Frontiers in Immunology · 2021 · 13 citations · 36 references
MedicineMolecular MedicinePathologyGenomicsMolecular PathologyTranscriptomicsGenomic ProfilingPediatric VasculitisRare Disease GenomicsVariant ClassificationVascular PathologyClinical Variant ClassificationDifferential Gene ExpressionDifferential Transcript ExpressionInflammatory EndotypesMedium-vessel VasculitisNeutrophil Degranulation DefectsUnsupervised ClusteringVasculitis Classification
Objectives: Chronic primary vasculitis describes a group of complex and rare diseases that are characterized by blood vessel inflammation. Classification of vasculitis subtypes is based predominantly on the size of the involved vessels and clinical phenotype. There is a recognized need to improve classification, especially for small-to-medium sized vessel vasculitides, that, ideally, is based on the underlying biology with a view to informing treatment. Methods: We performed RNA-Seq on blood samples from children (n = 41) and from adults (n = 11) with small-to-medium sized vessel vasculitis, and used unsupervised hierarchical clustering of gene expression patterns in combination with clinical metadata to define disease subtypes. Results: Differential gene expression at the time of diagnosis separated patients into two primary endotypes that differed in the expression of ~3,800 genes in children, and ~1,600 genes in adults. These endotypes were also present during disease flares, and both adult and pediatric endotypes could be discriminated based on the expression of just 20 differentially expressed genes. Endotypes were associated with distinct biological processes, namely neutrophil degranulation and T cell receptor signaling. Conclusions: Phenotypically similar subsets of small-to-medium sized vessel vasculitis may have different mechanistic drivers involving innate vs. adaptive immune processes. Discovery of these differentiating immune features provides a mechanistic-based alternative for subclassification of vasculitis.
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STAR: ultrafast universal RNA-seq aligner
Alexander Dobin, Carrie Davis, Felix Schlesinger et al. · Bioinformatics · 2012 · 56.2K citations · Full text
HTSeq—a Python framework to work with high-throughput sequencing data
Simon Anders, Paul Theodor Pyl, Wolfgang Huber · Bioinformatics · 2014 · 22.7K citations · Full text