Research Article

Distinct MicroRNAs Identified in Rabbit Blood Arising from Induced Diabetes and a Surgically Simulated Diabetic Ischemia Complication

Author(s): Girish J. Kotwal*, Sabine Waigel, Julia Chariker, Eric Rouchka and Sufan Chien

Volume 12, Issue 1, 2023

Published on: 30 November, 2022

Page: [22 - 28] Pages: 7

DOI: 10.2174/2211536611666221005091351

Price: $65

Abstract

Background: Diabetic complications have been studied extensively in recent years. There are very few biomarkers in body fluids that can pinpoint a distinct diabetic complication due to insufficient known specific biomarkers for ischemia.

Objective: Identifying microRNA in animal models for each complication could enable early diagnosis of a given complication if verified in humans. MicroRNA (miRNA) profiling has been done in rodent models for a number of diabetic complications, like diabetic glomerular injury, atherosclerosis, cognitive impairment, diabetic wound healing, angiopathy and other complications. Due to multiple differences between rodents and humans, the changes in rabbit skin, considered closer to humans than even pigs, may better simulate human diabetic complications of ischemia.

Methods: To study the miRNA profile of rabbits in which diabetes was induced or ischemia was surgically generated, we studied whether diabetes or ischemia-induced specific miRNA could be detected. MicroRNA from the blood of diabetic rabbits and rabbits with local ischemia was collected in PAXgene Blood RNA tubes specifically designed for miRNA isolation and extracted using the PAX gene miRNA extraction kit. The isolated RNA was quality controlled using an RNA analyzer, and further, using RNA seq technology, it was analyzed for distinct miRNAs that were detected in diabetic and non-diabetic rabbits induced with ischemia.

Results: A miRNA that was found to be expressed in diabetic rabbits and ischemic rabbits but not in untreated rabbits was miRNA-183. Several miRNAs were differentially expressed across comparison groups, and several upregulated miRNAs were identified being unique to each comparison. In rabbits with a potential diabetic complication of a long-term ischemic model, there was one distinct microRNA, which was highly significantly upregulated in ischemia rabbit (miRNA-133-3p). One miRNA that was highly significantly upregulated in diabetic rabbit but not in ischemic rabbits was miRNA-3074-5p. Only statistically significant results have been considered and analyzed.

Conclusion: These findings could lead to a precise and timely diagnosis of a potential single diabetic complication without invasive tissue biopsies and could be a novel tool in the management of diabetic patients developing complications due to the progression of diabetes.

Keywords: Micro RNAs, diabetes, ischemia, rabbit, diabetic complication, blood markers

Graphical Abstract

[1]
Zheng Y, Wang J, Zhang B, Li X. Diabetes mellitus and cause-specific mortality: A population-based study. Diabetes Metab J 2019; 43(3): 319-41.
[http://dx.doi.org/10.4093/dmj.2018.0060] [PMID: 31210036]
[2]
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018; 14(2): 88-98.
[http://dx.doi.org/10.1038/nrendo.2017.151] [PMID: 29219149]
[3]
Papamichou D, Panagiotakos DB, Itsiopoulos C. Dietary patterns and management of type 2 diabetes: A systematic review of randomised clinical trials. Nutr Metab Cardiovasc Dis 2019; 29(6): 531-43.
[http://dx.doi.org/10.1016/j.numecd.2019.02.004] [PMID: 30952576]
[4]
Riddle MC, Herman WH. The cost of diabetes care—an elephant in the room. Diabetes Care 2018; 41(5): 929-32.
[http://dx.doi.org/10.2337/dci18-0012] [PMID: 29678864]
[5]
Harding JL, Pavkov ME, Magliano DJ, Shaw JE, Gregg EW. Global trends in diabetic comlications:a review of current evidence. Diabetologia 2019; 14(2): 88-98.
[http://dx.doi.org/10.1038/nrendo.2017.151]
[6]
Sohel MMH, Sohel H. Circulating microRNAs as biomarkers in cancer diagnosis. Life Sci 2020; 248: 117473.
[http://dx.doi.org/10.1016/j.lfs.2020.117473] [PMID: 32114007]
[7]
Wu Y, Li Q, Zhang R, Dai X, Chen W, Xing D. Circulating microRNAs: Biomarkers of disease. Clin Chim Acta 2021; 516: 46-54.
[http://dx.doi.org/10.1016/j.cca.2021.01.008] [PMID: 33485903]
[8]
He X, Kuang G, Wu Y, Ou C. Emerging roles of exosomal miRNAs in diabetes mellitus. Clin Transl Med 2021; 11(6): e468.
[http://dx.doi.org/10.1002/ctm2.468] [PMID: 34185424]
[9]
Wang J, Wan R, Mo Y, Zhang Q, Sherwood LC, Chien S. Creating a long-term diabetic rabbit model. Exp Diabetes Res 2010; 2010: 1-10.
[http://dx.doi.org/10.1155/2010/289614] [PMID: 21234414]
[10]
Cock PJA, Fields CJ, Goto N, Heuer ML, Rice PM. The sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 2010; 38(6): 1767-71.
[http://dx.doi.org/10.1093/nar/gkp1137] [PMID: 20015970]
[11]
Illumina I. BaseSpace user guide. Rev E 2014; p. 15044182.
[12]
Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012; 40(1): 37-52.
[http://dx.doi.org/10.1093/nar/gkr688] [PMID: 21911355]
[13]
Robinson MD, McCarthy DJ, Smyth GK. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26(1): 139-40.
[http://dx.doi.org/10.1093/bioinformatics/btp616] [PMID: 19910308]
[14]
Andrews S. FastQC: A quality control tool for high throughput sequence data. 2014. Available from: http://bioinformatics.babraham.ac.uk/projects/fastqc/
[15]
Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30(15): 2114-20.
[http://dx.doi.org/10.1093/bioinformatics/btu170] [PMID: 24695404]
[16]
Metpally RPR, Nasser S, Malenica I, et al. Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model. Front Genet 2013; 4: 20.
[http://dx.doi.org/10.3389/fgene.2013.00020] [PMID: 23459507]
[17]
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9(4): 357-9.
[http://dx.doi.org/10.1038/nmeth.1923] [PMID: 22388286]
[18]
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: MicroRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006; 34(90001): D140-4.
[http://dx.doi.org/10.1093/nar/gkj112] [PMID: 16381832]
[19]
Hsu SD, Lin FM, Wu WY, et al. miRTarBase: A database curates experimentally validated microRNA–target interactions. Nucleic Acids Res 2011; 39(Database issue) (Suppl. 1): D163-9.
[http://dx.doi.org/10.1093/nar/gkq1107] [PMID: 21071411]
[20]
Flight RM, Harrison BJ, Mohammad F, et al. categoryCompare, an analytical tool based on feature annotations. Front Genet 2014; 5: 98.
[http://dx.doi.org/10.3389/fgene.2014.00098] [PMID: 24808906]
[21]
Harris MA, Clark J, Ireland A, et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004; 32(Database issue): D258-61.
[PMID: 14681407]
[22]
Esteves PJ, Abrantes J, Baldauf HM, et al. The wide utility of rabbits as models of human diseases. Exp Mol Med 2018; 50(5): 1-10.
[http://dx.doi.org/10.1038/s12276-018-0094-1] [PMID: 29789565]
[23]
Kotwal GJ, Martin MD, Chien S. Significant upregulation of U1 and U4 spliceosomal snRNAs by ATP nanoliposomes explains acceleration of wound healing, due to increased pre-mRNA processing to functional mRNA. Nanomedicine 2018; 14(4): 1289-99.
[http://dx.doi.org/10.1016/j.nano.2018.03.003] [PMID: 29627519]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy