pp 1–13 | Cite as

miR-409-3p is reduced in plasma and islet immune infiltrates of NOD diabetic mice and is differentially expressed in people with type 1 diabetes

  • Giuliana Ventriglia
  • Francesca Mancarella
  • Guido Sebastiani
  • Dana P. Cook
  • Roberto Mallone
  • Chantal Mathieu
  • Conny Gysemans
  • Francesco DottaEmail author



MicroRNAs (miRNAs) are a novel class of potential biomarkers emerging in many diseases, including type 1 diabetes. Here, we aim to analyse a panel of circulating miRNAs in non-obese diabetic (NOD) mice and individuals with type 1 diabetes.


We adopted standardised methodologies for extracting miRNAs from small sample volumes to evaluate a profiling panel of mature miRNAs in paired plasma and laser-captured microdissected immune-infiltrated islets of recently diabetic and normoglycaemic NOD mice. Moreover, we validated the findings during disease progression and remission after anti-CD3 therapy in NOD mice, as well as in individuals with type 1 diabetes.


Plasma levels of five miRNAs were downregulated in diabetic vs normoglycaemic mice. Of those, miR-409-3p was also downregulated in situ in the immune islet infiltrates of diabetic mice, suggesting an association with disease pathogenesis. Target-prediction tools linked miR-409-3p to immune- and metabolism-related signalling molecules. In situ miR-409-3p expression correlated with insulitis severity, and CD8+ central memory T cells were found to be enriched in miR-409-3p. Plasma miR-409-3p levels gradually decreased during diabetes development and improved with disease remission after anti-CD3 antibody therapy. Finally, plasma miR-409-3p levels were lower in people recently diagnosed with type 1 diabetes compared with a non-diabetic control group, and levels were inversely correlated with HbA1c levels.


We propose that miR-409-3p may represent a new circulating biomarker of islet inflammation and type 1 diabetes severity.


Anti-CD3 therapy Biomarker Inflammation MicroRNA Progression Type 1 diabetes 





Laser-captured microdissected


Monoclonal antibody




Non-obese resistant


Standard operating procedure


Central memory T cell


Effector memory T cell


3′-Untranslated region



We would like to thank L. Dusaer and J. Laureys (Laboratory for Clinical and Experimental Endocrinology, KU Leuven, Belgium) for their technical assistance.

Contribution statement

GV, FM and GS contributed to all aspects of this manuscript, including data acquisition and analysis and drafting and editing the manuscript. DPC, RM and CM were responsible for conception and experimental design, interpretation of the data and editing of the manuscript. FD and CG contributed to conception, experimental design and drafting the manuscript, provided final approval of the submitted manuscript and are guarantors of this work. All authors gave final approval of the version to be published.


This project has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement no. 115797 (INNODIA). This joint undertaking receives support from the Union’s Horizon 2020 research and innovation programme and EFPIA, JDRF and the Leona M. and Harry B. Helmsley Charitable Trust. FD is supported by the Italian Ministry of Research (grant no. 2015373Z39_007) and by Fondazione Roma. DPC is a PhD fellow of the FWO-Vlaanderen (Belgium) (11Y6716N).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2019_5026_MOESM1_ESM.pdf (1.3 mb)
ESM 1 (PDF 1.25 mb)


  1. 1.
    Finnegan EF, Pasquinelli AE (2013) MicroRNA biogenesis: regulating the regulators. Crit Rev Biochem Mol Biol 48:51–68. CrossRefPubMedGoogle Scholar
  2. 2.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wilczynska A, Bushell M (2015) The complexity of miRNA-mediated repression. Cell Death Differ 22:22–33. CrossRefPubMedGoogle Scholar
  4. 4.
    Guay C, Regazzi R (2017) Exosomes as new players in metabolic organ cross-talk. Diabetes Obes Metab 19(Suppl 1):137–146. CrossRefPubMedGoogle Scholar
  5. 5.
    Kosaka N, Iguchi H, Yoshioka Y et al (2010) Secretory mechanisms and intercellular transfer of microRNAs in living cells. J Biol Chem 285:17442–17452. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Fehlmann T, Ludwig N, Backes C et al (2016) Distribution of microRNA biomarker candidates in solid tissues and body fluids. RNA Biol 13:1084–1088. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Petrovic N, Ergün S, Isenovic ER (2017) Levels of microRNA heterogeneity in cancer biology. Mol Diagn Ther 21:511–523. CrossRefPubMedGoogle Scholar
  8. 8.
    Ventriglia G, Nigi L, Sebastiani G, Dotta F (2015) MicroRNAs: novel players in the dialogue between pancreatic islets and immune system in autoimmune diabetes. Biomed Res Int 2015:749734. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Zeng L, Cui J, Wu H, Lu Q (2014) The emerging role of circulating microRNAs as biomarkers in autoimmune diseases. Autoimmunity 47:419–429. CrossRefPubMedGoogle Scholar
  10. 10.
    Wang F, Chen C, Wang D (2014) Circulating microRNAs in cardiovascular diseases: from biomarkers to therapeutic targets. Front Med 8:404–418. CrossRefPubMedGoogle Scholar
  11. 11.
    Erener S, Marwaha A, Tan R et al (2017) Profiling of circulating microRNAs in children with recent onset of type 1 diabetes. JCI Insight 2:e89656. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Samandari N, Mirza AH, Nielsen LB et al (2017) Circulating microRNA levels predict residual beta cell function and glycaemic control in children with type 1 diabetes mellitus. Diabetologia 60:354–363. CrossRefPubMedGoogle Scholar
  13. 13.
    Snowhite IV, Allende G, Sosenko J et al (2017) Association of serum microRNAs with islet autoimmunity, disease progression and metabolic impairment in relatives at risk of type 1 diabetes. Diabetologia 60:1409–1422. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Nielsen LB, Wang C, Sørensen K et al (2012) Circulating levels of microRNA from children with newly diagnosed type 1 diabetes and healthy controls: evidence that miR-25 associates to residual beta-cell function and glycaemic control during disease progression. Exp Diabetes Res 896362:2012. CrossRefGoogle Scholar
  15. 15.
    Seyhan AA, Nunez Lopez YO, Xie H et al (2016) Pancreas-enriched miRNAs are altered in the circulation of subjects with diabetes: a pilot cross-sectional study. Sci Rep 6:31479. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Guay C, Regazzi R (2013) Circulating microRNAs as novel biomarkers for diabetes mellitus. Nat Rev Endocrinol 9:513–521. CrossRefPubMedGoogle Scholar
  17. 17.
    Robert S, Gysemans C, Takiishi T et al (2014) Oral delivery of glutamic acid decarboxylase (GAD)-65 and IL10 by Lactococcus lactis reverses diabetes in recent-onset NOD mice. Diabetes 63:2876–2887. CrossRefPubMedGoogle Scholar
  18. 18.
    Takiishi T, Korf H, Van Belle TL et al (2012) Reversal of autoimmune diabetes by restoration of antigen-specific tolerance using genetically modified Lactococcus lactis in mice. J Clin Invest 122:1717–1725. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Demeester S, Keymeulen B, Kaufman L et al (2015) Preexisting insulin autoantibodies predict efficacy of otelixizumab in preserving residual β-cell function in recent-onset type 1 diabetes. Diabetes Care 38:644–651. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bindea G, Galon J, Mlecnik B (2013) CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics 29:661–663. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Bindea G, Mlecnik B, Hackl H et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Magnuson AM, Thurber GM, Kohler RH et al (2015) Population dynamics of islet-infiltrating cells in autoimmune diabetes. Proc Natl Acad Sci USA 112:1511–1516. CrossRefPubMedGoogle Scholar
  23. 23.
    Willcox A, Richardson SJ, Bone AJ et al (2009) Analysis of islet inflammation in human type 1 diabetes. Clin Exp Immunol 155:173–181. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Coppieters KT, Dotta F, Amirian N et al (2012) Demonstration of islet-autoreactive CD8 T cells in insulitic lesions from recent onset and long-term type 1 diabetes patients. J Exp Med 209:51–60. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Arif S, Leete P, Nguyen V et al (2014) Blood and islet phenotypes indicate immunological heterogeneity in type 1 diabetes. Diabetes 63:3835–3845. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Rossi RL, Rossetti G, Wenandy L et al (2011) Distinct microRNA signatures in human lymphocyte subsets and enforcement of the naive state in CD4+ T cells by the microRNA miR-125b. Nat Immunol 12:796–803. CrossRefGoogle Scholar
  27. 27.
    Chatenoud L, Primo J, Bach JF (1997) CD3 antibody-induced dominant self tolerance in overtly diabetic NOD mice. J Immunol 158:2947–2954PubMedGoogle Scholar
  28. 28.
    Wallberg M, Recino A, Phillips J et al (2017) Anti-CD3 treatment up-regulates programmed cell death protein-1 expression on activated effector T cells and severely impairs their inflammatory capacity. Immunology 151:248–260. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Benson RA, Garcon F, Recino A et al (2018) Non-invasive multiphoton imaging of islets transplanted into the pinna of the NOD mouse ear reveals the immediate effect of anti-CD3 treatment in autoimmune diabetes. Front Immunol 9:1006. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Sebastiani G, Nigi L, Grieco GE et al (2017) Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging? J Endocrinol Invest 40:591–610. CrossRefPubMedGoogle Scholar
  31. 31.
    Zampetaki A, Mayr M (2012) Analytical challenges and technical limitations in assessing circulating miRNAs. Thromb Haemost 108:592–598. CrossRefPubMedGoogle Scholar
  32. 32.
    Åkerman L, Casas R, Ludvigsson J et al (2018) Serum miRNA levels are related to glucose homeostasis and islet autoantibodies in children with high risk for type 1 diabetes. PLoS One 13:e0191067. CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Yang M, Ye L, Wang B et al (2015) Decreased miR-146 expression in peripheral blood mononuclear cells is correlated with ongoing islet autoimmunity in type 1 diabetes patients. J Diabetes 7:158–165. CrossRefPubMedGoogle Scholar
  34. 34.
    Garcia-Contreras M, Shah SH, Tamayo A et al (2017) Plasma-derived exosome characterization reveals a distinct microRNA signature in long duration type 1 diabetes. Sci Rep 7:5998. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Wang H, Peng W, Ouyang X et al (2012) Circulating microRNAs as candidate biomarkers in patients with systemic lupus erythematosus. Transl Res 160:198–206. CrossRefPubMedGoogle Scholar
  36. 36.
    Wang G, Tam L-S, EK-M L et al (2010) Serum and urinary cell-free MiR-146a and MiR-155 in patients with systemic lupus erythematosus. J Rheumatol 37:2516–2522. CrossRefPubMedGoogle Scholar
  37. 37.
    Murata K, Furu M, Yoshitomi H et al (2013) Comprehensive microRNA analysis identifies miR-24 and miR-125a-5p as plasma biomarkers for rheumatoid arthritis. PLoS One 8:e69118. CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Osipova J, Fischer D-C, Dangwal S et al (2014) Diabetes-associated microRNAs in pediatric patients with type 1 diabetes mellitus: a cross-sectional cohort study. J Clin Endocrinol Metab 99:E1661–E1665. CrossRefPubMedGoogle Scholar
  39. 39.
    Assmann TS, Recamonde-Mendoza M, Puñales M et al (2018) MicroRNA expression profile in plasma from type 1 diabetic patients: case-control study and bioinformatic analysis. Diabetes Res Clin Pract 141:35–46. CrossRefPubMedGoogle Scholar
  40. 40.
    Wallace C, Smyth DJ, Maisuria-Armer M et al (2010) The imprinted DLK1-MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1 diabetes. Nat Genet 42:68–71. CrossRefPubMedGoogle Scholar
  41. 41.
    Abuhatzira L, Xu H, Tahhan G et al (2015) Multiple microRNAs within the 14q32 cluster target the mRNAs of major type 1 diabetes autoantigens IA-2, IA-2β, and GAD65. FASEB J 29:4374–4383. CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Zhang T, Zhang Z, Li F et al (2018) miR-143 regulates memory T cell differentiation by reprogramming T cell metabolism. J Immunol 201:2165–2175. CrossRefPubMedGoogle Scholar
  43. 43.
    Essig K, Hu D, Guimaraes JC et al (2017) Roquin suppresses the PI3K-mTOR Signaling pathway to inhibit T helper cell differentiation and conversion of Treg to Tfr cells. Immunity 47 e12:1067–1082. CrossRefGoogle Scholar
  44. 44.
    Ban YH, Oh S-C, Seo S-H et al (2017) miR-150-mediated Foxo1 regulation programs CD8+ T cell differentiation. Cell Rep 20:2598–2611. CrossRefPubMedGoogle Scholar
  45. 45.
    Ouimet M, Ediriweera H, Afonso MS et al (2017) microRNA-33 regulates macrophage autophagy in atherosclerosis. Arterioscler Thromb Vasc Biol 37:1058–1067. CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Liu X, Zhou F, Yang Y et al (2018) MiR-409-3p and MiR-1896 co-operatively participate in IL-17-induced inflammatory cytokine production in astrocytes and pathogenesis of EAE mice via targeting SOCS3/STAT3 signaling. Glia 67:101–112. CrossRefPubMedGoogle Scholar
  47. 47.
    Raud B, McGuire PJ, Jones RG et al (2018) Fatty acid metabolism in CD8+ T cell memory: Challenging current concepts. Immunol Rev 283:213–231. CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Christianson SW, Shultz LD, Leiter EH (1993) Adoptive transfer of diabetes into immunodeficient NOD-scid/scid mice. Relative contributions of CD4+ and CD8+ T-cells from diabetic versus prediabetic NOD.NON-Thy-1a donors. Diabetes 42:44–55CrossRefGoogle Scholar
  49. 49.
    Baeke F, Van Belle TL, Takiishi T et al (2012) Low doses of anti-CD3, ciclosporin A and the vitamin D analogue, TX527, synergise to delay recurrence of autoimmune diabetes in an islet-transplanted NOD mouse model of diabetes. Diabetologia 55:2723–2732. CrossRefPubMedGoogle Scholar
  50. 50.
    Herold KC, Bundy BN, Long SA et al (2019) An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes. N Engl J Med 381:603–613. CrossRefPubMedGoogle Scholar
  51. 51.
    Perdigoto AL, Preston-Hurlburt P, Clark P et al (2019) Treatment of type 1 diabetes with teplizumab: clinical and immunological follow-up after 7 years from diagnosis. Diabetologia 62:655–664. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Giuliana Ventriglia
    • 1
    • 2
    • 3
  • Francesca Mancarella
    • 1
    • 2
  • Guido Sebastiani
    • 1
    • 2
  • Dana P. Cook
    • 3
  • Roberto Mallone
    • 4
  • Chantal Mathieu
    • 3
  • Conny Gysemans
    • 3
  • Francesco Dotta
    • 1
    • 2
    Email author
  1. 1.Diabetes Unit, Department of Medicine, Surgery and NeurosciencesUniversity of SienaSienaItaly
  2. 2.Fondazione Umberto Di Mario ONLUS c/o Toscana Life SciencesSienaItaly
  3. 3.Clinical and Experimental Endocrinology (CEE)Katholieke Universiteit Leuven (KU LEUVEN)LeuvenBelgium
  4. 4.Inserm, U1016, CNRS, UMR8104, Paris Descartes University, Sorbonne Paris Cité, Cochin InstituteParisFrance

Personalised recommendations