Abstract
Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns.
Highlights
Complex spatio-temporal gene expression programs guide the progressive determination of pluripotent cells allowing cell fates to become sequentially restricted during embryonic development
This study is composed of 4 main components: (1) compiling a training set of founder cells (FCs) enhancers from multiple sources including the literature, testing of additional computational predictions from a previous study [5], increasing the size of the dataset through phylogenetic profiling, including the empirical validation of a subset of those predictions; (2) machine learning on the FC enhancer training set; (3) experimental validation of classifier predictions using transgenic reporter assays and whole embryo in situ hybridization with genespecific probes; and (4) functional examination of sequence features associated with the computational classification to define novel motifs and transcription factors (TFs) regulating myogenesis
We used the information derived from the abovementioned studies to examine the distribution of TF binding sites (TFBSs) across the entire set of known FC enhancers to ascertain the extent to which TF combinatorics contributes to the diversity of FC enhancer activities
Summary
Complex spatio-temporal gene expression programs guide the progressive determination of pluripotent cells allowing cell fates to become sequentially restricted during embryonic development These transitions in cell fate are encoded in the genome by cis regulatory DNA sequences such as transcriptional enhancers. Several groups have identified enhancers based on the presence of shared sequence features without the necessity of knowing the co-regulating TFs or their binding motifs [6,7,8,9,10,11,12] These enhancer modeling approaches generally take advantage of two data sources: (1) the non-coding sequences surrounding the members of a gene set of interest, or a set of previously validated enhancers associated with such genes; and (2) previously described sequence motifs from transcription factor binding site (TFBS) libraries and/or de novo motif discovery. A particular transcriptional regulatory model can be validated by assaying the functionality of the motifs that are found to be relevant for making predictions, and subsequently by identifying the DNA binding proteins that target these sequences
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