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Longitudinal multi-omics of host–microbe dynamics in prediabetes

Author

Listed:
  • Wenyu Zhou

    (Stanford University School of Medicine)

  • M. Reza Sailani

    (Stanford University School of Medicine)

  • Kévin Contrepois

    (Stanford University School of Medicine)

  • Yanjiao Zhou

    (The Jackson Laboratory for Genomic Medicine
    UConn Health)

  • Sara Ahadi

    (Stanford University School of Medicine)

  • Shana R. Leopold

    (The Jackson Laboratory for Genomic Medicine)

  • Martin J. Zhang

    (Stanford University)

  • Varsha Rao

    (Stanford University School of Medicine)

  • Monika Avina

    (Stanford University School of Medicine)

  • Tejaswini Mishra

    (Stanford University School of Medicine)

  • Jethro Johnson

    (The Jackson Laboratory for Genomic Medicine)

  • Brittany Lee-McMullen

    (Stanford University School of Medicine)

  • Songjie Chen

    (Stanford University School of Medicine)

  • Ahmed A. Metwally

    (Stanford University School of Medicine)

  • Thi Dong Binh Tran

    (The Jackson Laboratory for Genomic Medicine)

  • Hoan Nguyen

    (The Jackson Laboratory for Genomic Medicine)

  • Xin Zhou

    (The Jackson Laboratory for Genomic Medicine)

  • Brandon Albright

    (The Jackson Laboratory for Genomic Medicine)

  • Bo-Young Hong

    (The Jackson Laboratory for Genomic Medicine)

  • Lauren Petersen

    (The Jackson Laboratory for Genomic Medicine)

  • Eddy Bautista

    (The Jackson Laboratory for Genomic Medicine)

  • Blake Hanson

    (The Jackson Laboratory for Genomic Medicine)

  • Lei Chen

    (The Jackson Laboratory for Genomic Medicine)

  • Daniel Spakowicz

    (The Jackson Laboratory for Genomic Medicine)

  • Amir Bahmani

    (Stanford Center for Genomics and Personalized Medicine)

  • Denis Salins

    (Stanford University School of Medicine)

  • Benjamin Leopold

    (The Jackson Laboratory for Genomic Medicine)

  • Melanie Ashland

    (Stanford University School of Medicine)

  • Orit Dagan-Rosenfeld

    (Stanford University School of Medicine)

  • Shannon Rego

    (Stanford University School of Medicine)

  • Patricia Limcaoco

    (Stanford University School of Medicine)

  • Elizabeth Colbert

    (Stanford University School of Medicine)

  • Candice Allister

    (Stanford University School of Medicine)

  • Dalia Perelman

    (Stanford University School of Medicine)

  • Colleen Craig

    (Stanford University School of Medicine)

  • Eric Wei

    (Stanford University School of Medicine
    Stanford Center for Genomics and Personalized Medicine)

  • Hassan Chaib

    (Stanford University School of Medicine
    Stanford Center for Genomics and Personalized Medicine
    Stanford Diabetes Research Center)

  • Daniel Hornburg

    (Stanford University School of Medicine)

  • Jessilyn Dunn

    (Stanford University School of Medicine)

  • Liang Liang

    (Stanford University School of Medicine)

  • Sophia Miryam Schüssler-Fiorenza Rose

    (Veteran Affairs Palo Alto Health Care System
    Stanford University School of Medicine)

  • Kim Kukurba

    (Stanford University School of Medicine)

  • Brian Piening

    (Providence Cancer Center)

  • Hannes Rost

    (University of Toronto)

  • David Tse

    (Stanford University)

  • Tracey McLaughlin

    (Stanford University School of Medicine
    Stanford Diabetes Research Center)

  • Erica Sodergren

    (The Jackson Laboratory for Genomic Medicine)

  • George M. Weinstock

    (The Jackson Laboratory for Genomic Medicine)

  • Michael Snyder

    (Stanford University School of Medicine
    Stanford Center for Genomics and Personalized Medicine
    Stanford Diabetes Research Center)

Abstract

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

Suggested Citation

  • Wenyu Zhou & M. Reza Sailani & Kévin Contrepois & Yanjiao Zhou & Sara Ahadi & Shana R. Leopold & Martin J. Zhang & Varsha Rao & Monika Avina & Tejaswini Mishra & Jethro Johnson & Brittany Lee-McMullen, 2019. "Longitudinal multi-omics of host–microbe dynamics in prediabetes," Nature, Nature, vol. 569(7758), pages 663-671, May.
  • Handle: RePEc:nat:nature:v:569:y:2019:i:7758:d:10.1038_s41586-019-1236-x
    DOI: 10.1038/s41586-019-1236-x
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    Cited by:

    1. Suhas V. Vasaikar & Adam K. Savage & Qiuyu Gong & Elliott Swanson & Aarthi Talla & Cara Lord & Alexander T. Heubeck & Julian Reading & Lucas T. Graybuck & Paul Meijer & Troy R. Torgerson & Peter J. Sk, 2023. "A comprehensive platform for analyzing longitudinal multi-omics data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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