Biology subjects

Han, M. V.

Publications and source records attributed to Han, M. V..

4 recordsLinked to original sources

bioRxiv2026

Fasting disrupts the InsP6 HDAC3 axis to drive ER stress-mediated clearance of DNA-damaged cells and enforce tissue quality control.

Fasting drives metabolic adaptation but also elicits acute cellular stress. How this stress shapes tissue integrity is unknown. Here, we show that in the intestine, fasting depletes growth factor signaling, which triggers cellular stress. This response functions as a tissue quality-control checkpoint that selectively eliminates pre-existing DNA-damaged cells while sparing healthy counterparts. A short-term fast diminishes TGF-{beta} signaling and elicits endoplasmic reticulum (ER) stress, driving DNA-damaged intestinal cells beyond an apoptotic threshold, thereby reducing the inflammatory burden. Mechanistically, loss of TGF-{beta} signaling triggers FBXO22-Cullin1-mediated degradation of the inositol kinase IPMK, leading to depletion of inositol hexaphosphate (InsP). InsP loss attenuates HDAC3 activity and initiates coordinated epigenetic and post-translational reprogramming, thereby increasing CDK5RAP3 abundance. Elevated CDK5RAP3 inhibits ribosomal RPL26 UFMylation, thereby amplifying ER stress and selectively licensing apoptosis in DNA-damaged cells. Collectively, fasting disrupts a TGF-{beta}-InsP6-HDAC3 axis to drive ER stress-dependent clearance of DNA-damaged cells, enforcing tissue quality control.

cell biology↗

bioRxiv2023

Machine learning of enhancer-promoter specificity based on enhancer perturbation studies reveals a distinct class of enhancers.

MotivationUnderstanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer promoter relationships in a data driven manner. ResultsWe applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor and histone modification ChIP-seq. Based on the learned model, we confirmed previously reported rules governing enhancer driven transcription, and we gained some insights that generated new hypotheses, such as a novel role for protecting against replication-transcription conflict at the active enhancers in CHAMP1. We also identified a distinct class of enhancers that drives target promoter transcription, but is not in strong contact with the promoters. There were two clusters of such enhancers that regulated ATG2A and the histone 1 cluster respectively. These enhancers were different from other typical enhancers, in that they had other strong enhancers nearby, and they also had strong H3K4me3 marks at the target promoters, both patterns that typically predict reduced enhancer influence, but here contributing in the opposite way. In summary, we find that integrating genomic assays with enhancer perturbation studies increases the accuracy of the model, and provides novel insights into the understanding of enhancer driven transcription. Availabilitythe trained models and the source code are available at https://github.com/HanLabUNLV/abic. Contactmira.han@unlv.edu

bioinformatics↗

bioRxiv2023

Genome wide clustering on integrated chromatin states and Micro-C contacts reveals chromatin interaction signatures

Chromatin states based on various histone modifications are a common annotation for genomes, and have been shown to correspond to regulatory functions such as enhancers and transcription start sites. With the advent of Hi-C and other chromatin conformation capture technologies, we now have the ability to analyze 3-dimensional physical interactions of chromatin regions, in addition to the 1-dimensional regulatory annotation, but methods to integrate this information are lacking. We present a framework for integrating the chromatin state of interacting regions into a numeric vector through the contact-weighted sum of chromatin states. Unsupervised clustering on integrated chromatin states and Micro-C contacts reveals common patterns of chromatin interaction which we call chromatin interaction signatures. Our results indicate that most chromatin interaction signatures are found in all four cell types investigated here. Between 12-40% of the regions change chromatin interaction signatures between the cell types despite maintaining chromatin state, hinting at the dynamic nature of chromatin conformation. Although regions with similar chromatin states are often in contact as expected, subcategories of enhancers and transcription start sites have distinct chromatin interaction signatures that are associated with gene expression. The integrated information on the chromatin states that the region is in contact with adds another layer of annotation beyond chromatin state or Hi-C contact separately. In summary, we present the first set of chromatin interaction signatures for the human genome that provides an integrated view of the complex dynamics of concurrent change occurring in chromatin state and in chromatin interaction.

bioinformatics↗

bioRxiv2018

Transcriptome analysis reveals negative correlation between transcripts of mitochondrial genes and L1HS, and positive correlation between KRAB-ZFPs and older LINE elements in normal somatic tissue

BackgroundDespite the long-held assumption that transposons are normally only expressed in the germ-line, recent evidence shows that transcripts of transposable element (TE) sequences are frequently found in the somatic cells. However, the extent of variation in TE transcript levels across different tissues and different individuals are unknown, and the co-expression between TEs and host gene mRNAs have not been examined.\n\nResultsHere we report the variation in TE derived transcript levels across tissues and between individuals observed in the non-tumorous tissues collected for The Cancer Genome Atlas. We found core TE co-expression modules consisting mainly of transposons, showing correlated expression across broad classes of TEs. Despite this co-expression within tissues, there are individual TE loci that exhibit tissue-specific expression patterns, when compared across tissues. The core TE modules were negatively correlated with other gene modules that consisted of immune response genes in interferon signaling. KRAB Zinc Finger Proteins (KZFPs) were over-represented gene members of the TE modules, showing positive correlation across multiple tissues. But we did not find overlap between TE-KZFP pairs that are co-expressed and TE-KZFP pairs that are bound in published ChIP-seq studies.\n\nConclusionsWe find unexpected variation in TE derived transcripts, within and across non-tumorous tissues. We describe a broad view of the RNA state for non-tumorous tissues exhibiting higher level of TE transcripts. Tissues with higher level of TE transcripts have a broad range of TEs co-expressed, with high expression of a large number of KZFPs, and lower RNA levels of immune genes.

genomics↗