Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Overview of the Microbiome Among Nurses study (Micro-N) as an example of prospective characterization of the microbiome within cohort studies

Nature Protocols volume 16, pages 2724–2731 (2021)Cite this article

Subjects

A Publisher Correction to this article was published on 13 May 2021

This article has been updated

Abstract

A lack of prospective studies has been a major barrier for assessing the role of the microbiome in human health and disease on a population-wide scale. To address this significant knowledge gap, we have launched a large-scale collection targeting fecal and oral microbiome specimens from 20,000 women within the Nurses’ Health Study II cohort (the Microbiome Among Nurses study, or Micro-N). Leveraging the rich epidemiologic data that have been repeatedly collected from this cohort since 1989; the established biorepository of archived blood, urine, buccal cell, and tumor tissue specimens; the available genetic and biomarker data; the cohort’s ongoing follow-up; and the BIOM-Mass microbiome research platform, Micro-N furnishes unparalleled resources for future prospective studies to interrogate the interplay between host, environmental factors, and the microbiome in human health. These prospectively collected materials will provide much-needed evidence to infer causality in microbiome-associated outcomes, paving the way toward development of microbiota-targeted modulators, preventives, diagnostics and therapeutics. Here, we describe a generalizable, scalable and cost-effective platform used for stool and oral microbiome specimen and metadata collection in the Micro-N study as an example of how prospective studies of the microbiome may be carried out.

Over the past decade, population-scale human microbiome studies have provided tremendous evidence linking microorganisms in the gut and body-wide with various conditions from gastroenterological and periodontal illnesses to cardiovascular, neoplastic, respiratory, and neurologic disorders1. However, the majority of human data are limited to cross-sectional studies, making it inherently challenging to differentiate cause from effect, and are prone to reverse causation. Therefore, it is crucial to establish a biobank of samples collected prior to the onset of disease from well-characterized young and middle-aged populations with long-term longitudinal follow-up2. Leveraging ongoing large cohort studies provides both a relatively economical option and an ideal setting for the development of such microbiome biobanks, owing to the established infrastructure for biospecimen collection and outcome ascertainment, deep characterization of relevant risk factors, and complementary archival biospecimens that enable future linkage with the microbiome data.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the Nurses’ Health Study II cohort.
Fig. 2: Workflow of the Micro-N project generalizable by the BIOM-Mass platform.

Change history

References

  1. The Integrative HMP (iHMP) Research Network Consortium. The integrative human microbiome project. Nature 569, 641–648 (2019).

  2. Sinha, R. et al. Next steps in studying the human microbiome and health in prospective studies, Bethesda, MD, May 16–17, 2017. Microbiome 6, 210 (2018).

    Article  Google Scholar 

  3. Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).

    Article  CAS  Google Scholar 

  4. Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017).

    Article  CAS  Google Scholar 

  5. Bao, Y. et al. Origin, methods, and evolution of the three Nurses’ Health Studies. Am. J. Public Health 106, 1573–1581 (2016).

    Article  Google Scholar 

  6. Ogino, S. et al. Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine. Gut 67, 1168–1180 (2018).

    Article  CAS  Google Scholar 

  7. Rice, M. S. et al. Breast cancer research in the Nurses’ Health Studies: exposures across the life course. Am. J. Public Health 106, 1592–1598 (2016).

    Article  Google Scholar 

  8. Townsend, M. K., Aschard, H., De Vivo, I., Michels, K. B. & Kraft, P. Genomics, telomere length, epigenetics, and metabolomics in the Nurses’ Health Studies. Am. J. Public Health 106, 1663–1668 (2016).

    Article  Google Scholar 

  9. Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol 3, 356–366 (2018).

    Article  CAS  Google Scholar 

  10. Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol 3, 347–355 (2018).

    Article  CAS  Google Scholar 

  11. Huang, T. et al. The Mind-Body Study: study design and reproducibility and interrelationships of psychosocial factors in the Nurses’ Health Study II. Cancer Causes Control 30, 779–790 (2019).

    Article  Google Scholar 

  12. Bartosch, S., Fite, A., Macfarlane, G. T. & McMurdo, M. E. Characterization of bacterial communities in feces from healthy elderly volunteers and hospitalized elderly patients by using real-time PCR and effects of antibiotic treatment on the fecal microbiota. Appl. Environ. Microbiol. 70, 3575–3581 (2004).

    Article  CAS  Google Scholar 

  13. Nagata, N. et al. Effects of bowel preparation on the human gut microbiome and metabolome. Sci. Rep. 9, 4042 (2019).

    Article  Google Scholar 

  14. Jalanka, J. et al. Effects of bowel cleansing on the intestinal microbiota. Gut 64, 1562–1568 (2015).

    Article  Google Scholar 

  15. Sinha, R. et al. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol. Biomark. Prev. 25, 407–416 (2016).

    Article  Google Scholar 

  16. Carroll, I. M., Ringel-Kulka, T., Siddle, J. P., Klaenhammer, T. R. & Ringel, Y. Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS ONE 7, e46953 (2012).

    Article  CAS  Google Scholar 

  17. Wu, G. D. et al. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 10, 206 (2010).

    Article  Google Scholar 

  18. Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. & Fierer, N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307, 80–86 (2010).

    Article  CAS  Google Scholar 

  19. Dominianni, C., Wu, J., Hayes, R. B. & Ahn, J. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiol. 14, 103 (2014).

    Article  Google Scholar 

  20. Choo, J. M., Leong, L. E. & Rogers, G. B. Sample storage conditions significantly influence faecal microbiome profiles. Sci. Rep. 5, 16350 (2015).

    Article  CAS  Google Scholar 

  21. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    Article  CAS  Google Scholar 

  22. Drew, D. A. et al. Fecal microbiome in epidemiologic studies—letter. Cancer Epidemiol. Biomark. Prev. 25, 869 (2016).

    Article  Google Scholar 

  23. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article  CAS  Google Scholar 

  24. Human Microbiome Project, C. A framework for human microbiome research. Nature 486, 215–221 (2012).

    Article  Google Scholar 

  25. Dong, T. S. & Gupta, A. Influence of early life, diet, and the environment on the microbiome. Clin. Gastroenterol. Hepatol. 17, 231–242 (2019).

    Article  Google Scholar 

  26. Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).

    Article  CAS  Google Scholar 

  27. Jensen, M. A., Ferretti, V., Grossman, R. L. & Staudt, L. M. The NCI Genomic Data Commons as an engine for precision medicine. Blood 130, 453–459 (2017).

    Article  CAS  Google Scholar 

  28. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  Google Scholar 

  29. Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).

    Article  CAS  Google Scholar 

  30. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    Article  CAS  Google Scholar 

  31. Ianiro, G., Tilg, H. & Gasbarrini, A. Antibiotics as deep modulators of gut microbiota: between good and evil. Gut 65, 1906–1915 (2016).

    Article  CAS  Google Scholar 

  32. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    Article  CAS  Google Scholar 

  33. Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    Article  Google Scholar 

  34. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    Article  CAS  Google Scholar 

  35. Fu, B. C. et al. Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience. Ann. Epidemiol. 26, 373–379 (2016).

    Article  Google Scholar 

  36. Rockett, H. R., Berkey, C. S., Field, A. E. & Colditz, G. A. Cross-sectional measurement of nutrient intake among adolescents in 1996. Prev. Med 33, 27–37 (2001).

    Article  CAS  Google Scholar 

  37. Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems https://doi.org/10.1128/mSystems.00021-16 (2016).

  38. Anderson, E. L. et al. A robust ambient temperature collection and stabilization strategy: Enabling worldwide functional studies of the human microbiome. Sci. Rep. 6, 31731 (2016).

    Article  CAS  Google Scholar 

  39. Luo, T. et al. Effects of specimen collection methodologies and storage conditions on the short-term stability of oral microbiome taxonomy. Appl. Environ. Microbiol. 82, 5519–5529 (2016).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Massachusetts Life Sciences Center (MLSC), the National Institutes of Health (U01 CA176726, R24 DK110499, R00 CA215314, R35 CA253185, R01 CA202704 and R01 CA243454) and the Harvard T. H. Chan School of Public Health. J.I. is supported by Nebraska Tobacco Settlement Biomedical Research Development Funds. We thank staff at the Harvard T. H. Chan School of Public Health for their assistance (A. Spickard, M. Sinunu and S. Branstrator) and the investigators of other cohort studies for their participation of the microbiome working group and contribution to the questionnaire development. These investigators include J. Ahn (New York University), B. Blot (Vanderbilt University), R. Burk (Albert Einstein College of Medicine), M. Hullar (Fred Hutchinson Cancer Research Center), R. Kaplan (Albert Einstein College of Medicine), J. Lampe (Fred Hutchinson Cancer Research Center), L. Le Marchand (University of Hawaiʻi), K. Meyer (University of North Carolina at Chapel Hill), Q. Qi (Albert Einstein College of Medicine), T. Randolph (Fred Hutchinson Cancer Research Center), H. Sesso (Harvard Medical School/Brigham and Women’s Hospital), M. Shrubsole (Vanderbilt University), R. Sinha (National Cancer Institute), E. Vogtmann (National Cancer Institute), L. Wilkens (University of Hawaiʻi) and W. Zheng (Vanderbilt University). We also thank the participants and staff of the Nurses’ Health Study II for their valuable contributions—in particular, B. Hall, A. Scott, S. Al-Shanniek and E. Cornacchio for their dedication to sampling processing and handling and M. Atkinson for his database programming.

Author information

Author notes

  1. These authors contributed equally: Christine Everett, Chengchen Li, Jeremy E. Wilkinson, Andrew T. Chan, Wendy S. Garrett, Curtis Huttenhower, Eric B. Rimm, Mingyang Song.

Authors and Affiliations

  1. Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Christine Everett, Chengchen Li, Jeremy E. Wilkinson, Long H. Nguyen, Lauren J. McIver, Natalia Palacios, A. Heather Eliassen, Walter C. Willett, Alberto Ascherio, Qi Sun, Andrew T. Chan, Wendy S. Garrett, Curtis Huttenhower, Eric B. Rimm & Mingyang Song

  2. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Christine Everett, A. Heather Eliassen, Walter C. Willett, Alberto Ascherio, Qi Sun, Andrew T. Chan & Eric B. Rimm

  3. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Chengchen Li, Jeremy E. Wilkinson, Lauren J. McIver & Curtis Huttenhower

  4. Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Long H. Nguyen, Andrew T. Chan & Mingyang Song

  5. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Long H. Nguyen, Andrew T. Chan & Mingyang Song

  6. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Kerry Ivey, Natalia Palacios, Walter C. Willett, Alberto Ascherio, Qi Sun, Eric B. Rimm & Mingyang Song

  7. South Australian Health and Medical Research Institute, Infection and Immunity Theme, School of Medicine, Flinders University, Adelaide, Australia

    Kerry Ivey

  8. Department of Nutrition and Dietetics, College of Nursing and Health Sciences, Flinders University, Adelaide, South Australia, Australia

    Kerry Ivey

  9. Food Science and Technology Department, Institute of Agriculture and Natural Resources, University of Nebraska, Lincoln, NE, USA

    Jacques Izard

  10. Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska, USA

    Jacques Izard

  11. Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, USA

    Natalia Palacios

  12. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    A. Heather Eliassen, Walter C. Willett, Alberto Ascherio, Shelley S. Tworoger, Eric B. Rimm & Mingyang Song

  13. Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA

    Shelley S. Tworoger

  14. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Andrew T. Chan, Wendy S. Garrett & Curtis Huttenhower

  15. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Andrew T. Chan, Wendy S. Garrett & Curtis Huttenhower

  16. Department of Molecular Metabolism, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Wendy S. Garrett

  17. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Wendy S. Garrett

  18. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Wendy S. Garrett

Authors
  1. Christine Everett
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengchen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeremy E. Wilkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Long H. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lauren J. McIver
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kerry Ivey
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jacques Izard
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Natalia Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. A. Heather Eliassen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Walter C. Willett
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alberto Ascherio
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Qi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Shelley S. Tworoger
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Andrew T. Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Wendy S. Garrett
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Curtis Huttenhower
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Eric B. Rimm
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mingyang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study concept and design: A.H.E., W.C.W., A.T.C., W.S.G., C.H., E.B.R., M.S. Acquisition of data: C.E., C.L., J.E.W., L.H.N., L.J.M., K.I., J.I., N.P., A.H.E., W.C.W., A.A., Q.S., S.S.T., A.T.C., W.S.G., C.H., E.B.R., M.S. Drafting of the manuscript: A.T.C., W.S.G., C.H., E.B.R., M.S. Critical revision of the manuscript for important intellectual content: C.E., C.L., J.E.W., L.H.N., L.J.M., K.I., J.I., N.P., A.H.E., W.C.W., A.A., Q.S., S.S.T., A.T.C., W.S.G., C.H., E.B.R., M.S. Funding acquisition: A.H.E., W.C.W., A.T.C., W.S.G., C.H., E.B.R. Administrative, technical, or material support: A.T.C., W.S.G., C.H., E.B.R., M.S. Study supervision: A.T.C., W.S.G., C.H., E.B.R., M.S.

Corresponding author

Correspondence to Mingyang Song.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Muriel Derrien and John Penders for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods 1, Table 1, Methods 2 and 3 and Discussion.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Everett, C., Li, C., Wilkinson, J.E. et al. Overview of the Microbiome Among Nurses study (Micro-N) as an example of prospective characterization of the microbiome within cohort studies. Nat Protoc 16, 2724–2731 (2021). https://doi.org/10.1038/s41596-021-00519-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00519-z

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research