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Circulating miR-103 family as potential biomarkers for type 2 diabetes through targeting CAV-1 and SFRP4

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Abstract

Aims

MicroRNA-103 (miR-103) family plays important roles in regulating glucose homeostasis in type 2 diabetes mellitus (DM2). However, the underlying mechanisms remain poorly characterized. The objective of this study was to test the hypothesis that circulating miR-103a and miR-103b, which regulate CAV-1 and SFRP4, respectively, are novel biomarkers for diagnosis of DM2.

Methods

We determined the predictive potential of circulating miR-103a and miR-103b in pre-DM subjects (pre-DM), noncomplicated diabetic subjects, and normal glucose-tolerance individuals (control) using bioinformatic analysis, qRT-PCR, luciferase assays, and ELISA assays.

Results

We found that both miR-103a and miR-103b had high complementarity and conservation, modulated reporter gene expression through seed sequences in the 3′UTRs of CAV-1 and SFRP4 mRNA, and negatively regulated their mRNA and protein levels, respectively. We also found that increased miR-103a and decreased miR-103a in plasma were significantly and negatively correlated with reduced CAV-1 levels and elevated SFRP4 levels in pre-DM and DM2, respectively, and were significantly associated with glucose metabolism, HbA1c levels, and other DM2 risk factors for progression from a normal individual to one with pre-DM. Furthermore, we demonstrated that the reciprocal changes in circulating miR-103a and miR-103b not only provided high sensitivity and specificity to differentiate the pre-DM population but also acted as biomarkers for predicting DM2 with high diagnostic value.

Conclusions

These findings suggest that circulating miR-103a and miR-103b may serve as novel biomarkers for diagnosis of DM2, providing novel insight into the mechanisms underlying pre-DM.

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Abbreviations

DM2:

Type 2 diabetes mellitus

miR-103:

MicroRNA-103

NCDM:

Noncomplicated diabetic subjects

pre-DM:

Pre-diabetes mellitus

CAV-1:

Caveolin 1

SFRP4:

Secreted frizzled-related protein 4

FPG:

Fasting plasma glucose

2hPG:

2-hour post-load glucose

OGTT:

Oral glucose-tolerance test

BMI:

Body mass index

WC:

Waist circumference

TC:

Total cholesterol

TG:

Triglyceride

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

HbA1c:

Hemoglobin A1c

References

  1. Chaudhury A, Duvoor C, Reddy Dendi VS, Kraleti S, Chada A, Ravilla R, Marco A, Shekhawat NS, Montales MT, Kuriakose K, Sasapu A, Beebe A, Patil N, Musham CK, Lohani GP, Mirza W (2017) Clinical review of antidiabetic drugs: implications for type 2 diabetes mellitus management. Front Endocrinol 8:6

    Google Scholar 

  2. Karalliedde J, Gnudi L (2016) Diabetes mellitus, a complex and heterogeneous disease, and the role of insulin resistance as a determinant of diabetic kidney disease. Nephrol Dial Transplant 31:206–213

    CAS  PubMed  Google Scholar 

  3. American Diabetes A (2013) Diagnosis and classification of diabetes mellitus. Diabetes Care 36(Suppl 1):S67–S74

    Google Scholar 

  4. Bloomgarden ZT (2008) American College of Endocrinology pre-diabetes consensus conference: part three. Diabetes Care 31:2404–2409

    PubMed  PubMed Central  Google Scholar 

  5. Grundy SM (2012) Pre-diabetes, metabolic syndrome, and cardiovascular risk. J Am Coll Cardiol 59:635–643

    CAS  PubMed  Google Scholar 

  6. Stepanek L, Horakova D, Nakladalova M, Cibickova L, Karasek D, Zadrazil J (2018) Significance of prediabetes as a nosological entity. Biomedical Papers of the Medical Faculty of the University Palacky, Olomouc

    Google Scholar 

  7. Tuso P (2014) Prediabetes and lifestyle modification: time to prevent a preventable disease. Perm J 18:88–93

    PubMed  PubMed Central  Google Scholar 

  8. Vaishya S, Sarwade RD, Seshadri V (2018) MicroRNA, proteins, and metabolites as novel biomarkers for prediabetes, diabetes, and related complications. Front Endocrinol 9:180

    Google Scholar 

  9. Guay C, Regazzi R (2013) Circulating microRNAs as novel biomarkers for diabetes mellitus. Nat Rev Endocrinol 9:513–521

    CAS  PubMed  Google Scholar 

  10. Sebastiani G, Nigi L, Grieco GE, Mancarella F, Ventriglia G, Dotta F (2017) Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging? J Endocrinol Invest 40:591–610

    CAS  PubMed  Google Scholar 

  11. Tiwari J, Gupta G, de Jesus Andreoli Pinto T, Sharma R, Pabreja K, Matta Y, Arora N, Mishra A, Sharma R, Dua K (2018) Role of microRNAs (miRNAs) in the pathophysiology of diabetes mellitus. Panminerva Med 60:25–28

    PubMed  Google Scholar 

  12. Fernandez-Hernando C, Ramirez CM, Goedeke L, Suarez Y (2013) MicroRNAs in metabolic disease. Arterioscler Thromb Vasc Biol 33:178–185

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Chen HY, Lin YM, Chung HC, Lang YD, Lin CJ, Huang J, Wang WC, Lin FM, Chen Z, Huang HD, Shyy JY, Liang JT, Chen RH (2012) miR-103/107 promote metastasis of colorectal cancer by targeting the metastasis suppressors DAPK and KLF4. Can Res 72:3631–3641

    CAS  Google Scholar 

  14. Trajkovski M, Hausser J, Soutschek J, Bhat B, Akin A, Zavolan M, Heim MH, Stoffel M (2011) MicroRNAs 103 and 107 regulate insulin sensitivity. Nature 474:649–653

    CAS  PubMed  Google Scholar 

  15. Wang JX, Zhang XJ, Li Q, Wang K, Wang Y, Jiao JQ, Feng C, Teng S, Zhou LY, Gong Y, Zhou ZX, Liu J, Wang JL, Li PF (2015) MicroRNA-103/107 regulate programmed necrosis and myocardial ischemia/reperfusion injury through targeting FADD. Circ Res 117:352–363

    CAS  PubMed  Google Scholar 

  16. Woeller CF, Flores E, Pollock SJ, Phipps RP (2017) Editor’s highlight: Thy1 (CD90) expression is reduced by the environmental chemical tetrabromobisphenol-A to promote adipogenesis through induction of microRNA-103. Toxicol Sci 157:305–319

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Wilfred BR, Wang WX, Nelson PT (2007) Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways. Mol Genet Metab 91:209–217

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Vatandoost N, Amini M, Iraj B, Momenzadeh S, Salehi R (2015) Dysregulated miR-103 and miR-143 expression in peripheral blood mononuclear cells from induced prediabetes and type 2 diabetes rats. Gene 572:95–100

    CAS  PubMed  Google Scholar 

  19. Luo M, Li R, Deng X, Ren M, Chen N, Zeng M, Yan K, Xia J, Liu F, Ma W, Yang Y, Wan Q, Wu J (2015) Platelet-derived miR-103b as a novel biomarker for the early diagnosis of type 2 diabetes. Acta Diabetol 52:943–949

    CAS  PubMed  Google Scholar 

  20. Zhu H, Leung SW (2015) Identification of microRNA biomarkers in type 2 diabetes: a meta-analysis of controlled profiling studies. Diabetologia 58:900–911

    CAS  PubMed  Google Scholar 

  21. Chakraborty C, Doss CG, Bandyopadhyay S, Agoramoorthy G (2014) Influence of miRNA in insulin signaling pathway and insulin resistance: micro-molecules with a major role in type-2 diabetes. Wiley Interdiscip Rev RNA 5:697–712

    CAS  PubMed  Google Scholar 

  22. Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucl Acids Res 47:D155–D162

    CAS  PubMed  Google Scholar 

  23. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS (2009) MEME SUITE: tools for motif discovery and searching. Nucl Acids Res 37:W202–W208

    CAS  PubMed  Google Scholar 

  24. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649

    PubMed  PubMed Central  Google Scholar 

  25. Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R (2009) QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25:3045–3046

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20

    CAS  PubMed  Google Scholar 

  27. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, Tsai TR, Huang HT, Wang CY, Wu HY, Ho SY, Chen PR, Chuang CH, Hsieh PJ, Wu YS, Chen WL, Li MJ, Wu YC, Huang XY, Ng FL, Buddhakosai W, Huang PC, Lan KC, Huang CY, Weng SL, Cheng YN, Liang C, Hsu WL, Huang HD (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucl Acids Res 46:D296–D302

    CAS  PubMed  Google Scholar 

  28. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44:839–847

    CAS  PubMed  Google Scholar 

  29. Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucl Acids Res 43:D146–D152

    CAS  PubMed  Google Scholar 

  30. Wang B (2013) Base composition characteristics of mammalian miRNAs. J Nucl Acids 2013:951570

    Google Scholar 

  31. Hsueh WA, Orloski L, Wyne K (2010) Prediabetes: the importance of early identification and intervention. Postgrad Med 122:129–143

    PubMed  Google Scholar 

  32. Bergman M (2013) Pathophysiology of prediabetes and treatment implications for the prevention of type 2 diabetes mellitus. Endocrine 43:504–513

    CAS  PubMed  Google Scholar 

  33. Dorsey R, Songer T (2011) Lifestyle behaviors and physician advice for change among overweight and obese adults with prediabetes and diabetes in the United States, 2006. Prev Chronic Disease 8:A132

    Google Scholar 

  34. Eastwood SV, Tillin T, Sattar N, Forouhi NG, Hughes AD, Chaturvedi N (2015) Associations between prediabetes, by three different diagnostic criteria, and incident CVD differ in South Asians and Europeans. Diabetes Care 38:2325–2332

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Bhatia P, Raina S, Chugh J, Sharma S (2015) miRNAs: early prognostic biomarkers for Type 2 diabetes mellitus? Biomark Med 9:1025–1040

    CAS  PubMed  Google Scholar 

  36. Willeit P, Skroblin P, Moschen AR, Yin X, Kaudewitz D, Zampetaki A, Barwari T, Whitehead M, Ramírez CM, Goedeke L, Rotllan N, Bonora E, Hughes AD, Santer P, Fernández-Hernando C, Tilg H, Willeit J, Kiechl S, Mayr M (2017) Circulating microrna-122 is associated with the risk of new-onset metabolic syndrome and type 2 diabetes. Diabetes 66:347–357

    CAS  PubMed  Google Scholar 

  37. Yan S, Wang T, Huang S, Di Y, Huang Y, Liu X, Luo Z, Han W, An B (2016) Differential expression of microRNAs in plasma of patients with prediabetes and newly diagnosed type 2 diabetes. Acta Diabetol 53:693–702

    CAS  PubMed  Google Scholar 

  38. Al-Muhtaresh HA, Al-Kafaji G (2018) Evaluation of two-diabetes related microRNAs suitability as earlier blood biomarkers for detecting prediabetes and type 2 diabetes mellitus. J Clin Med 7

  39. Villard A, Marchand L, Thivolet C, Rome S (2015) Diagnostic value of cell-free circulating microRNAs for obesity and type 2 diabetes: a meta-analysis. J Mol Biomark Diagn 6

  40. Wang S, Wang N, Zheng Y, Zhang J, Zhang F, Wang Z (2017) Caveolin-1: an oxidative stress-related target for cancer prevention. Oxid Med Cell Long 2017:7454031

    Google Scholar 

  41. Feng H, Guo W, Han J, Li XA (2013) Role of caveolin-1 and caveolae signaling in endotoxemia and sepsis. Life Sci 93:1–6

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Cohen AW, Combs TP, Scherer PE, Lisanti MP (2003) Role of caveolin and caveolae in insulin signaling and diabetes. Am J Physiol Endocrinol Metab 285:E1151–E1160

    CAS  PubMed  Google Scholar 

  43. Catalan V, Gomez-Ambrosi J, Rodriguez A, Silva C, Rotellar F, Gil MJ, Cienfuegos JA, Salvador J, Frühbeck G (2008) Expression of caveolin-1 in human adipose tissue is upregulated in obesity and obesity-associated type 2 diabetes mellitus and related to inflammation. Clin Endocrinol 68:213–219

    CAS  Google Scholar 

  44. Carrillo-Sepulveda MA, Matsumoto T (2014) Phenotypic modulation of mesenteric vascular smooth muscle cells from type 2 diabetic rats is associated with decreased caveolin-1 expression. Cell Physiol Biochem 34:1497–1506

    CAS  PubMed  Google Scholar 

  45. Briand N, Le Lay S, Sessa WC, Ferre P, Dugail I (2011) Distinct roles of endothelial and adipocyte caveolin-1 in macrophage infiltration and adipose tissue metabolic activity. Diabetes 60:448–453

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mahdi T, Hanzelmann S, Salehi A, Muhammed SJ, Reinbothe TM, Tang Y, Axelsson AS, Zhou Y, Jing X, Almgren P, Krus U, Taneera J, Blom AM, Lyssenko V, Esguerra JL, Hansson O, Eliasson L, Derry J, Zhang E, Wollheim CB, Groop L, Renström E, Rosengren AH (2012) Secreted frizzled-related protein 4 reduces insulin secretion and is overexpressed in type 2 diabetes. Cell Metab 16:625–633

    CAS  PubMed  Google Scholar 

  47. Anand K, Vidyasagar S, Lasrado I, Pandey GK, Amutha A, Ranjani H, Mohan Anjana R, Mohan V, Gokulakrishnan K (2016) Secreted frizzled-related protein 4 (SFRP4): a novel biomarker of beta-cell dysfunction and insulin resistance in individuals with prediabetes and type 2 diabetes. Diabetes Care 39:e147–e148

    PubMed  Google Scholar 

  48. Zou D, Ye Y, Zou N, Yu J (2017) Analysis of risk factors and their interactions in type 2 diabetes mellitus: a cross-sectional survey in Guilin, China. J Diabetes Investig 8:188–194

    CAS  PubMed  Google Scholar 

  49. Liu L, Guan X, Yuan Z, Zhao M, Li Q, Zhang X, Zhang H, Zheng D, Xu J, Gao L, Guan Q, Zhao J, The Reaction Study Group (2019) Different contributions of dyslipidemia and obesity to the natural history of type 2 diabetes: 3-year cohort study in China. J Diabetes Res 2019:4328975

    PubMed  PubMed Central  Google Scholar 

  50. Biesenbach G (1989) Disorders of lipid metabolism in diabetes mellitus. Wien Med Wochenschr Suppl 105:9–17

    CAS  PubMed  Google Scholar 

  51. Yang Z, Chen H, Si H, Li X, Ding X, Sheng Q, Chen P, Zhang H (2014) Serum miR-23a, a potential biomarker for diagnosis of pre-diabetes and type 2 diabetes. Acta Diabetol 51:823–831

    CAS  PubMed  Google Scholar 

  52. Al-Kafaji G, Al-Mahroos G, Alsayed NA, Hasan ZA, Nawaz S, Bakhiet M (2015) Peripheral blood microRNA-15a is a potential biomarker for type 2 diabetes mellitus and pre-diabetes. Mol Med Rep 12:7485–7490

    CAS  PubMed  Google Scholar 

  53. Liu Y, Gao G, Yang C, Zhou K, Shen B, Liang H, Jiang X (2014) The role of circulating microRNA-126 (miR-126): a novel biomarker for screening prediabetes and newly diagnosed type 2 diabetes mellitus. Int J Mol Sci 15:10567–10577

    PubMed  PubMed Central  Google Scholar 

  54. Párrizas M, Brugnara L, Esteban Y, González-Franquesa A, Canivell S, Murillo S, Gordillo-Bastidas E, Cussó R, Cadefau JA, García-Roves PM, Servitja JM, Novials A (2015) Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention. J Clin Endocrinol Metab 100:E407–E415

    PubMed  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (81800434, 81570263), Grant of Sichuan Province Science and Technology Agency Grant (2019YJ0487), Foundation of Luzhou Municipal Science and Technology Bureau (2017LZXNYD-T05, 2016LZXNYD-J24), and the Ministry Science and Technology of China Grant (2016YFC0901200, 2016YFC0901205).

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Author notes

  1. Mao Luo and Chunrong Xu have contributed equally to this work.

Authors and Affiliations

  1. Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease of Sichuan Province, Drug Discovery Research Center, Southwest Medical University, Luzhou, Sichuan, China

    Mao Luo, Chunrong Xu, Gang Wang & Jianbo Wu

  2. Department of Endocrinology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China

    Qin Wan

  3. Laboratory for Cardiovascular Pharmacology of Department of Pharmacology, The School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China

    Mao Luo, Chunrong Xu, Gang Wang & Jianbo Wu

  4. GCP Center, Affiliated Hospital (T.C.M) of Southwest Medical University, Luzhou, Sichuan, China

    Yulin Luo

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  1. Mao Luo
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Contributions

The original idea of this study was proposed by QW. ML and CX performed experiments and analyzed the data and wrote the first draft of this manuscript. YL and GW performed and interpreted the experiments. JW and QW edited subsequent drafts. All authors have read and approved the final version of the manuscript for submission.

Corresponding author

Correspondence to Qin Wan.

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Conflict of interest

All the authors including Mao Luo, Chunrong Xu, Yulin Luo, Gang Wang, Jianbo Wu, and Qin Wan declare that they have no conflict of interest.

Ethical approval

All human subjects used in the study ‘‘Circulating miR-103 Family as Potential Biomarkers for Type 2 Diabetes through targeting CAV-1 and SFRP4’’ have been reviewed by the Research Ethics Committee of the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, P. R. China and have been performed in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All samples were collected with informed consent of all subjects. There is no security and privacy violation to the patient’s health in our study.

Human and animal rights

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the 1975 Helsinki declaration, as revised in 2008 (5).

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Informed consent was obtained from all individual participants included in the study.

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Luo, M., Xu, C., Luo, Y. et al. Circulating miR-103 family as potential biomarkers for type 2 diabetes through targeting CAV-1 and SFRP4. Acta Diabetol 57, 309–322 (2020). https://doi.org/10.1007/s00592-019-01430-6

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