Jiwani, S. | Search bioRxiv

bioRxiv2026

Preexisting chronic infection skews the epigenetic landscape of subsequent memory CD8 T cell responses

Previous studies suggest that preexisting chronic disease impairs immune responses to subsequent infection and vaccination. However, the underlying epigenetic mechanisms are understudied. Here, we show that preexisting chronic infection with LCMV clone 13 (CL13) compromised the formation of central memory CD8 T cells (TCM) to subsequent Listeria monocytogenes infection, despite not profoundly impacting effector responses. This correlated with a skewed cytokine milieu. Our chromatin-accessibility profiling of Listeria-specific CD8 T cells showed significant epigenetic skewing of both TCM and effector memory (TEM) in mice with preexisting LCMV-CL13, a skewing that started in memory-precursor effector cells (MPECs) during the effector phase. Transcription-factor binding sites (TFBS) analyses highlighted the interferon regulatory factor (IRF) family as major TFs implicated in this skewing. Thus, our results suggest that preexisting persistent inflammation skews the phenotypic and epigenetic landscape of subsequent memory responses, arguing for interventions to optimize vaccine-induced memory in hosts with preexisting chronic disease.

immunology↗

bioRxiv2022

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

bioinformatics↗