Abstract

IntroductionCirculating long noncoding RNAs (lncRNAs) are emerging as valuable biomarkers in numerous diseases. However the expression signature of circulating lncRNAs is yet to be identified in diabetic cardiomyopathy (DCM). We used computational and system biology approach to predict circulating lncRNAs using plasma RNA before and after DCM.HypothesisWe hypothesized that lncRNAs could serve as blood‐based prognostic and diagnostic biomarkers in DCM detection.MethodsThe left ventricular function was measured with echocardiography in control and db/db mice. Expression profiling of lncRNAs was performed using LncRNA Array v3.0 (8 × 60K, Arraystar).ResultsA total of 17,124 lncRNAs and 13,333 mRNAs were profiled of which 814 lncRNAs and 2707 mRNAs were deregulated, and more than half were intergenic lncRNAs and antisense lncRNAs. Ten dysregulated plasma lncRNAs were validated by quantitative PCR assays. For the gene ontologies related to plasma, the enrichment was observed for those mRNAs which were found to be significantly co‐expressed with the neighboring lncRNAs during DCM. Bioinformatics analysis revealed that the altered lncRNAs regulated the differentially expressed mRNAs which were profoundly enriched for pathways such as insulin signaling, fatty acid metabolism, MAPK signaling, and hypertrophy cardiomyopathy which in the past has been associated with diabetic pathogenesis. To evaluate the translational efficiency of our mouse model, we experimentally validated the results using human induced pluripotent cell‐derived cardiomyocytes (hiPSCs‐CMs) for the in vitro DCM model. We found that most of the gene expression patterns were repeated in hiPSCs‐CMs and several of these lncRNAs genes have been associated with DCM in the past.ConclusionCirculating lncRNAs are dysregulated in DCM, and our results depict the changing transcriptome as a function of disease progression and functional role of lncRNAs. These lncRNAs may serve as promising targets for DCM prevention, early diagnosis, and therapy.Support or Funding InformationThis work was supported, in part, by a National Institutes of Health research grant P01GM 066730 (to Dr. Bosnjak) from the United States Public Health Services, Bethesda, Maryland, USA. The funding body had no role in the design of the study, collection, analysis, and interpretation of data, and in writing the abstract.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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