Association of HLA-dependent islet autoimmunity with systemic antibody responses to intestinal commensal bacteria in children

Abstract

Microbiome sequence analyses have suggested that changes in gut bacterial composition are associated with autoimmune disease in humans and animal models. However, little is known of the mechanisms through which the gut microbiota influences autoimmune responses to distant tissues. Here, we evaluated systemic antibody responses against cultured human gut bacterial strains to determine whether observed patterns of anticommensal antibody (ACAb) responses are associated with type 1 diabetes (T1D) in two cohorts of pediatric study participants. In the first cohort, ACAb responses in sera collected from participants within 6 months of T1D diagnosis were compared with age-matched healthy controls and also with patients with recent onset Crohn’s disease. ACAb responses against multiple bacterial species discriminated among these three groups. In the second cohort, we asked whether ACAb responses present before diagnosis were associated with later T1D development and with HLA genotype in participants who were discordant for subsequent progression to diabetes. Serum IgG2 antibodies against Roseburia faecis and against a bacterial consortium were associated with future T1D diagnosis in an HLA DR3/DR4 haplotype–dependent manner. These analyses reveal associations between antibody responses to intestinal microbes and HLA-DR genotype and islet autoantibody specificity and with a future diagnosis of T1D. Further, we present a platform to investigate antibacterial antibodies in biological fluids that is applicable to studies of autoimmune diseases and responses to therapeutic interventions.

Gut microbial metabolites alter IgA immunity in type 1 diabetes

“…It was reported that secretory IgA has the capacity to bind bacteria and regulate the composition and function of gut microbiota (23). More recently, differences in IgA binding to bacteria have been linked to both inflammatory bowel disease and T1D (24,25). Thus, we investigated the proportion of IgA-bound bacteria in our study subjects.…”

“…Interestingly, bacteria bound by IgA are more colitogenic than non-IgA-bound bacteria in inducing colitis (24). To this end, a recent study showed that serum IgA from individuals with T1D has a differential ability to bind specific bacteria compared with that from healthy controls (25). Furthermore, IL-10 production from recirculating intestinal IgA + B cells protected mice from developing autoimmune encephalomyelitis (26).…”

Specific type 1 diabetes risk genes underpin age-at-diagnosis and indicate joint defects in immunity, beta-cell fragility and responses to viral infections in early-onset disease

“…Here, we show for the first time that the protective HLA class II haplotypes DRB1*15:01-DQB1*06:02 and DRB1*07:01-DQB1*03:03 are less prevalent amongst individuals diagnosed at <7 years compared with controls and those diagnosed at ≥13 years. Therefore, the earliest and most aggressive phenotypic subtype of T1D results primarily from carriage of high risk alleles and haplotypes of the HLA class II and I genes, which probably act at four levels: (i) altering the T cell receptor repertoire in favour of anti-islet antigen reactivity, for example preproinsulin, and/or reducing the protective repertoire of T regulatory cells; (ii) providing a strong autoantigen presentation environment in the islets and pancreatic draining lymph nodes enabling the infiltration and cytolytic activity of CD8 + T cells but also by disrupting B cell anergy 13 permitting binding and presentation of autoantigen to provide potent help to T cells in a self-reinforcing spiral of autoreactivity; (iii) affecting the immune response to the viral infections that are involved in the disease; (iv) affecting how the gut microbiome develops in early life, a system that is known to affect T1D susceptibility 14 .…”

Could a bacteria-stuffed pill cure autoimmune diseases?

“…Her team has built a platform to identify antibacterial antibodies in blood. Analysing samples from children at high risk of type 1 diabetes, the platform revealed important clues about who would develop the disease 6 .…”