Drinking water bacterial communities exhibit specific and selective necrotrophic growth

Physicochemical water disinfection methods result in the reduction of bacterial concentrations by orders of magnitude, but not in the total elimination of the bacterial community. As such, the dead bacterial biomass may act as a carbon and nutrient source for the survivor populations. The ability of bacterial strains to grow on dead bacterial cells has been described before as necrotrophy. We investigated the impact of killed bacterial biomass of two different bacterial strains on the growth potential of natural drinking water microbial communities. Many indigenous bacterial taxa could grow on dead biomass, with the total bacterial concentration increasing from 10(4) to 10(8) cells/ml. Necrotrophic growth was specific (43 enriched taxa) and selective (i.e. enriched taxa were dependent on the type of dead biomass). The potential of natural water communities to grow necrotrophically has remained underexplored. Nevertheless the phenomenon can have a big impact in water quality and deserves more attention

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities

Clustering environmental flow cytometry data by searching density peaks

Microbial single cells can be characterized by their phenotypic properties using flow cytometry. Therefore flow cytometry can be used to analyze various aspects of environmental microbial communities. In recent years, researchers have focused on fully exploiting the multivariate data that such analyses generate. As they are interested in the diversity of an environmental sample, we need a proper estimation of the number of species and their abundances. We modified a recently published algorithm to estimate the microbial diversity based on flow cytometry data. After giving a brief sketch of the problem setup, we will review this algorithm alongside its various implementations. Moreover we will present our current implementation combined with future challenges we foresee

A Clostridium group IV species dominates and suppresses a mixed culture fermentation by tolerance to medium chain fatty acids products

A microbial community is engaged in a complex economy of cooperation and competition for carbon and energy. In engineered systems such as anaerobic digestion and fermentation, these relationships are exploited for conversion of a broad range of substrates into products, such as biogas, ethanol, and carboxylic acids. Medium chain fatty acids (MCFAs), for example, hexanoic acid, are valuable, energy dense microbial fermentation products, however, MCFA tend to exhibit microbial toxicity to a broad range of microorganisms at low concentrations. Here, we operated continuous mixed population MCFA fermentations on biorefinery thin stillage to investigate the community response associated with the production and toxicity of MCFA. In this study, an uncultured species from the Clostridium group IV (related to Clostridium sp. BS-1) became enriched in two independent reactors that produced hexanoic acid (up to 8.1 g L−1), octanoic acid (up to 3.2 g L−1), and trace concentrations of decanoic acid. Decanoic acid is reported here for the first time as a possible product of a Clostridium group IV species. Other significant species in the community, Lactobacillus spp. and Acetobacterium sp., generate intermediates in MCFA production, and their collapse in relative abundance resulted in an overall production decrease. A strong correlation was present between the community composition and both the hexanoic acid concentration (p = 0.026) and total volatile fatty acid concentration (p = 0.003). MCFA suppressed species related to Clostridium sp. CPB-6 and Lactobacillus spp. to a greater extent than others. The proportion of the species related to Clostridium sp. BS-1 over Clostridium sp. CPB-6 had a strong correlation with the concentration of octanoic acid (p = 0.003). The dominance of this species and the increase in MCFA resulted in an overall toxic effect on the mixed community, most significantly on the Lactobacillus spp., which resulted in a decrease in total hexanoic acid concentration to 32 ± 2% below the steady-state average. As opposed to the current view of MCFA toxicity broadly leading to production collapse, this study demonstrates that varied tolerance to MCFA within the community can lead to the dominance of some species and the suppression of others, which can result in a decreased productivity of the fermentation

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

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 (R-2 = 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. IMPORTANCE 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

Reconciliation between operational taxonomic units and species boundaries

The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC

Plant and soil microbe responses to light, warming and nitrogen addition in a temperate forest

1. Temperate forests across Europe and eastern North America have become denser since the 1950s due to less intensive forest management and global environmental changes such as nitrogen deposition and climate warming. Denser tree canopies result in lower light availability at the forest floor. This shade may buffer the effects of nitrogen deposition and climate warming on understorey plant communities. 2. We conducted an innovative in situ field experiment to study the responses of co-occurring soil microbial and understorey plant communities to nitrogen addition, enhanced light availability and experimental warming in a full-factorial design. 3. We determined the effects of multiple environmental drivers and their interactions on the soil microbial and understorey plant communities, and assessed to what extent the soil microbial and understorey plant communities covary. 4. High light led to lower biomass of the soil microbes (analysed by phospholipid fatty acids), but the soil microbial structure, i.e. the ratio of fungal biomass to bacterial biomass, was not affected by light availability. The composition of the soil bacterial community (analysed by high-throughput sequencing) was affected by both light availability and warming (and their interaction), but not by nitrogen addition. Yet, the number of unique operational taxonomic units was higher in plots with nitrogen addition, and there were significant interactive effects of light and nitrogen addition. Light availability also determined the composition of the plant community; no effects of nitrogen addition and warming were observed. The soil bacterial and plant communities were co-structured, and light availability explained a large part of the variance of this co-structure. 5. We provide robust evidence for the key role of light in affecting both the soil microbial and plant communities in forest understoreys. Our results advocate for more multifactor global change experiments that investigate the mechanism underlying the (in) direct effects of light on the plant-soil continuum in forests

Gene expansion and positive selection as bacterial adaptations to oligotrophic conditions

We examined the genomic adaptations of prevalent bacterial taxa in a highly nutrient- and ion-depleted freshwater environment located in the secondary cooling water system of a nuclear research reactor. Using genome-centric metagenomics, we found that none of the prevalent bacterial taxa were related to typical freshwater bacterial lineages. We also did not identify strong signatures of genome streamlining, which has been shown to be one of the ecoevolutionary forces shaping the genome characteristics of bacterial taxa in nutrient-depleted environments. Instead, focusing on the dominant taxon, a novel Ramlibacter sp. which we propose to name Ramlibacter aquaticus, we detected extensive positive selection on genes involved in phosphorus and carbon scavenging pathways. These genes were involved in the high-affinity phosphate uptake and storage into polyphosphate granules, metabolism of nitrogen-rich organic matter, and carbon/energy storage into polyhydroxyalkanoate. In parallel, comparative genomics revealed a high number of paralogs and an accessory genome significantly enriched in environmental sensing pathways (i.e., chemotaxis and motility), suggesting extensive gene expansions in R. aquaticus. The type strain of R. aquaticus (LMG 30558(T)) displayed optimal growth kinetics and productivity at low nutrient concentrations, as well as substantial cell size plasticity. Our findings with R. aquaticus LMG 30558(T) demonstrate that positive selection and gene expansions may represent successful adaptive strategies to oligotrophic environments that preserve high growth rates and cellular productivity. IMPORTANCE By combining a genome-centric metagenomic approach with a culture-based approach, we investigated the genomic adaptations of prevalent populations in an engineered oligotrophic freshwater system. We found evidence for widespread positive selection on genes involved in phosphorus and carbon scavenging pathways and for gene expansions in motility and environmental sensing to be important genomic adaptations of the abundant taxon in this system. In addition, microscopic and flow cytometric analysis of the first freshwater representative of this population (Ramlibacter aquaticus LMG 30558(T)) demonstrated phenotypic plasticity, possibly due to the metabolic versatility granted by its larger genome, to be a strategy to cope with nutrient limitation. Our study clearly demonstrates the need for the use of a broad set of genomic tools combined with culture-based physiological characterization assays to investigate and validate genomic adaptations

Raman spectroscopy-based measurements of single-cell phenotypic diversity in microbial populations

Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This has been shown to affect community assembly and physiological processes (e.g., stress tolerance, virulence, or cellular metabolic activity). Metabolic stress is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species, or cell permeability. However, bulk community measurements do not take into account single-cell phenotypic diversity, which is important for a better understanding and the subsequent management of microbial populations. Raman spectroscopy is a nondestructive alternative that provides detailed information on the biochemical makeup of each individual cell. Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or nontreated and then in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein, and nucleic acid compositions changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial populations. IMPORTANCE Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction