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Randomized Lasso links microbial taxa with aquatic functional groups inferred from flow cytometry
Rubbens, P.; Schmidt, M.L.; Props, R.; Biddanda, B.A.; Boon, N.; Waegeman, W.; Denef, V.J. (2019). Randomized Lasso links microbial taxa with aquatic functional groups inferred from flow cytometry. mSystems 4(5): e00093-19. https://hdl.handle.net/10.1128/msystems.00093-19
In: mSystems. American Society for Microbiology: Washington, DC. ISSN 2379-5077, meer
Peer reviewed article  

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  • Rubbens, P., meer
  • Schmidt, M.L.
  • Props, R., meer
  • Biddanda, B.A.
  • Boon, N., meer
  • Waegeman, W., meer
  • Denef, V.J.

Abstract
    High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships.

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