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Novel micro-RNA and Microbiome Signature Profiles as Biomarkers for Predicting the Risk of Autoimmune Type 1 Diabetes Development

P. Chhabra1, G. Ricardo1, A. Spano2, V. Mas1, M. Timko2, K. Brayman1

1Dept of Surgery, University of Virginia Health System, Charlottesville, VA, 2Dept of Biology, University of Virginia Health System, Charlottesville, VA

Meeting: 2020 American Transplant Congress

Abstract number: A-327

Keywords: Autoimmunity, Immunoglobulins (Ig), Mice, NOD, Prediction models

Session Information

Session Name: Poster Session A: Biomarker Discovery and Immune Modulation

Session Type: Poster Session

Date: Saturday, May 30, 2020

Session Time: 3:15pm-4:00pm

 Presentation Time: 3:30pm-4:00pm

Location: Virtual

*Purpose: To identify reliable microRNA (miRNA) and microbiome biomarkers for predicting risk of autoimmune beta-cell destruction in type 1 diabetes (T1D) and to investigate alterations in miRNA expression and cecal microbial composition during the development of T1D with or without IgM therapy. *Background: The most commonly used biomarkers for predicting T1D development are serum autoantibodies against β-cell antigens, which are not very reliable. Identifying miRNA and microbiome signatures that accurately predict T1D risk has potential for guiding and implementing immuno-intervention therapies. IgM therapy prevents T1D, and miRNA and/or microbiome profile analysis may help monitor treatment efficacy.

*Methods: Female non-obese diabetic (NOD) mice treated with IgM or saline (n=20/grp) were divided into 5wks−old non-diabetic; 9-12wks−old prehyperglycemic stage-1; ≥13wks−old prehyperglycemic stage-2; and diabetic groups. a) Total RNA from pancreas and peripheral blood mononuclear cells (PBMCs) was used for GeneChip miRNA v3.0 array hybridization. Expression intensities were normalized using RMA algorithm. Pairwise t-test comparisons were fit (Significance: False-Discovery-Rate<10% and Fold-change>1.5). b)16S rRNA libraries were prepared from bacterial DNA and deep-sequenced.

*Results: Dysregulated pancreatic miRNA signatures (miR-130b, -17, -1224, -29a, -378) were identified in prehyperglycemic animals as predictive biomarkers for T1D. MiR-29a, -130b, -21 were dysregulated in pancreas and PBMCs of diabetic mice. Interestingly, IgM therapy reversed expression of dysregulated miRNAs, and two IgM-treatment−specific miRNAs induced immune-suppressor cells. Relevant miRNAs were validated by RT-PCR. Significant dysbiosis was observed in the cecal microbiome with the progression of T1D development, characterized by an increase in the bacteroidetes:firmicutes ratio. In contrast, IgM conserved normal bacteroidetes:firmicutes ratio and this effect was long-lasting. Furthermore, oral gavage using cecal content from IgM-treated mice significantly diminished the incidence of diabetes in NOD mice compared to cecal content from saline-injected mice. IgM maintained normal levels of S24-7 and Lachnospiraceae, increased Lacobacillus and Turicibacter, and decreased Clostridiales.

*Conclusions: The paradigm for identification of high-risk individuals using miRNA and microbiome profiles should be applicable to a variety of disease states requiring pharmacologic and/or surgical interventions.

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To cite this abstract in AMA style:

Chhabra P, Ricardo G, Spano A, Mas V, Timko M, Brayman K. Novel micro-RNA and Microbiome Signature Profiles as Biomarkers for Predicting the Risk of Autoimmune Type 1 Diabetes Development [abstract]. Am J Transplant. 2020; 20 (suppl 3). https://atcmeetingabstracts.com/abstract/novel-micro-rna-and-microbiome-signature-profiles-as-biomarkers-for-predicting-the-risk-of-autoimmune-type-1-diabetes-development/. Accessed May 12, 2025.

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