, using transgenic analysis in D. melanogaster of D. virils CRMs.

The work is generally well done and controlled. The broad directions of the conclusions appear reasonable, but I find there to be significant over interpretation. There are several issues that I hope the Authors will address.

1) I am unclear how unique the conclusions are. The "Concluding Remarks" section is unusual. It is two pages in length. It makes sweeping declarations about what the authors believe to be the case, without indicating which claims are novel and which consistent with prior work. It references only one paper for one small point. Many of the claims appear to be consistent with earlier work. For example, it is well known that "the loss or gain of TF binding at an enhancer.… can have diverse effects" and that there is "plasticity of enhancers to evolutionary change". I suggest converting the Concluding Remarks to a proper Discussion section that sets the work in context indicating what is novel and what results support prior research. The Introduction of the manuscript in fact discusses the state of the field quite well.

The authors claim that "cooperative interactions allow a TF to occupy an enhancer despite a loss of its functional motifs". I don't recall that claim being drawn out in the Results. I don't see any evidence for it. As has been repeatedly observed, one often sees TF occupancy in vivo to genomic regions where there is no match to a motif at a given cutoff. But the presence of a lower affinity match in the bound genomic region is not ruled out and indeed is likely. What are the authors basing their claim on and how many instances is there evidence for in the data? How does their result differ from other work?

2) The analysis in Figure 5 is interesting. However, the authors may have over interpreted some of the differences between the more marginally significant and non-significant cases. The exact significance threshold will in part depend on a number of issues unrelated to biology, including the technical quality of the experimental data and the details of the statistical methods used. For example, the authors use Benjamini-Hochberg corrected p-values in Figure 5A, which is a less stringent correction than say a Bonferroni correction. Had the Bonferroni correction been used fewer significant co-evolving pairs of TFs would have been identified. The unidirectionality of interactions is open to debate, for example. I would recommend a more measured discussion of the trends, with a focus on the clearest results.

3) An important advantage of this paper is that expression patterns driven by enhancers are compared. However, only seven CRMs are examined, which should temper the somewhat sweeping conclusions made. How were the seven chosen exactly? Were they selected at random from a pool of all CRMs with moderate or poor binding conservation? Or were additional ad hoc criteria used? The authors stress the high conservation in the patterns of output expression between D. mel. and D. vir., which is true for some CRMs, but for others the differences in expression appear large, a point that is glossed over and is not captured in the simple summary table.

Reviewer #2:

There are many challenges in understanding when and where genes are expressed in development; identifying all the transcription factors required for organogenesis and their binding specificities, determining whether they bind DNA in cis or function through protein;protein interactions. One strategy is to use evolution and experimental data to decipher a single gene regulatory network. In this manuscript the authors investigate orthologous putative cis-regulatory regions for function using five mesodermal embryonically expressed transcription factors TWI, TIN, MEF2, BAP and BIN. Using Chromatin Immunoprecipitation (ChIP) and two distantly related Drosophila species – melanogaster and virilis they identified almost 3,000 conserved predicted CRMs. For seven pairs of predicted enhancers they generated transgenes using the D. melanogaster and D. virilis predicted CRM and tested activity in D. melanogaster. They found that four of the predicted CRM pairs regulate expression concordantly with the SVM predictions (for spatio-temporal activity from the TF binding profile), one pair has partial concordance with the SVM prediction, one pair showed mesodermal expression albeit not the same and the final pair had no expression. Although they have only validated a fraction of the predicted regulatory pairs the paper illustrates a very productive strategy to address questions of the control of gene expression and deserves to be published in eLife after the authors consider the following comments.

1) The D. melanogaster genome assembly was updated in 2015 from Release 5 to Release 6. They may want to use the latest genomic assembly.

2) The authors use the term Cis-regulatory modules (CRMs) to describe the ChIP bound regions. These should be called predicted or putative CRMs. In the field, a region is designated as a CRMs when it is shown to function to regulate gene expression or transcription rates.

3) The authors used D. melanogaster ChIP-chip and compared that to D. virilis ChIP-seq. They should discuss any issues of resolution obtained with the two protocols.

4) Regions where all the TFs bind have been called "hot spots". For the regions bound by all five TFs are they also hot spots? Or are they important for mesoderm development?

5) The methods section describing the alignments is quite cryptic. It should be expanded to describe exactly what how they mapped the D. virilis peaks onto the D. melanogaster sequence. What is the percent similarity of the regions? What cutoffs were used? Are the regions syntenic?

6) Subsection “Conservation of TF binding is factor-dependent”. Need to define "14 conditions". Sounds like all 5 TFs were tested under 14 conditions for a total of 70 experiments. When in fact it is 14 experiments total for the 5 TFs at the developmental stages where they are most active. Please clarify.

7) Could get estimates of how many of the D. melanogaster predicted CRMs are functional by comparing to the literature curated experimentally proven CRMs from RedFly and from the Kvon et al., study Nature.

8) It is difficult to determine when the authors are referring to the gene or the protein (e.g. Subsection “Conservation of TF binding is factor-dependent”). Some conventions are to italicize genes and capitalize proteins. So "Twist directly regulates Tinman" would be "TWI directly regulates Tin”. The first time the TF is mentioned the abbreviation should follow in parenthesis. It will make the manuscript more clear.

9) How were the proteins generated for antibody production? Could you expand the Methods section (subsection “ChIP-seq on D. virilis embryos”)?

10) Figure 2 and all subsequent gene maps for the D. virilis panel shows D. melanogaster gene annotations that are poorly drawn – are these gene spans? Why aren't you showing the D. virilis gene model annotations (e.g. CG9253 is GJ16136)?

11) Figure 2 and all subsequent gene maps should show genome arm and coordinates (D. mel) and scaffolds (D. vir) since the scale bar is not clear. Is it only for the D. virilis panel or both?

Reviewer #3:

This paper reports the collection and analysis of binding data for 5 key mesodermal TFs at 5 developmental timepoints in Drosophila virilis. This dataset complements a comparable dataset collected by the same group in D. melanogaster and allows them to conduct systematic analyses of conservation of context dependent TF binding over developmental and evolutionary time. The dataset was carefully collected and will be useful to the fly community, and likely useful to the broader computational community studying enhancer evolution.

The paper is framed as a general study on enhancer evolution, but in my opinion, the most interesting and novel contribution is a direct test of the "TF collective" model that the authors put forth previously. In this model, TFs can bind to DNA via contacts with other TFs, such that a cognate DNA recognition motif is not strictly required. A clear prediction of this model is that over evolutionary time, binding of such a TF may be conserved while the underlying motif is not. They indeed observe this behavior for Tinman, and by tracking co-binding and loss of co-binding, identify the TFs that may work collectively with Tinman. I believe the article would be more effective if crafted more directly around testing this model, with the additional observations about enhancer evolution framed as observations that emerge from the dataset and provide additional evidence for well-established concepts in the field.

As currently written, the paper marches through many well-established concepts in the field and the novelty of the work is a bit lost. For example, their functional tests of 7 pairs of orthologs (14 reporter constructs total) demonstrate the concept that we cannot yet predict the relationship between enhancer sequence, TF occupancy and function. I think this is a key puzzle for the field, so more evidence of interesting discrepancies is welcome, but it is not a major take home message of the paper, as put forth in the abstract.

My only major technical criticism concerns their identification of orthologous enhancers based (apparently) just on synteny. They identify ChIP peaks separately in each species and then use pslMap to find corresponding peaks between D. mel and D.vir, which they assign as orthologous and use for all of the subsequent analyses. Given that enhancers with similar activity can move genomic location (e.g. Kalay and Wittkopp, PLoS Genetics 2010), assigning orthology based on synteny may not be straightforward. This is a critical part of their study, and they should clearly justify their strategy, and its potential pitfalls and impacts on their downstream analyses. It may be useful to cross-check their assignments by searching locally for better matches to D. mel enhancers based on sequence or occupancy profile; this would potentially give a sense of the error in their synteny based assignment.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for submitting your article "Uncoupling evolutionary changes in DNA sequence, transcription factor occupancy and enhancer activity" for consideration by eLife. Your article has been reviewed by three peer reviewers (one from the original submission plus two new reviewers), and the evaluation has been overseen by Patricia Wittkopp as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: David N Arnosti (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The changes made to this work have clarified its novel contributions to the field. The revised manuscript effectively summarizes the way in which the authors focus on a core set of putative CRM for further analysis, concisely indicates how overall patterns for binding show a high degree of conservation in developmental windows, and the extent to which DNA sequence is able to predict occupancy on CRMs classified as highly bound or not, and specific cases for which the "collective" model of enhancer occupancy is substantiated (particularly for elements involving Tinman). This last point represents substantial progress over their previous studies, in which the lack of observable cognate binding motifs was suggested to be evidence for occupancy driven largely by non-DNA mediated interactions with neighboring factors. Here, the authors clearly show that for this group of CRM, there are a sizeable number that appear to fit this category, and they have sufficient information to assess the frequency with which altered binding of factor A is linked to changes in motifs for factor B. We also appreciated the more detailed and measured statistical analysis of Figure 5 and that the approach for choosing CRMs for transgenic analysis was made more transparent and the results more thoroughly discussed. Overall, the revisions make this paper a strong contribution to the literature that will be of great interest to the evo/devo and genomic research communities. We do, however, also have some additional questions/concerns that need to be addressed prior to publication.

Essential revisions:

1) The use of FDR is standard for the field and must be provided in addition to any other metrics the authors wish to present. We do not anticipate this will change the results. In addition, if the IDR results remain, please add language recognizing that it is a relative measure of reproducibility between treatment replicas and does not control for systematic biases due to, for example, preferential amplification of some genomic regions over others.

Additional details including the control used and threshold applied must also be provided for readers to properly interpret the q value calculations presented.

2) The authors should make clear that only replica antiserum against the same set of epitopes was used for each protein. In contrast, studies by other groups have used two sera reacting with different portions of the ChIP'd protein as a "gold standard" for ChIP-seq. The revised manuscript must clearly state what might be missed by using only serum to common epitopes, rather than two antisera recognizing different parts of the same protein. We encourage the authors to use reagents recognizing different epitopes or domains of the proteins of interest in the future, as other researchers have done.

3) Additional methodological detail is needed. The Materials and methods are missing some important information; details on chromatin preparation (presumably similar or identical to earlier work, but not cited), numbers of reads obtained from different ChIP-seq experiments.

4) It seems the authors are considering only direct cooperativity between proteins. Indirect cooperativity is also a major mechanism where the binding of one protein impacts the binding of another protein via an effect of nucleosome occupancy and should be mentioned

5) The claim that Mef2 binds a less conserved set of peaks in early time points is somewhat driven by the large number of peaks in D. melanogaster at time point 2, compared to D. virilis, which is in contrast the rest of the data, where D. virilis peaks far outnumber D. melanogaster peaks. Is this difference because of differential expression dynamics/levels of Mef2 in the two species? In general, do we know if the expression dynamics/levels of these factors are conserved between these species? How might this impact the measured conservation of binding?

6) Please provide additional pieces of information to confirm the claims that Tin binding is less dependent on sequence as binding complexity increases. For example, no statistical test was provided for the motif density comparison in subsection “TF binding site divergence is tolerated in the context of collective TF binding”. Please provide the reader with more information as to how to compare the numbers of red/green dots in Figure 4C; there seems to be different numbers of data points in the two plots and fractions are not reported/compared.

7) It would be interesting to know if the SVM from Zinzen 2009 already accounts for the fact that differential TF occupancy can lead to the same expression output in the manner observed here. For example, if the SVM were given the binding profile of the D. vir CRM 10323, would it predict SM expression? Or given the D. vir CRM 2469 binding profile, would it predict VM expression? This would indicate whether this divergence in TF binding leading to the same outcomes is already observed within D. melanogaster or whether new and unique regulatory logic is being employed in D. virilis, which may indicate the involvement of other TFs.

https://doi.org/10.7554/eLife.28440.029

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Reviewer #1:

Khoueiry et al. provide ChIP-seq in vivo DNA binding of five transcription factors in D. virilis embryos at several stages of embryogenesis. They compare these data to existing ChIP-chip data of the same TFs at equivalent stages in D. melanogaster. As expected many regions bound strongly in both species are identified, including a set of ~3,400 likely cis regulatory modules (CRMs). The temporal patterns of DNA binding are well conserved over a large evolutionary distance. The most interesting results are the analysis of changes in DNA binding and the presence of recognition motifs between species. Consistent with earlier work, there is a clear correspondence between loss of recognition site motifs and loss of measured DNA binding in vivo for a subset of regions. For a subset of pair-wise combinations of transcription factors, there appear to be modest preferences for DNA binding to co-evolve. Unlike many prior studies, changes in the transcriptional output of enhancers is also examined, using transgenic analysis in D. melanogaster of D. virils CRMs.

The work is generally well done and controlled. The broad directions of the conclusions appear reasonable, but I find there to be significant over interpretation. There are several issues that I hope the Authors will address.

1) I am unclear how unique the conclusions are. The "Concluding Remarks" section is unusual. It is two pages in length. It makes sweeping declarations about what the authors believe to be the case, without indicating which claims are novel and which consistent with prior work. It references only one paper for one small point. Many of the claims appear to be consistent with earlier work. For example, it is well known that "the loss or gain of TF binding at an enhancer can have diverse effects" and that there is "plasticity of enhancers to evolutionary change". I suggest converting the Concluding Remarks to a proper Discussion section that sets the work in context indicating what is novel and what results support prior research. The Introduction of the manuscript in fact discusses the state of the field quite well.

We wrote the paper in the style of a mixed ‘results and discussion’ section, followed by a short concluding remarks, as the paper is quite long and we had also described the state of the field in the introduction, which we appreciate the reviewer’s comment on. Although those are the main reasons, it also stems from a personal preference of not putting too much weight on the Discussion sections of the published literature. However, given the reviewers comment, we have now changed the format to a discussion.

The authors claim that "cooperative interactions allow a TF to occupy an enhancer despite a loss of its functional motifs". I don't recall that claim being drawn out in the Results. I don't see any evidence for it. As has been repeatedly observed, one often sees TF occupancy in vivo to genomic regions where there is no match to a motif at a given cutoff. But the presence of a lower affinity match in the bound genomic region is not ruled out and indeed is likely. What are the authors basing their claim on and how many instances is there evidence for in the data? How does their result differ from other work?

We apologize for the lack of clarity in our statement leading to this confusion. The statement is suggested by a combination of two main observations, described below. But first, just to clarify, to avoid exactly this motif cut-off issue raised by the reviewer, we purposely used STAP, a threshold free approach. Our observations are therefore based on first estimating the net motif presence in a CRM using STAP, which utilizes a thermodynamics-based model of occupancy taking both high and low affinity sites into account. These estimates are then compared between orthologs and this ‘ΔSTAP’ score is then used as a measure of motif loss or conservation.

Our two main observations are the following: First, Figure 4B shows that changes in Tin binding (ΔChIP) between orthologous CRMs is relatively well explained by changes in Tin motif presence (ΔSTAP) when we consider ‘singleton’ CRMs, but that this relationship is significantly weaker when examining CRMs occupied by multiple TFs (‘high occupancy’ class). We interpret this as implying that Tin binding is less dependent on its motif in the context of other functionally related ‘partner’ TFs due to protein::protein interactions, consistent with the TF collective model. We note that this is not a simple matter of inadequate motif modelling, since the same score of motif presence is used in examining the two classes of CRMs. Moreover, the observation is not merely about TF binding and its relation to motif presence but about change in TF binding and its relation to change in motif presence. We have observed a statistically significant difference in how this expected relationship manifests in singleton CRMs versus high occupancy CRMs.

Second, motivated by the above observation and its possible implications, we tested if change in Tin binding (ΔChIP) between orthologous CRMs is significantly associated with changes in motif presence of a secondary mesodermal TF such as Twi, Mef2, Bap or Bin, all of which are known to be important regulators of the system under study. As shown in Figure 5B, we found that this is indeed the case, for Bap (uncorrected p-value 2.09E-4), Bin (uncorrected p-value 1.68E-2), and marginally so for Twi (uncorrected p-value 4.4E-2).

Taken together, these finding suggest that conservation of TF occupancy, i.e. whether a TF’s ChIP peak remains occupied in an orthologous CRM, is partly determined by whether a secondary TF’s motif is conserved or not. We have now reworded the quoted statement in the above comment to reflect this interpretation more accurately. We have also included a model figure in the discussion (Figure 7), which highlights what has been observed in other studies and what is new in ours. It shows three different motif:TF binding relationships, one of which (TF collective) is novel to this study.

We believe the novelty of our claim is in its evolutionary context, in a regulatory system where we know that the TFs examined are genetically required for this tissues development, and therefore these TF bound enhancers are very likely to be functionally relevant to the gene’s expression in this spatio-temporal domain. The reviewer correctly points out that the role of secondary TFs in shaping occupancy of a TF has been amply recorded in the literature. So the evolutionary loss of one TF’s binding can be due to the loss of another TFs binding, while the first TF’s binding site is conserved. A number of groups have observed this (which we reference), and we see this as well in our data (now highlighted in the new Figure 4C, and Figure 7). But, importantly, we also find examples of the opposite: conservation of a TFs occupancy when its motif has diverged. This is consistent with the increased flexibility in TF occupancy predicted by the ‘TF collective model’, and this the first large scale cross-species study showing that that is the case (Figure 4 and 7). The TF collective model implies a greater neutral neighbourhood for evolutionary changes than has been noted. We have expanded on this aspect of the paper as suggested by reviewer 3.

2) The analysis in Figure 5 is interesting. However, the authors may have over interpreted some of the differences between the more marginally significant and non-significant cases. The exact significance threshold will in part depend on a number of issues unrelated to biology, including the technical quality of the experimental data and the details of the statistical methods used. For example, the authors use Benjamini-Hochberg corrected p-values in Figure 5A, which is a less stringent correction than say a Bonferroni correction. Had the Bonferroni correction been used fewer significant co-evolving pairs of TFs would have been identified.

We fully agree with the reviewer’s point. Selecting significant thresholds and/or the multiple testing correction method is subject to both common practices (e.g. selecting a 0.05 significance threshold) and the analysis context (FDR thresholds up to 20% are often used in screening contexts when eliminating false positives is less important than missing true positives). Although the Benjamini-Hochberg procedure is a less stringent correction than the Bonferroni correction, the overall conclusions are not strongly affected by using the Bonferroni correction (see table below). Only the Bin-Bap associations become not significant with the Bonferroni correction (0.04 BH corrected p-value vs 0.16 Bonferroni corrected p-value). Given the low number of counts, we decided to use the well-established Benjamini-Hochberg correction, which evaluates the False Discovery Rate (as opposed to controlling the Family Wise Error Rate in the Bonferroni case) method. We fully agree that the significance of the Bin-Bap association should be regarded with caution. We now mention this association, but do not expand on it.

A comparison of the Benjamini-Hochberg (BH) and Bonferroni corrected p-values is provided for all pair-wise comparisons below (cons = conserved; Exp = expected). Yellow indicates the 4 significant cases under discussion.

TF1TF2TF1 & TF2
cons
TF1 cons
TF2 lost
TF1 lost TF2 consTF1 & TF2 lostExp TF1
& TF2 lost
PvalBH (FDR)

Bonfer roni
twibin1171042822237.55E-017.55E-01

1.00E+00
twitin443854020112.12E-037.06E-03
twibap6496929246.45E-021.29E-01

6.45E-01

twimef24202976129361.10E-011.83E-01

1.00E+00
bintin104326030251.28E-011.83E-01

1.00E+00
binbap49671241341.58E-023.95E-021.58E-01
binmef21575912656535.06E-015.62E-01

1.00E+00
tinbap9082147286.00E-126.00E-116.00E-11
tinmef23231546769491.15E-045.74E-041.15E-03
bapmef2671711743392.77E-013.46E-01

1.00E+00

The unidirectionality of interactions is open to debate, for example. I would recommend a more measured discussion of the trends, with a focus on the clearest results.

This is a fair point, and we can now see how the table in Figure 5B (now Figure 5C) was confusing. We previously reported all interactions, while we were primarily interested in testing the associations between the binding loss of TF1 and the loss of sequence support for a TF2, in particular for the Tin-Twist, Tin-Mef2 and Tin-Bap pairs (and to a lesser extend Bin-Bap) i.e. 6 tests (or 8 tests when considering Bin-Bap), but we presented the results of all 20 directional tests. We acknowledge the reviewer’s advice to focus on the clearest results- we now realize it was detracting from our main point to present the results from all 20 tests and we have therefore modified the table to present the clearest results, which is for Tin-Twist, Tin-Mef2 and Tin-Bap pairs only. In this context, the p-values for Mef2-Tin (1.96E-4), Tin-Bap (2.09E-4) and Bap-Tin (8.44E-4) remain clearly significant even under Bonferroni correction, while the p-values for Twi-Tin (4.3E-2) and Tin-Twi (4.4E-2) should be considered with care. The manuscript was already cautious about the Tin-Twi results (“Our results also suggest …”) and we therefore left this part of the text unmodified.

3) An important advantage of this paper is that expression patterns driven by enhancers are compared. However, only seven CRMs are examined, which should temper the somewhat sweeping conclusions made. How were the seven chosen exactly? Were they selected at random from a pool of all CRMs with moderate or poor binding conservation? Or were additional ad hoc criteria used?

Thank you for recognizing the importance of testing the expression patterns of the orthologous enhancers. In our manuscript we tried to move away from simply examining conserved or non–conserved binding to rather compare what effect conservation/divergence in TF binding has on enhancer activity – which is in the end is what selection will be acting on. We note that ‘only seven CRMs’ means in effect generating 14 transgenic strains, as we tested the enhancer from each species.

The CRM pairs chosen for in vivo testing were not chosen randomly, as we specifically wanted to test the assumption that divergence of occupancy means diverged enhancer activity. We state the criteria used to select the enhancers for testing in the manuscript (subsection “Linking evolutionary changes in TF occupancy to their effects on enhancer activity”): “(1) high SVM specificity (spec. > 0.98) of the predicted spatio-temporal expression of the D. melanogaster CRM, (2) medium to low Jaccard index (J <= 0.5) indicative of a medium to high turn-over in TFs binding on orthologous CRMs (Table S10) and (3) good alignment of orthologous loci at the corresponding CRM regions” to ensure that the true orthologous enhancer is cloned. We only retained pairs bound in at least 5 different conditions (altogether) to avoid reviewing CRM pairs with marginal changes in their binding profiles. Of the 161 orthologous pairs passing all these criteria, we then checked manually for the presence of a good alignment of orthologous loci using the Integrative Genome Viewer (IGV). These “objective” qualitative criteria ensured that the integrity of the CRM and its surrounding locus is preserved and constitute thus a valid option for the subsequent in-vivo analysis. We now also add these criteria to the text.

The authors stress the high conservation in the patterns of output expression between D. mel. and D. vir., which is true for some CRMs, but for others the differences in expression appear large, a point that is glossed over and is not captured in the simple summary table.

Yes, for some enhancers the expression is highly conserved, while for others it has partially diverged, having conserved activity in some tissues and non-conserved in others. We have now extended the text to highlight this. For some enhancer pairs this may be due to the binding of other TFs, which were not assayed in our ChIP-seq, on the tested CRM pairs. The text now states “We note that, although the TF binding is highly conserved, three pairs (D. mel CRM-6087/D. vir CRM-459; D. mel CRM-4682/D. vir CRM-12291; D. mel CRM-4725/D. vir CRM-12156) showed only a partial overlap in their activity, probably due to the binding of additional TFs not assessed here.”

Reviewer #2:

[…] 1) The D. melanogaster genome assembly was updated in 2015 from Release 5 to Release 6. They may want to use the latest genomic assembly.

While we agree that this would be great (and the lab is migrating to Dm6), we regret that this is not possible for this project. The dm6 version was released in 2015, as the reviewer mentioned, when a substantial amount of the analysis was already completed, including all reads alignment, motif discovery analysis, chains used for peak translation between species as well as many other complementary analysis. However, we will provide Dm6 peaks, generated by liftover to dm6 coordinates as it is generally done, as well as Dm3 peaks on our web page when published, as we routinely do for all studies.

2) The authors use the term Cis-regulatory modules (CRMs) to describe the ChIP bound regions. These should be called predicted or putative CRMs. In the field a region is designated as a CRMs when it is shown to function to regulate gene expression or transcription rates.

We completely agree with the reviewer. We typically use ChIP-CRM to describe putative enhancers, and enhancers when the region has been shown to function as an enhancer in transgenic embryos. For this data, we first defined this for the melanogaster data in 2009 “…8,008 ChIP defined cis-regulatory modules (ChIP-CRMs), henceforth called CRMs”. In the current paper we state “…8,008 ChIP defined putative CRMs (ChIP-CRMs), henceforth called CRMs”. We state this “CRM” definition at the outset in the paper (subsection “Conserved enhancer occupancy is associated with enhancer function”).

3) The authors used D. melanogaster ChIP-chip and compared that to D. virilis ChIP-seq. They should discuss any issues of resolution obtained with the two protocols.

This was something that we were concerned with. We show in Figure—figure supplement 1that signals of TF occupancy identified by ChIP-chip globally correlates with peaks identified by ChIP-seq for the same factor (Mef2) at the same time point (6-8h) and in the same species (D. melanogaster). This indicates that the overall resolution difference between the two technologies does not have a major effect. To control for the different base-pair resolution of ChIP-seq compared to ChIPchip, we performed the bulk of our analyses based on the ChIP peak center, extended by a defined number of bases, to minimize any resolution effects. For instance, CRM calling in both species was based on TF peak centers and defining CRMs upon clustering nearby cases (Methods). Also, motif discovery considers peak centers extended by 100bp.

4) Regions where all the TFs bind have been called "hot spots". For the regions bound by all five TFs are they also hot spots? Or are they important for mesoderm development?

Kvon et al., nicely demonstrated that HOT regions function as patterned developmental enhancers in their Genes Dev. 2012 paper. In addition, in our Zinzen et al., 2009 paper, one of the CRMs we demonstrated functions as an enhancer in transgenic is a CRM bound by the 5 different TFs studied here (Figure 5B, CRM 4726). Therefore, both HOT regions and regions bound by our 5 mesodermal factors function as enhancers, and are likely important for development. To specifically answer the reviewer’s question, we split the HOT regions (downloaded from http://www.modencode.org/publications/files/fly/DataS8.gff) and, as described in Kvon et al., (Genes Dev. 2012), split them into HOT (complexity score strictly >8, 1854 regions), WARM (3 < complexity score ≤8 and strictly, 8467 regions) and COLD (complexity score ≤3, 28241 regions) groups. We then assessed if the 137 Dmel ChIP-CRMs bound by the 5 TFs overlaps more with the HOT group compared to the other two groups. Requesting for at least 50% overlap of the CRMs, we find that 50, 48 and 20 CRMs bound by the 5 TFs overlap with HOT, WARM and COLD regions, respectively. These numbers indicate that Dmel ChIP-CRMs bound by the 5 TFs are equally associated with HOT and WARM regions, confirming the results from Kvon et al., but also that 87 of the 137 CRMs (63%) are not HOT regions.

5) The methods section describing the alignments is quite cryptic. It should be expanded to describe exactly what how they mapped the D. virilis peaks onto the D. melanogaster sequence. What is the percent similarity of the regions? What cutoffs were used? Are the regions syntenic?

We have now expanded this part of the Methods section. The regions are a bit more than syntenic, as captured by the alignment. We did not use a cutoff here. We have elaborated and described the pslmap algorithm in the methods. Briefly, Pslmap uses a precomputed alignment, as the liftover used in He, Bardet et al., and assigns orthologous information based on alignment scores. It does this base-by-base. The advantage over liftover (which does block-level mappings) is that pslmap takes into consideration rearrangements including insertions and deletions, allowing expansion or contraction of block sizes.

6) Subsection “Conservation of TF binding is factor-dependent”. Need to define "14 conditions". Sounds like all 5 TFs were tested under 14 conditions for a total of 70 experiments. When in fact it is 14 experiments total for the 5 TFs at the developmental stages where they are most active. Please clarify.

We agree, we have now rephrased it as follows “The number of time-points examined for each TF reflects the developmental stages that the TF is expressed, totaling 14 conditions (TF and time point).

7) Could get estimates of how many of the D. melanogaster predicted CRMs are functional by comparing to the literature curated experimentally proven CRMs from RedFly and from the Kvon et al., study Nature.

We agree – we actually did such a comparison in Figure 2D, comparing experimentally validated and curated CRMs from RedFly and Kvon et al., study (Figure 2D). We state, in methods subsection “Enrichments of characterized enhancers”: To assess if our conserved set of CRMs are enriched for known mesodermal enhancers, we extended our previously built CRM Activity Database (CAD (Zinzen, 2009) by adding the activity of active tiles from Kvon and colleagues (Kvon, 2014) and a set of new entries from RedFly (Gallo, 2011).

8) It is difficult to determine when the authors are referring to the gene or the protein (e.g. Subsection “Conservation of TF binding is factor-dependent”). Some conventions are to italicize genes and capitalize proteins. So "Twist directly regulates Tinman" would be "TWI directly regulates Tin”. The first time the TF is mentioned the abbreviation should follow in parenthesis. It will make the manuscript more clear.

We now updated the text to reflect the reviewer’s valid point. All protein instances are now capitalized and abbreviated (i.e. Twi for Twist) and gene instances are lower case and italicized (i.e. twi for the twist gene). In this case Twi (the protein) regulates tin (the gene).

9) How were the proteins generated for antibody production? Could you expand the Methods section (subsection “ChIP-seq on D. virilis embryos”)?

The antibodies directed against the virilis proteins were generated as follows – first the protein was expressed in, and purified from, E. coli as a SUMO fusion protein. This was then injected into rabbits, in the EMBL animal house, to raise antibodies. We then used the SUMO tag purified protein to purify the antibodies. We now add more information on this to the method section.

10) Figure 2 and all subsequent gene maps for the D. virilis panel shows D. melanogaster gene annotations that are poorly drawn – are these gene spans? Why aren't you showing the D. virilis gene model annotations (e.g. CG9253 is GJ16136)?

Yes, those are gene spans. The orthologous information we had was mapping D. mel genes to D. vir gene spans. We updated the figures now to include all gene information with exons and introns clearly visible.

11) Figure 2 and all subsequent gene maps should show genome arm and coordinates (D. mel) and scaffolds (D. vir) since the scale bar is not clear. Is it only for the D. virilis panel or both?

The same scale was used for screenshots of both species. We now state that in figure legends by saying (i.e. in Figure 2 legend): “The same scale was used for both species and indicated on the lower right corner”.

Reviewer #3:

[…] My only major technical criticism concerns their identification of orthologous enhancers based (apparently) just on synteny. They identify ChIP peaks separately in each species and then use pslMap to find corresponding peaks between D. mel and D.vir, which they assign as orthologous and use for all of the subsequent analyses. Given that enhancers with similar activity can move genomic location (e.g. Kalay and Wittkopp, PLoS Genetics 2010), assigning orthology based on synteny may not be straightforward. This is a critical part of their study, and they should clearly justify their strategy, and its potential pitfalls and impacts on their downstream analyses. It may be useful to cross-check their assignments by searching locally for better matches to D. mel enhancers based on sequence or occupancy profile; this would potentially give a sense of the error in their synteny based assignment.

We also shared the reviewer’s concern in how best to assign orthologous enhancers, and actually explored different ways to do this. “pslmap”, unlike liftover used previously (i.e. He, Bardet et al), does a “base-by-base mapping, which may insert or delete bases within a block”. This is thus not a “simple synteny” approach, where an anchor point is used to define syntenic elements. The mappings produced by liftOver is block-level, which may expand or contract the size of blocks and is thus not ideal for defining orthologous regions between distantly related species.

Additionally, and most importantly, we translated CRMs from D. virilis to D. melanogaster and not peaks or reads. This is important, as if one translates the reads first (from D. vir to D. mel) and then call peaks, the peak calling is significantly affected by the liftover of each short read in such distant species. Therefore, we defined significant TF peaks in D. virilis, and defined D. virilis CRMs, after lifting over the D. virilis CRMs to D. mel we maintain the binding information (i.e. number and quality TFs bound) to that of D. virilis, as measured. We are not assigning TF peak to peak (D. virilis to D. melanogaster), which would be the case if we translated the data at the level of peaks. By translating CRMs, for example, a Bap TP3 peak in D. virilis (7-10h) is considered conserved if it falls in D. virilis CRM whose translation overlaps with a D. mel CRM with a Bap TP3 (6-8h) in it, independent of whether both peaks overlap. Figure 2A gives a faithful example of that. For instance, Figure 2A upper panel shows a strong conservation pair with a Jaccard index of 0.66 with both the red and green TFs conserved, although not aligned. This shows that we did not restrict our conservation analysis to aligned peaks (strict synteny) from both species, but rather allow assignments of conserved peaks by “searching locally”, in the CRM element itself. Zooming out, it is true (and we are aware) that enhancers themselves can move around significantly during evolution. An enhancer, for example 5’ of the gene, might move into an intron or 3’, just as an example. In this case, we would not have called an orthologous CRM, as there would be no CRM overlapping our D. virilis element. We are aware of this, but our goal here was to obtain a stringent set of orthologous elements (high specificity), rather than casting the net (genomic distance) wider with the risk of defining artificial orthologous pairs.

Given the reviewers suggestion we have now examined this issue, i.e. how much ‘CRM movement’ is there locally, by assessing the frequency of binding events in CRMs in close proximity to our orthologous CRM pairs with conserved or lost binding. If a locally better match CRM exists, in terms of matching patterns of TF occupancy, the average distance to those matches should be small, taking the compact genome of both assessed Drosophila species. For this, we split CRMs assigned as orthologous into two categories: Those with a conserved binding for the assessed condition (TF and Time point) and those with a binding loss in D. mel or in D. virilis (they are still defined as CRMs since they have a binding for another factor). In each case, we calculated the distance to the closest binding event (i.e. for Twi_TP1, we calculated the distance to the closest Twi binding at the first time point). In all 14 conditions, the median distance to the closest TF exceeded 5kb when we look in D. melanogaster. This was also the case when we looked in D. virilis, except for Twi at the second time point and Tin at both time points where the medians were between 1kb and 5kb. As a reminder, the median size of D. virilis and D. melanogaster CRMs is 200bp.

This analysis (Author response image 1)shows that, in average, we need to search for additional binding events (a putative orthologous CRM) as far away as 5kb, which as you know is a large distance for Drosophila’s relatively compact genome. Importantly, in 68% of these cases we had already assigned the distant CRM to an orthologous CRM pair that overlapped in both species. In these cases we would therefore have to consider two-to-one cross-species matches, one overlapping and one several kb away. For comparison, the median spacing between all consecutive CRMs is 3.2kb in D. virilis (Mean = 10.5kb, 14386 CRMs) and 3.3kb in D. melanogaster (Mean = 14.4 kb, 8008 CRMs). We therefore feel we risk assigning false pairs at larger distances. Moreover, owing to their modular and separable nature, CRMs as close as 500 bp can have completely distinct activity. For example, we previously showed that the 1.2 kb Combo region (Zinzen et al., 2009) harbors two CRMs, with some shared binding, where one drives activity in the somatic muscle and the other in the visceral muscle. Finally, given that we already have nearly 3000 orthologous pairs using a conservative overlapping approach, we feel this is ample numbers to examine the global properties of orthologous binding events. For other, more ‘loose’ definitions of orthologous enhancer pairs, all data will be available for future studies to explore.

Author response image 1
Boxplots represent the distribution of distances to closest peaks (in logarithmic scale) for each of the 14 conditions assayed.

The distribution of distances of a TF binding event lost in D. mel (BindingLostInDmel) at the indicated condition (i.e. Twi_TP1) to the closest peak for the identical condition in another CRM. Similarly, BindingLostInDvir shows the distance distribution of a TF binding event lost in D. virilis at the indicated condition to the closest peak for the identical condition in another CRM. BindingConsDmelCoor shows distance distribution for D. melanogaster CRMs that have their binding conserved and similarly for BindingConsDvirCoor..

https://doi.org/10.7554/eLife.28440.025

In addition to the above analysis, we calculated the absolute number of CRMs that have a close binding event, for different distances and categories. Distances to closest binding event were assigned to one of the 5 categories (<100bp, 100-200bp, 200-500bp, 500-1000bp and > 1000bp). Again, one must go to distances >1kb from the CRM to find region bound by one of the same TFs at the same time point (TP).

Author response image 2
Count of CRMs with a close binding event falling in one of the five distance categories.

y-axis shows absolute counts (also displayed as numbers above corresponding bars) and x-axis the 5 distance categories. The analysis was done for all 4 categories of CRMs: Binding lost in D. mel, binding lost in D. virilis, binding conserved (for D. mel CRMs) and binding conserved (for D. virilis CRMs).

https://doi.org/10.7554/eLife.28440.026

[Editors' note: the author responses to the re-review follow.]

Essential revisions:

1) The use of FDR is standard for the field and must be provided in addition to any other metrics the authors wish to present. We do not anticipate this will change the results. In addition, if the IDR results remain, please add language recognizing that it is a relative measure of reproducibility between treatment replicas and does not control for systematic biases due to, for example, preferential amplification of some genomic regions over others.

Additional details including the control used and threshold applied must also be provided for readers to properly interpret the q value calculations presented.

We apologize for the lack of peak calling details in the Materials and methods.

We have amended the manuscript to reflect the reviewer’s point as follows:

The main text was changed from “The number of high confidence peaks, called at a 1% Irreproducible Discovery Rate (IDR) threshold (Materials and methods)” to “The number of high confidence peaks, called at a 1% Irreproducible Discovery Rate (IDR, a measure ensuring equivalent reproducibility between replicas) threshold, corresponding to peak calling q-value < 0.001 in all conditions (Materials and methods)”.

Additionally, the method section “ChIP-seq sample processing” was enriched with all peak-calling details.

2) The authors should make clear that only replica antiserum against the same set of epitopes was used for each protein. In contrast, studies by other groups have used two sera reacting with different portions of the ChIP'd protein as a "gold standard" for ChIP-seq. The revised manuscript must clearly state what might be missed by using only serum to common epitopes, rather than two antisera recognizing different parts of the same protein. We encourage the authors to use reagents recognizing different epitopes or domains of the proteins of interest in the future, as other researchers have done.

We thank the reviewer for highlighting this important technical issue. In fact, we have used two different sera for two of our transcription factor ChIP, Mef2 and Twist. In the case of Mef2, the sera were generated against the DNA binding domain while for Twist we used the full-length protein. Although we cannot guarantee that the sera from both animals targets different portions of the protein, it is also not a 100% guaranteed that both sera, i.e. that the two different animals will raise antibodies targeting the same portion of the protein as we generated polyclonal antibodies. For the other three transcription factors assayed here, we have only used one antibody, as there is only one good ‘ChIP grade’ antibody available. While we completely agree with the reviewer’s point, that the best practice is to use two antibodies generated from different parts of the protein, we note that the vast majority of ChIP experiments against Drosophila TFs have only used one antibody, as there are very few antibodies commercially available. In all of our previous ChIP studies in melanogaster we have used two antibodies (although again they were generated against the same protein domains (usually the entire protein)).

To address the reviewers point, we have now added the following to the ChIP Methods: “Two independent biological replicates were generated for each transcription factor at each time-point, using two independent antibodies when possible (for Twi and Mef2, targeting the full-length and DNA binding domains, respectively). Although we have no guarantee that both sera target different portions of the protein, these polyclonals were generated in different rabbits and therefore have a different background level of IgG antibodies in the sera.”

3) Additional methodological detail is needed. The Materials and methods are missing some important information; details on chromatin preparation (presumably similar or identical to earlier work, but not cited), numbers of reads obtained from different ChIP-seq experiments.

We apologize for the lack of information related to this matter. We have now added this to the materials and method: “Chromatin from D. virilis staged embryos was extracted as described previously for D. melanogaster (Sandmann et al., 2006).”

Also, we extended Table S1 to include the final number of reads (duplicates and multi-mapped reads removed) for both replicates used for each TF, time point combination as well as for the matching input replicates.

4) It seems the authors are considering only direct cooperativity between proteins. Indirect cooperativity is also a major mechanism where the binding of one protein impacts the binding of another protein via an effect of nucleosome occupancy and should be mentioned.

We thank the reviewer for pointing to this additional alternative. You are right, we did mainly focus on direct protein:protein and protein:DNA interactions. To highlight this alternative, we now mention at the end of paragraph “Co-divergence in TF occupancy identifies potential cooperative binding of TFs”, that “…the occupancy of Tin is highly cooperative, which could involve direct cooperativity that is dependent on the interplay between protein: DNA and protein: protein interactions, although we note this could also involve indirect co-operativity through co-factor recruitment or nucleosome displacement”.

5) The claim that Mef2 binds a less conserved set of peaks in early time points is somewhat driven by the large number of peaks in D. melanogaster at time point 2, compared to D. virilis, which is in contrast the rest of the data, where D. virilis peaks far outnumber D. melanogaster peaks. Is this difference because of differential expression dynamics/levels of Mef2 in the two species? In general, do we know if the expression dynamics/levels of these factors are conserved between these species? How might this impact the measured conservation of binding?

We agree with the reviewer that the differences at this time-point are striking. We don’t think this is due to a difference in expression dynamics between the two species. We actually spent quite some effort in assessing that, in order to select comparable time-windows for the experiments. Our in-situ (data not shown) and immunostaining data (Figure 1C and Figure—figure supplement 1) don’t show any delay in the expression of mef2 transcripts or Mef2 protein. Actually the expression pattern of Mef2 is beautifully conserved, even though it is complex and very dynamic.

One possibility is that the binding of Mef2, rather than its expression, is slightly delayed in D. virilis compared to D. melanogaster. There is a D. melanogaster protein, Him, that was shown to inhibit Mef2 binding early in development (Liotta et al., 2007) – although the timing of action of Him doesn’t fit this observation, it indicates that Mef2’s ability to bind to its targets can be inhibited by other proteins. Perhaps there is another protein or mechanism in D. virilis. To account for this option, we have now added the following: “This could represent a shift (or delay) in the initiation of Mef2 binding between species, rather than in Mef2 expression (which is conserved as seen in Figure 1C and Figure—figure supplement 1), which would impact the ability of Mef2 to bind at early stages.”

Although standard in-situ hybridization and immune-stains are not fully quantitative, we found no observable differences in the levels of Mef2 at equivalent stages between species, although Mef2 expression does increase over time within each species, as previously reported. One alternative possibility is that D. melanogaster Mef2 antibody used here on D. virilis cross-react less efficiently at earlier time points, where Mef2 is less “functionally” required, as known from Mef2 LOF mutants. However, this is less likely to be the case since the same antibody was used efficiently for later time points.

6) Please provide additional pieces of information to confirm the claims that Tin binding is less dependent on sequence as binding complexity increases. For example, no statistical test was provided for the motif density comparison in subsection “TF binding site divergence is tolerated in the context of collective TF binding”. Please provide the reader with more information as to how to compare the numbers of red/green dots in Figure 4C; there seems to be different numbers of data points in the two plots and fractions are not reported/compared.

We have now added the result of the binomial test assessing if the observed motif density at high-bound Tin enhancers equals that at singletons (subsection “TF binding site divergence is tolerated in the context of collective TF binding”). We also noted that the reported motif densities (7.6 and 10.6, for high-bound and singleton enhancer, respectively) were slightly wrong and were corrected to 7.96 and 10.41. We apologize for this error (but are thankful to have found it). Importantly, with these corrected numbers, the difference in motifs densities is highly significant – p-value < 5e-7, binomial test.

We have now added the numbers of red and green peaks in the Figure 4C legend, and more details on the analysis presented in this new Figure 4C.

Briefly, the plot shows 150 singleton and 488 high-bound peaks (as delta ChIP scores must be computed per peak (as measured) and not per enhancer):

· “High dChIP & Low dSTAP” population (green dots): 2 and 21 peaks selected for singleton and high-bound peaks, respectively (p=0.06 in one sided Fisher exact);

· “Low dChIP & High dSTAP” population (red dots): 0 and 28 peaks selected for singleton and high-bound peaks, respectively (p=0.0005 in one sided Fisher exact).

7) It would be interesting to know if the SVM from Zinzen 2009 already accounts for the fact that differential TF occupancy can lead to the same expression output in the manner observed here. For example, if the SVM were given the binding profile of the D. vir CRM 10323, would it predict SM expression? Or given the D. vir CRM 2469 binding profile, would it predict VM expression? This would indicate whether this divergence in TF binding leading to the same outcomes is already observed within D. melanogaster or whether new and unique regulatory logic is being employed in D. virilis, which may indicate the involvement of other TFs.

We concur with the reviewer and we have started an analysis along this line as part of another study with a new student in the group. Briefly, The SVM is able to recapitulate fully or partially the correct expression given D. virilis TF binding profiles in approximately 66% of the cases. Although high, this analysis shows that the expression of a substantial number of CRMs (34%) is not faithfully predicted. This may be biological due to divergence in activity, or technical, due to the fact that the SVM training is based on D. melanogaster enhancers and the test is based on D. virilis binding data. Our current study shows that for some enhancer’s their output can be conserved despite a change in TF binding. Given this, we anticipate that training using D. virilis TF occupancy and D. melanogaster activity will impact the accuracy of the SVMs predictions, however we need to explore this further. The D. melanogaster SVM is also not fully applicable to the D. virilis ChIP data, as we have one less time-point for Tinman ChIP in D. virilis (the first D. mel time point) and thus a new SVM would need to be recomputed. If we were to initiate this today, we would explore different machine learning approaches, which is by itself a major new study.

https://doi.org/10.7554/eLife.28440.030

Article and author information

Author details

  1. Pierre Khoueiry

    European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    Present address
    Department of Biochemistry and Molecular Genetics, American University of Beirut, Beirut, Lebanon
    Contribution
    PK, Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0001-7643-3310
  2. Charles Girardot

    European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    Contribution
    CG, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0003-4301-3920
  3. Lucia Ciglar

    European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    Contribution
    LC, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
  4. Pei-Chen Peng

    Carl R. Woese Institute of Genomic Biology, University of Illinois, Champaign, United States
    Contribution
    P-CP, Validation, Investigation, Visualization, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
  5. E Hilary Gustafson

    European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    Contribution
    EHG, Formal analysis, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
  6. Saurabh Sinha

    1. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    2. Carl R. Woese Institute of Genomic Biology, University of Illinois, Champaign, United States
    Contribution
    SS, Resources
    Competing interests
    The authors declare that no competing interests exist.
  7. Eileen EM Furlong

    European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    Contribution
    EEMF, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing
    For correspondence
    furlong@embl.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-9544-8339

Funding

Marie Curie (ITN EvoNet)

  • Pierre Khoueiry
  • Eileen EM Furlong

Deutsche Forschungsgemeinschaft (DFG FU750)

  • Eileen EM Furlong

National Institutes of Health (R01GM114341)

  • Saurabh Sinha

European Molecular Biology Organization (Long-term fellowship)

  • Pierre Khoueiry

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are very grateful to all members of the Furlong lab for discussions and comments, especially David Garfield. This work was technically supported by the EMBL Genomics Core facility and the public resources of FlyBase, BDGP and RedFly. The work was financially supported by the Marie Curie EvoNet ITN and DFG FU 750 grants to EEF, NIH grant R01GM114341 to SS and an EMBO long-term fellowship 1125-2010 to PK.

Reviewing Editor

  1. Patricia J Wittkopp, Reviewing Editor, University of Michigan, United States

Publication history

  1. Received: May 8, 2017
  2. Accepted: July 21, 2017
  3. Version of Record published: August 9, 2017 (version 1)

Copyright

© 2017, Khoueiry et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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