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Autoimmune disease-associated pathobionts: mechanisms and therapeutic potential of phage-based approaches

Sep 2026 · Frontiers in Immunology · Vol 17 · 0 citations · 80 references
Medicine

Abstract

The gut microbiota is a critical regulator of systemic immune homeostasis; accumulating evidence implicates specific commensal bacteria, termed “pathobionts,” in autoimmune disease pathogenesis. However, the definition of pathobionts remains context-dependent, as their effects are influenced by host genetics and host–microbe interactions. In this review, we summarize representative pathobionts supported by functional evidence in selected extraintestinal autoimmune diseases and discuss how these mechanisms may inform phage-based microbiome-targeted interventions. Mechanistically, pathobionts contribute to autoimmune disease through multiple pathways, including molecular mimicry, induction of intestinal T helper 17 and T follicular helper cell responses, disruption of regulatory T cell homeostasis, intestinal barrier dysfunction, and bacterial translocation from the gut to extraintestinal sites. These processes highlight the central role of gut-associated lymphoid tissue in initiating systemic autoimmunity, and targeting disease-associated microbes represents a promising therapeutic strategy. Whole-phage therapy, which enables highly specific bacterial elimination, has shown efficacy in preclinical immune-mediated disease models, but may be affected by variable in vivo replication, bacterial receptor-mediated resistance, anti-phage immune responses, and ecological effects on the resident microbiome. Phage-derived enzymes that lyse bacterial cell walls, such as endolysins, represent a complementary therapeutic modality that specifically targets bacterial peptidoglycan through cell wall-binding and catalytic domains. Collectively, these findings support the concept that pathobiont-targeted interventions, particularly phage-based strategies, may provide microbiome-directed, immunosuppression-sparing therapeutic approaches for selected patient subsets.

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