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Drug treatment

ABSTRACT: Transcription factors (TFs) and transcriptional coregulators represent an emerging and exciting class of targets. By quantifying target gene modulation, gene regulatory networks (GRNs) delineate disease biology and evaluate pharmacological agents targeting these regulators. However, none of the existing methods are explicitly designed to measure the effects of perturbations in which TF expression is decoupled from its activity. We present Epiregulon, a method that constructs GRNs from single-cell ATAC and RNA data for accurate prediction of TF activity. Our weight estimation, based on co-occurrence of TF expression and chromatin accessibility, avoids erroneous inflation of TF activity as seen with TF expression only approaches. Furthermore, our utilization of ChIP-seq data expands inference to include transcriptional coregulators lacking defined motifs. Our extensive network of regulators facilitates identification of cell-state specific interaction partners. Using Epiregulon, we uncover divergent cell fate transitions of prostate cancer cells driven by NKX2-1 and GATA6 expression. We accurately predicted the effects of AR inhibition across various drug modalities. Finally, Epiregulon was able to infer the context-dependent activity of a chromatin remodeler lacking a defined motif sequence and recapitulate the unique etiologies of prostate cancer. By mapping out the network of key regulators across a multitude of perturbations, Epiregulon can accelerate the discovery of new therapeutics targeting transcription factors.

ORGANISM(S): Homo sapiens

PROVIDER: GSE251977 | GEO | 2025/01/21

REPOSITORIES: GEO

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Project description:In the budding yeast Saccharomyces cerevisiae, transcription factors (TFs) regulate the periodic expression of many genes during the cell cycle, including gene products required for progression through cell-cycle events. Experimental evidence coupled with quantitative models suggest that a network of interconnected TFs is capable of regulating periodic genes over the cell cycle. Importantly, these dynamical models were built on transcriptomics data and assumed that TF protein levels and activity are directly correlated with mRNA abundance. To ask whether TF transcripts match protein expression levels as cells progress through the cell cycle, we applied a multiplexed targeted mass spectrometry approach (parallel reaction monitoring) on synchronized populations of cells. We found that protein expression of many TFs and cell-cycle regulators closely followed their respective mRNA transcript dynamics in cycling wild-type cells. Discordant mRNA/protein expression dynamics were also observed for a subset of cell-cycle TFs and for proteins targeted for degradation by E3 ubiquitin ligase complexes such as SCF (Skp1/Cul1/F-box) and APC/C (anaphase-promoting complex/cyclosome). We further profiled mutant cells lacking B-type cyclin/CDK activity (clb1-6), where oscillations in ubiquitin ligase activity, cyclin/CDKs, and cell-cycle progression are halted. We found that a number of proteins were no longer periodically degraded in clb1-6 mutants compared to wild type, highlighting the importance of post-transcriptional regulation. Finally, the TF complexes responsible for activating G1/S transcription (SBF and MBF) were more constitutively expressed at the protein level than their periodic mRNA expression levels in both wild-type and mutant cells. This comprehensive investigation of cell-cycle regulators reveals that multiple layers of regulation (transcription, protein stability, and proteasome targeting) affect protein expression dynamics during the cell cycle.

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Drug treatment

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