Transcription factors interact with RNA to regulate genes.

Transcription factors (TFs) orchestrate the gene expression programs that define each cell's identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA binding contributes to TF function by promoting the dynamic association between DNA, RNA, and TF on chromatin. TF-RNA interactions are a conserved feature important for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA, and protein is a general property of many TFs and is fundamental to their gene regulatory function.

Mediator Condensates Localize Signaling Factors to Key Cell Identity Genes.

The gene expression programs that define the identity of each cell are controlled by master transcription factors (TFs) that bind cell-type-specific enhancers, as well as signaling factors, which bring extracellular stimuli to these enhancers. Recent studies have revealed that master TFs form phase-separated condensates with the Mediator coactivator at super-enhancers. Here, we present evidence that signaling factors for the WNT, TGF-ß, and JAK/STAT pathways use their intrinsically disordered regions (IDRs) to enter and concentrate in Mediator condensates at super-enhancers. We show that the WNT coactivator ß-catenin interacts both with components of condensates and DNA-binding factors to selectively occupy super-enhancer-associated genes. We propose that the cell-type specificity of the response to signaling is mediated in part by the IDRs of the signaling factors, which cause these factors to partition into condensates established by the master TFs and Mediator at genes with prominent roles in cell identity.

The dynamic clustering of insulin receptor underlies its signaling and is disrupted in insulin resistance.

Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in metabolic diseases such as type 2 diabetes. We report here that IR is incorporated into dynamic clusters at the plasma membrane, in the cytoplasm and in the nucleus of human hepatocytes and adipocytes. Insulin stimulation promotes further incorporation of IR into these dynamic clusters in insulin-sensitive cells but not in insulin-resistant cells, where both IR accumulation and dynamic behavior are reduced. Treatment of insulin-resistant cells with metformin, a first-line drug used to treat type 2 diabetes, can rescue IR accumulation and the dynamic behavior of these clusters. This rescue is associated with metformin's role in reducing reactive oxygen species that interfere with normal dynamics. These results indicate that changes in the physico-mechanical features of IR clusters contribute to insulin resistance and have implications for improved therapeutic approaches.

Combined global localization analysis and transcriptome data identify genes that are directly coregulated by Adr1 and Cat8.

In Saccharomyces cerevisiae, glucose depletion causes a profound alteration in metabolism, mediated in part by global transcriptional changes. Many of the transcription factors that regulate these changes act combinatorially. We have analyzed combinatorial regulation by Adr1 and Cat8, two transcription factors that act during glucose depletion, by combining genome-wide expression and genome-wide binding data. We identified 32 genes that are directly activated by Adr1, 28 genes that are directly activated by Cat8, and 14 genes that are directly regulated by both. Our analysis also uncovered promoters that Adr1 binds but does not regulate and promoters that are indirectly regulated by Cat8, stressing the advantage of combining global expression and global localization analysis to find directly regulated targets. At most of the coregulated promoters, the in vivo binding of one factor is independent of the other, but Adr1 is required for optimal Cat8 binding at two promoters with a poor match to the Cat8 binding consensus. In addition, Cat8 is required for Adr1 binding at promoters where Adr1 is not required for transcription. These data provide a comprehensive analysis of the direct, indirect, and combinatorial requirements for these two global transcription factors.