Lignocellulose Fermentation Products Generated by Giant Panda Gut Microbiomes Depend Ultimately on pH Rather than Portion of Bamboo: A Preliminary Study.

Giant pandas feed almost exclusively on bamboo but miss lignocellulose-degrading genes. Their gut microbiome may contribute to their nutrition; however, the limited access to pandas makes experimentation difficult. In vitro incubation of dung samples is used to infer gut microbiome activity. In pandas, such tests indicated that green leaves are largely fermented to ethanol at neutral pH and yellow pith to lactate at acidic pH. Pandas may feed on either green leaves or yellow pith within the same day, and it is unclear how pH, dung sample, fermentation products and supplied bamboo relate to one another. Additionally, the gut microbiome contribution to solid bamboo digestion must be appropriately assessed. Here, gut microbiomes derived from dung samples with mixed colors were used to ferment green leaves, also by artificially adjusting the initial pH. Gut microbiomes digestion of solid lignocellulose accounted for 30-40% of the detected final fermentation products. At pH 6.5, mixed-color dung samples had the same fermentation profile as green dung samples (mainly alcohols), while adjusting the initial pH to 4.5 resulted in the profile of yellow dung samples (mainly lactate). Metaproteomics confirmed that gut microbiomes attacked hemicellulose, and that the panda's alpha amylase was the predominant enzyme (up to 75%).

Cyclical Patterns Affect Microbial Dynamics in the Water Basin of a Nuclear Research Reactor.

The BR2 nuclear research reactor in Mol, Belgium, runs in successive phases of operation (cycles) and shutdown, whereby a water basin surrounding the reactor vessel undergoes periodic changes in physico-chemical parameters such as flow rate, temperature, and radiation. The aim of this study was to explore the microbial community in this unique environment and to investigate its long-term dynamics using a 16S rRNA amplicon sequencing approach. Results from two sampling campaigns spanning several months showed a clear shift in community profiles: cycles were mostly dominated by two Operational Taxonomic Units (OTUs) assigned to unclassified Gammaproteobacterium and Pelomonas, whereas shutdowns were dominated by an OTU assigned to Methylobacterium. Although 1 year apart, both campaigns showed similar results, indicating that the system remained stable over this 2-year period. The community shifts were linked with changes in physico-chemical parameters by Non-metric Multidimensional Scaling (NMDS) and correlation analyses. In addition, radiation was hypothesized to cause a decrease in cell number, whereas temperature had the opposite effect. Chemoautotrophic use of H2 and dead cell recycling are proposed to be used as a strategies for nutrient retrieval in this extremely oligotrophic environment.

Predicting the Presence and Abundance of Bacterial Taxa in Environmental Communities through Flow Cytometric Fingerprinting.

Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average R2 of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods. IMPORTANCE Monitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. In recent years, flow cytometry-based measurements of community diversity have been shown to correlate well with those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting that there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundances of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated into routine monitoring schemes and early warning systems for biotechnological applications.

Triangulation of microbial fingerprinting in anaerobic digestion reveals consistent fingerprinting profiles.

The anaerobic digestion microbiome has been puzzling us since the dawn of molecular methods for mixed microbial community analysis. Monitoring of the anaerobic digestion microbiome can either take place via a non-targeted holistic evaluation of the microbial community through fingerprinting or by targeted monitoring of selected taxa. Here, we compared four different microbial community fingerprinting methods, i.e., amplicon sequencing, metaproteomics, metabolomics and cytomics, in their ability to characterise the full-scale anaerobic digestion microbiome. Cytometric fingerprinting through cytomics reflects a, for anaerobic digestion, novel, single cell-based approach of direct microbial community fingerprinting by flow cytometry. Three different digester types, i.e., sludge digesters, digesters treating agro-industrial waste and dry anaerobic digesters, each reflected different operational parameters. The α-diversity analysis yielded inconsistent results, especially for richness, across the different methods. In contrast, ß-diversity analysis resulted in comparable profiles, even when translated into phyla or functions, with clear separation of the three digester types. In-depth analysis of each method's features i.e., operational taxonomic units, metaproteins, metabolites, and cytometric traits, yielded certain similar features, yet, also some clear differences between the different methods, which was related to the complexity of the anaerobic digestion process. In conclusion, cytometric fingerprinting through flow cytometry is a reliable, fast method for holistic monitoring of the anaerobic digestion microbiome, and the complementary identification of key features through other methods could give rise to a direct interpretation of anaerobic digestion process performance.

PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.

Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

Computational Analysis of Microbial Flow Cytometry Data.

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Cytometric fingerprints of gut microbiota predict Crohn's disease state.

Variations in the gut microbiome have been associated with changes in health state such as Crohn's disease (CD). Most surveys characterize the microbiome through analysis of the 16S rRNA gene. An alternative technology that can be used is flow cytometry. In this report, we reanalyzed a disease cohort that has been characterized by both technologies. Changes in microbial community structure are reflected in both types of data. We demonstrate that cytometric fingerprints can be used as a diagnostic tool in order to classify samples according to CD state. These results highlight the potential of flow cytometry to perform rapid diagnostics of microbiome-associated diseases.

Rearing water microbiomes in white leg shrimp (Litopenaeus vannamei) larviculture assemble stochastically and are influenced by the microbiomes of live feed products.

The development of effective management strategies to reduce the occurrence of diseases in aquaculture is hampered by the limited knowledge on the microbial ecology of these systems. In this study, the dynamics and dominant community assembly processes in the rearing water of Litopenaeus vannamei larviculture tanks were determined. Additionally, the contribution of peripheral microbiomes, such as those of live and dry feeds, to the rearing water microbiome were quantified. The community assembly in the hatchery rearing water over time was dominated by stochasticity, which explains the observed heterogeneity between replicate cultivations. The community undergoes two shifts that match with the dynamics of the algal abundances in the rearing water. Source tracking analysis revealed that 37% of all bacteria in the hatchery rearing water were introduced either by the live or dry feeds, or during water exchanges. The contribution of the microbiome from the algae was the largest, followed by that of the Artemia, the exchange water and the dry feeds. Our findings provide fundamental knowledge on the assembly processes and dynamics of rearing water microbiomes and illustrate the crucial role of these peripheral microbiomes in maintaining health-promoting rearing water microbiomes.

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.

Substrate-Dependent Fermentation of Bamboo in Giant Panda Gut Microbiomes: Leaf Primarily to Ethanol and Pith to Lactate.

The giant panda is known worldwide for having successfully moved to a diet almost exclusively based on bamboo. Provided that no lignocellulose-degrading enzyme was detected in panda's genome, bamboo digestion is believed to depend on its gut microbiome. However, pandas retain the digestive system of a carnivore, with retention times of maximum 12 h. Cultivation of their unique gut microbiome under controlled laboratory conditions may be a valid tool to understand giant pandas' dietary habits, and provide valuable insights about what component of lignocellulose may be metabolized. Here, we collected gut microbiomes from fresh fecal samples of a giant panda (either entirely green or yellow stools) and supplied them with green leaves or yellow pith (i.e., the peeled stem). Microbial community composition was substrate dependent, and resulted in markedly different fermentation profiles, with yellow pith fermented to lactate and green leaves to lactate, acetate and ethanol, the latter to strikingly high concentrations (∼3%, v:v, within 3.5 h). Microbial metaproteins pointed to hemicellulose rather than cellulose degradation. The alpha-amylase from the giant panda (E.C. 3.2.1.1) was the predominant identified metaprotein, particularly in reactors inoculated with pellets derived from fecal samples (up to 60%). Gut microbiomes assemblage was most prominently impacted by the change in substrate (either leaf or pith). Removal of soluble organics from inocula to force lignocellulose degradation significantly enriched Bacteroides (in green leaf) and Escherichia/Shigella (in yellow pith). Overall, different substrates (either leaf or pith) markedly shaped gut microbiome assemblies and fermentation profiles. The biochemical profile of fermentation products may be an underestimated factor contributing to explain the peculiar dietary behavior of giant pandas, and should be implemented in large scale studies together with short-term lab-scale cultivation of gut microbiomes.

Temperature and Nutrient Levels Correspond with Lineage-Specific Microdiversification in the Ubiquitous and Abundant Freshwater Genus Limnohabitans.

Most freshwater bacterial communities are characterized by a few dominant taxa that are often ubiquitous across freshwater biomes worldwide. Our understanding of the genomic diversity within these taxonomic groups is limited to a subset of taxa. Here, we investigated the genomic diversity that enables Limnohabitans, a freshwater genus key in funneling carbon from primary producers to higher trophic levels, to achieve abundance and ubiquity. We reconstructed eight putative Limnohabitans metagenome-assembled genomes (MAGs) from stations located along broad environmental gradients existing in Lake Michigan, part of Earth's largest surface freshwater system. De novo strain inference analysis resolved a total of 23 strains from these MAGs, which strongly partitioned into two habitat-specific clusters with cooccurring strains from different lineages. The largest number of strains belonged to the abundant LimB lineage, for which robust in situ strain delineation had not previously been achieved. Our data show that temperature and nutrient levels may be important environmental parameters associated with microdiversification within the Limnohabitans genus. In addition, strains predominant in low- and high-phosphorus conditions had larger genomic divergence than strains abundant under different temperatures. Comparative genomics and gene expression analysis yielded evidence for the ability of LimB populations to exhibit cellular motility and chemotaxis, a phenotype not yet associated with available Limnohabitans isolates. Our findings broaden historical marker gene-based surveys of Limnohabitans microdiversification and provide in situ evidence of genome diversity and its functional implications across freshwater gradients.IMPORTANCELimnohabitans is an important bacterial taxonomic group for cycling carbon in freshwater ecosystems worldwide. Here, we examined the genomic diversity of different Limnohabitans lineages. We focused on the LimB lineage of this genus, which is globally distributed and often abundant, and its abundance has shown to be largely invariant to environmental change. Our data show that the LimB lineage is actually comprised of multiple cooccurring populations for which the composition and genomic characteristics are associated with variations in temperature and nutrient levels. The gene expression profiles of this lineage suggest the importance of chemotaxis and motility, traits that had not yet been associated with the Limnohabitans genus, in adapting to environmental conditions.

Short-term supplementation of celecoxib-shifted butyrate production on a simulated model of the gut microbial ecosystem and ameliorated in vitro inflammation.

Celecoxib has been effective in the prevention and treatment of chronic inflammatory disorders through inhibition of altered cyclooxygenase-2 (COX-2) pathways. Despite the benefits, continuous administration may increase risk of cardiovascular events. Understanding microbiome-drug-host interactions is fundamental for improving drug disposition and safety responses of colon-targeted formulations, but little information is available on the bidirectional interaction between individual microbiomes and celecoxib. Here, we conducted in vitro batch incubations of human faecal microbiota to obtain a mechanistic proof-of-concept of the short-term impact of celecoxib on activity and composition of colon bacterial communities. Celecoxib-exposed microbiota shifted metabolic activity and community composition, whereas total transcriptionally active bacterial population was not significantly changed. Butyrate production decreased by 50% in a donor-dependent manner, suggesting that celecoxib impacts in vitro fermentation. Microbiota-derived acetate has been associated with inhibition of cancer markers and our results suggest uptake of acetate for bacterial functions when celecoxib was supplied, which potentially favoured bacterial competition for acetyl-CoA. We further assessed whether colon microbiota modulates anti-inflammatory efficacy of celecoxib using a simplified inflammation model, and a novel in vitro simulation of the enterohepatic metabolism. Celecoxib was responsible for only 5% of the variance in bacterial community composition but celecoxib-exposed microbiota preserved barrier function and decreased concentrations of IL-8 and CXCL16 in a donor-dependent manner in our two models simulating gut inflammatory milieu. Our results suggest that celecoxib-microbiome-host interactions may not only elicit adaptations in community composition but also in microbiota functionality, and these may need to be considered for guaranteeing efficient COX-2 inhibition.

Microhabitats are associated with diversity-productivity relationships in freshwater bacterial communities.

Eukaryotic communities commonly display a positive relationship between biodiversity and ecosystem function (BEF) but the results have been mixed when assessed in bacterial communities. Habitat heterogeneity, a factor in eukaryotic BEFs, may explain these variable observations but it has not been thoroughly evaluated in bacterial communities. Here, we examined the impact of habitat on the relationship between diversity assessed based on the (phylogenetic) Hill diversity metrics and heterotrophic productivity. We sampled co-occurring free-living (more homogenous) and particle-associated (more heterogeneous) bacterial habitats in a freshwater, estuarine lake over three seasons: spring, summer and fall. There was a strong, positive, linear relationship between particle-associated bacterial richness and heterotrophic productivity that strengthened when considering dominant taxa. There were no observable BEF trends in free-living bacterial communities for any diversity metric. Biodiversity, richness and Inverse Simpson's index, were the best predictors of particle-associated production whereas pH was the best predictor of free-living production. Our findings show that heterotrophic productivity is positively correlated with the effective number of taxa and that BEF relationships are associated with microhabitats. These results add to the understanding of the highly distinct contributions to diversity and functioning contributed by bacteria in free-living and particle-associated habitats.

Adaptation and characterization of thermophilic anammox in bioreactors.

Anammox, the oxidation of ammonium with nitrite, is a key microbial process in the nitrogen cycle. Under mesophilic conditions (below 40 °C), it is widely implemented to remove nitrogen from wastewaters lacking organic carbon. Despite evidence of the presence of anammox bacteria in high-temperature environments, reports on the cultivation of thermophilic anammox bacteria are limited to a short-term experiment of 2 weeks. This study showcases the adaptation of a mesophilic inoculum to thermophilic conditions, and its characterization. First, an attached growth technology was chosen to obtain the process. In an anoxic fixed-bed biofilm bioreactor (FBBR), a slow linear temperature increase from 38 to over 48 °C (0.05-0.07 °C d-1) was imposed to the community over 220 days, after which the reactor was operated at 48 °C for over 200 days. Maximum total nitrogen removal rates reached up to 0.62 g N L-1 d-1. Given this promising performance, a suspended growth system was tested. The obtained enrichment culture served as inoculum for membrane bioreactors (MBR) operated at 50 °C, reaching a maximum total nitrogen removal rate of 1.7 g N L-1 d-1 after 35 days. The biomass in the MBR had a maximum specific anammox activity of 1.1 ± 0.1 g NH4+-N g-1 VSS d-1, and the growth rate was estimated at 0.075-0.19 d-1. The thermophilic cultures displayed nitrogen stoichiometry ratios typical for mesophilic anammox: 0.93-1.42 g NO2--Nremoved g-1 NH4+-Nremoved and 0.16-0.35 g NO3--Nproduced g-1 NH4+-Nremoved. Amplicon and Sanger sequencing of the 16S rRNA genes revealed a disappearance of the original "Ca. Brocadia" and "Ca. Jettenia" taxa, yielding Planctomycetes members with only 94-95% similarity to "Ca. Brocadia anammoxidans" and "Ca. B. caroliniensis", accounting for 45% of the bacterial FBBR community. The long-term operation of thermophilic anammox reactors and snapshot views on the nitrogen stoichiometry, kinetics and microbial community open up the development path of thermophilic partial nitritation/anammox. A first economic assessment highlighted that treatment of sludge reject water from thermophilic anaerobic digestion of sewage sludge may become attractive.

Discriminating Bacterial Phenotypes at the Population and Single-Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting.

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

Flow cytometry for rapid characterisation of microbial community dynamics in waste stabilisation ponds.

Algal and bacterial communities play a major role in the treatment performance and efficiency of waste stabilisation ponds (WSPs); however, the study of these WSP microbial communities has been challenging. Flow cytometry (FCM) has been used widely as a rapid, culture-independent method of characterising algae and/or bacteria in a range of freshwater and marine environments, and in conventional wastewater treatment processes, but its application to WSP wastewater has been underexplored. In this study, a method for the characterisation of both algal and bacterial microbial populations in WSP wastewater is presented and standardised, using cultures and field samples. We show that SYTO 16 dye is more effective than SYBR Green I for the concurrent detection of both algae and bacteria in samples. Through gating and phenotypic diversity analysis, the FCM results show both spatial and temporal shifts in pond microbial communities. The ability to rapidly determine the spatiotemporal shifts in pond populations is not only important for the improvement of pond operation and monitoring strategies, but also for the planning and management. Flow cytometry has the potential to become a diagnostic tool for ponds to assess treatment performance and determine the most optimal operating conditions.

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.

Propionate-Producing Consortium Restores Antibiotic-Induced Dysbiosis in a Dynamic in vitro Model of the Human Intestinal Microbial Ecosystem.

Metabolic syndrome is a growing public health concern. Efforts at searching for links with the gut microbiome have revealed that propionate is a major fermentation product in the gut with several health benefits toward energy homeostasis. For instance, propionate stimulates satiety-inducing hormones, leading to lower energy intake and reducing weight gain and associated risk factors. In (disease) scenarios where microbial dysbiosis is apparent, gut microbial production of propionate may be decreased. Here, we investigated the effect of a propionogenic bacterial consortium composed of Lactobacillus plantarum, Bacteroides thetaiotaomicron, Ruminococcus obeum, Coprococcus catus, Bacteroides vulgatus, Akkermansia muciniphila, and Veillonella parvula for its potential to restore in vitro propionate concentrations upon antibiotic-induced microbial dysbiosis. Using the mucosal simulator of the human intestinal microbial ecosystem (M-SHIME), we challenged the simulated colon microbiome with clindamycin. Addition of the propionogenic consortium resulted in successful colonization and subsequent restoration of propionate levels, while a positive effect on the mitochondrial membrane potential (ΔΨm) was observed in comparison with the controls. Our results support the development and application of next generation probiotics, which are composed of multiple bacterial strains with diverse functionality and phylogenetic background.

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 organization 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. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxon-dependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations.IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic 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.