Biology subjects

Brobeil, A.

Publications and source records attributed to Brobeil, A..

4 recordsLinked to original sources

bioRxiv2026

Metabolic specialization structures gut bacterial niches and drives colorectal cancer progression

Despite the established association between the gut microbiome and colorectal cancer (CRC), the functional distinction between microbial passengers and drivers of CRC progression remains unresolved. Here, we collected stool, blood, as well as paired tumor, and normal mucosa tissues from seventy-seven CRC patients to characterize the systemic and localized impact of the gut microbiome on early- and late-stage CRC. By deep shotgun metagenomic sequencing, we identified distinct bacterial species and functions residing in tumor versus normal mucosa, highlighting an enrichment of oral-associated bacteria in tumor tissues. Several of these species remained undetected in the stool microbiome analysis. We further combined bacterial culturing with untargeted metabolomics of bacteria enriched in tumor and normal mucosa tissues, revealing distinct clusters of metabolic potential. Functional testing of multiple members from one cluster comprising both tumor- and mucosa-enriched species revealed Leptotrichia wadei as a pro-tumorigenic bacterium in a murine CRC model. Single-nucleus RNA sequencing and in vitro experiments further demonstrated that L. wadei and its secretome induces M2 macrophage polarization to promote tumor growth. Overall, our study shows that metabolic specialization structures microbial colonization niches, while species-specific metabolic outputs identify functional drivers of CRC progression, and uncovers L. wadei as an oncogenic bacterium in CRC.

microbiology↗

bioRxiv2025

Obesity promotes conserved inflammatory and metabolic transcriptional programs in mouse and human colon tumors

BackgroundThe global prevalence of obesity, an established risk and progression factor for colon cancer, is high and rising. Unfortunately, the mechanisms underlying the obesity-colon cancer association are incompletely understood, and new molecular targets enabling more effective intervention strategies to break the obesity-colon cancer link are urgently needed. ObjectiveThis study integrated RNA sequencing data from mouse and human colon tumor samples, as well as human adipose samples, to rigorously establish obesity-associated transcriptomic signatures conserved between the two species. MethodsWe employed a mouse colon cancer model with colonoscopy-guided orthotopic transplantation of syngeneic Apc-null;KrasG12D/+;Trp53-null;Smad4-null;tdTomato colon tumor organoids. Epithelial cell adhesion molecule (EpCAM)-positive cells from murine tumors, and 193 human colon tumors and 188 human mesenteric adipose tissue samples from the ColoCare cohort underwent transcriptomic analyses. ResultsDiet-induced obesity reduced survival in the mouse model of colon cancer. Integrated transcriptomic analyses of EpCAM-positive murine tumor cells and bulk human tumors revealed obesity-driven enrichment of inflammation and metabolic pathways, including upregulation of genes involved in innate immune sensing (TLR2, MYD88, IRF4) and tumor microenvironment remodeling (MMP9, TGFB1, SERPINE1). Analysis of paired mesenteric visceral adipose tissue and tumor samples from the ColoCare cohort indicated that obesity amplifies inflammatory signaling pathways through unique adipose ligand-tumor receptor interactions. ConclusionsThese results establish obesity-associated adipose tissue dysregulation as a key inter-tissue modulator of biology, with concordant cross-species effects on tumor cell-intrinsic inflammatory and metabolic programs.

cancer biology↗

bioRxiv2025

Spatialproteomics - an interoperable toolbox for analyzing highly multiplexed fluorescence image data

SummaryHighly multiplexed immunofluorescence imaging is a recent method to characterize tissues at single-cell resolution on the protein level, offering low cost, high scalability, and the ability to analyze paraffin-embedded tissue samples. However, the analysis of these data involves a sequence of steps, including segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis, each of which involves a multitude of method and parameter choices that need to be adapted to the data and analytical objective at hand. Moreover, variations in data quality can be high and unpredictable, which necessitates further flexibility and interactivity. While individual components exist, there is an unmet need for a coherent toolbox that offers end-to-end coverage of the workflow, flexibility, and automation. We present spatialproteomics, a Python package that addresses these challenges. Built on top of xarray and dask, spatialproteomics can process images that are larger than the working memory. It supports synchronization of shared coordinates across data modalities such as images, segmentation masks, and expression matrices, which facilitates easy and safe subsetting and transformation. We demonstrate spatialproteomics on a set of images of reactive lymph nodes or different forms of B cell Non-Hodgkin lymphomas (BNHL) from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of cell type composition and spatial distribution across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process gigapixel whole slide images. Altogether, we propose spatialproteomics as an easy-to-install, easy-to-learn, comprehensive toolbox for constructing powerful end-to-end image analysis solutions for highly multiplexed immunofluorescence imaging. Availability and ImplementationThe source code for spatialproteomics is freely available at https://github.com/sagar87/spatialproteomics under the MIT license. Contactwolfgang.huber@embl.org, Peter-Martin.Bruch@med.uni-duesseldorf.de

bioinformatics↗

bioRxiv2022

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

T-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+ TCF7- but not PD1+ TCF7+ cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+ regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

cancer biology↗