Biology subjects

Gounari, F.

Publications and source records attributed to Gounari, F..

2 recordsLinked to original sources

bioRxiv2026

Maternal obesity induces developmental programming of Intestinal stem cells through an IL-17A/PPAR immune-epithelial axis

Maternal obesity is associated with increased risk of sporadic colorectal cancer (CRC) in offspring, suggesting that early-life environmental exposures durably shape disease susceptibility. Intestinal stem cells (ISCs), long-lived drivers of epithelial renewal and tumor initiation, are well poised to mediate this effect; however, how maternal obesity influences ISC programming during development remains poorly understood. Using mouse models of diet-induced obesity, we show that exposure to a maternal high-fat Western diet (mHFD) during pre- and postnatal development stably programs colonic ISCs. Offspring exhibit increased ISC proliferation, enhanced self-renewal, a hypermetabolic state, and altered epithelial lineage composition that persists into adulthood despite dietary normalization. These changes are accompanied by increased tumor burden following loss of Apc heterozygosity. Mechanistically, we identify the pro-inflammatory cytokine IL-17A as a key extrinsic driver and PPARd/a nuclear receptors as intrinsic mediators of the mHFD phenotype, revealing an immune-epithelial axis that programs ISC function during early life. Together, our findings demonstrate that maternal metabolic environments durably enhance stem cell fitness, providing a mechanistic link between developmental exposure and adult disease risk.

cell biology↗

bioRxiv2020

FiberAI: A Deep Learning model for automated analysis of nascent DNA Fibers

All life forms undergo cell division and are dependent on faithful DNA replication to maintain the stability of their genomes. Both intrinsic and extrinsic factors can stress the replication process and multiple checkpoint mechanisms have evolved to ensure genome stability. Understanding these molecular mechanisms is crucial for preventing and treating genomic instability associated diseases including cancer. DNA replicating fiber fluorography is a powerful technique that directly visualizes the replication process and a cells response to replication stress. Analysis of DNA-fiber microscopy images provides quantitative information about replication fitness. However, a bottleneck for high throughput DNA-fiber studies is that quantitative measurements are laborious when performed manually. Here we introduce FiberAI, which uses state-of-the art deep learning frameworks to detect and quantify DNA-fibers in high throughput microscopy images. FiberAI efficiently detects DNA fibers, achieving a bounding box average precision score of 0.91 and a segmentation average precision score of 0.90. We then use FiberAI to measure the integrity of replication checkpoints. FiberAI is publicly available and allows users to view model predicted selections, add their own manual selections, and easily analyze multiple image sets. Thus, FiberAI can help elucidate DNA replication processes by streamlining DNA-fiber analyses.

bioinformatics↗