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Scheiding, S.

Publications and source records attributed to Scheiding, S..

2 recordsLinked to original sources

UC-associated autoantibodies to αvβ6 inhibit mucosal TGFβ activation and predispose to intestinal inflammation

Ulcerative colitis (UC) is characterized by epithelial barrier dysfunction and dysregulated mucosal immune responses; however, the mechanisms driving disease onset remain poorly defined. Autoantibodies against the epithelial-restricted integrin v{beta}6 are a highly specific biomarker of UC that can precede clinical diagnosis by up to 10 years. Because v{beta}6 activates TGF{beta} at epithelial surfaces, we hypothesized that UC-associated v{beta}6 autoantibodies inhibit mucosal TGF{beta} activation and disrupt epithelial homeostasis. We showed that v{beta}6 autoantibodies were enriched in UC and that IgG from autoantibody-positive individuals inhibited v{beta}6-dependent activation of TGF{beta}. v{beta}6 blockade dampened TGF{beta} signaling and altered differentiation-associated gene programs in human intestinal epithelial cells. In mice, deletion of v caused expansion of inflammation-associated goblet cells in the colon and changes in intestinal immune cells. Using a novel mouse model, we showed that v{beta}6-specific autoantibody disrupted epithelial-immune crosstalk and increased susceptibility to DSS colitis. Together, these findings establish anti-v{beta}6 autoantibodies as active inhibitors of epithelial TGF{beta} signaling, constituting a de facto anti-cytokine response, rather than passive biomarkers. By linking preclinical seropositivity to impaired epithelial signaling and heightened susceptibility to colitis, this work identifies epithelial v{beta}6-dependent TGF{beta} activation as a pathway that may be leveraged to modify disease risk or limit disease severity. One Sentence SummaryUC-associated autoantibodies impair epithelial TGF{beta} activation, alter mucosal homeostasis, and predispose to colitis.

immunology↗

A simple strategy for sample annotation error detection in cytometry datasets

Mislabeling samples or data with the wrong participant information can impact study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of HLA class I alleles. We measured HLA-A*02 and HLA-B*07 expression in 3 longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

immunology↗