ArticleEBioMedicine2026
IgG4-related disease has a specific intestinal microbiota signature.
Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A Step-by-Step Guide to Successful Multiparametric Microbiota Flow Cytometry.Current protocols · 2026Article
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10 authors.
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Abstract
backgroundWhile the intestinal microbiome has been implicated in Immunoglobulin-4 related disease (IgG4-RD), it remains poorly characterised. Therefore, we performed a comprehensive microbiome characterisation to identify disease-specific alterations.
methodsIn this cross-sectional study, cryopreserved stool samples from 28 patients with IgG4-RD were characterised by 16S rRNA gene sequencing and by multiparameter microbiota flow-cytometry to determine their taxonomic composition and phenotype at the single cell level. These data were evaluated in comparison with 24 healthy controls (HC) and assessed for their potential to classify IgG4-RD using random forest classification, with an independent validation cohort (12 IgG4-RD, 12 HC).
findingsPatients with IgG4-RD exhibited reduced taxonomic diversity and disease-specific alterations in the microbiome compared to HC, characterised by significantly elevated levels of several species within the Bacillota phylum. These taxonomic alterations classified patients and HC with an AUROC of 0.87 (95% CI: 0.77-0.97) but showed reduced performance in the validation cohort (AUROC 0.58, 95% CI: 0.29-0.87). Flow cytometry revealed distinct phenotypic microbiota alterations, robustly distinguishing patients with IgG4-RD from HC in both the training (AUROC 0.9, 95% CI: 0.81-0.99) and validation cohort (AUROC 0.78, 95% CI: 0.59-0.97). The IgG4-RD microbiota were predominantly DNA-low and showed no enhanced endogenous IgG4 coating, neither natively nor after in vitro incubation with autologous serum.
interpretationOur study revealed specific alterations in the intestinal microbiota on taxonomic and phenotypic level in IgG4-RD, which potentially reflect different mechanisms of adaptations of the gut microbiota to immune disturbances specific to IgG4-RD. We provide proof-of-concept that this "microbiota fingerprint" may be suitable to identify IgG4-RD in a machine-learning approach and may provide important insights into the complexity of intestinal microbiota alterations in IgG4-RD.
fundingThis work was supported by grants from Rolf M. Schwiete Foundation, DFG (German Research Foundation), Innovative Medicines Initiative 2 Joint Undertaking (3 TR), and EFRE-Project.
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