subset of all slou-expressing cells (in particular, those which correspond to the lateral oblique 1 (LO1) and ventral transverse 1 (VT1) muscles). This result suggests that additional regulatory elements must account for the complete expression pattern of this FC gene [55], unlike the situation for the majority of FC enhancers. The predicted enhancer associated with slp1 is also located upstream of the gene and directs reporter activity both to FCs (Figure 4C) and to mesodermal and ectodermal stripes which are known to express slp1 [56]. Of note, the intronic enhancer for dve, a gene which was tested for expression in FCs based on the presence of this predicted FC enhancer (Figure 4A), was not active during the FC stage of myogenesis but did direct reporter expression slightly later when myotubes develop (Figure 4D). It remains possible that the activity of this element occurs at the FC stage but is insufficiently strong to be detected by the present assay. Alternatively, a separate enhancer may be directing the early FC activity of dve, consistent with the additional candidate enhancers associated with this gene (Table S3). In this case, the classifier appears to be detecting features shared by early- and late-acting muscle enhancers without discriminating FC-specific elements, which is not surprising given that many TFs are expressed and active in the same cell types at different stages of development [20,54]. Thus, while the classifier has some predictive value for FC enhancers, the regulatory network specifying these cells is sufficiently diverse and complex that the available training set is insufficient to provide a higher success rate for identifying new FC enhancers. To begin unraveling the complexities of this network, we need to define a more extensive collection of myogenic transcription factors and the DNA sequences to which they bind. To this end, we turned to an examination and validation of the novel sequence motifs detected by the classifier. Identification of Novel Sequence Motifs within FC Enhancers To begin constructing a more comprehensive myogenic network, we examined the sequence features associated with the computational classification of FC enhancers. These features included position weight matrices of known TF binding specific- ities found within the TRANSFAC database, as well as motifs not represented in this database that can be identified by the PRIORITY algorithm [51]. In the case of linear SVMs, features irrelevant to the classification receive zero weight, whereas those associated with the signal and control set receive positive and negative weights, respectively (see Materials and Methods). Since a finite number of TFs is expected to regulate FC gene expression, only some of all possible motifs will be relevant to the classification. Indeed, out of the original 945 features, 200 contributed to approximately 50% of the weights in the decision function of the classifier, suggesting their importance in the prediction of FC enhancers. Sixty-percent of these 200 motifs were associated with positive weights and correspond to almost 60 distinct TFs (Figure S4 and Table S4). Most of these TFs belong to only a few families having similar binding profiles, which we are unable to individualize (Figure 5). This diverse compilation of motifs suggests that the motif signature of FC enhancers is complex. However, this interpreta- tion should be considered with caution, since training on a set of enhancers with diverse expression (Table S1) is likely to lead to the identification of multiple enhancer signatures. In any case, we were encouraged by the fact that known myogenic regulatory motifs, including Ets, Mef2 and MyoD (due to similarities in binding preferences, E-boxes may represent motifs for Twi, MyoD or other TFs having basic-helix-loop-helix DNA binding domains), are among those with the highest discriminatory power. Other Machine Learning Transcriptional Regulators identified motifs, including those for Stat [57] and homeodomain proteins [35,58,59], appear to play critical roles in myogenesis. In addition to known TFBSs, de novo motifs make a key contribution to the classification and presumably account for binding sites of TFs missing from TFBS libraries, or constitute more accurate representations for the binding specificities of incompletely characterized TFs (Figure S5). In particular, we found that the most relevant de novo motif represents the binding specificity of Tin [60], consistent with the well-established mesodermal regulatory functions of this TF [61]. Predicted Motifs Regulate Enhancer Function in FCs To determine if the newly identified motifs are functionally relevant to FC gene expression, we employed site-directed mutagenesis of such putative binding site sequences in otherwise wild-type FC enhancers. We initially concentrated on the potential role of Ets, Myb, POU homeodomain (POUHD) and Fkh binding motifs (see Figure 5, Figure 6A, and Figure S7B). Each of these motifs is over-represented in both individual FC enhancers and their orthologous sequences when compared to controls (Figure S6). Sequence matches to Myb and POUHD motifs in the wild- type Ndg enhancer and a version in which the sites are mutated are shown in Figure 6B (also see Table S4). To compare activities of the different constructs, we crossed Drosophila strains containing wild-type or mutant enhancer transgenes driving different reporters (either GFP or lacZ) to each other. Mutagenesis of all motifs affected activity of the reporter as compared to wild-type versions of the enhancer (Figure 6C). For example, elimination of POUHD binding sites (Figure 6D) from an otherwise wild-type version of the Ndg enhancer reduced or eliminated enhancer activity in subsets of cells which express wild- type Ndg-lacZ (Figure 6D), whereas mutagenesis of Myb motifs caused an extensive de-repression of the reporter into additional somatic mesodermal cells (compare Figure 6E and 6E9). In addition, we found that the activity of Ets binding sites is critical for the full activity of the Ndg enhancer (Figure S7A), as had previously been demonstrated for another FC regulatory element [28]. Finally, mutagenesis of the Fkh binding sites in the apterous (ap) FC enhancer lead to a complete loss of reporter expression in those FCs in which the wild-type enhancer is active (compare Figure S7C and S7D). Collectively, the present experiments validating the functions of specific TFBSs in known FC enhancers document the critical role played by classifier-defined motifs in regulating specific gene expression patterns. Identifying a Novel Myogenic Transcription Factor from Motifs Over-Represented in FC Enhancers The preceding analyses indicate that the regulatory motifs learned by the classifier are critical for the normal functions of FC enhancers. Next, we used classifier results not only to discover a new cis-acting motif but also to identity the corresponding TF that binds to this sequence and to functionally characterize it as a previously unrecognized myogenic regulator. One of the top-scoring classifying features of the enhancer training set was a motif that binds to T-box TFs (Figure 5, Figure 7A, Figure S4, and Figure S6). This finding could either reflect the existence of a novel myogenic regulator or, since the training set of FC enhancers also contain many elements with heart activity (Table S1), it could simply indicate the functions of known cardiogenic T-box TFs [62–64]. To distinguish between these possibilities, we first defined the expression pattern of every Drosophila T-box TF (Table S5), which confirmed that Dorsocross 3 (Doc3) and optomotor-blind-related-gene-1 (org-1) are the only T-box TFs expressed in muscle FCs [22,62]. In particular, org-1 is co- PLoS Genetics | www.plosgenetics.org 9 March 2012 | Volume 8 | Issue 3 | e1002531