- The relationship between biological processes and the seasons is not well-understood.
- Usually, seasonal patterns are identified using calendar dates.
- Researchers used deep longitudinal multiomics profiling to identify biological seasonal patterns on diverse molecular data.
- The study includes 105 individuals over the course of 4 years.
- Multiomics is a biological analysis approach that uses different data which include the genome, proteome, transcriptome, epigenome, metabolome, and microbiome.
- With multiomics, scientists can analyze complex biological data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology.
- Researchers report more than 1000 seasonal variations in omics analytes and clinical measures.
- Analyte is a chemical substance that is the subject of chemical analysis.
- The different molecules group into two major seasonal patterns.
- The two seasonal patterns 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.
- Researchers also identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals.
- The results suggest important implications in healthcare.
- This study highlights the value of considering seasonality when assessing population wide health risk and management.
Sailani, M.R., Metwally, A.A., Zhou, W. et al. Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California. Nat Commun 11, 4933 (2020). https://doi.org/10.1038/s41467-020-18758-1