Koshkin, A. | bioRxiv Science

bioRxiv2025

Dynamic remodeling of the pancreas immune landscape in obesity.

Obesity is a known risk factor for diseases of the pancreas, including diabetes, pancreatic cancer and pancreatitis, but mechanisms remain unclear. To elucidate how obesity impacts pancreatic immune homeostasis, we performed spatial, transcriptomic and functional profiling of human pancreatic immune cells from obese and non-obese organ donors. Obesity was associated with higher density of tissue resident memory T-cells (TRM) in the exocrine pancreas which display high cytotoxic functions and aggregated around macrophages. Single cell sequencing of pancreatic macrophages revealed two main subsets - FOLR2+ CD11c- fetal-derived macrophages with pro-repair and immunoregulatory function and a FOLR2- CD11c+ monocyte-derived macrophages with greater T-cell interactions and pro-inflammatory function. In obesity, the pancreatic macrophage landscape shifts to lower predominance of FOLR2+ CD11c- macrophages and higher FOLR2- CD11c+ macrophages which interact selectively with the TRM and inflamed exocrine epithelium. Together, these results identify macrophage-T cell circuits and immune epithelial interactions that fuel chronic pancreatic inflammation in obesity - a potential unifying mechanism for obesity-related pancreatic diseases.

immunology↗

bioRxiv2023

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

bioinformatics↗