Biology subjects

AY, F.

Publications and source records attributed to AY, F..

3 recordsLinked to original sources

bioRxiv2026

IL-27 induces a cytotoxic state in CD4+ T cells distinct from the Th1 lineage

CD4+ cytotoxic T lymphocytes (CD4-CTLs) are understudied immune mediators with ambiguous origins. Despite expressing RUNX3, granzyme B (GZMB), and perforin (PRF1), CD4-CTLs are frequently classified as Th1 extensions due to shared interferon-{gamma} (IFN{gamma}) and T-BET expression. Here, we identify interleukin-27 (IL-27) as a independent inducer of a distinct CD4-CTL program. Proteomics reveals that while IL-27-polarized CD4+ T cells share protein signatures with conventional Th1s and CD8+ T cells, they possess a unique molecular landscape with re-wired cytokine signaling networks and a potent cytotoxic protein profile. Mechanistically, this program requires STAT1 and T-BET but operates independently of the autocrine IFN{gamma} feedback that sustains Th1 cells. During acute murine cytomegalovirus infection, IL-27 receptor signaling contributes to CD4-CTL differentiation in vivo, as its loss leads to reduced GZMB expression and skews CD4+ T cells toward IFN{gamma}+ and FOXP3+ subsets. Together, these findings establish IL-27 as a potent and previously unappreciated inducer of CD4-CTLs.

immunology↗

bioRxiv2026

EpiExpr: Predicting gene expression using epigenetic data and chromatin interactions

Decoding gene expression from epigenomic landscapes remains a fundamental challenge in genomics. We introduce EpiExpr, a flexible deep learning framework that predicts gene expression from 1D epigenetic tracks (EpiExpr-1D) and integrates 3D chromatin interactions (EpiExpr-3D) to capture distal regulatory effects. Leveraging residual convolutional networks and graph neural networks, including graph attention and graph transformer models, EpiExpr models both local and long-range regulatory influences. Applied to GM12878 and K562 cells, EpiExpr-1D and 3D improve gene expression prediction relative to reference approaches. Analysis using CRISPRi-FlowFISH validated enhancers confirms that EpiExpr-3D accurately prioritizes regulatory elements, compatible with activity-by-contact scores. Remarkably, EpiExpr achieves performance comparable to DNA sequence-based transformer models without requiring sequence embeddings, offering a computationally efficient alternative. This approach provides a scalable, multi-resolution framework (https://github.com/souryacs/3CExpr) for dissecting the contributions of epigenetic modifications and 3D genome organization to gene regulation, enabling broader application across cell types and experimental settings.

bioinformatics↗

bioRxiv2025

DiffHiChIP: Identifying differential chromatin contacts from HiChIP data

High-resolution conformation capture assays such as HiChIP are commonly used for profiling chromatin loops among cis-regulatory elements including enhancers and promoters. Detection of differential loops between two conditions (e.g., same cell type different genotypes or before/after perturbations) help contextualize condition-specific activities of genes in connection with such cis-regulatory elements. Existing differential loop callers for HiChIP data employ count-based models that are designed with gene expression data in mind and, hence, do not account for the distance decay of contact counts from HiChIP data. These approaches are not ideal for detection of differential long-range (>400Kb) loops, a limitation that persists even with the use of implicit or explicit corrections for this distance effect. We have implemented DiffHiChIP, the first comprehensive framework to call differential loops from HiChIP and similar 3C protocols. DiffHiChIP supports both DESeq2 and edgeR using either complete contact map or a subset of contacts (filtered) for background estimation, incorporates edgeR with generalized linear model (GLM) using either quasi-likelihood F-test or likelihood ratio test, and implements independent hypothesis weighting (IHW) as well as a distance stratification technique for modeling distance decay of contacts in estimating their statistical significance. Our results on 5 different datasets, each with two conditions or cell types, suggest that edgeR GLM-based models with IHW correction capture differential interactions, including long-range, that are supported by published Hi-C data and reference studies. Given the increasing trend of generating and utilizing HiChIP data for modeling chromatin regulation, DiffHiChIP promises to have broad impact and utility in this field.

bioinformatics↗