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

Publication ,  Journal Article
Alcazar, O; Hernandez, LF; Nakayasu, ES; Nicora, CD; Ansong, C; Muehlbauer, MJ; Bain, JR; Myer, CJ; Bhattacharya, SK; Buchwald, P; Abdulreda, MH
Published in: Biomolecules
March 4, 2021

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.

Duke Scholars

Published In

Biomolecules

DOI

EISSN

2218-273X

Publication Date

March 4, 2021

Volume

11

Issue

3

Location

Switzerland

Related Subject Headings

  • Software
  • Proteomics
  • MicroRNAs
  • Metabolomics
  • Humans
  • Genomics
  • Diabetes Mellitus, Type 1
  • Biomarkers
  • 3206 Medical biotechnology
  • 3102 Bioinformatics and computational biology
 

Citation

APA
Chicago
ICMJE
MLA
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Alcazar, O., Hernandez, L. F., Nakayasu, E. S., Nicora, C. D., Ansong, C., Muehlbauer, M. J., … Abdulreda, M. H. (2021). Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes. Biomolecules, 11(3). https://doi.org/10.3390/biom11030383
Alcazar, Oscar, Luis F. Hernandez, Ernesto S. Nakayasu, Carrie D. Nicora, Charles Ansong, Michael J. Muehlbauer, James R. Bain, et al. “Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.Biomolecules 11, no. 3 (March 4, 2021). https://doi.org/10.3390/biom11030383.
Alcazar O, Hernandez LF, Nakayasu ES, Nicora CD, Ansong C, Muehlbauer MJ, et al. Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes. Biomolecules. 2021 Mar 4;11(3).
Alcazar, Oscar, et al. “Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.Biomolecules, vol. 11, no. 3, Mar. 2021. Pubmed, doi:10.3390/biom11030383.
Alcazar O, Hernandez LF, Nakayasu ES, Nicora CD, Ansong C, Muehlbauer MJ, Bain JR, Myer CJ, Bhattacharya SK, Buchwald P, Abdulreda MH. Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes. Biomolecules. 2021 Mar 4;11(3).

Published In

Biomolecules

DOI

EISSN

2218-273X

Publication Date

March 4, 2021

Volume

11

Issue

3

Location

Switzerland

Related Subject Headings

  • Software
  • Proteomics
  • MicroRNAs
  • Metabolomics
  • Humans
  • Genomics
  • Diabetes Mellitus, Type 1
  • Biomarkers
  • 3206 Medical biotechnology
  • 3102 Bioinformatics and computational biology