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Sansom, S. N.

Publications and source records attributed to Sansom, S. N..

2 recordsLinked to original sources

IRF5 promotes intestinal inflammation by guiding monocyte differentiation towards a pathogenic CD11c+ macrophage phenotype

Mononuclear phagocytes (MNPs) play a key role in maintaining intestinal homeostasis but also in triggering immunopathology in response to acute microbial stimulation, which induces the recruitment of masses of Ly6Chi monocytes to the gut. The regulators that control monocyte tissue adaptation in the gut remain poorly understood. Interferon Regulatory Factor 5 (IRF5) is a transcription factor previously shown to play a key role in maintaining the inflammatory phenotype of macrophages. Here we investigate the impact of IRF5 on the MNP system and physiology of the gut at homeostasis and during inflammation. We demonstrate that IRF5 deficiency has a limited impact on colon physiology at steady state, but ameliorates immunopathology during Helicobacter hepaticus induced colitis. Inhibition of IRF5 activity in MNPs phenocopies global IRF5 deficiency. Using a combination of bone marrow chimera and single cell RNA-sequencing approaches we compare the differentiation trajectories of wild type and IRF5 deficient monocytes in a shared inflammatory environment and demonstrate that IRF5 stipulates a choice in monocyte differentiation towards macrophages. Specifically, IRF5 promotes the generation of pathogenic CD11c+ macrophages and controls the production of inflammatory mediators by these cells. Thus, we identify IRF5 as a key transcriptional controller of pathogenic monocyte differentiation in the gut.

immunology

CGAT-core: a python framework for building scalable, reproducible computational biology workflows

In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudoalignment.\n\nAvailabilityCGAT-core is freely available under an MIT licence for installation and use, including source code at https://github.com/cgat-developers/cgat-core\n\nContactandreas.heger@imm.ox.ac.uk (AH), david.sims@imm.ox.ac.uk (DS), adam.cribbs@imm.ox.ac.uk (AC)\n\nSupplementary information:

genomics