High-throughput amplicon sequencing has become a well-established approach for microbial community profiling. Correlating shifts in the relative abundances of bacterial taxa with environmental gradients is the goal of many microbiome surveys. As the abundances generated by this technology are semi-quantitative by definition, the observed dynamics may not accurately reflect those of the actual taxon densities. We combined the sequencing approach (16S rRNA gene) with robust single-cell enumeration technologies (flow cytometry) to quantify the absolute taxon abundances. A detailed longitudinal analysis of the absolute abundances resulted in distinct abundance profiles that were less ambiguous and expressed in units that can be directly compared across studies. We further provide evidence that the enrichment of taxa (increase in relative abundance) does not necessarily relate to the outgrowth of taxa (increase in absolute abundance). Our results highlight that both relative and absolute abundances should be considered for a comprehensive biological interpretation of microbiome surveys. The ISME Journal (2017) 11, 584-587; doi:10.1038/ismej.2016 published online 9 September 2016 Recent advancements in high-throughput sequencing of marker genes, such as the 16S rRNA gene, have provided microbial ecologists the tools to accurately infer the relative composition of microbial communities (Franzosa et al., 2015). This resulted in a widespread application of the technology in longitudinal studies where shifts in community structure are related to environmental variables and functional outputs (Faust et al., 2015;Wilhelm et al., 2015). An inherent limitation of the sequencing technology is that the calculated taxon abundances comprise relative values (Widder et al., 2016). Hence, caution must be taken with the biological interpretation of these values, since inter-sample differences in cell density are not considered. To our knowledge, there are no descriptive studies that assess the extent to which relative abundances deliver a skewed image of the actual microbial community dynamics. In this study, we combined robust cell density measurements from flow cytometry (Prest et al., 2013;Van Nevel et al., 2013) with the relative abundances derived from 16S rRNA gene amplicon sequencing. We performed two extensive longitudinal surveys on the central water reservoir of a cooling water system. This engineered freshwater ecosystem was subjected to highly controlled operational phases (Supplementary Information and data set). We quantified the absolute taxon abundances and assessed whether additional insights could be attained with the combined approach.Based on the sample-specified total cell density, the absolute taxon abundances were calculated for each time point. Individual taxon densities ranged from 0.5 to 1 679 cells per μl. Several inter-taxon differences became apparent by performing ordinary least squares regression analysis between the relative and absolute abundances. We focused on the three most abundant taxa, which ...

Summary Measuring the microbial diversity in natural and engineered environments is important for ecosystem characterization, ecosystem monitoring and hypothesis testing. Although the conventional assessment through single marker gene surveys has resulted in major advances, the complete procedure remains slow (i.e. weeks to months), labour‐intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest, in highly resolved, temporal surveys of microbial diversity, necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort. Here, we demonstrate that sensitive single‐cell measurements of phenotypic attributes, obtained via flow cytometry, can provide fast (i.e. within minutes) first‐line assessments of microbial diversity dynamics, without demanding extensive sample preparation and downstream data processing. We developed a data processing pipeline that fits bivariate kernel density functions to phenotypic parameter combinations of an entire microbial community and concatenates them to a single one‐dimensional phenotypic fingerprint. By calculating established diversity metrics from such phenotypic fingerprints, we construct an alternative interpretation of the microbial diversity that incorporates distinct phenotypic traits underlying cell‐to‐cell heterogeneity (i.e. morphology and nucleic acid content). Based on a detailed longitudinal study of a highly dynamic microbial ecosystem, our approach delivered temporal alpha diversity profiles that strongly correlated with the reference diversity, as estimated by 16S rRNA amplicon sequencing. This strongly suggests that the distribution of a limited amount of phenotypic features within a microbial community already provides sufficient resolving power for the measurement of diversity dynamics at the species level. We present a fast, robust analysis method for monitoring the microbial biodiversity of natural and engineered ecosystems that correlates well with the conventional marker gene surveys. Our work has both applied and fundamental implications that stretch from ecosystem monitoring and studies on microbial community dynamics, to supervised sampling strategies. Furthermore, our approach offers perspectives for the development of online and in situ monitoring systems for microbial ecosystems.

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 (ΔΨ m ) 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.

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.

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

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.

Disc shaped microbial aggregates enriched in Ruminococcaceae and lactic acid bacteria produced caproic acid from solid-free thin stillage.

BackgroundThe microbial community in anaerobic digestion is mainly monitored by means of DNA-based methods. This may lead to incorrect interpretation of the community parameters, because microbial abundance does not necessarily reflect activity. In this research, the difference between microbial community response on DNA (total community) and RNA (active community) based on the 16S rRNA (gene) with respect to salt concentration and response time was evaluated.ResultsThe application of higher NaCl concentrations resulted in a decrease in methane production. A stronger and faster response to salt concentration was observed on RNA level. This was reflected in terms of microbial community composition and organization, as richness, evenness, and overall diversity were differentially impacted. A higher divergence of community structure was observed on RNA level as well, indicating that total community composition depends on deterministic processes, while the active community is determined by stochastic processes. Methanosaeta was identified as the most abundant methanogen on DNA level, but its relative abundance decreased on RNA level, related to salt perturbation.ConclusionsThis research demonstrated the need for RNA-based community screening to obtain reliable information on actual community parameters and to identify key species that determine process stability.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0652-5) contains supplementary material, which is available to authorized users.