mojavensis ap enhancer is indeed active in muscle FCs, but only in a subset of the cells that express the reporter driven by the D. melanogaster enhancer (Figure 3B). We also observed interesting patterns of TFBS reshuffling between the orthologs of some FC enhancers. For example, a 643 bp sequence in the first intron of the D. melanogaster Nidogen (Ndg) gene activates reporter expression in a subset of muscle FCs, pericardial and cardial cells of the heart, and cells of the central nervous system (Figure 3C, 3D and data not shown), and was originally identified based on the presence of binding sites for Pnt, Twi and Tin [5]. GFP reporter constructs of Ndg enhancer candidates from D. persimilis and D. ananassae were tested in transgenic D. melanogaster embryos. To compare the activities of the ortholgous enhancers, we crossed D. persimilis Ndg-GFP or D. ananassae Ndg-GFP reporter constructs to a D. melanogaster Ndg-lacZ reporter strain (Figure 3C and 3D). The orthologous enhancers co- activate their respective reporters in D. melanogaster Ndg-expressing FCs, albeit a minority, with extensive additional activity evident in other mesodermal cells. The finding of distinct expression patterns for all tested Ndg enhancer sequences is noteworthy as there is significant conservation of Pnt, Twi and Tin binding sites between D. melanogaster and D. ananassae but not D. persimilis versions of the Ndg enhancer (Figure S3C). This finding suggests that different ordering and spacing of TF binding sequences (both conserved and non-conserved) can be employed by an enhancer to activate gene expression in FCs and other mesodermal cells [41], although precise cellular specificity is dependent on a fixed arrangement of binding sites. We note, however, that such inferences are based entirely on sequence comparisons, and that a more detailed understanding of the significance of the apparent evolutionary shuffling of TFBSs would require extensive in vivo functional testing. Finally, we observed variable ordering and distances between individual TFBSs among the orthologs of FC enhancers, as exemplified by even skipped (eve). This gene is expressed in two pericardial cells of the heart and a single dorsal somatic muscle FC [48]. Eve expression is positively regulated by the Wingless (Wg), Decapentapalegic (Dpp) and receptor tyrosine kinase (RTK)/Ras signaling pathways, and the gene is active in domains of the mesoderm in which Twi and Tin are critical [28,49]. An enhancer that integrates these convergent inputs was isolated and shown to contain clusters of binding sites for T cell factor (Tcf), Mothers against dpp (Mad), and Pointed (Pnt), TFs acting downstream of Wg, Dpp and RTK/Ras signals, respectively, as well as binding sites for Twi and Tin [28]. Here we show that the orthologous D. persimilis sequence is expressed in an identical pattern (Figure 3E). Interestingly, the D. persimilis eve muscle and heart enhancer contains clusters of Tcf, Mad, Pnt, Twi and Tin binding sites, but the precise positions of these sites are generally not well conserved (Figure S3D). The orthologous D. virilis eve enhancer has a similar structure in which all 5 of these TF binding site classes are present [29]. In total, 5 out of 5 tested orthologous sequences drove expression in a pattern that is similar (eve and lbl), though often not identical (Ndg), to the D. melanogaster enhancer. The imprecise activities of some of the orthologous enhancers may reflect the partial level of sequence identity that could affect as yet unidentified binding sites, may result from the extensive shuffling of known binding sites for co-regulatory TFs, or might simply be a reflection of differential gene expression in the orthologous flies [26,27,40,50]. Importantly, the general preservation of enhancer activity in the absence of extensive sequence conservation—a point which is further confirmed by the apparent shuffling of binding sites for known co-regulatory TF binding sites—suggests Machine Learning Transcriptional Regulators that these elements share other common sequence features. Thus, increasing the training set with orthologous sequences should minimize potential over-fitting caused by training on an otherwise small set of validated enhancers. Machine Learning of the FC Enhancer Code Results in an Accurate FC Enhancer Classifier The FC training set consisted of 16 D. melanogaster FC enhancers plus 24 orthologous sequences. However, as previously noted, the activity of these enhancers is not restricted to FCs, with only 2 out of 16 tested enhancers displaying such localized activity (Table S1). Therefore, any computational model for FC enhancer classifica- tion will likely predict enhancers having broad mesodermal expression patterns that include but are not restricted to FCs. As a control set, we randomly sampled 1000 non-coding D. melanogaster sequences with length, GC- and repeat-content distributions similar to those of the FC training set. To discriminate between FC enhancers and other non-coding sequences, we modified a machine learning approach that was previously developed for the prediction of mammalian heart enhancers, with many of those results validated in vivo [7]. This method captures sequence patterns specific to a set of similarly acting non-coding sequences, relying on known TFBSs, as well as de novo motif discovery, to account for unascertained TF binding specificities. Known TFBSs were obtained from the literature and available databases (see Materials and Methods). De novo motif discovery was performed using PRIORITY [51], a Gibbs sampling approach that searches for over-represented motifs in a set of sequences. With the aim of discovering TFs with critical roles in FC co- regulation, we assumed no prior knowledge of active TFs. Each sequence in the training and control set was represented by the number of occurrences per base pair of each of the 945 considered motifs. A linear Support Vector Machine (SVM) was trained to distinguish between FC enhancers and control sequences based on TFBS occurrences. The ability of the classifier to accurately predict regulatory activity was assessed by a 10-fold cross- validation procedure. The performance of the classifier was evaluated using the AUC, a value ranging from 0.5 (random classification) to 1.0 (perfect classification). The obtained AUC value of 0.89 indicates reliable detection of FC enhancers by the developed classifier (see Materials and Methods and Figure 2B). We next applied the classifier for de novo discovery of FC enhancers in the D. melanogaster genome. We used a sliding window approach to score ,140,000 overlapping non-coding 1000 base pair-long sequences spanning the complete genome. Keeping a low false-positive prediction rate (FPR) of 5%, approximately 5,500 sequences were annotated as putative FC enhancers (Table S3). Similar to what we observed for the training set, the individual conservation profile of the D. melanogaster putative FC enhancers generally reflects the phylogenetic distances of the species involved in the analysis, with most orthologs in the 50–80% sequence identity range in D. yakuba, D. erecta, D. ananassae, D. pseudoobscura and D. persimillis. However, putative FC enhancer sequences tend to be more deeply conserved than background genomic sequence (P-value,0.05, computed using the Binomial test, corrected for multiple testing using Bonferroni’s method), and thus, probably functional (see Text S1). Although it was not surprising that the scores of the FC enhancers in the training set were positively-skewed (Figure 2C), it was reassuring to find that putative FC enhancers are strongly associated with genes that are expressed in FCs. For example, we found that at a FPR of 5%, 222 enhancer predictions are associated with 77 genes expressed in FCs, a number that is 1.5-fold higher than would be PLoS Genetics | www.plosgenetics.org 7 March 2012 | Volume 8 | Issue 3 | e1002531