Fast and flexible analysis of linked microbiome data with mako.

Mako is a software tool that converts microbiome data and networks into a graph database and visualizes query results, thus allowing users without programming knowledge to carry out network-based queries. Mako is accompanied by a database compiled from 60 microbiome studies that is easily extended with the user's own data. We illustrate mako's strengths by enumerating association partners linked to propionate production and comparing frequencies of different network motifs across habitat types.

The virota and its transkingdom interactions in the healthy infant gut.

SignificanceMicrobes colonizing the infant gut during the first year(s) of life play an important role in immune system development. We show that after birth the (nearly) sterile gut is rapidly colonized by bacteria and their viruses (phages), which often show a strong cooccurrence. Most viruses infecting the infant do not cause clinical signs and their numbers strongly increase after day-care entrance. The infant diet is clearly reflected by identification of plant-infecting viruses, whereas fungi and parasites are not part of a stable gut microbiota. These temporal high-resolution baseline data about the gut colonization process will be valuable for further investigations of pathogenic viruses, dynamics between phages and their bacterial host, as well as studies investigating infants with a disturbed microbiota.

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.

BACKGROUND: Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. RESULTS: Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. CONCLUSIONS: Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

Quantifying the impact of ecological memory on the dynamics of interacting communities.

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

Thermocaproicibacter melissae gen. nov., sp. nov., a thermophilic chain-elongating bacterium, producing n-caproate from polymeric carbohydrates.

Strain MDTJ8T is a chain-elongating thermophilic bacterium isolated from a thermophilic acidogenic anaerobic digestor treating human waste while producing the high commodity chemical n-caproate. The strain grows and produces formate, acetate, n-butyrate, n-caproate and lactate from mono-, di- and polymeric saccharides at 37-60 °C (optimum, 50-55 °C) and at pH 5.0-7.0 (optimum, pH 6.5). The organism is an obligate anaerobe, is motile and its cells form rods (0.3-0.5×1.0-3.0 µm) that stain Gram-positive and occur primarily as chains. Phylogenetic analysis of both the 16S rRNA gene and full genome sequence shows that strain MDTJ8T belongs to a group that consists of mesophylic chain-elongating bacteria within the family Oscillospiraceae, being nearest to Caproicibacter fermentans EA1T (94.8 %) and Caproiciproducens galactitolivorans BS-1T (93.7 %). Its genome (1.96 Mbp) with a G+C content of 49.6 mol% is remarkably smaller than those of other chain-elongating bacteria of the family Oscillospiraceae. Pairwise average nucleotide identity and DNA-DNA hybridization values between strain MDJT8T and its mesophilic family members are less than 70 and 35 %, respectively, while pairwise average amino acid identity values are less than 68 %. In addition, strain MDJT8T uses far less carbohydrate and non-carbohydrate substrates compared to its nearest family members. The predominant cellular fatty acids of strain MDTJ8T are C14 : 0, C14 : 0 DMA (dimethyl acetal) and C16 : 0, while its polar lipid profile shows three unidentified glycophospholipids, 11 glycolipids, 13 phospholipids and six unidentified lipids. No respiratory quinones and polyamines are detected. Based on its phylogenetic, genotypic, morphological, physiological, biochemical and chemotaxonomic characteristics, strain MDTJ8T represents a novel species and novel genus of the family Oscillospiraceae and Thermocaproicibacter melissae gen. nov., sp. nov. is proposed as its name. The type strain is MDTJ8T (=DSM 114174T=LMG 32615T=NCCB 100883T).

Deciphering Interactions Within a 4-Strain Riverine Bacterial Community.

The dynamics of a community of four planktonic bacterial strains isolated from river water was followed in R2 broth for 72 h in batch experiments. These strains were identified as Janthinobacterium sp., Brevundimonas sp., Flavobacterium sp. and Variovorax sp. 16S rRNA gene sequencing and flow cytometry analyses were combined to monitor the change in abundance of each individual strain in bi-cultures and quadri-culture. Two interaction networks were constructed that summarize the impact of the strains on each other's growth rate in exponential phase and carrying capacity in stationary phase. The networks agree on the absence of positive interactions but also show differences, implying that ecological interactions can be specific to particular growth phases. Janthinobacterium sp. was the fastest growing strain and dominated the co-cultures. However, its growth rate was negatively affected by the presence of other strains 10 to 100 times less abundant than Janthinobacterium sp. In general, we saw a positive correlation between growth rate and carrying capacity in this system. In addition, growth rate in monoculture was predictive of carrying capacity in co-culture. Taken together, our results highlight the necessity to take growth phases into account when measuring interactions within a microbial community. In addition, evidence that a minor strain can greatly influence the dynamics of a dominant one underlines the necessity to choose population models that do not assume a linear dependency of interaction strength to abundance of other species for accurate parameterization from such empirical data.

Regime transition Shapes the Composition, Assembly Processes, and Co-occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake.

At certain nutrient concentrations, shallow freshwater lakes are generally characterized by two contrasting ecological regimes with disparate patterns of biodiversity and biogeochemical cycles: a macrophyte-dominated regime (MDR) and a phytoplankton-dominated regime (PDR). To reveal ecological mechanisms that affect bacterioplankton along the regime shift, Illumina MiSeq sequencing of the 16S rRNA gene combined with a novel network clustering tool (Manta) were used to identify patterns of bacterioplankton community composition across the regime shift in Taihu Lake, China. Marked divergence in the composition and ecological assembly processes of bacterioplankton community was observed under the regime shift. The alpha diversity of the bacterioplankton community consistently and continuously decreased with the regime shift from MDR to PDR, while the beta diversity presents differently. Moreover, as the regime shifted from MDR to PDR, the contribution of deterministic processes (such as environmental selection) to the assembly of bacterioplankton community initially decreased and then increased again as regime shift from MDR to PDR, most likely as a consequence of differences in nutrient concentration. The topological properties, including modularity, transitivity and network diameter, of the bacterioplankton co-occurrence networks changed along the regime shift, and the co-occurrences among species changed in structure and were significantly shaped by the environmental variables along the regime transition from MDR to PDR. The divergent environmental state of the regimes with diverse nutritional status may be the most important factor that contributes to the dissimilarity of bacterioplankton community composition along the regime shift.

Consumer Safety Considerations of Skin and Oral Microbiome Perturbation.

Microbiomes associated with human skin and the oral cavity are uniquely exposed to personal care regimes. Changes in the composition and activities of the microbial communities in these environments can be utilized to promote consumer health benefits, for example, by reducing the numbers, composition, or activities of microbes implicated in conditions such as acne, axillary odor, dandruff, and oral diseases. It is, however, important to ensure that innovative approaches for microbiome manipulation do not unsafely disrupt the microbiome or compromise health, and where major changes in the composition or activities of the microbiome may occur, these require evaluation to ensure that critical biological functions are unaffected. This article is based on a 2-day workshop held at SEAC Unilever, Sharnbrook, United Kingdom, involving 31 specialists in microbial risk assessment, skin and oral microbiome research, microbial ecology, bioinformatics, mathematical modeling, and immunology. The first day focused on understanding the potential implications of skin and oral microbiome perturbation, while approaches to characterize those perturbations were discussed during the second day. This article discusses the factors that the panel recommends be considered for personal care products that target the microbiomes of the skin and the oral cavity.

The Dynamic of a River Model Bacterial Community in Two Different Media Reveals a Divergent Succession and an Enhanced Growth of Most Strains Compared to Monocultures.

The dynamic of a community of 20 bacterial strains isolated from river water was followed in R2 broth and in autoclaved river water medium for 27 days in batch experiments. At an early stage of incubation, a fast-growing specialist strain, Acinetobater sp., dominated the community in both media. Later on, the community composition in both media diverged but was highly reproducible across replicates. In R2, several strains previously reported to degrade multiple simple carbon sources prevailed. In autoclaved river water, the community was more even and became dominated by several strains growing faster or exclusively in that medium. Those strains have been reported in the literature to degrade complex compounds. Their growth rate in the community was 1.5- to 7-fold greater than that observed in monoculture. Furthermore, those strains developed simultaneously in the community. Together, our results suggest the existence of cooperative interactions within the community incubated in autoclaved river water.

Structure of association networks in food bacterial communities.

The structure of microbial association networks was investigated for seventeen studies on food bacterial communities using the CoNet app. The results were compared with those for host and environmental microbiomes. Microbial association networks of food bacterial communities shared several properties with those of host microbiomes, although they were less complex and lacked a scale-free, small world structure that is characteristic of environmental microbial communities. This may depend on both the initial contamination pattern, whose main source is the raw material microbiome, and on the copiotrophic nature of food environments, with lack of well defined, specific niches. The selective factors which are characteristic of fermentation and spoilage drastically simplified microbial association networks and showed the emergence of negative hubs. Co-presence and mutual exclusion networks had a radically different structure, with high clustering coefficient in the first and high heterogeneity in the latter. Node properties (degree, positive degree, betweenness centrality, abundance) can be combined in plots, which allow a rapid identification of hub species. The combined use of three network inference tools (CoNet, SparCC, and SPIEC-EASI) confirmed that microbial association network detection is method specific, but several coherent copresence or mutual exclusion relationships were detected by at least two different methods.

Fire modifies the phylogenetic structure of soil bacterial co-occurrence networks.

Fire alters ecosystems by changing the composition and community structure of soil microbes. The phylogenetic structure of a community provides clues about its main assembling mechanisms. While environmental filtering tends to reduce the community phylogenetic diversity by selecting for functionally (and hence phylogenetically) similar species, processes like competitive exclusion by limiting similarity tend to increase it by preventing the coexistence of functionally (and phylogenetically) similar species. We used co-occurrence networks to detect co-presence (bacteria that co-occur) or exclusion (bacteria that do not co-occur) links indicative of the ecological interactions structuring the community. We propose that inspecting the phylogenetic structure of co-presence or exclusion links allows to detect the main processes simultaneously assembling the community. We monitored a soil bacterial community after an experimental fire and found that fire altered its composition, richness and phylogenetic diversity. Both co-presence and exclusion links were more phylogenetically related than expected by chance. We interpret such a phylogenetic clustering in co-presence links as a result of environmental filtering, while that in exclusion links reflects competitive exclusion by limiting similarity. This suggests that environmental filtering and limiting similarity operate simultaneously to assemble soil bacterial communities, widening the traditional view that only environmental filtering structures bacterial communities.

A web application for sample size and power calculation in case-control microbiome studies.

UNLABELLED: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the 'HMP' R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power. AVAILABILITY AND IMPLEMENTATION: The web interface is written in R code using Shiny (RStudio Inc., 2016) and it is available at https://fedematt.shinyapps.io/shinyMB The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP CONTACT: Federico.Mattiello@UGent.be.

Species-sorting and mass-transfer paradigms control managed natural metacommunities.

A complex microbial system consisting of six different interconnected localities was thoroughly investigated at full scale for over a year. The metacommunity concept originating from macro-ecology was used to uncover mechanisms of community assembly by observing microbial interrelationships in and between the different localities via correlation and network analysis. The individual-based observation approach was applied using high-throughput microbial community cytometry in addition to next generation sequencing. We found robust α-diversity values for each of the six localities and high ß-diversity values despite directed connectivity between localities, classifying for endpoint assembly of organisms in each locality. Endpoint characteristics were based on subcommunities with high cell numbers whereas those with lower cell numbers were involved in dispersal. Perturbation caused abiotic parameters to alter local community assembly with especially the rare cells announcing community restructuration processes. The mass-effect paradigm as part of the metacommunity concept was identified by an increase in interlocality biotic correlations under perturbation which, however, did not unbalance the predominant species-sorting paradigm in the studied full scale metacommunity. Data as generated in this study might contribute to the development of individual-based models for controlling managed multispecies natural systems in future.

Identifying genomic and metabolic features that can underlie early successional and opportunistic lifestyles of human gut symbionts.

We lack a deep understanding of genetic and metabolic attributes specializing in microbial consortia for initial and subsequent waves of colonization of our body habitats. Here we show that phylogenetically interspersed bacteria in Clostridium cluster XIVa, an abundant group of bacteria in the adult human gut also known as the Clostridium coccoides or Eubacterium rectale group, contains species that have evolved distribution patterns consistent with either early successional or stable gut communities. The species that specialize to the infant gut are more likely to associate with systemic infections and can reach high abundances in individuals with Inflammatory Bowel Disease (IBD), indicating that a subset of the microbiota that have adapted to pioneer/opportunistic lifestyles may do well in both early development and with disease. We identified genes likely selected during adaptation to pioneer/opportunistic lifestyles as those for which early succession association and not phylogenetic relationships explain genomic abundance. These genes reveal potential mechanisms by which opportunistic gut bacteria tolerate osmotic and oxidative stress and potentially important aspects of their metabolism. These genes may not only be biomarkers of properties associated with adaptation to early succession and disturbance, but also leads for developing therapies aimed at promoting reestablishment of stable gut communities following physiologic or pathologic disturbances.

Soil microbiome responses to the short-term effects of Amazonian deforestation.

Slash-and-burn clearing of forest typically results in increase in soil nutrient availability. However, the impact of these nutrients on the soil microbiome is not known. Using next generation sequencing of 16S rRNA gene and shotgun metagenomic DNA, we compared the structure and the potential functions of bacterial community in forest soils to deforested soils in the Amazon region and related the differences to soil chemical factors. Deforestation decreased soil organic matter content and factors linked to soil acidity and raised soil pH, base saturation and exchangeable bases. Concomitant to expected changes in soil chemical factors, we observed an increase in the alpha diversity of the bacterial microbiota and relative abundances of putative copiotrophic bacteria such as Actinomycetales and a decrease in the relative abundances of bacterial taxa such as Chlamydiae, Planctomycetes and Verrucomicrobia in the deforested soils. We did not observe an increase in genes related to microbial nutrient metabolism in deforested soils. However, we did observe changes in community functions such as increases in DNA repair, protein processing, modification, degradation and folding functions, and these functions might reflect adaptation to changes in soil characteristics due to forest clear-cutting and burning. In addition, there were changes in the composition of the bacterial groups associated with metabolism-related functions. Co-occurrence microbial network analysis identified distinct phylogenetic patterns for forest and deforested soils and suggested relationships between Planctomycetes and aluminium content, and Actinobacteria and nitrogen sources in Amazon soils. The results support taxonomic and functional adaptations in the soil bacterial community following deforestation. We hypothesize that these microbial adaptations may serve as a buffer to drastic changes in soil fertility after slash-and-burning deforestation in the Amazon region.

Microbial co-occurrence relationships in the human microbiome.

The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.

Metabolic models of human gut microbiota: Advances and challenges.

The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.