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Trajkovski, M.

Publications and source records attributed to Trajkovski, M..

2 recordsLinked to original sources

Cold Exposure Protects from Neuroinflammation Through Immunologic Reprogramming

Autoimmunity is energetically costly, but the impact of a metabolically active state on immunity and immune-mediated diseases is unclear. Ly6Chi monocytes are key effectors in CNS autoimmunity with elusive role in priming naive autoreactive T cells. Here we provide unbiased analysis of the immune changes in various compartments during cold exposure, and show that this energetically costly stimulus markedly ameliorates active experimental autoimmune encephalomyelitis (EAE). Cold exposure decreases MHCII on monocytes at steady-state and in various inflammatory mouse models, and suppresses T cell priming and pathogenicity through the modulation of monocytes. Genetic, or antibody-mediated monocyte depletion, or adoptive transfer of Th1- or Th17-polarized cells for EAE abolish the cold-induced effects on T cells or EAE, respectively. These findings provide a mechanistic link between environmental temperature and neuroinflammation, and suggest competition between cold-induced metabolic adaptations and autoimmunity as energetic trade-off beneficial for the immune-mediated diseases.

immunology

ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data

BackgroundMetagenomics and metatranscriptomics studies provide valuable insight into the composition and function of microbial populations from diverse environments, however the data processing pipelines that rely on mapping reads to gene catalogs or genome databases for cultured strains yield results that underrepresent the genes and functional potential of uncultured microbes. Recent improvements in sequence assembly methods have eased the reliance on genome databases, thereby allowing the recovery of genomes from uncultured microbes. However, configuring these tools, linking them with advanced binning and annotation tools, and maintaining provenance of the processing continues to be challenging for researchers.\n\nResultsHere we present ATLAS, a software package for customizable data processing from raw sequence reads to functional and taxonomic annotations using state-of-the-art tools to assemble, annotate, quantify, and bin metagenome and metatranscriptome data. Genome-centric resolution and abundance estimates are provided for each sample in a dataset. ATLAS is written in Python and the workflow implemented in Snakemake; it operates in a Linux environment, and is compatible with Python 3.5+ and Anaconda 3+ versions. The source code for ATLAS is freely available, distributed under a BSD-3 license.\n\nConclusionATLAS provides a user-friendly, modular and customizable Snakemake workflow for metagenome and metatranscriptome data processing; it is easily installable with conda and maintained as open-source on GitHub at https://github.com/metagenome-atlas/atlas.

bioinformatics