Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

Alcazar, Oscar; Hernandez, Luis F.; Nakayasu, Ernesto S.; Nicora, Carrie D.; Ansong, Charles; Muehlbauer, Michael J.; Bain, James R.; Myer, Ciara J.; Bhattacharya, Sanjoy K.; Buchwald, Peter; Abdulreda, Midhat H.

doi:10.3390/biom11030383

Title: Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

Journal Article · Thu Mar 04 00:00:00 EST 2021 · Biomolecules

DOI:https://doi.org/10.3390/biom11030383· OSTI ID:1782438

Alcazar, Oscar ^[1]; Hernandez, Luis F. ^[1];

^[2]; Nicora, Carrie D. ^[2]; Ansong, Charles ^[2]; Muehlbauer, Michael J. ^[3];

^[3]; Myer, Ciara J. ^[4]; Bhattacharya, Sanjoy K. ^[4];

^[5];

^[6]

Univ. of Miami, Miami, FL (United States). Miller School of Medicine, Diabetes Research Institute
Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Div.
Duke Univ., Durham, NC (United States). Medical Center, Duke Molecular Physiology Institute
Univ. of Miami, Miami, FL (United States). Miller School of Medicine, Dept. of Ophthalmology and Miami Integrative Metabolomics Research Center
Univ. of Miami, Miami, FL (United States). Miller School of Medicine, Diabetes Research Institute, Dept. of Molecular and Cellular Pharmacology
Univ. of Miami, Miami, FL (United States). Miller School of Medicine, Diabetes Research Institute, Dept. of Surgery, Dept. of Microbiology and Immunology, and Ophthalmology

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. Results: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. Conclusions: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; Diabetes Research Institute Foundation; National Institutes of Health (NIH)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1782438

Report Number(s):: PNNL-SA-158353

Journal Information:: Biomolecules, Vol. 11, Issue 3; ISSN 2218-273X

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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