IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002257.html
   My bibliography  Save this article

Metabolic Regulation in Progression to Autoimmune Diabetes

Author

Listed:
  • Marko Sysi-Aho
  • Andrey Ermolov
  • Peddinti V Gopalacharyulu
  • Abhishek Tripathi
  • Tuulikki Seppänen-Laakso
  • Johanna Maukonen
  • Ismo Mattila
  • Suvi T Ruohonen
  • Laura Vähätalo
  • Laxman Yetukuri
  • Taina Härkönen
  • Erno Lindfors
  • Janne Nikkilä
  • Jorma Ilonen
  • Olli Simell
  • Maria Saarela
  • Mikael Knip
  • Samuel Kaski
  • Eriika Savontaus
  • Matej Orešič

Abstract

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes. Author Summary: We have recently found that distinct metabolic disturbances precede β-cell autoimmunity in children who later progress to type 1 diabetes (T1D). Here we performed a murine study using non-obese diabetic (NOD) mice that recapitulated the protocol used in human, followed up by independent studies where NOD mice were studied in relation to risk of diabetes progression. We found that young female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum subgroup. The metabolic phenotypes observed in our study could be relevant as end points for studies investigating T1D pathogenesis and/or responses to interventions. By proceeding from a clinical study via metabolomics and modeling to an experimental model using a similar study design, then evolving further to tissue-specific studies, we hereby also present a conceptually novel approach to reversed translation that may be useful in future therapeutic studies in the context of prevention and treatment of T1D as well as of other diseases characterized by long prodromal periods.

Suggested Citation

  • Marko Sysi-Aho & Andrey Ermolov & Peddinti V Gopalacharyulu & Abhishek Tripathi & Tuulikki Seppänen-Laakso & Johanna Maukonen & Ismo Mattila & Suvi T Ruohonen & Laura Vähätalo & Laxman Yetukuri & Tain, 2011. "Metabolic Regulation in Progression to Autoimmune Diabetes," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
  • Handle: RePEc:plo:pcbi00:1002257
    DOI: 10.1371/journal.pcbi.1002257
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002257
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002257&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002257?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Graham M. Lord & Giuseppe Matarese & Jane K. Howard & Richard J. Baker & Stephen R. Bloom & Robert I. Lechler, 1998. "Leptin modulates the T-cell immune response and reverses starvation-induced immunosuppression," Nature, Nature, vol. 394(6696), pages 897-901, August.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    3. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Li Wen & Ruth E. Ley & Pavel Yu. Volchkov & Peter B. Stranges & Lia Avanesyan & Austin C. Stonebraker & Changyun Hu & F. Susan Wong & Gregory L. Szot & Jeffrey A. Bluestone & Jeffrey I. Gordon & Alexa, 2008. "Innate immunity and intestinal microbiota in the development of Type 1 diabetes," Nature, Nature, vol. 455(7216), pages 1109-1113, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rasmus Madsen & Viqar Showkat Banday & Thomas Moritz & Johan Trygg & Kristina Lejon, 2012. "Altered Metabolic Signature in Pre-Diabetic NOD Mice," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santu Ghosh & Alan M. Polansky, 2022. "Large-Scale Simultaneous Testing Using Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 808-843, August.
    2. Ming Yuan & Christina Kendziorski, 2006. "A Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification," Biometrics, The International Biometric Society, vol. 62(4), pages 1089-1098, December.
    3. Simina M. Boca & Héctor Céorrada Bravo & Brian Caffo & Jeffrey T. Leek & Giovanni Parmigiani, 2013. "A Decision-Theory Approach to Interpretable Set Analysis for High-Dimensional Data," Biometrics, The International Biometric Society, vol. 69(3), pages 614-623, September.
    4. Leek Jeffrey T & Storey John D., 2011. "The Joint Null Criterion for Multiple Hypothesis Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, June.
    5. Hongkai Ji & Wing Hung Wong, 2006. "Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes," Biometrics, The International Biometric Society, vol. 62(3), pages 645-663, September.
    6. Hwang J.T. Gene & Liu Peng, 2010. "Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-35, October.
    7. Huiping Zhang & Fan Wang & Henry R Kranzler & Hongyu Zhao & Joel Gelernter, 2013. "Profiling of Childhood Adversity-Associated DNA Methylation Changes in Alcoholic Patients and Healthy Controls," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    8. Dørum Guro & Snipen Lars & Solheim Margrete & Sæbø Solve, 2009. "Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, July.
    9. N. Bochkina & S. Richardson, 2007. "Tail Posterior Probability for Inference in Pairwise and Multiclass Gene Expression Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1117-1125, December.
    10. Ji Tieming & Liu Peng & Nettleton Dan, 2012. "Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-29, May.
    11. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    12. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    13. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    14. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    15. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    16. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    17. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    18. A Bottle & P Aylin, 2011. "Predicting the false alarm rate in multi-institution mortality monitoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1711-1718, September.
    19. Van Hanh Nguyen & Catherine Matias, 2014. "On Efficient Estimators of the Proportion of True Null Hypotheses in a Multiple Testing Setup," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1167-1194, December.
    20. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1002257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.