Dataset Information

STAT5 Regulation of Sex-Dependent Hepatic CpG Methylation at Distal Regulatory Elements Mapping to Sex-Biased Genes.

ABSTRACT: Growth hormone-activated STAT5b is an essential regulator of sex-differential gene expression in mouse liver; however, its impact on hepatic gene expression and epigenetic responses is poorly understood. Here, we found a substantial, albeit incomplete loss of liver sex bias in hepatocyte-specific STAT5a/STAT5b (collectively, STAT5)-deficient mouse liver. In male liver, many male-biased genes were downregulated in direct association with the loss of STAT5 binding; many female-biased genes, which show low STAT5 binding, were derepressed, indicating an indirect mechanism for repression by STAT5. Extensive changes in CpG methylation were seen in STAT5-deficient liver, where sex differences were abolished at 88% of ∼1,500 sex-differentially methylated regions, largely due to increased DNA methylation upon STAT5 loss. STAT5-dependent CpG hypomethylation was rarely found at proximal promoters of STAT5-dependent genes. Rather, STAT5 primarily regulated the methylation of distal enhancers, where STAT5 deficiency induced widespread hypermethylation at genomic regions enriched for accessible chromatin, enhancer histone marks (histone H3 lysine 4 monomethylation [H3K4me1] and histone H3 lysine 27 acetylation [H3K27ac]), STAT5 binding, and DNA motifs for STAT5 and other transcription factors implicated in liver sex differences. Thus, the sex-dependent binding of STAT5 to liver chromatin is closely linked to the sex-dependent demethylation of distal regulatory elements linked to STAT5-dependent genes important for liver sex bias.

SUBMITTER: Hao P

PROVIDER: S-EPMC8093489 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

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STAT5 Regulation of Sex-Dependent Hepatic CpG Methylation at Distal Regulatory Elements Mapping to Sex-Biased Genes.

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