Biology subjects

Wehbe, F.

Publications and source records attributed to Wehbe, F..

3 recordsLinked to original sources

bioRxiv2025

Maladaptive inflammatory signaling in old mice impairs colonic regeneration by promoting a sustained fetal-like epithelial state

Aging is associated with a decline in the regenerative capacity of many tissues. Central to this decline is a complex interplay between inflammation and stem cell function. How these two processes are linked and influence regenerative capacity remains unclear. Here, we undertake a comprehensive assessment of age-related changes in the mouse colon at single-cell resolution. A survey of immune and epithelial compartments revealed a hyperactivated inflammatory state in the colon of old mice characterized by the induction of an interferon {gamma} (IFN{gamma}) response signature in immune cells. This does not result in increased inflammation under homeostasis, but triggers a disproportionate inflammatory response, disrupting regeneration after challenge with the enteropathogen Citrobacter rodentium. Colons of old mice exhibit higher production of IFN{gamma} by T and innate lymphoid cells (ILCs) that are associated with reduced Lgr5+ stem cells and decreased epithelial proliferation. Interestingly, we find aged intestinal epithelial cells to be hypersensitive to IFN{gamma} signaling, inducing a regeneration-associated fetal-like gene expression signature that, in turn, renders these cells more sensitive to IFN{gamma}-induced apoptosis. Our findings reveal an age-related imbalance in the interaction between the immune and epithelial compartments in the colon, priming the system for excessive inflammatory responses and the emergence of a hypersensitive epithelial cell state thus derailing proper repair of the intestinal epithelium after injury.

immunology↗

bioRxiv2024

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for identifying functional enhancers in mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian cortex and to designing more efficient genetic tools to target cortical cell types.

genomics↗

bioRxiv2023

Inferring Disease Progressive Stages in Single-Cell Transcriptomics Using Weakly-Supervised Deep Learning Approach

BackgroundApplication of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in human. However, individual cells in patient-derived tissues are in different pathological stages, and hence such cellular variability impedes subsequent differential gene expression analyses. ResultTo overcome such heterogeneity issue, we present a novel deep learning approach, scIDST, that infers disease progressive levels of individual cells with weak supervision framework. The inferred disease progressive cells displayed significant differential expression of disease-relevant genes, which could not be detected by comparative analysis between patients and healthy donors. In addition, we demonstrated that pre-trained models by scIDST are applicable to multiple independent data resources, and advantageous to infer cells related to certain disease risks and comorbidities. ConclusionTaken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease-associated molecular features.

genetics↗