Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California

M. Reza Sailani, Ahmed A. Metwally, Wenyu Zhou, Sophia Miryam Schüssler-Fiorenza Rose, Sara Ahadi, Kevin Contrepois, Tejaswini Mishra, Martin Jinye Zhang, Łukasz Kidziński, Theodore J. Chu and Michael P. Snyder ()
Additional contact information
M. Reza Sailani: Stanford University
Ahmed A. Metwally: Stanford University
Wenyu Zhou: Stanford University
Sophia Miryam Schüssler-Fiorenza Rose: Stanford University
Sara Ahadi: Stanford University
Kevin Contrepois: Stanford University
Tejaswini Mishra: Stanford University
Martin Jinye Zhang: Stanford University
Łukasz Kidziński: Stanford University
Theodore J. Chu: Division of Allergy and Immunology, Stanford University
Michael P. Snyder: Stanford University

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract The influence of seasons on biological processes is poorly understood. In order to identify biological seasonal patterns based on diverse molecular data, rather than calendar dates, we performed a deep longitudinal multiomics profiling of 105 individuals over 4 years. Here, we report more than 1000 seasonal variations in omics analytes and clinical measures. The different molecules group into two major seasonal patterns which correlate with peaks in late spring and late fall/early winter in California. The two patterns are enriched for molecules involved in human biological processes such as inflammation, immunity, cardiovascular health, as well as neurological and psychiatric conditions. Lastly, we identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals. The results of our study have important implications in healthcare and highlight the value of considering seasonality when assessing population wide health risk and management.

Date: 2020
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DOI: 10.1038/s41467-020-18758-1

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