Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry

• DOI: 10.1128/msystems.00093-19
• Publication Date: 2019-09-09T12:35:46Z
• Authors: Peter Rubbens; Marian L. Schmidt; Ruben Props; Bopaiah A. Biddanda; Nico Boon; Willem Waegeman; Vincent J. Denef
• Keywords: PHYLOGENETIC SIGNAL; DIVERSITY; NUCLEIC-ACID-CONTENT; Microbiology; 03 medical and health sciences; RATES; 14. Life underwater; 16S rRNA; 0303 health sciences; bacterioplankton; flow cytometry; Biology and Life Sciences; HETEROTROPHIC BACTERIA; aquatic microbiology; 15. Life on land; QR1-502; 6. Clean water; SIZE; machine learning; 13. Climate action; CELLS; heterotrophic productivity; PROTEIN-SYNTHESIS; FEATURE-SELECTION; GROWTH; variable selection; Research Article

A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production.