Boon, N. | bioRxiv Science

bioRxiv2018

Coculturing bacteria leads to reduced phenotypic heterogeneities

Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organisation remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. We have demonstrated that interactions between these bacteria lead to an adjustment of their individual phenotypic diversities, and that this adjustment is conditional on the bacterial taxon.\n\nImportanceLaboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. As phenotypic heterogeneity is known to play an important role in pathogenicity and virulence, antibiotics resistance, biotechnological applications and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

microbiology↗

bioRxiv2018

Using machine learning to associate bacterial taxa with functional groups through flow cytometry, 16S rRNA gene sequencing, and productivity data

High-(HNA) and low-nucleic acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. HNA cell density often 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 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, with the highest performance at the OTU level. Selected OTUs ranged from low to high relative abundance and were mostly lake system-specific (89.5%-99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5%-33.3%) suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of systems-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. ImportanceA major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Research is limited by the ability to readily culture most bacteria present in the environment and the difference in bacterial physiology in situ compared to in laboratory culture. 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 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.

ecology↗