Chromatin loop anchors predict transcript and exon usage

Comprehensive multiomics analyses reveal pervasive involvement of aberrant cohesin binding in transcriptional and chromosomal disorder of cancer cells

Chromatin organization, whose malfunction causes various diseases including cancer, is fundamentally controlled by cohesin. While cancer cells have been found with mutated or misexpressed cohesin genes, there is no comprehensive survey about the presence and role of abnormal cohesin binding in cancer cells. Here, we systematically identified ∼1% of cohesin-binding sites (701-2,633) as cancer-aberrant binding sites of cohesin (CASs). We integrated CASs with large-scale transcriptomics, epigenomics, 3D genomics, and clinical information. CASs represent tissue-specific epigenomic signatures enriched for cancer-dysregulated genes with functional and clinical significance. CASs exhibited alterations in chromatin compartments, loops within topologically associated domains, and cis-regulatory elements, indicating that CASs induce dysregulated genes through misguided chromatin structure. Cohesin depletion data suggested that cohesin binding at CASs actively regulates cancer-dysregulated genes. Overall, our comprehensive investigation suggests that aberrant cohesin binding is an essential epigenomic signature responsible for dysregulated chromatin structure and transcription in cancer cells.

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention

BACKGROUND

Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore, it is plausible that transcription factors can regulate splicing outcomes.

RESULTS

We provide evidence for the hypothesis that transcription factors are involved in the regulation of intron retention by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events.

CONCLUSIONS

This work opens up opportunities for further studies that elucidate the mechanisms by which transcription factors affect intron retention and other forms of splicing.

AVAILABILITY

Source code available at https://github.com/fahadahaf/chromir.

Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

DNA replication initiation is a complex process involving various genetic and epigenomic signatures. The correct identification of replication origins (ORIs) could provide important clues for the study of a variety of diseases caused by replication. Here, we design a computational approach named iORI-Epi to recognize ORIs by incorporating epigenome-based features, sequence-based features, and 3D genome-based features. The iORI-Epi displays excellent robustness and generalization ability on both training datasets and independent datasets of K562 cell line. Further experiments confirm that iORI-Epi is highly scalable in other cell lines (MCF7 and HCT116). We also analyze and clarify the regulatory role of epigenomic marks, DNA motifs, and chromatin interaction in DNA replication initiation of eukaryotic genomes. Finally, we discuss gene enrichment pathways from the perspective of ORIs in different replication timing states and heuristically dissect the effect of promoters on replication initiation. Our computational methodology is worth extending to ORI identification in other eukaryotic species.