Microbial interactions: from networks to models

Disentangling Interactions in the Microbiome: A Network Perspective

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

Systems-level metabolism of the altered Schaedler flora, a complete gut microbiota

The altered Schaedler flora (ASF) is a model microbial community with both in vivo and in vitro relevance. Here we provide the first characterization of the ASF community in vitro, independent of a murine host. We compared the functional genetic content of the ASF to wild murine metagenomes and found that the ASF functionally represents wild microbiomes better than random consortia of similar taxonomic composition. We developed a chemically defined medium that supported growth of seven of the eight ASF members. To elucidate the metabolic capabilities of these ASF species-including potential for interactions such as cross-feeding-we performed a spent media screen and analyzed the results through dynamic growth measurements and non-targeted metabolic profiling. We found that cross-feeding is relatively rare (32 of 3570 possible cases), but is enriched between Clostridium ASF356 and Parabacteroides ASF519. We identified many cases of emergent metabolism (856 of 3570 possible cases). These data will inform efforts to understand ASF dynamics and spatial distribution in vivo, to design pre- and probiotics that modulate relative abundances of ASF members, and will be essential for validating computational models of ASF metabolism. Well-characterized, experimentally tractable microbial communities enable research that can translate into more effective microbiome-targeted therapies to improve human health.

Natural products as mediators of disease

Covering: up to 2016Humans are walking microbial ecosystems, each harboring a complex microbiome with the genetic potential to produce a vast array of natural products. Recent sequencing data suggest that our microbial inhabitants are critical for maintaining overall health. Shifts in microbial communities have been correlated to a number of diseases including infections, inflammation, cancer, and neurological disorders. Some of these clinically and diagnostically relevant phenotypes are a result of the presence of small molecules, yet we know remarkably little about their contributions to the health of individuals. Here, we review microbe-derived natural products as mediators of human disease.

On the pathogenesis of insulin-dependent diabetes mellitus: the role of microbiota

Type 1 diabetes (T1D) is an autoimmune disorder characterized by the selective destruction of insulin-producing β cells as result of a complex interplay between genetic, stochastic and environmental factors in genetically susceptible individuals. An increasing amount of experimental data from animal models and humans has supported the role played by imbalanced gut microbiome in T1D pathogenesis. The commensal intestinal microbiota is fundamental for several physiologic mechanisms, including the establishment of immune homeostasis. Alterations in its composition have been correlated to changes in the gut immune system, including defective tolerance to food antigens, intestinal inflammation and enhanced gut permeability. Early findings reported differences in the intestinal microbiome of subjects affected by prediabetes or overt disease compared to healthy individuals. The present review focuses on microbiota-host homeostasis, its alterations, factors that influence microbiome composition and discusses their putative correlation with T1D development. Further studies are necessary to clarify the role played by microbiota modifications in the processes that cause enhanced permeability and the autoimmune mechanisms responsible for T1D onset.

Monitoring bacterial indicators of water quality in a tidally influenced delta: A Sisyphean pursuit

The Sacramento-San Joaquin Delta Estuary (Delta) is the confluence of two major watersheds draining the Western Sierra Nevada mountains into the Central Valley of California, ultimately terminating into San Francisco Bay. We sampled 88 sites once a month for two years (2006-2008) over 87 separate sampling events for a total of 1740 samples. Water samples were analyzed for fecal indicator bacteria (Escherichia coli, enterococci and fecal coliforms), and 53 other physiochemical, land use, and environmental characteristics. The purpose of the study was to create a baseline of microbial water quality in the Delta and to identify various factors (climatic, land use, tidal, etc.) that were associated with elevated concentrations of indicator bacteria. Fecal indicator bacteria generally had weak to modest relationships to environmental conditions; the strength and direction of which varied for each microbial indicator, drainage region, and across seasons. Measured and unmeasured, site-specific effects accounted for large portions of variance in model predictions (ρ=0.086 to 0.255), indicating that spatial autocorrelation was a major component of water quality outcomes. The effects of tidal cycling and lack of connectivity between waterways and surrounding landscapes likely contributed to the lack of association between local land uses and microbial outcomes, though weak associations may also be indicative of mismatched spatiotemporal scales. The complex nature of this system necessitates continued monitoring and regular updates to statistical models designed to predict microbial water quality.

Consistent metagenomic biomarker detection via robust PCA

BACKGROUND

Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms.

RESULTS

We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance.

CONCLUSIONS

The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations.

REVIEWERS

This article was reviewed by Masanori Arita and Zoltan Gaspari.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

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.

Virus-host interactions and their roles in coral reef health and disease

Coral reefs occur in nutrient-poor shallow waters, constitute biodiversity and productivity hotspots, and are threatened by anthropogenic disturbance. This Review provides an introduction to coral reef virology and emphasizes the links between viruses, coral mortality and reef ecosystem decline. We describe the distinctive benthic-associated and water-column- associated viromes that are unique to coral reefs, which have received less attention than viruses in open-ocean systems. We hypothesize that viruses of bacteria and eukaryotes dynamically interact with their hosts in the water column and with scleractinian (stony) corals to influence microbial community dynamics, coral bleaching and disease, and reef biogeochemical cycling. Last, we outline how marine viruses are an integral part of the reef system and suggest that the influence of viruses on reef function is an essential component of these globally important environments.

Balance Trees Reveal Microbial Niche Differentiation

Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss. Author Video: An author video summary of this article is available.

Impact of polymicrobial biofilms in catheter-associated urinary tract infections

Recent reports have demonstrated that most biofilms involved in catheter-associated urinary tract infections are polymicrobial communities, with pathogenic microorganisms (e.g. Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) and uncommon microorganisms (e.g. Delftia tsuruhatensis, Achromobacter xylosoxidans) frequently co-inhabiting the same urinary catheter. However, little is known about the interactions that occur between different microorganisms and how they impact biofilm formation and infection outcome. This lack of knowledge affects CAUTIs management as uncommon bacteria action can, for instance, influence the rate at which pathogens adhere and grow, as well as affect the overall biofilm resistance to antibiotics. Another relevant aspect is the understanding of factors that drive a single pathogenic bacterium to become prevalent in a polymicrobial community and subsequently cause infection. In this review, a general overview about the IMDs-associated biofilm infections is provided, with an emphasis on the pathophysiology and the microbiome composition of CAUTIs. Based on the available literature, it is clear that more research about the microbiome interaction, mechanisms of biofilm formation and of antimicrobial tolerance of the polymicrobial consortium are required to better understand and treat these infections.

Toward Accurate and Quantitative Comparative Metagenomics

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.

Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotroph MDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes.

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

Disentangling the influence of earthworms in sugarcane rhizosphere

For the last 150 years many studies have shown the importance of earthworms for plant growth, but the exact mechanisms involved in the process are still poorly understood. Many important functions required for plant growth can be performed by soil microbes in the rhizosphere. To investigate earthworm influence on the rhizosphere microbial community, we performed a macrocosm experiment with and without Pontoscolex corethrurus (EW+ and EW−, respectively) and followed various soil and rhizosphere processes for 217 days with sugarcane. In EW+ treatments, N₂O concentrations belowground (15 cm depth) and relative abundances of nitrous oxide genes (nosZ) were higher in bulk soil and rhizosphere, suggesting that soil microbes were able to consume earthworm-induced N₂O. Shotgun sequencing (total DNA) revealed that around 70 microbial functions in bulk soil and rhizosphere differed between EW+ and EW− treatments. Overall, genes indicative of biosynthetic pathways and cell proliferation processes were enriched in EW+ treatments, suggesting a positive influence of worms. In EW+ rhizosphere, functions associated with plant-microbe symbiosis were enriched relative to EW− rhizosphere. Ecological networks inferred from the datasets revealed decreased niche diversification and increased keystone functions as an earthworm-derived effect. Plant biomass was improved in EW+ and worm population proliferated.

Spectral consensus strategy for accurate reconstruction of large biological networks

BACKGROUND

The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches.

RESULTS

We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem.

CONCLUSIONS

The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.

A family of interaction-adjusted indices of community similarity

Interactions between taxa are essential drivers of ecological community structure and dynamics, but they are not taken into account by traditional indices of β diversity. In this study, we propose a novel family of indices that quantify community similarity in the context of taxa interaction networks. Using publicly available datasets, we assessed the performance of two specific indices that are Taxa INteraction-Adjusted (TINA, based on taxa co-occurrence networks), and Phylogenetic INteraction-Adjusted (PINA, based on phylogenetic similarities). TINA and PINA outperformed traditional indices when partitioning human-associated microbial communities according to habitat, even for extremely downsampled datasets, and when organising ocean micro-eukaryotic plankton diversity according to geographical and physicochemical gradients. We argue that interaction-adjusted indices capture novel aspects of diversity outside the scope of traditional approaches, highlighting the biological significance of ecological association networks in the interpretation of community similarity.

Effect of Formula Containing Lactobacillus reuteri DSM 17938 on Fecal Microbiota of Infants Born by Cesarean-Section

OBJECTIVES

Microbiota modulation by probiotics in infants born by cesarean (C)-section is poorly understood. We aimed at assessing the response of C-section-delivered infant microbiota to a formula containing Lactobacillus reuteri Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSM) 17938 and comparing it with that of vaginally delivered infants.

METHODS

Infants delivered by C-section (C) and vaginally (V) were randomized to receive either control formula (CCt, n = 10; VCt, n = 10) or the same formula containing L reuteri (CLr, n = 11; VLr, n = 9) within 72 hours following birth. Stool samples were collected at 2 weeks and 4 months of age. Microbial DNA was extracted, amplified, and pyrosequenced.

RESULTS

The phylogenetic profiles of the CLr, VCt, and VLr microbiota were not significantly different at any age but diverged from that of CCt at 2 weeks. Compared with VCt, CCt displayed lower Bifidobacterium and higher Enterobacter, unclassified Enterobacteriaceae, Enterococcus, Clostridium, and unclassified Clostridiaceae relative abundance at 2 weeks, as well as lower Collinsella and higher Enterococcus and Coprococcus abundance at 4 months. The level of most of these taxa was not significantly different between the CLr and the vaginal-delivery groups. Compared with VCt, the only difference observed in VLr microbiota was higher Lactobacillus at the 2 study ages and Coprococcus at 4 months.

CONCLUSIONS

Our results show that a formula containing L reuteri DSM 17938 does not essentially alter the microbiota in vaginally born infants. In C-section-delivered infants, however, this strain seems to play the role of keystone species by modulating the early development of the microbiota toward the composition found after vaginal delivery.

Using network analysis to discern compositional patterns in ultrahigh-resolution mass spectrometry data of dissolved organic matter

RATIONALE

Marine dissolved organic matter (DOM) has long been recognized as a large and dynamic component of the global carbon cycle. Yet, DOM is chemically varied and complex and these attributes present challenges to researchers interested in addressing questions about the role of DOM in global biogeochemical cycles.

METHODS

Organic matter extracts from seawater were analyzed by direct infusion with electrospray ionization into a Fourier transform ion cyclotron resonance mass spectrometer. Network analysis was used to quantify the number of chemical transformations between mass-to-charge values in each sample. The network of chemical transformations was calculated using the MetaNetter plug-in within Cytoscape. The chemical transformations serve as markers for the shared structural characteristics of compounds within complex DOM.

RESULTS

Network analysis revealed that transformations involving selected sulfur-containing moieties and isomers of amino acids were more prevalent in the deep sea than in the surface ocean. Common chemical transformations were not significantly different between the deep sea and surface ocean. Network analysis complements existing computational tools used to analyze ultrahigh-resolution mass spectrometry data.

CONCLUSIONS

This combination of ultrahigh-resolution mass spectrometry with novel computational tools has identified new potential building blocks of organic compounds in the deep sea, including the unexpected importance of dissolved organic sulfur components. The method described here can be readily applied by researchers to analyze heterogeneous and complex DOM. Copyright © 2016 John Wiley & Sons, Ltd.

The Interaction between Dietary Fiber and Fat and Risk of Colorectal Cancer in the Women's Health Initiative

Combined intakes of specific dietary fiber and fat subtypes protect against colon cancer in animal models. We evaluated associations between self-reported individual and combinations of fiber (insoluble, soluble, and pectins, specifically) and fat (omega-6, omega-3, and docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), specifically) and colorectal cancer (CRC) risk in the Women's Health Initiative prospective cohort (n = 134,017). During a mean 11.7 years (1993-2010), 1952 incident CRC cases were identified. Cox regression models computed multivariate adjusted hazard ratios to estimate the association between dietary factors and CRC risk. Assessing fiber and fat individually, there was a modest trend for lower CRC risk with increasing intakes of total and insoluble fiber (p-trend 0.09 and 0.08). An interaction (p = 0.01) was observed between soluble fiber and DHA + EPA, with protective effects of DHA + EPA with lower intakes of soluble fiber and an attenuation at higher intakes, however this association was no longer significant after correction for multiple testing. These results suggest a modest protective effect of higher fiber intake on CRC risk, but not in combination with dietary fat subtypes. Given the robust results in preclinical models and mixed results in observational studies, controlled dietary interventions with standardized intakes are needed to better understand the interaction of specific fat and fiber subtypes on colon biology and ultimately CRC susceptibility in humans.

Hydrocarbon degraders establish at the costs of microbial richness, abundance and keystone taxa after crude oil contamination in permafrost environments

Oil spills from pipeline ruptures are a major source of terrestrial petroleum pollution in cold regions. However, our knowledge of the bacterial response to crude oil contamination in cold regions remains to be further expanded, especially in terms of community shifts and potential development of hydrocarbon degraders. In this study we investigated changes of microbial diversity, population size and keystone taxa in permafrost soils at four different sites along the China-Russia crude oil pipeline prior to and after perturbation with crude oil. We found that crude oil caused a decrease of cell numbers together with a reduction of the species richness and shifts in the dominant phylotypes, while bacterial community diversity was highly site-specific after exposure to crude oil, reflecting different environmental conditions. Keystone taxa that strongly co-occurred were found to form networks based on trophic interactions, that is co-metabolism regarding degradation of hydrocarbons (in contaminated samples) or syntrophic carbon cycling (in uncontaminated samples). With this study we demonstrate that after severe crude oil contamination a rapid establishment of endemic hydrocarbon degrading communities takes place under favorable temperature conditions. Therefore, both endemism and trophic correlations of bacterial degraders need to be considered in order to develop effective cleanup strategies.

MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles

Background

The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data.

Results

In this paper, we present the Lotka-Volterra model based tool, the Metagenomic Microbial Interacticon Simulator (MetaMIS), which is designed to analyze the time series data of microbial community profiles. MetaMIS first infers underlying microbial interactions from abundance tables for operational taxonomic units (OTUs) and then interprets interaction networks using the Lotka-Volterra model. We also embed a Bray-Curtis dissimilarity method in MetaMIS in order to evaluate the similarity to biological reality. MetaMIS is designed to tolerate a high level of missing data, and can estimate interaction information without the influence of rare microbes. For each interaction network, MetaMIS systematically examines interaction patterns (such as mutualism or competition) and refines the biotic role within microbes. As a case study, we collect a human male fecal microbiome and show that Micrococcaceae, a relatively low abundance OTU, is highly connected with 13 dominant OTUs and seems to play a critical role. MetaMIS is able to organize multiple interaction networks into a consensus network for comparative studies; thus we as a case study have also identified a consensus interaction network between female and male fecal microbiomes.

Conclusions

MetaMIS provides an efficient and user-friendly platform that may reveal new insights into metagenomics data. MetaMIS is freely available at: https://sourceforge.net/projects/metamis/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1359-0) contains supplementary material, which is available to authorized users.

Networks Depicting the Fine-Scale Co-Occurrences of Fungi in Soil Horizons

Fungi in soil play pivotal roles in nutrient cycling, pest controls, and plant community succession in terrestrial ecosystems. Despite the ecosystem functions provided by soil fungi, our knowledge of the assembly processes of belowground fungi has been limited. In particular, we still have limited knowledge of how diverse functional groups of fungi interact with each other in facilitative and competitive ways in soil. Based on the high-throughput sequencing data of fungi in a cool-temperate forest in northern Japan, we analyzed how taxonomically and functionally diverse fungi showed correlated fine-scale distributions in soil. By uncovering pairs of fungi that frequently co-occurred in the same soil samples, networks depicting fine-scale co-occurrences of fungi were inferred at the O (organic matter) and A (surface soil) horizons. The results then led to the working hypothesis that mycorrhizal, endophytic, saprotrophic, and pathogenic fungi could form compartmentalized (modular) networks of facilitative, antagonistic, and/or competitive interactions in belowground ecosystems. Overall, this study provides a research basis for further understanding how interspecific interactions, along with sharing of niches among fungi, drive the dynamics of poorly explored biospheres in soil.

Spatiotemporal dynamics and correlation networks of bacterial and fungal communities in a membrane bioreactor

To systematically study biofilm communities responsible for biofouling in membrane bioreactors (MBRs), we characterized the spatiotemporal dynamics of bacterial and fungal biofilm communities, and their networks, in a pilot-scale flat-sheet MBR treating actual municipal wastewater. Activated sludge (AS) and membrane samples were collected on days 4 and 8. The membranes were cut into 18 tiles, and bacterial and fungal communities were analyzed using next generation sequencing. Nonmetric multidimensional scaling (NMDS) plots revealed significant temporal variations in bacterial and fungal biofilm communities due to changes in the abundances of a few dominant members. Although the experimental conditions and inoculum species pools remained constant, variogram plots of bacterial and fungal communities revealed decay in local community similarity with geographic distance at each sampling time. Variogram modeling (exponential rise to maximum, R² ≥ 0.79) revealed that decay patterns of both communities were different between days 4 and 8. In addition, networks of bacteria or fungi alone were distinct in network composition between days 4 and 8. The day-8 networks were more compact and clustered than those of the earlier time point. Bacteria-fungi networks show that the number of inter-domain associations decreased from 113 to 40 with time, confirming that membrane biofilm is a complex consortium of bacteria and fungi. Spatiotemporal succession in biofilm communities may be common on MBR membranes, resulting from different geographic distributions of initial microbial populations and their priority effects.

Metagenomics: Probing pollutant fate in natural and engineered ecosystems

Polluted environments are a reservoir of microbial species able to degrade or to convert pollutants to harmless compounds. The proper management of microbial resources requires a comprehensive characterization of their genetic pool to assess the fate of contaminants and increase the efficiency of bioremediation processes. Metagenomics offers appropriate tools to describe microbial communities in their whole complexity without lab-based cultivation of individual strains. After a decade of use of metagenomics to study microbiomes, the scientific community has made significant progress in this field. In this review, we survey the main steps of metagenomics applied to environments contaminated with organic compounds or heavy metals. We emphasize technical solutions proposed to overcome encountered obstacles. We then compare two metagenomic approaches, i.e. library-based targeted metagenomics and direct sequencing of metagenomes. In the former, environmental DNA is cloned inside a host, and then clones of interest are selected based on (i) their expression of biodegradative functions or (ii) sequence homology with probes and primers designed from relevant, already known sequences. The highest score for the discovery of novel genes and degradation pathways has been achieved so far by functional screening of large clone libraries. On the other hand, direct sequencing of metagenomes without a cloning step has been more often applied to polluted environments for characterization of the taxonomic and functional composition of microbial communities and their dynamics. In this case, the analysis has focused on 16S rRNA genes and marker genes of biodegradation. Advances in next generation sequencing and in bioinformatic analysis of sequencing data have opened up new opportunities for assessing the potential of biodegradation by microbes, but annotation of collected genes is still hampered by a limited number of available reference sequences in databases. Although metagenomics is still facing technical and computational challenges, our review of the recent literature highlights its value as an aid to efficiently monitor the clean-up of contaminated environments and develop successful strategies to mitigate the impact of pollutants on ecosystems.

Contrasting Ecological Processes and Functional Compositions Between Intestinal Bacterial Community in Healthy and Diseased Shrimp

Intestinal bacterial communities play a pivotal role in promoting host health; therefore, the disruption of intestinal bacterial homeostasis could result in disease. However, the effect of the occurrences of disease on intestinal bacterial community assembly remains unclear. To address this gap, we compared the multifaceted ecological differences in maintaining intestinal bacterial community assembly between healthy and diseased shrimps. The neutral model analysis shows that the relative importance of neutral processes decreases when disease occurs. This pattern is further corroborated by the ecosphere null model, revealing that the bacterial community assembly of diseased samples is dominated by stochastic processes. In addition, the occurrence of shrimp disease reduces the complexity and cooperative activities of species-to-species interactions. The keystone taxa affiliated with Alphaproteobacteria and Actinobacteria in healthy shrimp gut shift to Gammaproteobacteria species in diseased shrimp. Changes in intestinal bacterial communities significantly alter biological functions in shrimp. Within a given metabolic pathway, the pattern of enrichment or decrease between healthy and deceased shrimp is correlated with its functional effects. We propose that stressed shrimp are more prone to invasion by alien strains (evidenced by more stochastic assembly and higher migration rate in diseased shrimp), which, in turn, disrupts the cooperative activity among resident species. These findings greatly aid our understanding of the underlying mechanisms that govern shrimp intestinal community assembly between health statuses.

Deciphering the Pathobiome: Intra- and Interkingdom Interactions Involving the Pathogen Erysiphe alphitoides

Plant-inhabiting microorganisms interact directly with each other, forming complex microbial interaction networks. These interactions can either prevent or facilitate the establishment of new microbial species, such as a pathogen infecting the plant. Here, our aim was to identify the most likely interactions between Erysiphe alphitoides, the causal agent of oak powdery mildew, and other foliar microorganisms of pedunculate oak (Quercus robur L.). We combined metabarcoding techniques and a Bayesian method of network inference to decipher these interactions. Our results indicate that infection with E. alphitoides is accompanied by significant changes in the composition of the foliar fungal and bacterial communities. They also highlight 13 fungal operational taxonomic units (OTUs) and 13 bacterial OTUs likely to interact directly with E. alphitoides. Half of these OTUs, including the fungal endophytes Mycosphaerella punctiformis and Monochaetia kansensis, could be antagonists of E. alphitoides according to the inferred microbial network. Further studies will be required to validate these potential interactions experimentally. Overall, we showed that a combination of metabarcoding and network inference, by highlighting potential antagonists of pathogen species, could potentially improve the biological control of plant diseases.

Long-term successional dynamics of microbial association networks in anaerobic digestion processes

It is of great interest to elucidate underlying mechanisms to maintain stability of anaerobic digestion, an important process in waste treatment. By operating triplicate anaerobic digesters continuously for two years, we found that microbial community composition shifted over time despite stable process performance. Using an association network analysis to evaluate microbial interactions, we detected a clear successional pattern, which exhibited increasing modularity but decreasing connectivity among microbial populations. Phylogenetic diversity was the most important factor associated with network topology, showing positive correlations with modularity but negative correlations with network complexity, suggesting induced niche differentiation over time. Positive, but not negative, correlation strength was significantly related (p < 0.05) to phylogeny. Furthermore, among populations exhibiting consistent positive correlations across networks, close phylogenetic linkages were evident (e.g. Clostridiales organisms). Clostridiales organisms were also identified as keystone populations in the networks (i.e., they had large effects on other species), suggestive of an important role in maintaining process stability. We conclude that microbial interaction dynamics of anaerobic digesters evolves over time during stable process performance.

The full-scale anaerobic digestion microbiome is represented by specific marker populations

Anaerobic digestion is a well-established microbial-based technology for the treatment of organic waste streams and subsequent biogas recovery. A robust and versatile microbial community to ensure overall stability of the process is essential. Four full-scale anaerobic digestion plants were followed for one year to link operational characteristics with microbial community composition and structure. Similarities between digesters, community dynamics and co-occurrence between bacteria and archaea within each digester were analysed. Free ammonia concentration (>200 mg N L^-1) and conductivity (>30 mS cm^-1) hindered acetoclastic methanogenesis by Methanosaetaceae. Thus, methanogenesis was pushed to the hydrogenotrophic pathway carried out by Methanobacteriales and Methanomicrobiales. Firmicutes dominated the overall bacterial community in each of the digesters (>50%), however, principal coordinate analysis of Bray-Curtis indices showed that each of the four digesters hosted a unique microbial community. The uniqueness of this community was related to two phylotypes belonging to the Syntrophomonas genus (Phy32 and Phy34) and to one unclassified bacterium (Phy2), which could both be considered marker populations in the community. A clear differentiation in co-occurrence of methanogens with several bacteria was observed between the different digesters. Our results demonstrated that full-scale anaerobic digestion plants show constant dynamics and co-occurrence patterns in function of time, but are unique in terms of composition, related to the presence of marker populations.

Microbial interactions: ecology in a molecular perspective

The microorganism–microorganism or microorganism–host interactions are the key strategy to colonize and establish in a variety of different environments. These interactions involve all ecological aspects, including physiochemical changes, metabolite exchange, metabolite conversion, signaling, chemotaxis and genetic exchange resulting in genotype selection. In addition, the establishment in the environment depends on the species diversity, since high functional redundancy in the microbial community increases the competitive ability of the community, decreasing the possibility of an invader to establish in this environment. Therefore, these associations are the result of a co-evolution process that leads to the adaptation and specialization, allowing the occupation of different niches, by reducing biotic and abiotic stress or exchanging growth factors and signaling. Microbial interactions occur by the transference of molecular and genetic information, and many mechanisms can be involved in this exchange, such as secondary metabolites, siderophores, quorum sensing system, biofilm formation, and cellular transduction signaling, among others. The ultimate unit of interaction is the gene expression of each organism in response to an environmental (biotic or abiotic) stimulus, which is responsible for the production of molecules involved in these interactions. Therefore, in the present review, we focused on some molecular mechanisms involved in the microbial interaction, not only in microbial–host interaction, which has been exploited by other reviews, but also in the molecular strategy used by different microorganisms in the environment that can modulate the establishment and structuration of the microbial community.

Integrated analysis of bacterial and microeukaryotic communities from differentially active mud volcanoes in the Gulf of Cadiz

The present study assesses the diversity and composition of sediment bacterial and microeukaryotic communities from deep-sea mud volcanoes (MVs) associated with strike-slip faults in the South-West Iberian Margin (SWIM). We used a 16S/18S rRNA gene based pyrosequencing approach to characterize and correlate the sediment bacterial and microeukaryotic communities from MVs with differing gas seep regimes and from an additional site with no apparent seeping activity. In general, our results showed significant compositional changes of bacterial and microeukaryotic communities in sampling sites with different seepage regimes. Sediment bacterial communities were enriched with Methylococcales (putative methanotrophs) but had lower abundances of Rhodospirillales, Nitrospirales and SAR202 in the more active MVs. Within microeukaryotic communities, members of the Lobosa (lobose amoebae) were enriched in more active MVs. We also showed a strong correlation between Methylococcales populations and lobose amoeba in active MVs. This study provides baseline information on the diversity and composition of bacterial and microeukaryotic communities in deep-sea MVs associated with strike-slip faults.

Biotic Interactions Shape the Ecological Distributions of Staphylococcus Species

Many metagenomic sequencing studies have observed the presence of closely related bacterial species or genotypes in the same microbiome. Previous attempts to explain these patterns of microdiversity have focused on the abiotic environment, but few have considered how biotic interactions could drive patterns of microbiome diversity. We dissected the patterns, processes, and mechanisms shaping the ecological distributions of three closely related Staphylococcus species in cheese rind biofilms. Paradoxically, the most abundant species (S. equorum) is the slowest colonizer and weakest competitor based on growth and competition assays in the laboratory. Through in vitro community reconstructions, we determined that biotic interactions with neighboring fungi help resolve this paradox. Species-specific stimulation of the poor competitor by fungi of the genus Scopulariopsis allows S. equorum to dominate communities in vitro as it does in situ. Results of comparative genomic and transcriptomic experiments indicate that iron utilization pathways, including a homolog of the S. aureus staphyloferrin B siderophore operon pathway, are potential molecular mechanisms underlying Staphylococcus-Scopulariopsis interactions. Our integrated approach demonstrates that fungi can structure the ecological distributions of closely related bacterial species, and the data highlight the importance of bacterium-fungus interactions in attempts to design and manipulate microbiomes.

Decades of culture-based studies and more recent metagenomic studies have demonstrated that bacterial species in agriculture, medicine, industry, and nature are unevenly distributed across time and space. The ecological processes and molecular mechanisms that shape these distributions are not well understood because it is challenging to connect in situ patterns of diversity with mechanistic in vitro studies in the laboratory. Using tractable cheese rind biofilms and a focus on coagulase-negative Staphylococcus (CNS) species, we demonstrate that fungi can mediate the ecological distributions of closely related bacterial species. One of the Staphylococcus species studied, S. saprophyticus, is a common cause of urinary tract infections. By identifying processes that control the abundance of undesirable CNS species, cheese producers will have more precise control on the safety and quality of their products. More generally, Staphylococcus species frequently co-occur with fungi in mammalian microbiomes, and similar bacterium-fungus interactions may structure bacterial diversity in these systems.

Response of Core Microbial Consortia to Chronic Hydrocarbon Contaminations in Coastal Sediment Habitats

Traditionally, microbial surveys investigating the effect of chronic anthropogenic pressure such as polyaromatic hydrocarbons (PAHs) contaminations consider just the alpha and beta diversity and ignore the interactions among the different taxa forming the microbial community. Here, we investigated the ecological relationships between the three domains of life (i.e., Bacteria, Archaea, and Eukarya) using 454 pyrosequencing on the 16S rRNA and 18S rRNA genes from chronically impacted and pristine sediments, along the coasts of the Mediterranean Sea (Gulf of Lion, Vermillion coast, Corsica, Bizerte lagoon and Lebanon) and the French Atlantic Ocean (Bay of Biscay and English Channel). Our approach provided a robust ecological framework for the partition of the taxa abundance distribution into 859 core Operational taxonomic units (OTUs) and 6629 satellite OTUs. OTUs forming the core microbial community showed the highest sensitivity to changes in environmental and contaminant variations, with salinity, latitude, temperature, particle size distribution, total organic carbon (TOC) and PAH concentrations as main drivers of community assembly. The core communities were dominated by Gammaproteobacteria and Deltaproteobacteria for Bacteria, by Thaumarchaeota, Bathyarchaeota and Thermoplasmata for Archaea and Metazoa and Dinoflagellata for Eukarya. In order to find associations among microorganisms, we generated a co-occurrence network in which PAHs were found to impact significantly the potential predator – prey relationship in one microbial consortium composed of ciliates and Actinobacteria. Comparison of network topological properties between contaminated and non-contaminated samples showed substantial differences in the network structure and indicated a higher vulnerability to environmental perturbations in the contaminated sediments.

Transitioning from Microbiome Composition to Microbial Community Interactions: The Potential of the Metaorganism Hydra as an Experimental Model

Animals are home to complex microbial communities, which are shaped through interactions within the community, interactions with the host, and through environmental factors. The advent of high-throughput sequencing methods has led to novel insights in changing patterns of community composition and structure. However, deciphering the different types of interactions among community members, with their hosts and their interplay with their environment is still a challenge of major proportion. The emerging fields of synthetic microbial ecology and community systems biology have the potential to decrypt these complex relationships. Studying host-associated microbiota across multiple spatial and temporal scales will bridge the gap between individual microorganism studies and large-scale whole community surveys. Here, we discuss the unique potential of Hydra as an emerging experimental model in microbiome research. Through in vivo, in vitro, and in silico approaches the interaction structure of host-associated microbial communities and the effects of the host on the microbiota and its interactions can be disentangled. Research in the model system Hydra can unify disciplines from molecular genetics to ecology, opening up the opportunity to discover fundamental rules that govern microbiome community stability.

Metabolic Complementation in Bacterial Communities: Necessary Conditions and Optimality

Bacterial communities may display metabolic complementation, in which different members of the association partially contribute to the same biosynthetic pathway. In this way, the end product of the pathway is synthesized by the community as a whole. However, the emergence and the benefits of such complementation are poorly understood. Herein, we present a simple model to analyze the metabolic interactions among bacteria, including the host in the case of endosymbiotic bacteria. The model considers two cell populations, with both cell types encoding for the same linear biosynthetic pathway. We have found that, for metabolic complementation to emerge as an optimal strategy, both product inhibition and large permeabilities are needed. In the light of these results, we then consider the patterns found in the case of tryptophan biosynthesis in the endosymbiont consortium hosted by the aphid Cinara cedri. Using in-silico computed physicochemical properties of metabolites of this and other biosynthetic pathways, we verified that the splitting point of the pathway corresponds to the most permeable intermediate.

Microbial Succession and Flavor Production in the Fermented Dairy Beverage Kefir

Traditional fermented foods represent relatively low-complexity microbial environments that can be used as model microbial communities to understand how microbes interact in natural environments. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein. In the process, the link between individual species, and associated pathways, with flavor compounds is revealed and several genes that could be responsible for the purported gut health-associated benefits of consuming kefir are identified. Ultimately, in addition to providing an important fundamental insight into microbial interactions, this information can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods.

Kefir is a putatively health-promoting dairy beverage that is produced when a kefir grain, consisting of a consortium of microorganisms, is added to milk to initiate a natural fermentation. Here, a detailed analysis was carried out to determine how the microbial population, gene content, and flavor of three kefirs from distinct geographic locations change over the course of 24-h fermentations. Metagenomic sequencing revealed that Lactobacillus kefiranofaciens was the dominant bacterial species in kefir during early stages of fermentations but that Leuconostoc mesenteroides became more prevalent in later stages. This pattern is consistent with an observation that genes involved in aromatic amino acid biosynthesis were absent from L. kefiranofaciens but were present in L. mesenteroides. Additionally, these shifts in the microbial community structure, and associated pathways, corresponded to changes in the levels of volatile compounds. Specifically, Acetobacter spp. correlated with acetic acid; Lactobacillus spp. correlated with carboxylic acids, esters and ketones; Leuconostoc spp. correlated with acetic acid and 2,3-butanedione; and Saccharomyces spp. correlated with esters. The correlation data suggest a causal relationship between microbial taxa and flavor that is supported by observations that addition of L. kefiranofaciens NCFB 2797 increased the levels of esters and ketones whereas addition of L. mesenteroides 213M0 increased the levels of acetic acid and 2,3-butanedione. Finally, we detected genes associated with probiotic functionalities in the kefir microbiome. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein and can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods.

IMPORTANCE Traditional fermented foods represent relatively low-complexity microbial environments that can be used as model microbial communities to understand how microbes interact in natural environments. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein. In the process, the link between individual species, and associated pathways, with flavor compounds is revealed and several genes that could be responsible for the purported gut health-associated benefits of consuming kefir are identified. Ultimately, in addition to providing an important fundamental insight into microbial interactions, this information can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods.

Author Video: An author video summary of this article is available.

Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Living together in biofilms: the microbial cell factory and its biotechnological implications

In nature, bacteria alternate between two modes of growth: a unicellular life phase, in which the cells are free-swimming (planktonic), and a multicellular life phase, in which the cells are sessile and live in a biofilm, that can be defined as surface-associated microbial heterogeneous structures comprising different populations of microorganisms surrounded by a self-produced matrix that allows their attachment to inert or organic surfaces. While a unicellular life phase allows for bacterial dispersion and the colonization of new environments, biofilms allow sessile cells to live in a coordinated, more permanent manner that favors their proliferation. In this alternating cycle, bacteria accomplish two physiological transitions via differential gene expression: (i) from planktonic cells to sessile cells within a biofilm, and (ii) from sessile to detached, newly planktonic cells. Many of the innate characteristics of biofilm bacteria are of biotechnological interest, such as the synthesis of valuable compounds (e.g., surfactants, ethanol) and the enhancement/processing of certain foods (e.g., table olives). Understanding the ecology of biofilm formation will allow the design of systems that will facilitate making products of interest and improve their yields.

Comparison of microbial communities of activated sludge and membrane biofilm in 10 full-scale membrane bioreactors

Operation of membrane bioreactors (MBRs) for wastewater treatment is hampered by the membrane biofouling resulting from microbial activities. However, the knowledge of the microbial ecology of both biofilm and activated sludge in MBRs has not been sufficient. In this study, we scrutinized microbial communities of biofilm and activated sludge from 10 full-scale MBR plants. Overall, Flavobacterium, Dechloromonas and Nitrospira were abundant in order of abundance in biofilm, whereas Dechloromonas, Flavobacterium and Haliscomenobacter in activated sludge. Community structure was analyzed in either biofilm or activated sludge. Among MBRs, as expected, not only diversity of microbial community but also its composition was different from one another (p < 0.05). Between the biofilm and activated sludge, community composition made significant difference, but its diversity measures (i.e., alpha diversity, e.g., richness, diversity and evenness) did not (p > 0.05). Effects of ten environmental factors on community change were investigated using Spearman correlation. MLSS, HRT, F/M ratio and SADm explained the variation of microbial composition in the biofilm, whereas only MLSS did in the activated sludge. Microbial networks were constructed with the 10 environmental factors. The network results revealed that there were different topological characteristics between the biofilm and activated sludge networks, in which each of the 4 factors had different associations with microbial nodes. These results indicated that the different microbial associations were responsible for the variation of community composition between the biofilm and activated sludge.