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Abstract Background Transcription factor (TF) proteins are a key component of the gene regulatory networks that control cellular fates and function. TFs bind DNA regulatory elements in a sequence-specific manner and modulate target gene expression through combinatorial interactions with each other, cofactors, and chromatin-modifying proteins. Large-scale studies over the last two decades have helped shed light on the complex network of TFs that regulate development in Drosophila melanogaster. Results Here, we present a detailed characterization of expression of all known and predicted Drosophila TFs in two well-established embryonic cell lines, Kc167 and S2 cells. Using deep coverage RNA sequencing approaches we investigate the transcriptional profile of all 707 TF coding genes in both cell types. Only 103 TFs have no detectable expression in either cell line and 493 TFs have a read count of 5 or greater in at least one of the cell lines. The 493 TFs belong to 54 different DNA-binding domain families, with significant enrichment of those in the zf-C2H2 family. We identified 123 differentially expressed genes, with 57 expressed at significantly higher levels in Kc167 cells than S2 cells, and 66 expressed at significantly lower levels in Kc167 cells than S2 cells. Network mapping reveals that many of these TFs are crucial components of regulatory networks involved in cell proliferation, cell–cell signaling pathways, and eye development. Conclusions We produced a reference TF coding gene expression dataset in the extensively studied Drosophila Kc167 and S2 embryonic cell lines, and gained insight into the TF regulatory networks that control the activity of these cells.

The core promoter elements are important DNA sequences for the regulation of RNA polymerase II transcription in eukaryotic cells. Despite the broad evolutionary conservation of these elements, there is extensive variation in the nucleotide composition of the actual sequences. In this study, we aim to improve our understanding of the complexity of this sequence variation in the TATA box and initiator core promoter elements in Drosophila melanogaster. Using computational approaches, including an enhanced version of our previously developed MARZ algorithm that utilizes gapped nucleotide matrices, several sequence landscape features are uncovered, including an interdependency between the nucleotides in position 2 and 5 in the initiator. Incorporating this information in an expanded MARZ algorithm improves predictive performance for the identification of the initiator element. Overall our results demonstrate the need to carefully consider detailed sequence composition features in core promoter elements in order to make more robust and accurate bioinformatic predictions.

Abstract Drosophila melanogaster