Biology subjects

Dzutsev, A.

Publications and source records attributed to Dzutsev, A..

3 recordsLinked to original sources

bioRxiv2026

IL-17RC signaling connects intestinal microbiota and neuroimmune interactions in atherosclerosis

While dysbiosis and inflammation were previously implicated in cardiovascular diseases, the circuits of how microbiota drives distant perivascular innervation, neuroinflammation and atherosclerosis remains unknown. Here, we report that IL-17RC signaling in intestine protects from atherosclerosis controlling intestinal barrier and microbiota, and loss of IL-17RC in intestinal epithelial cells alters microbiota, enhances perivascular innervation and aortic inflammation, augmenting the disease. Neuronal outgrowth is functionally dependent on microbiota and is essential for neuroinflammation and augmentation of atherosclerosis as chemical denervation reduces inflammation, macrophage activation and disease progression. Microbiota-dependent IL-17A producing {gamma}{delta} T cells accumulate in aorta to promote neuronal outgrowth and activation that can be reversed by {gamma}{delta} T cell blockade. Perivascular neuron activation is further dependent on cell autonomous IL-17 signaling as IL-17RC ablation in sympathetic neurons protected mice from microbiota-driven atherosclerosis. Together, our data illuminate how intestinal cytokine signaling distantly restrains neuroimmune interactions in aorta and uncovers a novel link between IL-17 signaling, microbiota, perivascular innervation and neuroimmune pro-inflammatory crosstalk instrumental for atherosclerosis progression. SummaryIL-17RC signaling regulates intestinal dysbiosis and perivascular neuronal outgrowth that modulates inflammation in atherosclerosis.

immunology↗

bioRxiv2025

Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival

BackgroundCanine cancer remains a leading cause of death in dogs, yet advances in veterinary oncology lag behind human medicine, particularly in immunotherapy. While immune checkpoint inhibitors are just entering clinical trials in dogs, other immunotherapies, such as anti-EGFR/HER2 vaccines, have shown promise. In parallel, mounting evidence in human oncology links gut microbiota composition to immunotherapy response. However, this relationship remains unexplored in canine patients. In this pilot study, we analyzed the gut microbiome of dogs enrolled in a clinical trial of anti-EGFR/HER2 immunotherapy to identify microbial biomarkers associated with survival outcomes. MethodsRectal swab samples of 51 dogs were collected at the time of first vaccine administration (baseline microbiota) and underwent 16S rRNA gene sequencing according to standard protocols. ResultsMicrobiome composition showed no significant differences by cancer type, sex, or breed, suggesting no inherent microbiome bias in the cohort. However, Cox regression analysis revealed 11 bacterial taxa whose abundances were significantly associated with overall survival (FDR < 0.1), independently of cancer type. Seven taxa were linked to increased mortality risk, while four were associated with prolonged survival. These associations remained significant after adjusting for confounders such as hemangiosarcoma diagnosis and advanced age. ConclusionsTo our knowledge, this is the first study to identify gut microbial signatures associated with survival in dogs undergoing cancer immunotherapy. These findings suggest that specific bacterial taxa may serve as prognostic biomarkers for immunotherapy outcomes in canine cancer, laying the groundwork for microbiota-targeted strategies to improve therapeutic efficacy in veterinary oncology.

cancer biology↗

bioRxiv2023

JAMS - A framework for the taxonomic and functional exploration of microbiological genomic data

Shotgun microbiome sequencing analysis presents several challenges to accurately and consistently depict sample composition and functional potential. Here we present a two-part framework - JAMS (Just a Microbiology System) - whereby with raw fastq files and metadata as input, meaningful analysis within a sample and between a sample can be performed with ease for either shotgun or 16S sequences. JAMS is the first package to provide seamless deconvolution of functions into their taxonomic contributors. We validated our JAMS framework on two human gut shotgun metagenome test datasets against the popular tool MetaPhlAn 4. We further demonstrate the application of the JAMS package, particularly the plotting functions, on a mouse shotgun dataset.

bioinformatics↗