The gene expression pattern specified by an animal regulatory sequence is generally viewed as arising from the particular arrangement of transcription factor binding sites it contains. However, we demonstrate here that regulatory sequences whose binding sites have been almost completely rearranged can still produce identical outputs. We sequenced the even-skipped locus from six species of scavenger flies (Sepsidae) that are highly diverged from the model species Drosophila melanogaster, but share its basic patterns of developmental gene expression. Although there is little sequence similarity between the sepsid eve enhancers and their well-characterized D. melanogaster counterparts, the sepsid and Drosophila enhancers drive nearly identical expression patterns in transgenic D. melanogaster embryos. We conclude that the molecular machinery that connects regulatory sequences to the transcription apparatus is more flexible than previously appreciated. In exploring this diverse collection of sequences to identify the shared features that account for their similar functions, we found a small number of short (20–30 bp) sequences nearly perfectly conserved among the species. These highly conserved sequences are strongly enriched for pairs of overlapping or adjacent binding sites. Together, these observations suggest that the local arrangement of binding sites relative to each other is more important than their overall arrangement into larger units of cis-regulatory function.
The transformation of a fertilized egg into a complex, multicellular organism is a carefully choreographed process in which thousands of genes are turned on and off in specific spatial and temporal patterns that confer distinct physical properties and behaviors on emerging cells and tissues. To understand how an organism's genome specifies its form and function, it is therefore necessary to understand how patterns of gene expression are encoded in DNA. Decades of analysis of the fruit fly Drosophila melanogaster have identified numerous regulatory sequences, but have not fully illuminated how they work. Here we harness the record of natural selection to probe the function of these sequences. We identified regulatory sequences from scavenger fly species that diverged from Drosophila over 100 million years ago. While these regulatory sequences are almost completely different from their Drosophila counterparts, they drive identical expression patterns in Drosophila embryos, demonstrating extreme flexibility in the molecular machines that interpret regulatory DNA. Yet, the identical outputs produced by these sequences mean they must have something in common, and we describe one shared feature of regulatory sequence organization and function that has emerged from these comparisons. Our approach can be generalized to any regulatory system and species, and we believe that a growing collection of regulatory sequences with dissimilar sequences but similar outputs will reveal the molecular logic of gene regulation.
Citation: Hare EE, Peterson BK, Iyer VN, Meier R, Eisen MB (2008) Sepsid even-skipped Enhancers Are Functionally Conserved in Drosophila Despite Lack of Sequence Conservation. PLoS Genet 4(6): e1000106. https://doi.org/10.1371/journal.pgen.1000106
Editor: Norbert Perrimon, Harvard Medical School, Howard Hughes Medical Institute, United States of America
Received: February 28, 2008; Accepted: May 22, 2008; Published: June 27, 2008
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: This work was supported by NIH grant HG002779 to MBE by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, and by NSF Grant No. 0334948 to RM. EEH was supported by an NSF Graduate Research Fellowship. Sequencing was conducted under a Community Sequencing Grant from the Department of Energy's Joint Genome Institute.
Competing interests: The authors have declared that no competing interests exist.
Recent studies revealing how the gain, loss and repositioning of transcription factor binding sites within regulatory sequences can alter gene expression with observable phenotypic consequences  have focused efforts to understand the molecular basis for organismal diversity on the evolution of regulatory DNA. However, a growing body of work has demonstrated that alterations of binding-site composition and organization often leave regulatory sequence function unchanged –.
The potential for significant changes in regulatory sequences to have no functional consequences complicates efforts to identify sequence changes that are likely to affect gene expression and phenotype. But precisely because many of these changes do not affect regulatory output, they provide a powerful opportunity to understand how the arrangement of transcription factor binding sites in a regulatory sequence determines its output. We believe that identifying divergent enhancers that drive similar patterns of expression, and distilling the common principles that unite them, will allow us to decipher the molecular logic of gene regulation.
We began to explore the effectiveness of this approach with the extensively studied regulatory systems of the early D. melanogaster embryo , using the recently sequenced genomes of 12 Drosophila species to document the evolutionary fate of transcription factor binding sites in early embryonic enhancers (Peterson, Hare, Iyer, Eisen, unpublished). A consistent pattern emerged: while binding site turnover is common, a large fraction of the binding sites in most enhancers are conserved across the genus (see Figure 1).
Predicted binding sites for the five factors known to regulate expression from the eve stripe 2 enhancer in the twelve sequenced Drosophila species . Sites were predicted independently in each species using PATSER  and mapped onto an MLAGAN  multiple alignment of the eve stripe 2 enhancer sequences. The height of the box representing each binding site is scaled by its PATSER p-value (taller boxes represent sites with higher predicted affinities). The top panel (grey shading) shows the positions of biochemically-verified (in vitro footprinting) binding sites . The indicated coordinates are for the multiple-alignment, which is longer than individual enhancers due to the high frequency of alignment gaps.
The extent to which variation in enhancers from sequenced Drosophila species represented all of the possible variation in these sequences was unclear. Perhaps the conserved sites were an imperturbable core essential for each enhancer's function. Or, perhaps, there had simply not been enough time since the divergence of the genus for mutation to have generated alternative configurations that would produce identical expression patterns. To resolve this ambiguity it was necessary to reconstruct binding site turnover events that occurred over longer evolutionary timescales by comparing Drosophila enhancers to their counterparts in species from outside the genus. The appropriate species for such comparisons would share basic patterning mechanisms with Drosophila species, but be sufficiently diverged from Drosophila to provide significant additional data on the constraints on binding site turnover. Ideally, these species would be amenable to experimental analysis and have fully sequenced genomes.
Unfortunately, the closest available genome sequences were from several very distantly related mosquito species , whose most recent common ancestor with Drosophila lived approximately 220 million years ago. These sequences were unlikely to be informative because of several important differences between early-embryonic patterning in Drosophila and mosquitoes. Mosquitoes, for example, lack the primary anterior morphogen in Drosophila, the modified Hox gene Bicoid, which is found only in higher cyclorrhaphan Diptera (the “true flies”) .
With essentially no information on non-coding sequences and regulatory networks from flies outside the Drosophilidae, we reasoned that other groups within the Acalyptratae, the speciose 100 million year-old division of Diptera that includes Drosophila, represented the best compromise between our aims to maximize sequence divergence and minimize regulatory network divergence.
We selected three families, Sepsidae, Diopsidae and Tephritidae, that span acalyptrate diversity, have well-characterized phylogenies, and contain multiple species whose specimens could be readily obtained. In this paper we present results on gene regulation in sepsids, which, due to their small genomes, were the most amenable to genome analysis.
Specifically, we report the sequence and experimental characterization of the even-skipped locus from six sepsid species. The particular species were selected to include the major sepsid lineages, and, in several cases, because of the amenability of the species for embryological study. We chose to characterize multiple sepsid species to facilitate the identification of sepsid enhancers by intra-family comparisons , and to enable comparisons of enhancer evolution between sepsids and drosophilids.
Sequencing Sepsid eve Loci
The six sepsid species we selected for this study, Sepsis punctum, Sepsis cynipsea, Dicranosepsis sp., Themira superba, Themira putris and Themira minor, have genome sizes that range from 134 Mb to 285 Mb (Table 1). We generated a whole-genome fosmid library for each species, identified eve-containing clones by hybridization with a species-specific eve probe generated by degenerate PCR, and shotgun sequenced the clones to an average 13× coverage (Table S1). We annotated the assembled sequences (Table S2) to identify all protein-coding genes with homologs in D. melanogaster (Figure S1).
All of the sequenced clones contained clear eve orthologs, and the organization of the eve locus is very similar in sepsids and drosophilids (Figure 2). The sepsid loci are slightly larger (Table 1), consistent with their overall larger genome sizes. The genes flanking eve, however, are different between the families.
The eve locus (20 kb flanking the eve protein-coding gene) is shown for two Drosophila (D. melanogaster and D. virilis) and two sepsid species (S. cynipsea and T. putris), centered on the eve homeodomain. Black lines represent significant BLASTZ  hits on the plus strand, red lines on the minus strand (BLASTZ parameters: K = 1800, with chaining). Verified D. melanogaster enhancers are shown in green and predicted sepsid enhancers in black. Note that only a subset of D. melanogaster eve enhancers are shown here; all BLASTZ matches between D. melanogaster and S. cynipsea fall within known enhancers with the exception of those falling within 500 bp of the transcription start site in D. melanogaster.
The Evolutionary Relationship of Sepsid and Drosophilid Species
A maximum likelihood tree calculated using seven protein-coding gene sequences in all six sepsids and a subset of Drosophila species demonstrates that the sepsid species are about twice as diverged from D. melanogaster than D. melanogaster is from the most distantly related Drosophila species (Figure 3A).
(A) Maximum likelihood tree of protein-coding genes inferred from seven genes using CODEML module of PAML . Branch lengths are in substitutions per codon using the [F3×4] model. (B) Maximum likelihood non-coding trees of six Drosophila and six sepsids computed using the BASEML module of PAML . Branch lengths are in substitutions per site using the HKY model.
Minimal Non-Coding Sequence Similarity between Sepsids and Drosophilids
Examination of the eve locus from sequenced Drosophila species shows that there is readily detectable non-coding sequence conservation spanning the entire locus, even between the most distantly related species (Figure 2). The average pairwise noncoding match score (a BLASTZ  based measure of sequence similarity; see Materials and Methods) between D. melanogaster and members of the virilis-repleta clade is 20% (Table S3). We observe a similar pattern in the sepsid eve loci. The average pairwise noncoding match score between S. cynipsea and Themira species is 17% (Table S3). However, there is minimal non-coding sequence conservation between families outside of a few small (approximately 20–30 bp) blocks of extremely high conservation scattered across the locus (Figure 2). The average pairwise noncoding match score between D. melanogaster and the sepsids is 4% (Table S3). Maximum likelihood non-coding trees from the eve locus in sepsids and Drosophila reveal that the two families span roughly the same amount of non-coding divergence (Figure 3B).
Characterization of the Trans-Regulatory Network in the Sepsid Themira minor
We established a colony of the T. minor from adults captured in Sacramento, CA, and developed protocols to recover and fix T. minor embryos. The overall morphology and pattern of embryonic development is very similar in sepsids and Drosophila (Figure S2). As expected from studies of other dipterans, T. minor eve is expressed in a characteristic set of seven stripes in blastoderm embryos (Figure 4D,H).
Expression patterns were visualized by in situ hybridization with species-specific digoxigenin-labeled antisense RNA probes. The gap transcription factors hb, gt and Kr are expressed in similar domains during stage 5 in D. melanogaster (A–C) and T. minor (E–G). eve is expressed in seven transverse stripes during cellularization in both species (D, H). Embryos are oriented with anterior to the left and dorsal up.
We were additionally interested in comparing the trans-regulatory network of this sepsid to that of drosophilids. In D. melanogaster, eve expression in the blastoderm is regulated by the transcription factors Bicoid (BCD), Caudal (CAD), Hunchback (HB), Giant (GT), Krüppel (KR) Knirps (KNI) and Sloppy-paired 1 (SLP1).
hb, gt and Kr are expressed in T. minor in patterns that mimic those of their orthologs in D. melanogaster embryos (Figure 4A–C,E–G). This is in contrast to AP patterning factors in the mosquito, in which there have been shifts in expression domains, and presumably changes in regulation of the downstream genes .
We were unable to clone the kni, slp1 and cad genes from T. minor. In D. melanogaster, bcd RNAs are tethered to the anterior pole of the embryo, with BCD protein diffusing away from the pole to create a strong anterior to posterior gradient. BCD antibodies were not cross-reactive in T. minor, and we were unable to characterize the T. minor BCD gradient.
Key elements of the heart regulatory network are conserved between flies and vertebrates . As we therefore expect this network to be conserved between the sepsid and Drosophila species, and our supply of T. minor embryos was limited, we did not examine the expression of heart regulators.
Identification of Sepsid Enhancers
Since the sepsid and Drosophila trans-regulatory networks regulating eve expression appear to be similar, we reasoned that sepsid enhancers would contain similar collections of transcription factor binding sites as their Drosophila counterparts. In D. melanogaster, clusters of HB, CAD, KNI, KR, and BCD binding sites in the eve locus have been shown to correspond to known stripe enhancers . We therefore examined the density of predicted HB, CAD, KNI, KR, GT and BCD binding sites across each fosmid sequence (Figure S3) and identified 18 candidate sepsid stripe enhancers (Table S4) (We recently generated GT in vitro binding data which was not available when the initial D. melanogaster work was carried out).
Each of these predicted enhancers contained a small number of short (20–30 bp) sequences conserved between sepsids and drosophilids, which established presumptive orthology with specific regions of the D. melanogaster genome. In essence, the binding site plots showed us where sepsid enhancers could be found, and the small islands of sequence conservation suggested their likely function.
We also identified putative eve muscle-heart enhancers (MHE) (Table S4) in the sepsid species by looking for short blocks (20–30 bp) of high similarity (>90%) that overlap functionally verified transcription binding sites from the D. melanogaster MHE in pairwise alignments between the D. melanogaster MHE and each of the sepsid intergenic regions.
Function of Sepsid eve Enhancers in Transgenic D. melanogaster Embryos
We chose to test whether candidate enhancers from one species in each of the two sepsid clades were capable of driving expression in D. melanogaster embryos. Enhancer-reporter cassettes for each of these 8 constructs were introduced into the D. melanogaster genome via Phi-C31 phage-mediated targeted integration ,. Remarkably, despite their extensive sequence differences, all of the tested sepsid sequences drive very similar expression patterns to those driven by their orthologous D. melanogaster enhancers (Figure 5), although there are some small and intriguing differences. This confirms that these sepsid sequences are functional eve enhancers that, with their high degree of sequence divergence, represent markedly different examples of how to construct an eve enhancer.
Expression patterns of eve stripe 2, stripe 3+7, stripe 4+6 and muscle-heart enhancers from sepsids S. cynipsea, T. putris and T. superba were compared to their D. melanogaster counterparts in transgenic D. melanogaster embryos by RNA in situ hybridization with digoxigenin-labeled antisense RNA probes against the reporter genes lacZ (A, D, G) and CFP (B,C,E,F,H,I,K,L), or staining with βGal antibodies (J). (A–C) Sepsid stripe 2 enhancers drive strong expression in an anterior stripe corresponding to D. melanogaster stripe 2. (D–F) Sepsid stripe 3+7 enhancers drive expression within the limits of D. melanogaster stripe 3 and 7, with additional expression in the posterior. (G–I) Sepsid stripe 4+6 enhancers drive expression within the limits of D. melanogaster stripes 4 and 6. (J–L) Sepsid MHE enhancers are expressed in metameric clusters in the dorsal mesoderm in stage, as in D. melanogaster. Embryos were imaged during cellularization and are oriented with anterior to the left and dorsal up.
The D. melanogaster minimal stripe 2 element drives expression in a single stripe in the stage 5 blastoderm from 63–57% egg-length through activation by broad anterior gradients of BCD and HB and localized repression by GT and SLP1 in the anterior and KR in the posterior  (Figure 5A; Table 2). The sepsid stripe 2 enhancers in the transgenics similarly drive expression from 62–55% egg-length (Figure 5B,C; Table 2). In 78% of embryos containing the S. cynipsea enhancer and 55% of embryos containing the T. putris enhancer, we observe expression in stripe 7 from the sepsid stripe 2 enhancers; similar behavior has also been observed for D. melanogaster stripe 2 constructs .
The D. melanogaster stripe 3+7 enhancer (Figure 5D) is broadly activated by dStat and Tailless (TLL) (stripe 7 only), and the two stripes of expression at 53–47% and 21–12% egg-length (Table 2) are carved out by domains of HB, KNI, and SLP1 repression . Stripe 3 expression in the transgenics containing sepsid stripe 3+7 enhancers agrees well with D. melanogaster (Figure 5E,F; Table 2). The anterior border of stripe 7 corresponds to that in D. melanogaster, but in embryos containing either the S. cynipsea or T. putris stripe 3+7 element, stripe 7 expression extends posteriorly. Significantly, the stripe 3+7 enhancer has been inverted in the Sepsis species relative to the other sepsids and Drosophila. This strongly suggests that these enhancers are orientation-independent in their native genomic context.
The D. melanogaster stripe 4+6 enhancer drives expression in 2 stripes from 47–40% and 30–22% (Figure 5G). There is some evidence that stripe 4+6 expression is activated broadly by Dichaete and restricted to 2 stripes by HB and KNI repression, but the precise details of its regulation are less well understood ,. This pattern is reproduced in our transgenics, with expression from 46–40% and 31–25% egg-length (Figure 5H,I; Table 2).
In stage 11 D. melanogaster embryos, eve is expressed in laterally-symmetric, metameric pairs of pericardial cells in the dorsal mesoderm (Figure 5J) . The eve MHE integrates activation and repression from multiple signaling pathways, including DPP and WG from the dorsal ectoderm and RAS in the dorsal mesoderm . In addition, broad domains of TIN and TWI in the dorsal mesoderm activate expression. This metameric pattern is faithfully reproduced by the sepsid MHE enhancers (Figure 5K,L).
Binding Site Composition and Organization in Sepsid Enhancers
That enhancers with minimal sequence conservation have conserved function suggests that they share some common features beyond primary sequence. In order to examine what these shared properties might be, we examined and compared the composition and organization of predicted transcription factor binding sites in all of the characterized eve enhancers. We restricted our analysis of each enhancer to those factors known to be involved in the activity of the particular enhancer.
We aligned enhancer sequences from within each family, and plotted predicted transcription factor binding sites on these alignments (Figure 6). 92% of D. melanogaster binding sites are found in the same location in enhancers from other species within the closely related melanogaster subgroup (Table S3), 29% of sites are similarly conserved between D. melanogaster and the species of the virilis-repleta clade (Table S3). An average of 22% of sites are conserved between S. cynipsea and Themira species. The non-coding divergence between these two sepsid clades is similar to that between D. melanogaster and the virilis-repleta clade (Figure 3). This is likely an underestimate of the conserved sites within the sepsids as these are not minimal enhancers and thus should contain a larger portion of non-conserved background sites.
Predicted binding sites for the five factors known to regulate expression from the eve stripe 2 enhancer in six Drosophila species  and six sepsid species. Sites were predicted independently in each species using PATSER  and mapped onto an MLAGAN  multiple alignment of the eve stripe 2 enhancer sequences. The height of the box representing each binding site is scaled by its PATSER p-value (taller boxes represent sites with higher predicted affinities). The top panel (grey shading) shows the positions of biochemically-verified (in vitro footprinting) binding sites . Binding sites conserved within families are indicated by solid boxes. A BCD-KR site pair conserved across families is indicated by a dashed box. Alignment coordinates are indicated.
The lack of sequence similarity between families made nucleotide level alignment of sepsid enhancers to their Drosophila orthologs impossible. However, the previously described small blocks of high sequence conservation allowed us to orient and crudely align the sepsid and drosophilid enhancers to each other. In examining plots like this for all four enhancers, it was clear that few of the binding sites conserved within each family were conserved between families (Figure 6; Figure S4). Only 5% of D. melanogaster binding sites are conserved in pairwise comparisons with sepsid species, representing an additional 84% reduction in conserved sites compared to the virilis-repleta clade (Table S3).
However, we note that all of the highly conserved blocks contained at least one, and often several, highly conserved binding sites, and that most of these sites correspond to known in vitro footprints for the corresponding factor in D. melanogaster  (Figure S4).
Conservation of Binding Site Composition
Most early embryonic enhancers in D. melanogaster contain unusually large numbers – compared to random non-coding sequence – of predicted binding sites for the factors involved in their regulation ,, although the exact relationship between binding site density and function remains to be elucidated. Binding site density is conserved between enhancers in D. melanogaster and D. pseudoobscura ,, but it is not clear how much of this conservation is due to selection to maintain binding sites, and how much is due to the overall high level of sequence conservation between D. melanogaster and D. pseudoobscura.
Deep Conservation of Paired Binding Sites
Given the overall lack of sequence and binding site conservation between sepsid and Drosophila enhancers, we were particularly interested in the characteristics of the small sequence blocks that are conserved between the families. We noticed that all of these blocks contained overlapping or tightly spaced binding sites.
To analyze this more rigorously, we classified predicted D. melanogaster binding sites for footprinted factors in the eve MHE, stripe 2 and stripe 3+7 enhancers into four categories ranging from non-conserved (present only in D. melanogaster and its immediate sister taxa) to extremely highly conserved (present in Drosophila and sepsids). We then classified sites based on their proximity to other predicted binding sites: overlapping sites that share one or more bases with another binding site, neighboring sites that are within 10 bases of another site but do not overlap, and isolated sites. Overlapping sites are more often extremely conserved, close sites are more often highly conserved and isolated sites are more often minimally or non-conserved than expected by chance (Figure 7; p<0.007, p<0.01, p<.049, Chi-squared test). However, the number of sites is too small to detect relationships between conservation and the spacing of pairs of sites for specific factors.
Binding sites in the stripe 2, stripe 3+7, and MHE enhancers were classified as “overlapping” if they shared at least one base pair with a site for a different factor, “close” if the nearest base of another site (for a different factor) is within 10 bp, and “isolated” if neither condition is met. Binding sites in D. melanogaster were classified as non-conserved, minimally conserved (only within melanogaster subgroup), highly conserved (within 12 sequenced Drosophila species) and extremely conserved (12 Drosophila and 6 sepsids). (A) The distribution of conservation scores as a function of binding-site proximity shows overlapping and close sites are more likely to be highly or extremely conserved than isolated sites. (B) The fraction of each conservation category in different proximity groups again shows that extremely and highly conserved sites are strongly enriched for overlapping and close binding sites.
We have demonstrated here that complex animal regulatory sequences can tolerate nearly complete rearrangement of their transcription factor binding sites without appreciably altering their transcriptional output. Thus, while the global organization of binding sites within regulatory sequences plays an important role in determining their function, strong evolutionary constraint to maintain expression patterns does not require the maintenance of any single binding site architecture. Despite this flexibility in the overall organization of regulatory sequences, our analysis of the small number of binding sites conserved between Drosophila and sepsid species suggests strong selection to maintain overlapping and adjacent pairs of binding sites.
Although Drosophila has been the subject of more extensive genome sequencing than any other animal genus, these observations were not evident from comparing the 12 sequenced Drosophila genomes. Only with the inclusion of species with similar development but substantially more highly diverged genomes did these properties emerge.
Binding Site Turnover and the Regulatory Machinery
Our work extends in both the extent of divergence and number of enhancers examined the pioneering work on binding site turnover of Ludwig and Kreitman, who showed in a series of papers that the eve stripe 2 enhancer from other Drosophila species drives a stripe 2 pattern in transgenic D. melanogaster embryos despite the imperfect conservation of functional binding sites ,,.
Although several examples of Drosophila regulatory sequence conservation over long evolutionary distances had been reported prior to Ludwig and Kreitman's work on eve stripe 2 ,, eve regulation has become the preeminent model for the study of binding site turnover. It remains one of the few cases where observations of expression pattern conservation have been followed up with studies of functional complementation .
We have nearly doubled the evolutionary distance analyzed by Ludwig and Kreitman. Furthermore, in their comparisons the majority of binding sites were conserved, while our species sample has very few conserved binding sites. We have also generalized their observation to include additional enhancers responding to a different suite of transcription factors, including one (the MHE) active following gastrulation. Previous reports of the functional equivalence of divergent enhancers in Drosophila have involved blastoderm enhancers, leaving open the possibility that the observed binding site turnover was a byproduct of the syncitial nature of the early Drosophila embryo. Our data on the MHE demonstrates that extreme binding site turnover with functional conservation occurs in enhancers active in a cellular context.
A handful of isolated case studies support our findings. For example, the tailless enhancer from the house fly Musca domestica  and the single-minded enhancer from the mosquito Anopheles gambiae  drive similar patterns as their endogenous orthologs in D. melanogaster embryos despite having different organization of binding sites, and non-coding sequences from the human RET locus drive ret-specific expression in zebrafish despite the absence of detectable sequence similarity between human and zebrafish RET non-coding DNA . Nonetheless, in each of these cases simple transcription factor “grammars” were conserved, offering a ready molecular explanation for the conserved function. No such grammar is as of yet apparent in the eve enhancers.
Such remarkable flexibility in the organization of enhancers suggests that the protein-protein and protein-DNA interactions that mediate the activity of developmental enhancers are not highly structured as, for example, is seen in enhanceosomes . If they were, it is hard to imagine how such wildly different sequences could produce identical expression patterns in the same trans-regulatory context. The extent of binding site turnover is consistent instead with the recently proposed “billboard” model of enhancer activity in which enhancers contain multiple sub-elements that independently interact with cofactors and the basal machinery to dictate transcriptional output –. In proposing the billboard model, Kulkarni and Arnosti proposed that billboard enhancers would be more evolutionarily pliable than enhanceosomes, and suggested that the eve stripe 2 results from Ludwig and Kreitman were understandable if eve stripe 2 were a billboard enhancer . Their model does not, however, predict how evolutionarily flexible billboard enhancers should be. Our discovery of extreme sequence and binding site divergence between functionally equivalent sepsid and Drosophila enhancers shows that they are extremely flexible, a fact that must be accounted for in future models of enhancer activity.
However even billboard enhancers are not infinitely flexible. One remarkable aspect of enhancer evolution is that despite the clearly frequent repositioning or replacement of transcription factor binding sites within enhancers, the enhancers themselves remain fairly compact. There must, therefore, be selection to keep the different sub-elements that contribute to an enhancer's output within the one to two kilobase span of a typical enhancer. This spatial constraint implies some functional interaction between enhancer sub-elements not currently captured by the billboard model.
The Importance of Paired Sites in Gene Regulation
Given the extent of non-coding divergence between Drosophila and sepsids across most non-coding DNA, we were surprised to observe small islands of very strong sequence conservation. Our finding that there is a significant enrichment of overlapping or adjacent binding sites within conserved blocks lends evolutionary support to long-standing suggestions of the importance of direct competitive and cooperative interactions between bound transcription factors.
Numerous studies have demonstrated that appropriate regulation of the eve stripe enhancers (and other enhancers) relies on the close proximity of multiple binding sites for both activators and repressors , , –.
Of the 12 footprinted BCD, HB, KR, and GT sites in the minimal stripe 2 element, 8 fall into 2 clusters of about 50 base pairs each containing overlapping activator (HB or BCD) and repressor (KR or GT) sites. In transient transfection experiments using these binding site clusters, BCD and HB dependent activation was repressed by DNA binding of GT or KR, consistent with the short-range repression mechanisms of quenching or competition . Knirps also mediates short-range repression in a range of 50–100 bp through quenching or direct repression of the transcriptional machinery when bound near a promoter .
Similarly, HB and BCD co-expression in transient transfection experiments results in multiplicative activation of a reporter construct containing a subset of the eve minimal stripe 2 element . Mutation of single activator sites in the minimal stripe 2 element results in a significant reduction in expression, again suggesting that HB and BCD bind cooperatively to this enhancer .
The local quenching and cooperativity models predict that binding sites in close proximity to each other should be under strong purifying selection to remain close to each other. Under the generally accepted model of binding site turnover, sites are lost in one region of an enhancer when new mutations create a complementary site elsewhere in the same enhancer. The appearance of new sites is the rate-limiting step as there are more mutational steps required to create a new site from random sequence than to destroy an existing site. Since random mutations are far less likely to produce pairs of adjacent sites than single sites, we expect functionally linked pairs of sites to be subject to far lower rates of binding site turnover. In contrast, if binding site turnover is driven by base substitutions, we expect functionally independent sites that are adjacent or even partially overlapping to have essentially the same rates of binding site turnover as isolated sites. The conserved blocks we observed between sepsids and Drosophila were generally larger than individual sites, as has been previously reported within Drosophila , consistent with the former model. Our observation that proximal sites are preferentially conserved additionally supports their direct functional linkage.
However, we note that insertions and deletions are a major source of sequence variation in Drosophila, with D. melanogaster having a strong deletion bias  and deletion is thought to contribute significantly to binding site turnover . Taking this into account, we expect to observe reduced turnover in even functionally independent binding sites if they are overlapping or adjacent, as some fraction of the deletions that would remove a binding site with a complementary site elsewhere would also affect adjacent, and presumably uncompensated sites. These deletions would be subject to purifying selection, and the rate of turnover for the proximal sites would be reduced. Assessing whether such an effect could explain our observation requires more data on relative rates of nucleotide substitution and insertion and deletions of different sizes in sepsids, which will be accomplished with the sequencing of sepsid genomes.
We can, however, test the significance of our observation directly. The linked function model predicts that the paired binding sites we observe to be conserved between families should be more sensitive to manipulations that alter the spacing between the sites than paired binding sites that are not conserved.
Variation in eve Stripe Patterns
Though expression of the sepsid eve enhancers in D. melanogaster embryos is qualitatively very similar to the patterns driven by the D. melanogaster enhancers, there are subtle and interesting differences. Expression of stripe 7 exhibits the most variability across all enhancers in transgenics, including those enhancers from D. melanogaster. It was previously observed that stripe 7 is weakly expressed in D. melanogaster stripe 2 transgenics, and stripe 7 expression is weaker than the endogenous stripe in stripe 3+7 transgenics ,,. We frequently observed stripe 7 expression in all our non-Drosophila stripe 2 transgenics, and stripe 7 expression did not perfectly recapitulate endogenous expression, suggesting that regulatory information specifying this stripe is distributed across the upstream region, thus challenging the model of enhancer modularity in agreement with . Information may be more diffusely spread across the locus in sepsids, resulting in missing information in our discrete cloned enhancers, in which case the native D. melanogaster pattern should be more accurately reproduced by cloning a larger regulatory region. Alternately, there could be changes within the non-Drosophila enhancers which result in expression differences in D. melanogaster despite conserved native eve expression, suggesting co-adaptation of each enhancer and its native trans environment.
Species Choice and the Value of More Distant Comparisons
We began this study seeking taxa that were significantly more diverged from D. melanogaster than any Drosophila species, but which had sufficiently conserved cis-regulatory networks that their enhancers would have similar function to their D. melanogaster counterparts. Our choice of sepsids was guided by their relatively close – but not too close – position to Drosophila on published trees of Diptera , by their relatively similar morphology suggestive of similar developmental mechanisms, and by practical considerations such as genome size and availability.
We have now shown that the extensive sequence divergence between sepsids and Drosophila was not accompanied by extensive differentiation of early embryonic patterning mechanisms. Thus sepsids provide a valuable model for comparative analysis of Drosophila embryology and developmental cis-regulation. We were also able to establish a colony of sepsids (T. minor) in the lab from flies caught locally, and collect embryos for the developmental gene expression and morphology data presented here. Based on our experience, we believe that more extensive embryological and molecular work with sepsids is very feasible, although some may find the need to provide the colonies with fresh cow dung objectionable.
The additional sequence divergence has enabled us to reach two important conclusions that could not be obtained in analyses of the 12 sequenced Drosophila genomes. Previous analyses of binding site turnover in Drosophila revealed substantial numbers of conserved binding sites within the genus, leaving open the question of whether these sites represented an imperturbable core necessary for enhancer function, or if there had simply not been sufficient divergence time for mutation to generate alternative configurations. We have now largely answered this question, at least for the eve enhancers – there does not appear to be an imperturbable core of sites at the level of overall enhancer organization.
Although binding site conservation in Drosophila has been extensively studied, our observations about the relationship between conservation and binding site proximity were never described because this pattern was simply not evident in examinations of the multitude of conserved binding sites across the Drosophila genome. This relationship only became apparent when we observed just how striking the conservation of a small subset of sites was.
More generally, this study highlights the value of the infrequently studied (at least by molecular biologists) Dipteran species outside of the genus Drosophila. It also points to a general strategy for dissecting the still elusive molecular mechanisms of enhancer function in which genome sequencing and functional studies are combined to catalog the diverse ways in which regulatory sequences with common function can be generated. Our initial foray into this domain has yielded exciting and unanticipated results. With the cost of genome sequencing plummeting, and with great improvements in Drosophila transgenesis, we expect this approach to be even more productive in the years to come.
Sepsis punctum, Sepsis cynipsea, Themira superba, Themira putris and Dicranosepsis sp. stocks were maintained in the Evolutionary Biology Laboratory at the National University of Singapore. Themira minor cultures were established at LBNL from specimens collected at McKinley Park in Sacramento, CA. Samples for genome sizing and genomic DNA isolation were flash-frozen adult flies.
Genome Size Determination
Genome sizing methods were adapted from . Five adult heads for each species were dissected into 1.5 mL of Galbraith buffer on ice, homogenized with 15 strokes of an A pestle in a 15 mL Kontes Dounce tissue homogenizer, and filtered through 30 um nylon mesh. T. superba heads were combined with 5 D. virilis heads before homogenization. 7 uL of 1∶10 chicken red blood cells (diluted in PBS) and 50 uL of 1 mg/mL propidium iodide were added and samples were stained for 4 hours rocking at 4 degrees in the dark. Mean fluorescence of co-stained nuclei was quantified on a Beckman-Coulter EPICS XL-MCL flow cytometer with an argon laser (emission at 488 nm/15 mW power). The propidium iodide fluorescence and genome size of Gallus domesticus (red blood cell standard, 1,225 Mb) were used to calculate the unknown genome sizes. For T. superba, D. virilis at 328 Mb, was used as a second internal standard.
Fosmid Library Preparation
High molecular weight genomic DNA was obtained from approximately 500 mg of frozen adult flies using the Qiagen 500/G Genomic-tip protocol for isolation of genomic DNA from flies (Qiagen Cat. No. 10262). Fosmid libraries were generated according to the Fosmid (40 kb) Library Creation Protocol developed at the DOE Joint Genome Institute (http://www.jgi.doe.gov/sequencing/protocols/prots_production.html) with the following modifications. DNA was end-repaired without hydro shearing, phenol-extracted, and precipitated a second time after gel-purification to increase cloning efficiency. Ligation reactions were incubated overnight at 16°C with T4 DNA ligase then packaged according to the JGI protocol. All libraries are at approximately 5× coverage with an average insert size of 39.5 kb.
Species specific sequence for target genes was obtained by degenerate PCR with primers designed based on Drosophila protein sequences, with additional fly sequences used where available. 40 bp overlapping oligonucleotide probes were synthesized by Klenow extension of 24 bp oligos overlapping by 8 bp with radiolabeled dATP/dCTP. Oligos were designed against target gene regions with 50–55% GC and no matches to known PFAM domains. Overgo probes were hybridized in pools of 6–10 probes to high density colony array filters at 60 degrees C overnight as described in  and visualized on a Molecular Dynamics Storm 860 phosphorimager. Positive clones were isolated and fosmid DNA was extracted and printed in 12×8 arrays on nylon membranes for hybridization with single overgo probes, protocol as above. 1–3 fosmid clones for each gene in each species were selected by EcoRI and BglII restriction mapping from final dot blot positives and were shotgun sequenced.
Sequencing and Assembly
Selected fosmids were subcloned and sequenced at the Joint Genome Center; protocols are available at http://www.jgi.doe.gov/sequencing/protocols/prots_production.html.
Chromatograms were reanalyzed using PHRED v0.020425.c ,, using the phredPhrap Perl script supplied with the CONSED distribution to call bases and assign quality scores. The ARACHNE assembler , was then used to build scaffolds (Table S2). After assembly, contigs from fosmids tiling across a given locus for a particular species were further merged by alignment using BLAT  (version 25; run with default parameters). Where matches exceeded 98% identity and extended to within 100 basepairs of either: a) both ends of a single contig, or b) one end of both contigs, one of the two sequences for the match region was chosen at random to construct a single representative sequence for the entire region, despite heterozygosity in fosmid libraries.
Fosmid sequences and combined locus sequences are available as Dataset S1.
Protein-coding gene annotation of the fosmids was performed with reference to the Flybase D. melanogaster 4.3 annotations. D. melanogaster translations were compared to the fosmid sequences translated in six frames using BLASTX. GeneWise  was used to construct gene models on scaffolds having hits with e-value ≤1e−10, with the query translation as template. Gene models were then filtered by requiring that the model translation find the original D. melanogaster query translation among the top hits in a reciprocal BLASTP search against the D. melanogaster translation set (e-value threshold 1e–10).
Coding and Non-Coding Trees
We obtained established phylogenies of Drosophila  and Sepsidae . Branch lengths for coding regions were determined using nucleotide sequence aligned in amino acid space with T-Coffee . Codeml from the PAML package (version 3.13d, codon frequencies estimated from base frequencies [F3×4], no clock, single dN/dS across all branches estimated with a starting value of 0.4, transition/transversion ratio estimated starting at 2) was used to estimate branch lengths over 10 sequenced Drosophila species (not including D. simulans or D. sechellia) and the 6 sepsid species reported here for orthologs of seven genes (bcd, CG8386, CG9119, eve, odd, stumps, zen) independently, as well as for the concatenation of all seven, and for the seven arrangements of all but one gene. The 15 resulting trees were compared both by visual inspection, and RMSD of branch lengths. Single gene trees constructed from alignments with 115 or fewer informative positions (eve, CG9119) estimated many zero-length internal branches and higher RMSD from the full concatenation (up to 390% average branch length), however no leave-one-out tree deviated from the seven gene concatenation by more than 15% of average branch length, suggesting that no single gene dramatically distorts the overall estimate of branch lengths in the full set concatenation. We therefore report final results for per-codon rate estimates of the full concatenation of 1566 informative positions.
Phylogenies of noncoding regions surrounding the eve gene were estimated in each family separately using baseml from then the PAML package  (Model: HKY, transition/transversion ratio estimated as above, alpha estimated starting at 0.5). A total of 966 sites in Drosophila and 958 sites in Sepsid alignments proved informative in upstream and downstream regions combined. Final estimates from these upstream + downstream concatenations in each family are reported as per-base rates.
Determination of Endogenous Expression Patterns in Sepsids
Fresh cow dung was obtained from free-ranging, grass fed, and antibiotic-free Milking Shorthorn cows (Bos taurus) in the Tilden Regional Park in Berkeley, CA. Resting cows were approached with caution and startled by loud shouting, whereupon the cows rapidly stood up, defecated, and moved away from the source of the annoyance. Dung was collected in ZipLoc bags (1 gallon), snap-frozen and stored at −80 C. Dung aliquots were thawed at 4 C and moistened slightly before use.
T. minor embryos were collected at room temperature in a 100 mm petri dish of fresh cow dung. Embryos were removed from the top layer of dung under a dissecting microscope then filtered through course mesh to remove grass and debris. Fixation was as previously described for D. melanogaster in 50% fixation buffer (1.3× PBS, 66 mM EGTA pH 8.0) containing 9.25% formaldehyde . 500–1000 bp of coding sequence for each gene were amplified from genomic DNA by degenerate PCR and cloned into the pGEM-T-Easy vector, amplified with M13 forward and reverse primers, and gel-purified with Qia-quick PCR columns. 4 uL of product were used in 20 uL transcription reactions with digoxigenin-11-UTP as described by the manufacturer (Roche DIG RNA Labeling Kit, Cat. No. 11 175 025 910). Probes were then incubated in 100 uL of 1× carbonate buffer (120 mM Na2CO3, pH 10.2) for 20 minutes, and reactions were stopped by addition of 100 uL stop solution (0.2 M NaOAc, pH 6.0). Probes were precipitated with 8 uL of 4 M LiCl and 600 uL EtOH then resuspended in 1 mL hybridization buffer. Hybridizations were performed as described previously with 18–20 hour hybridizations . Embryos were imaged on a Nikon Eclipse 80i scope equipped with a Nikon Digital Sight DS-U1 camera.
We picked regions of the fosmid to test for enhancer activity based on manual inspection of two types of data: (1) D. melanogaster enhancers mapped to each fosmid sequence via pairwise alignments, and (2) conservation of putative binding sites for BCD, CAD, KR, KNI, GT, HB. We computed pairwise LAGAN (Brudno et al. 2003) alignments of each sepsid fosmid to all of the other sepsid fosmids and the eve locus (defined as 10 kb upstream and downstream of the annotated eve protein-coding gene). In all cases, short blocks of high sequence similarity between D. melanogaster enhancers and the sepsid fosmids allowed us to determine the rough location of the likely sepsid enhancer. We used PATSER  and position weight matrixes for BCD, CAD, KR, KNI and HB from  and GT from data in  to predict sites for each factor across each fosmid using a ln(p-value) cutoff of −6. We assigned a conservation score to each site equal to the number of species in which a site for the same factor was predicted at an overlapping position in the pairwise alignment of the species. We examined the mean conservation score in 100 bp windows surrounding each mapped enhancer and selected boundaries for tested fragments to include regions of high single genome and conserved site density surrounding the region mapped from D. melanogaster. In the Themira clade, we tested stripe enhancer predictions from T. putris and the MHE prediction from T. superba as initially there was insufficient flanking sequence to recover all enhancers from the same species.
Generation of D. melanogaster Transgenics
Enhancers were cloned into either the NotI or BglII site in pBΦY-ayeCFP vector (modified from pBDP-Gt81, kindly provided by Barret Pfeiffer). Reporter constructs were injected into the D. mel attP2 landing pad strain  by Genetic Services, Inc. Injection survivors were pooled and red-eyed progeny were screened from the F1 generation. Integration was confirmed by two PCR reactions, one which amplifies across the cassette integration site (fw: CGGCGGCAACCCTCAGCGGATG; rv: GCGAAGAGATAGCGGACGCAGCGG) and one which amplifies the enhancer within pBΦY (fw: AAATAGGGGTTCCGCGCACAT; rv: CCCCGCGCCCTTTTATACCG). S. cynipsea stripe 2 was confirmed with the cassette integration primers and primers that amplify within the enhancer (fw: TGGCACAAGAGCGCCTCGAA; rv: GCGAGCCCCTTTTCCGTTGG).
Imaging of Transgene Expression Patterns
D. melanogaster eve stripe 2 and stripe 3+7 transgenic lines were kindly provided by Steven Small , and D. melanogaster stripe 4+6 from Miki Fujioka . For the stripe enhancers, transgenic embryos were collected for 2 hours then aged for 2 hours at room temperature. For the MHE, embryos were collected for 6 hours and aged 4 hours. Fixation, CFP and lacZ probe synthesis, hybridization conditions and microscopy were as described above.
Sequence and Binding Site Conservation Metrics
Pairwise BLASTZ Score: BLASTZ  was run with default parameters except for MSP cutoff (K) of 1800, outputting but not extending chains (C = 1). BLASTZ hits can occur on either strand, and to any position in the other sequence, thus opposite strand matches (outside of the described inversion) and “non-local” matches (i.e. those to sequences far from the orthologous region defined by the synteny of matches overall) are a straightforward internal description of the precision of this method, and the cutoff selected. The chosen cutoff was selected in order to preserve hits that overlapped the “conserved cores” found by MLAGAN in each enhancer, while minimizing the number of minus strand and non-syntenic hits. BLASTZ alignments were generated for D. melanogaster enhancers in the eve locus against the nine Drosophila species and six Sepsid species shown in Table S4 and Table S5. The pairwise BLASTZ score is defined as the product of percent identity and the total length of HSP chains as reported by BLASTZ.
D. melanogaster binding sites in BLASTZ hits: Using BLASTZ alignments calculated above, we tabulated the number of binding sites predicted by PATSER in D. melanogaster enhancer sequences that occurred within BLASTZ aligned regions. Numbers reported reflect the total number of binding sites for factors in each enhancer as follows: HB, KNI, DSTAT in the stripe 3+7 enhancer; BCD, HB, GT, KR, SLP1 in the stripe 2 enhancer; TWI, PNT, PAN, MED, MAD, TIN in the muscle-heart enhancer; HB, KNI in the stripe 4+6 enhancer. A p-value cutoff of ln(p-value) <−6 was imposed on PATSER output.
Conserved binding sites in BLASTZ hits: We further tabulated the number of binding sites in a given D. melanogaster enhancer that fall within a BLASTZ HSP, in which the aligned sequence from the comparison species also contained an above-cutoff site prediction for the same transcription factor. In order for a site to be called “conserved” in a species pair, the comparison species binding site must overlap the D. melanogaster site by at least 1 bp.
Conserved binding sites in multiple alignment: MLAGAN was used to compute multiple alignments of the four enhancers listed above (stripe 2, stripe 3+7, stripe 4+6 and MHE) with default alignment parameters. Pairwise comparisons between D. melanogaster and each other species in an alignment were conducted as follows: each binding site prediction in D. melanogaster calculated as above was categorized as conserved in that species if a binding site better than ln(p-value) <−6 was present aligned within 5 bp of the boundaries of the site prediction in D. melanogaster (see alignment error correction, binding site dynamics in paper methods).
Binding Site Dynamics Analysis
Binding sites were predicted in each species for each experimentally determined enhancer with published DNaseI footprints (stripe 2, stripe 3+7 and the Muscle Heart Enhancer) using PATSER  (version 3e) with a range of ln(p-value) cutoffs from −5 to −7. Position Weight Matrices for factors known to regulate expression driven from each enhancer were drawn from , except for GT (N. Ogawa and M.D. Biggin, unpublished) DSTAT , PAN/dTCF, PNT, and TIN  and TWI (D. Pollard, unpublished). PATSER was run with a GC content for each enhancer calculated from the entire eve locus in that species. D. melanogaster site predictions scoring above the cutoff were categorized into one of the following categories: “overlapping” if they overlap an above-cutoff site for a different factor by at least one basepair, “close” if the nearest base of another site (for a different factor) is within 10 bp, and “isolated” if neither condition is met Analyses described here use ln(p-value) <−6; results were robust to p-value cutoffs over the range described above (data not shown). Next, binding site predictions for each species were mapped onto a multiple alignment of all 18 species (12 Drosophila and 6 Sepsids) generated using MLAGAN (Brudno et al. 2003) (version 2.0, default run parameters). Finally, for each D. melanogaster site, the nearest aligned bases in each other species (plus/minus 5 bp for alignment error) were evaluated for presence of a binding site for the same factor. Three clades of increasing evolutionary distance were considered: “minimal” (melanogaster subgroup), “high” (all sequenced Drosophila), or “extreme” (12 Drosophila and 6 Sepsids). A given site in D. melanogaster was categorized by the largest clade in which it is conserved, where conservation is defined as presence in at least 80% of the species in that group (4/5 in subgroup, 10/12 in Drosophila, 15/18 in all species considered here). Thus, each binding site in D. melanogaster is categorized both by proximity to other binding sites in D. melanogaster, and by evolutionary stability across increasingly divergent species groups. Tabulations of these two properties were examined for relatedness by G-test of independence with Williams' correction for small sample size .
Binding Site Plots
Enhancers were chosen for this analysis and those described above based on previously available data, specifically regarding the location of binding sites for important regulators. We analyzed eve enhancers for which DNase I footprinted in-vitro binding sites had been determined, and each such enhancer examined the minimal sequence interval that mediated the complete expression pattern, extended to include all footprinted binding sites for relevant regulators. Binding site predictions for each enhancer, as calculated above, were plotted in alignment position coordinates for each enhancer described here. Alignment position coordinates for binding site matches that overlapped a gap (in the species in which the site was predicted) were plotted at the midpoint of the gap. P-value cutoffs above which to plot glyphs for each factor were chosen independently from among predictions over the range described above (ln(p-value) between −5 and −7 in steps of 0.5) in order to maximize concordance with known DNase I footprinted binding sites in the D. melanogaster sequence for each enhancer. The height of each glyph is proportional to the score of that site prediction, and heights for the top scoring site match for each factor are normalized across all factors plotted in that enhancer.
Protein coding genes flanking the sepsid eve loci. The eve locus for each sepsid species is shown along with predicted orthologs of protein-coding genes from D. melanogaster. The displayed loci consist of a single scaffold, except for S. punctum and D. sp. for which multiple scaffolds were aligned and merged. Gene models were constructed using GeneWise  on scaffolds having BLASTX hits to D. melanogaster translations with e-value ≤1e−10.
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Developmental timecourse of Themira minor. DIC images of fixed Themira minor embryos illustrate the major morphological stages of embryological development. Stages were determined according to Campos-Ortega & Hartenstein, 1985. The length of embryogenesis is similar in Themira minor and D. melanogaster at 25°C, with developmental stages corresponding closely and no obvious cases of heterochrony.
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Enhancer prediction at the eve locus. Mean percent identity and mean percent gapped bases were calculated in 100 bp non-overlapping windows from all pairwise LAGAN alignments. Binding sites for BCD, CAD, KR, KNI, GT and HB were predicted in each fosmid using PATSER (ln (p)<−6). A conservation score was assigned to each site equal to the number of species in which a site for the same factor was predicted at an overlapping position in the pairwise alignment of the species. Single genome counts, conserved counts, and gapped counts of binding sites were calculated in 50 bp non-overlapping windows. Single genome counts and conserved counts of predicted binding sites in regions mapping to known D. melanogaster enhancers were predominately used for defining borders of enhancer predictions. Blue boxes show the mapped locations of D. melanogaster enhancers. Shaded grey boxes show the locations of tested enhancers.
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Fine-scale sequence and binding site heterogeneity for stripe 3+7, stripe 4+6 and the MHE. Predicted binding sites for factors known to regulate expression from the (A) eve stripe 3+7, (B) stripe 4+6 and (C) muscle heart enhancers in six Drosophila species  and six sepsid species. Sites were predicted independently in each species using PATSER  and mapped onto an MLAGAN  multiple alignment of the eve enhancer sequences. The height of the box representing each binding site is scaled by its PATSER p-value (taller boxes represent sites with higher predicted affinities). The top panel (grey shading) shows the positions of biochemically-verified (in vitro footprinting) binding sites . The indicated coordinates are for the multiple-alignment, which is longer than individual enhancers due to the high frequency of alignment gaps.
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even-skipped containing scaffolds or fused scaffolds used in analyses.
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Pairwise sequence and binding site comparisons.
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Predicted and tested sepsid enhancers.
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Analyzed Drosophila enhancers.
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We thank the members of the Eisen Lab for their helpful comments on the manuscript and figures, the staff of the Department of Energy's Joint Genome Institute for their support and assistance throughout the duration of this project and several anonymous cows for their contribution of research materials.
Conceived and designed the experiments: EH BP ME. Performed the experiments: EH BP. Analyzed the data: EH BP VI ME. Contributed reagents/materials/analysis tools: EH BP RM. Wrote the paper: EH ME.
- 1. Wray GA (2007) The evolutionary significance of cis-regulatory mutations. Nat Rev Genet 8: 206–216.GA Wray2007The evolutionary significance of cis-regulatory mutations.Nat Rev Genet8206216
- 2. Costas J, Casares F, Vieira J (2003) Turnover of binding sites for transcription factors involved in early Drosophila development. Gene 310: 215–220.J. CostasF. CasaresJ. Vieira2003Turnover of binding sites for transcription factors involved in early Drosophila development.Gene310215220
- 3. Dermitzakis ET, Bergman CM, Clark AG (2003) Tracing the evolutionary history of Drosophila regulatory regions with models that identify transcription factor binding sites. Mol Biol Evol 20: 703–714.ET DermitzakisCM BergmanAG Clark2003Tracing the evolutionary history of Drosophila regulatory regions with models that identify transcription factor binding sites.Mol Biol Evol20703714
- 4. Dermitzakis ET, Clark AG (2002) Evolution of transcription factor binding sites in Mammalian gene regulatory regions: conservation and turnover. Mol Biol Evol 19: 1114–1121.ET DermitzakisAG Clark2002Evolution of transcription factor binding sites in Mammalian gene regulatory regions: conservation and turnover.Mol Biol Evol1911141121
- 5. Ludwig MZ, Bergman C, Patel NH, Kreitman M (2000) Evidence for stabilizing selection in a eukaryotic enhancer element. Nature 403: 564–567.MZ LudwigC. BergmanNH PatelM. Kreitman2000Evidence for stabilizing selection in a eukaryotic enhancer element.Nature403564567
- 6. Ludwig MZ, Kreitman M (1995) Evolutionary dynamics of the enhancer region of even-skipped in Drosophila. Mol Biol Evol 12: 1002–1011.MZ LudwigM. Kreitman1995Evolutionary dynamics of the enhancer region of even-skipped in Drosophila.Mol Biol Evol1210021011
- 7. Ludwig MZ, Palsson A, Alekseeva E, Bergman CM, Nathan J, et al. (2005) Functional evolution of a cis-regulatory module. PLoS Biol 3: e93.MZ LudwigA. PalssonE. AlekseevaCM BergmanJ. Nathan2005Functional evolution of a cis-regulatory module.PLoS Biol3e93
- 8. Ludwig MZ, Patel NH, Kreitman M (1998) Functional analysis of eve stripe 2 enhancer evolution in Drosophila: rules governing conservation and change. Development 125: 949–958.MZ LudwigNH PatelM. Kreitman1998Functional analysis of eve stripe 2 enhancer evolution in Drosophila: rules governing conservation and change.Development125949958
- 9. Moses AM, Pollard DA, Nix DA, Iyer VN, Li XY, et al. (2006) Large-scale turnover of functional transcription factor binding sites in Drosophila. PLoS Comput Biol 2: e130.AM MosesDA PollardDA NixVN IyerXY Li2006Large-scale turnover of functional transcription factor binding sites in Drosophila.PLoS Comput Biol2e130
- 10. Gallo SM, Li L, Hu Z, Halfon MS (2006) REDfly: a Regulatory Element Database for Drosophila. Bioinformatics 22: 381–383.SM GalloL. LiZ. HuMS Halfon2006REDfly: a Regulatory Element Database for Drosophila.Bioinformatics22381383
- 11. Holt RA, Subramanian GM, Halpern A, Sutton GG, Charlab R, et al. (2002) The genome sequence of the malaria mosquito Anopheles gambiae. Science 298: 129–149.RA HoltGM SubramanianA. HalpernGG SuttonR. Charlab2002The genome sequence of the malaria mosquito Anopheles gambiae.Science298129149
- 12. Stauber M, Jackle H, Schmidt-Ott U (1999) The anterior determinant bicoid of Drosophila is a derived Hox class 3 gene. Proc Natl Acad Sci U S A 96: 3786–3789.M. StauberH. JackleU. Schmidt-Ott1999The anterior determinant bicoid of Drosophila is a derived Hox class 3 gene.Proc Natl Acad Sci U S A9637863789
- 13. Berman BP, Pfeiffer BD, Laverty TR, Salzberg SL, Rubin GM, et al. (2004) Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura. Genome Biol 5: R61.BP BermanBD PfeifferTR LavertySL SalzbergGM Rubin2004Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura.Genome Biol5R61
- 14. Sinha S, Schroeder MD, Unnerstall U, Gaul U, Siggia ED (2004) Cross-species comparison significantly improves genome-wide prediction of cis-regulatory modules in Drosophila. BMC Bioinformatics 5: 129.S. SinhaMD SchroederU. UnnerstallU. GaulED Siggia2004Cross-species comparison significantly improves genome-wide prediction of cis-regulatory modules in Drosophila.BMC Bioinformatics5129
- 15. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, et al. (2003) Human-mouse alignments with BLASTZ. Genome Res 13: 103–107.S. SchwartzWJ KentA. SmitZ. ZhangR. Baertsch2003Human-mouse alignments with BLASTZ.Genome Res13103107
- 16. Goltsev Y, Hsiong W, Lanzaro G, Levine M (2004) Different combinations of gap repressors for common stripes in Anopheles and Drosophila embryos. Dev Biol 275: 435–446.Y. GoltsevW. HsiongG. LanzaroM. Levine2004Different combinations of gap repressors for common stripes in Anopheles and Drosophila embryos.Dev Biol275435446
- 17. Cripps RM, Olson EN (2002) Control of cardiac development by an evolutionarily conserved transcriptional network. Dev Biol 246: 14–28.RM CrippsEN Olson2002Control of cardiac development by an evolutionarily conserved transcriptional network.Dev Biol2461428
- 18. Berman BP, Nibu Y, Pfeiffer BD, Tomancak P, Celniker SE, et al. (2002) Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc Natl Acad Sci U S A 99: 757–762.BP BermanY. NibuBD PfeifferP. TomancakSE Celniker2002Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome.Proc Natl Acad Sci U S A99757762
- 19. Fish MP, Groth AC, Calos MP, Nusse R (2007) Creating transgenic Drosophila by microinjecting the site-specific phiC31 integrase mRNA and a transgene-containing donor plasmid. Nat Protoc 2: 2325–2331.MP FishAC GrothMP CalosR. Nusse2007Creating transgenic Drosophila by microinjecting the site-specific phiC31 integrase mRNA and a transgene-containing donor plasmid.Nat Protoc223252331
- 20. Groth AC, Fish M, Nusse R, Calos MP (2004) Construction of transgenic Drosophila by using the site-specific integrase from phage phiC31. Genetics 166: 1775–1782.AC GrothM. FishR. NusseMP Calos2004Construction of transgenic Drosophila by using the site-specific integrase from phage phiC31.Genetics16617751782
- 21. Small S, Blair A, Levine M (1992) Regulation of even-skipped stripe 2 in the Drosophila embryo. Embo J 11: 4047–4057.S. SmallA. BlairM. Levine1992Regulation of even-skipped stripe 2 in the Drosophila embryo.Embo J1140474057
- 22. Small S, Blair A, Levine M (1996) Regulation of two pair-rule stripes by a single enhancer in the Drosophila embryo. Dev Biol 175: 314–324.S. SmallA. BlairM. Levine1996Regulation of two pair-rule stripes by a single enhancer in the Drosophila embryo.Dev Biol175314324
- 23. Fujioka M, Emi-Sarker Y, Yusibova GL, Goto T, Jaynes JB (1999) Analysis of an even-skipped rescue transgene reveals both composite and discrete neuronal and early blastoderm enhancers, and multi-stripe positioning by gap gene repressor gradients. Development 126: 2527–2538.M. FujiokaY. Emi-SarkerGL YusibovaT. GotoJB Jaynes1999Analysis of an even-skipped rescue transgene reveals both composite and discrete neuronal and early blastoderm enhancers, and multi-stripe positioning by gap gene repressor gradients.Development12625272538
- 24. Clyde DE, Corado MS, Wu X, Pare A, Papatsenko D, et al. (2003) A self-organizing system of repressor gradients establishes segmental complexity in Drosophila. Nature 426: 849–853.DE ClydeMS CoradoX. WuA. PareD. Papatsenko2003A self-organizing system of repressor gradients establishes segmental complexity in Drosophila.Nature426849853
- 25. Frasch M, Hoey T, Rushlow C, Doyle H, Levine M (1987) Characterization and localization of the even-skipped protein of Drosophila. Embo J 6: 749–759.M. FraschT. HoeyC. RushlowH. DoyleM. Levine1987Characterization and localization of the even-skipped protein of Drosophila.Embo J6749759
- 26. Han Z, Fujioka M, Su M, Liu M, Jaynes JB, et al. (2002) Transcriptional integration of competence modulated by mutual repression generates cell-type specificity within the cardiogenic mesoderm. Dev Biol 252: 225–240.Z. HanM. FujiokaM. SuM. LiuJB Jaynes2002Transcriptional integration of competence modulated by mutual repression generates cell-type specificity within the cardiogenic mesoderm.Dev Biol252225240
- 27. Bergman CM, Carlson JW, Celniker SE (2005) Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster. Bioinformatics 21: 1747–1749.CM BergmanJW CarlsonSE Celniker2005Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster.Bioinformatics2117471749
- 28. Markstein M, Markstein P, Markstein V, Levine MS (2002) Genome-wide analysis of clustered Dorsal binding sites identifies putative target genes in the Drosophila embryo. Proc Natl Acad Sci U S A 99: 763–768.M. MarksteinP. MarksteinV. MarksteinMS Levine2002Genome-wide analysis of clustered Dorsal binding sites identifies putative target genes in the Drosophila embryo.Proc Natl Acad Sci U S A99763768
- 29. Mitsialis SA, Kafatos FC (1985) Regulatory elements controlling chorion gene expression are conserved between flies and moths. Nature 317: 453–456.SA MitsialisFC Kafatos1985Regulatory elements controlling chorion gene expression are conserved between flies and moths.Nature317453456
- 30. Bello B, Couble P (1990) Specific expression of a silk-encoding gene of Bombyx in the anterior salivary gland of Drosophila. Nature 346: 480–482.B. BelloP. Couble1990Specific expression of a silk-encoding gene of Bombyx in the anterior salivary gland of Drosophila.Nature346480482
- 31. Wratten NS, McGregor AP, Shaw PJ, Dover GA (2006) Evolutionary and functional analysis of the tailless enhancer in Musca domestica and Drosophila melanogaster. Evol Dev 8: 6–15.NS WrattenAP McGregorPJ ShawGA Dover2006Evolutionary and functional analysis of the tailless enhancer in Musca domestica and Drosophila melanogaster.Evol Dev8615
- 32. Markstein M, Zinzen R, Markstein P, Yee KP, Erives A, et al. (2004) A regulatory code for neurogenic gene expression in the Drosophila embryo. Development 131: 2387–2394.M. MarksteinR. ZinzenP. MarksteinKP YeeA. Erives2004A regulatory code for neurogenic gene expression in the Drosophila embryo.Development13123872394
- 33. Fisher S, Grice EA, Vinton RM, Bessling SL, McCallion AS (2006) Conservation of RET regulatory function from human to zebrafish without sequence similarity. Science 312: 276–279.S. FisherEA GriceRM VintonSL BesslingAS McCallion2006Conservation of RET regulatory function from human to zebrafish without sequence similarity.Science312276279
- 34. Panne D, Maniatis T, Harrison SC (2007) An atomic model of the interferon-beta enhanceosome. Cell 129: 1111–1123.D. PanneT. ManiatisSC Harrison2007An atomic model of the interferon-beta enhanceosome.Cell12911111123
- 35. Kulkarni MM, Arnosti DN (2003) Information display by transcriptional enhancers. Development 130: 6569–6575.MM KulkarniDN Arnosti2003Information display by transcriptional enhancers.Development13065696575
- 36. Arnosti DN, Barolo S, Levine M, Small S (1996) The eve stripe 2 enhancer employs multiple modes of transcriptional synergy. Development 122: 205–214.DN ArnostiS. BaroloM. LevineS. Small1996The eve stripe 2 enhancer employs multiple modes of transcriptional synergy.Development122205214
- 37. Arnosti DN (2003) Analysis and function of transcriptional regulatory elements: insights from Drosophila. Annu Rev Entomol 48: 579–602.DN Arnosti2003Analysis and function of transcriptional regulatory elements: insights from Drosophila.Annu Rev Entomol48579602
- 38. Gray S, Levine M (1996) Short-range transcriptional repressors mediate both quenching and direct repression within complex loci in Drosophila. Genes Dev 10: 700–710.S. GrayM. Levine1996Short-range transcriptional repressors mediate both quenching and direct repression within complex loci in Drosophila.Genes Dev10700710
- 39. Gray S, Szymanski P, Levine M (1994) Short-range repression permits multiple enhancers to function autonomously within a complex promoter. Genes Dev 8: 1829–1838.S. GrayP. SzymanskiM. Levine1994Short-range repression permits multiple enhancers to function autonomously within a complex promoter.Genes Dev818291838
- 40. Small S, Kraut R, Hoey T, Warrior R, Levine M (1991) Transcriptional regulation of a pair-rule stripe in Drosophila. Genes Dev 5: 827–839.S. SmallR. KrautT. HoeyR. WarriorM. Levine1991Transcriptional regulation of a pair-rule stripe in Drosophila.Genes Dev5827839
- 41. Stanojevic D, Small S, Levine M (1991) Regulation of a segmentation stripe by overlapping activators and repressors in the Drosophila embryo. Science 254: 1385–1387.D. StanojevicS. SmallM. Levine1991Regulation of a segmentation stripe by overlapping activators and repressors in the Drosophila embryo.Science25413851387
- 42. Arnosti DN, Gray S, Barolo S, Zhou J, Levine M (1996) The gap protein knirps mediates both quenching and direct repression in the Drosophila embryo. Embo J 15: 3659–3666.DN ArnostiS. GrayS. BaroloJ. ZhouM. Levine1996The gap protein knirps mediates both quenching and direct repression in the Drosophila embryo.Embo J1536593666
- 43. Bergman CM, Kreitman M (2001) Analysis of conserved noncoding DNA in Drosophila reveals similar constraints in intergenic and intronic sequences. Genome Res 11: 1335–1345.CM BergmanM. Kreitman2001Analysis of conserved noncoding DNA in Drosophila reveals similar constraints in intergenic and intronic sequences.Genome Res1113351345
- 44. Petrov DA (2002) DNA loss and evolution of genome size in Drosophila. Genetica 115: 81–91.DA Petrov2002DNA loss and evolution of genome size in Drosophila.Genetica1158191
- 45. Huang W, Nevins JR, Ohler U (2007) Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools. Genome Biol 8: R225.W. HuangJR NevinsU. Ohler2007Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools.Genome Biol8R225
- 46. Janssens H, Hou S, Jaeger J, Kim AR, Myasnikova E, et al. (2006) Quantitative and predictive model of transcriptional control of the Drosophila melanogaster even skipped gene. Nat Genet 38: 1159–1165.H. JanssensS. HouJ. JaegerAR KimE. Myasnikova2006Quantitative and predictive model of transcriptional control of the Drosophila melanogaster even skipped gene.Nat Genet3811591165
- 47. Yeates DK, Wiegmann BM, Courtney GW, Meier R, CLambkin C, et al. (2007) Phylogeny and systematics of Diptera: Two decades of progress and prospects. Zootaxa 1668: 595–590.DK YeatesBM WiegmannGW CourtneyR. MeierC. CLambkin2007Phylogeny and systematics of Diptera: Two decades of progress and prospects.Zootaxa1668595590
- 48. Bennett MD, Leitch IJ, Price HJ, Johnston JS (2003) Comparisons with Caenorhabditis (approximately 100 Mb) and Drosophila (approximately 175 Mb) using flow cytometry show genome size in Arabidopsis to be approximately 157 Mb and thus approximately 25% larger than the Arabidopsis genome initiative estimate of approximately 125 Mb. Ann Bot (Lond) 91: 547–557.MD BennettIJ LeitchHJ PriceJS Johnston2003Comparisons with Caenorhabditis (approximately 100 Mb) and Drosophila (approximately 175 Mb) using flow cytometry show genome size in Arabidopsis to be approximately 157 Mb and thus approximately 25% larger than the Arabidopsis genome initiative estimate of approximately 125 Mb.Ann Bot (Lond)91547557
- 49. Ross MT, LaBrie S, McPherson J, Stanton VP (1999) Screening large-insert libraries by hybridization. In: Dracopoli NC, Haines JL, editors. Current Protocols in Human Genetics. New York: Wiley. pp. 5.6.1–5.6.52.MT RossS. LaBrieJ. McPhersonVP Stanton1999Screening large-insert libraries by hybridization.NC DracopoliJL HainesCurrent Protocols in Human GeneticsNew YorkWiley220.127.116.11.52
- 50. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 8: 186–194.B. EwingP. Green1998Base-calling of automated sequencer traces using phred. II. Error probabilities.Genome Res8186194
- 51. Ewing B, Hillier L, Wendl MC, Green P (1998) Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 8: 175–185.B. EwingL. HillierMC WendlP. Green1998Base-calling of automated sequencer traces using phred. I. Accuracy assessment.Genome Res8175185
- 52. Batzoglou S, Jaffe DB, Stanley K, Butler J, Gnerre S, et al. (2002) ARACHNE: a whole-genome shotgun assembler. Genome Res 12: 177–189.S. BatzoglouDB JaffeK. StanleyJ. ButlerS. Gnerre2002ARACHNE: a whole-genome shotgun assembler.Genome Res12177189
- 53. Jaffe DB, Butler J, Gnerre S, Mauceli E, Lindblad-Toh K, et al. (2003) Whole-genome sequence assembly for mammalian genomes: Arachne 2. Genome Res 13: 91–96.DB JaffeJ. ButlerS. GnerreE. MauceliK. Lindblad-Toh2003Whole-genome sequence assembly for mammalian genomes: Arachne 2.Genome Res139196
- 54. Kent WJ (2002) BLAT–the BLAST-like alignment tool. Genome Res 12: 656–664.WJ Kent2002BLAT–the BLAST-like alignment tool.Genome Res12656664
- 55. Birney E, Clamp M, Durbin R (2004) GeneWise and Genomewise. Genome Res 14: 988–995.E. BirneyM. ClampR. Durbin2004GeneWise and Genomewise.Genome Res14988995
- 56. Clark AG, Eisen MB, Smith DR, Bergman CM, Oliver B, et al. (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature 450: 203–218.AG ClarkMB EisenDR SmithCM BergmanB. Oliver2007Evolution of genes and genomes on the Drosophila phylogeny.Nature450203218
- 57. Feng S, Yi K, Kutty S, Meier R (2008) Morphology versus molecules: the phylogenetic relationships of Sepsidae (Diptera: Cyclorrhapha) based on morphology and DNA sequence data from ten genes. Molecular Phylogenetics and Evolution submitted. S. FengK. YiS. KuttyR. Meier2008Morphology versus molecules: the phylogenetic relationships of Sepsidae (Diptera: Cyclorrhapha) based on morphology and DNA sequence data from ten genes.Molecular Phylogenetics and Evolution submitted
- 58. Notredame C, Higgins DG, Heringa J (2000) T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 302: 205–217.C. NotredameDG HigginsJ. Heringa2000T-Coffee: A novel method for fast and accurate multiple sequence alignment.J Mol Biol302205217
- 59. Yang Z (1997) PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci 13: 555–556.Z. Yang1997PAML: a program package for phylogenetic analysis by maximum likelihood.Comput Appl Biosci13555556
- 60. Jiang J, Kosman D, Ip YT, Levine M (1991) The dorsal morphogen gradient regulates the mesoderm determinant twist in early Drosophila embryos. Genes Dev 5: 1881–1891.J. JiangD. KosmanYT IpM. Levine1991The dorsal morphogen gradient regulates the mesoderm determinant twist in early Drosophila embryos.Genes Dev518811891
- 61. Hertz GZ, Stormo GD (1999) Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 15: 563–577.GZ HertzGD Stormo1999Identifying DNA and protein patterns with statistically significant alignments of multiple sequences.Bioinformatics15563577
- 62. Yan R, Small S, Desplan C, Dearolf CR, Darnell JE Jr (1996) Identification of a Stat gene that functions in Drosophila development. Cell 84: 421–430.R. YanS. SmallC. DesplanCR DearolfJE Darnell Jr1996Identification of a Stat gene that functions in Drosophila development.Cell84421430
- 63. Halfon MS, Grad Y, Church GM, Michelson AM (2002) Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model. Genome Res 12: 1019–1028.MS HalfonY. GradGM ChurchAM Michelson2002Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.Genome Res1210191028
- 64. Williams DA (1976) Improved likelihood ratio test for complete contingency tables. Biometrika 63: 33–37.DA Williams1976Improved likelihood ratio test for complete contingency tables.Biometrika633337
- 65. Brudno M, Do CB, Cooper GM, Kim MF, Davydov E, et al. (2003) LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA. Genome Res 13: 721–731.M. BrudnoCB DoGM CooperMF KimE. Davydov2003LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA.Genome Res13721731