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

The MTIST platform: a microbiome time series inference standardized test

The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.

Formation of a constructed microbial community in a nutrient-rich environment indicates bacterial interspecific competition

Understanding the organizational principles of microbial communities is essential for interpreting ecosystem stability. Previous studies have investigated the formation of bacterial communities under nutrient-poor conditions or obligate relationships to observe cooperative interactions among different species. How microorganisms form stabilized communities in nutrient-rich environments, without obligate metabolic interdependency for growth, is still not fully disclosed. In this study, three bacterial strains isolated from the Populus deltoides rhizosphere were co-cultured in complex medium, and their growth behavior was tracked. These strains co-exist in mixed culture over serial transfer for multiple growth-dilution cycles. Competition is proposed as an emergent interaction relationship among the three bacteria based on their significantly decreased growth levels. The effects of different initial inoculum ratios, up to three orders of magnitude, on community structure were investigated, and the final compositions of the mixed communities with various starting composition indicate that community structure is not dependent on the initial inoculum ratio. Furthermore, the competitive relationships within the community were not altered by different initial inoculum ratios. The community structure was simulated by generalized Lotka-Volterra and dynamic flux balance analysis to provide mechanistic predictions into emergence of community structure under a nutrient-rich environment. Metaproteomic analyses provide support for the metabolite exchanges predicted by computational modeling and for highly altered physiologies when microbes are grown in co-culture. These findings broaden our understanding of bacterial community dynamics and metabolic diversity in higher-order interactions and could be significant in the management of rhizospheric bacterial communities.

IMPORTANCE

Bacteria naturally co-exist in multispecies consortia, and the ability to engineer such systems can be useful in biotechnology. Despite this, few studies have been performed to understand how bacteria form a stable community and interact with each other under nutrient-rich conditions. In this study, we investigated the effects of initial inoculum ratios on bacterial community structure using a complex medium and found that the initial inoculum ratio has no significant impact on resultant community structure or on interaction patterns between community members. The microbial population profiles were simulated using computational tools in order to understand intermicrobial relationships and to identify potential metabolic exchanges that occur during stabilization of the bacterial community. Studying microbial community assembly processes is essential for understanding fundamental ecological principles in microbial ecosystems and can be critical in predicting microbial community structure and function.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions

The human gut is home to a complex array of microorganisms interacting with the host and each other, forming a community known as the microbiome. This community has been linked to human health and disease, but understanding the underlying interactions is still challenging for researchers. Standard studies typically use high-throughput sequencing to analyze microbiome distribution in patient samples. Recent advancements in meta-omic data analysis have enabled computational modeling strategies to integrate this information into an in silico model. However, there is a need for improved parameter fitting and data integration features in microbial community modeling. This study proposes a novel alternative strategy utilizing state-of-the-art dynamic flux balance analysis (dFBA) to provide a simple protocol enabling accurate replication of abundance data composition through dynamic parameter estimation and integration of metagenomic data. We used a recurrent optimization algorithm to replicate community distributions from three different sources: mock, in vitro, and clinical microbiome. Our results show an accuracy of 98% and 96% when using in vitro and clinical bacterial abundance distributions, respectively. The proposed modeling scheme allowed us to observe the evolution of metabolites. It could provide a deeper understanding of metabolic interactions while taking advantage of the high contextualization features of GEM schemes to fit the study case. The proposed modeling scheme could improve the approach in cases where external factors determine specific bacterial distributions, such as drug intake.

A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester

Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study

Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.

Modeling Microbial Community Networks: Methods and Tools

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

Shrimp AHPND Causing Vibrio anguillarum Infection: Quantitative Diagnosis and Identifying Antagonistic Bacteria

Acute hepatopancreatic necrosis disease (AHPND) is one of the most common and serious diseases in shrimp aquaculture. Relevant works have focused on the gut microbiota-disease relationship when serious AHPND occurs. In contrast, little is known about how the gut microbiota responds to pathogen infection over AHPND progression, whereas this knowledge is fundamental to uncover the etiology of AHPND. Here, we explored the temporal succession of shrimp gut microbiota during Vibrio anguillarum (a causal pathogen of AHPND) challenge. The successful infection of V. anguillarum was confirmed by linearly increased abundance of the pathogen in the shrimp gut over AHPND progression. V. anguillarum infection caused an irreversible disruption in the shrimp gut microbiota, of which infection and hours post infection (hpi) respectively constrained 6.2% and 10.2% of variation in the data. Furthermore, the predicted functional pathways involved in immunity and metabolism significantly decreased, while those facilitating infectious diseases significantly enriched in the infected shrimp. Intriguingly, after ruling out the effect of background changes in gut microbiota, we identified 20 infection-discriminatory taxa that could be served as independent variables for accurately (89.4%) diagnosing V. anguillarum infection, even at the early infection stage, i.e., 24 hpi. Using a consensus network, we identified several Vibrio and Pseudoalteromonas taxa that directly antagonized V. anguillarum, following the Darwin's niche theory. This is one of the few attempts to identify gut bioindicators for diagnosing pathogen infection. In addition, the antagonistic commensals of V. anguillarum might be the candidate probiotics for preventing AHPND.

Characterization of tetracycline-resistant microbiome in soil-plant systems by combination of H218O-based DNA-Stable isotope probing and metagenomics

The emergence and spread of antibiotic resistance have been considered as a global health threat. However, effective methods to identify antibiotic-resistant bacteria (ARB) in complex microbial community are lacking, and the potential transmission pathways of ARB and antibiotic resistance genes (ARGs) in the soil-plant system remain scarce. Here in this study, tetracycline was chosen as the target antibiotic due to its globally wide usage and clinical significance. DNA-based stable isotope probing with H₂¹⁸O was applied to identify the tetracycline-resistant bacteria from soil-plant systems. Eighteen-year organic fertilization significantly changed the composition of the tetracycline-resistant microbiome in the soil-wheat system and resulted in a higher relative abundance of ARGs in the wheat endophyte. Rhizosphere harboring the most diverse ARGs and mobile genetic elements was identified as a hot spot for horizontal gene transfer and an important bridge between bulk soil and wheat endophyte. Micrococcaceae and Sphingomonadaceae carrying ARGs associated with abundant mobile genetic elements, were identified as the core bacterial taxa in long-term manure-amended and untreated soil-wheat systems, respectively. This method contributes to a more precise track of ARB in the environment, and our work depicts the high potential of ARG transfer in the rhizosphere mediated by the core species.

Diversity and Interactomics of Bacterial Communities Associated with Dominant Trees During Tropical Forest Recovery

Bacterial communities have been identified as functional key members in soil ecology. A deep relation with these communities maintains forest coverture. Trees harbor particular bacteriomes in the rhizosphere, endosphere, or phyllosphere, different from bulk-soil representatives. Moreover, the plant microbiome appears to be specific for the plant-hosting species, varies through season, and responsive to several environmental factors. This work reports the changes in bacterial communities associated with dominant pioneer trees [Tabebuia rosea and Handroanthus chrysanthus [(Bignoniaceae)] during tropical forest recovery chronosequence in the Mayan forest in Campeche, Mexico. Massive 16S sequencing approach leads to identifying phylotypes associated with rhizosphere, bulk-soil, or recovery stage. Lotka-Volterra interactome modeling suggests the presence of putative regulatory roles of some phylotypes over the rest of the community. Our results may indicate that bacterial communities associated with pioneer trees may establish more complex regulatory networks than those found in bulk-soil. Moreover, modeled regulatory networks predicted from rhizosphere samples resulted in a higher number of nodes and interactions than those found in the analysis of bulk-soil samples.

Microbial ecology in selenate-reducing biofilm communities: Rare biosphere and their interactions with abundant phylotypes

Selenate (SeO₄ ^2- ) reduction in hydrogen (H₂ )-fed membrane biofilm reactors (H₂ -MBfRs) was studied in combinations with other common electron acceptors. We employed H₂ -MBfRs with two distinctly different conditions: R1, with ample electron-donor availability and acceptors SeO₄ ^2- and sulfate (SO₄ ^2- ), and R2, with electron-donor limitation and the presence of electron acceptors SeO₄ ^2- , nitrate (NO₃ ^- ), and SO₄ ^2- . Even though H₂ was available to reduce all input SeO₄ ^2- and SO₄ ^2- in R1, SeO₄ ^2- reduction was preferred over SO₄ ^2- reduction. In R2, co-reduction of NO₃ ^- and SeO₄ ^2- occurred, and SO₄ ^2- reduction was mostly suppressed. Biofilms in all MBfRs had high microbial diversity that was influenced by the "rare biosphere" (RB), phylotypes with relative abundance less than 1%. While all MBfR biofilms had abundant members, such as Dechloromonas and Methyloversatilis, the bacterial communities were significantly different between R1 and R2. For R1, abundant genera were Methyloversatilis, Melioribacter, and Propionivibrio; for R2, abundant genera were Dechloromonas, Hydrogenophaga, Cystobacter, Methyloversatilis, and Thauera. Although changes in electron-acceptor or -donor loading altered the phylogenetic structure of the microbial communities, the biofilm communities were resilient in terms of SeO₄ ^2- and NO₃ ^- reductions, because interacting members of the RB had the capacity of respiring these electron acceptors.

The Interplay between Campylobacter and the Caecal Microbial Community of Commercial Broiler Chickens over Time

Campylobacter is the most frequent foodborne zoonotic bacteria worldwide, with chicken meat being overwhelmingly the most important reservoir for human infections. Control measures implemented at the farm level (i.e., biosecurity or vaccination), which have been successfully applied to limit other pathogens, such as Salmonella, have not been effective in reducing Campylobacter occurrence. Thus, new approaches are needed to fully understand the ecological interactions of Campylobacter with host animals to effectively comprehend its epidemiology. The objective of this study was to analyse longitudinally the gut microbiota composition of Campylobacter-infected and non-infected farms to identify any difference that could potentially be indicative of gut colonization by Campylobacter spp. Differences in the colonization rate and timing were observed at the farms that became positive for Campylobacter jejuni over the investigated time points, even though in positive tests, the occurrence of Campylobacter jejuni gut colonization was not observed before the second week of the life of the birds. Significant differences were observed in the abundances of specific bacterial taxa between the microbiota of individuals belonging to farms that became Campylobacter positive during the study and those who remained negative with particular reference to Bacteroidales and Clostridiales, respectively. Moreover, Campylobacter colonization dramatically influenced the microbiota richness, although to a different extent depending on the infection timing. Finally, a key role of Faecalibacterium and Lactobacillus genera on the Campylobacter microbial network was observed. Understanding the ecology of the Campylobacter interaction with host microbiota during infection could support novel approaches for broiler microbial barrier restoration. Therefore, evidence obtained through this study can be used to identify options to reduce the incidence of infection at a primary production level based on the targeted influence of the intestinal microbiota, thus helping develop new control strategies in order to mitigate the risk of human exposure to Campylobacter by chicken meat consumption.

A network-based approach to deciphering a dynamic microbiome's response to a subtle perturbation

Over the past decades, one main issue that has emerged in ecological and environmental research is how losses in biodiversity influence ecosystem dynamics and functioning, and consequently human society. Although biodiversity is a common indicator of ecosystem functioning, it is difficult to measure biodiversity in microbial communities exposed to subtle or chronic environmental perturbations. Consequently, there is a need for alternative bioindicators to detect, measure, and monitor gradual changes in microbial communities against these slight, chronic, and continuous perturbations. In this study, microbial networks before and after subtle perturbations by adding S. acidaminiphila showed diverse topological niches and 4-node motifs in which microbes with co-occurrence patterns played the central roles in regulating and adjusting the intertwined relationships among microorganisms in response to the subtle environmental changes. This study demonstrates that microbial networks are a good bioindicator for chronic perturbation and should be applied in a variety of ecological investigations.

Higher-Order Interactions Dampen Pairwise Competition in the Zebrafish Gut Microbiome

The microbial communities resident in animal intestines are composed of multiple species that together play important roles in host development, health, and disease. Due to the complexity of these communities and the difficulty of characterizing them in situ, the determinants of microbial composition remain largely unknown. Further, it is unclear for many multispecies consortia whether their species-level makeup can be predicted based on an understanding of pairwise species interactions or whether higher-order interactions are needed to explain emergent compositions. To address this, we examine commensal intestinal microbes in larval zebrafish, initially raised germfree, to allow the introduction of controlled combinations of bacterial species. Using a dissection and plating assay, we demonstrate the construction of communities of one to five bacterial species and show that the outcomes from the two-species competitions fail to predict species abundances in more complex communities. With multiple species present, interbacterial interactions become weaker, suggesting that higher-order interactions in the vertebrate gut stabilize complex communities.IMPORTANCE Understanding the rules governing the composition of the diverse microbial communities that reside in the vertebrate gut environment will enhance our ability to manipulate such communities for therapeutic ends. Synthetic microbial communities, assembled from specific combinations of microbial species in germfree animals, allow investigation of the fundamental question of whether multispecies community composition can be predicted solely based on the combined effects of interactions between pairs of species. If so, such predictability would enable the construction of communities with desired species from the bottom up. If not, the apparent higher-order interactions imply that emergent community-level characteristics are crucial. Our findings using up to five coexisting native bacterial species in larval zebrafish, a model vertebrate, provide experimental evidence for higher-order interactions and, moreover, show that these interactions promote the coexistence of microbial species in the gut.

A network approach to investigating the key microbes and stability of gut microbial communities in a mouse neuropathic pain model

Background

Neuropathic pain is an abnormally increased sensitivity to pain, especially from mechanical or thermal stimuli. To date, the current pharmacological treatments for neuropathic pain are still unsatisfactory. The gut microbiota reportedly plays important roles in inducing neuropathic pain, so probiotics have also been used to treat it. However, the underlying questions around the interactions in and stability of the gut microbiota in a spared nerve injury-induced neuropathic pain model and the key microbes (i.e., the microbes that play critical roles) involved have not been answered. We collected 66 fecal samples over 2 weeks (three mice and 11 time points in spared nerve injury-induced neuropathic pain and Sham groups). The 16S rRNA gene was polymerase chain reaction amplified, sequenced on a MiSeq platform, and analyzed using a MOTHUR- UPARSE pipeline.

Results

Here we show that spared nerve injury-induced neuropathic pain alters gut microbial diversity in mice. We successfully constructed reliable microbial interaction networks using the Metagenomic Microbial Interaction Simulator (MetaMIS) and analyzed these networks based on 177,147 simulations. Interestingly, at a higher resolution, our results showed that spared nerve injury-induced neuropathic pain altered both the stability of the microbial community and the key microbes in a gut micro-ecosystem. Oscillospira, which was classified as a low-abundance and core microbe, was identified as the key microbe in the Sham group, whereas Staphylococcus, classified as a rare and non-core microbe, was identified as the key microbe in the spared nerve injury-induced neuropathic pain group.

Conclusions

In summary, our results provide novel experimental evidence that spared nerve injury-induced neuropathic pain reshapes gut microbial diversity, and alters the stability and key microbes in the gut.

IMPARO: inferring microbial interactions through parameter optimisation

BACKGROUND

Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Abundance profiles are often interpreted with the Lotka Volterra model in research. However existing research fails to consider a biologically meaningful underlying mathematical model for MINs or to address the possibility of multiple solutions.

RESULTS

In this paper we present IMPARO, a method for inferring microbial interactions through parameter optimisation. We use biologically meaningful models for both the abundance profile, as well as the MIN. We show how multiple MINs could be inferred with similar reconstructed abundance profile accuracy, and argue that a unique solution is not always satisfactory. Using our method, we successfully inferred clear interactions in the gut microbiome which have been previously observed in in-vitro experiments.

CONCLUSIONS

IMPARO was used to successfully infer microbial interactions in human microbiome samples as well as in a varied set of simulated data. The work also highlights the importance of considering multiple solutions for MINs.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Deciphering microbiomes in anaerobic reactors with superior trichloroethylene dechlorination performance at low pH conditions

Different pH conditions have been demonstrated to affect the activities of dechlorinating populations participating in the successive dechlorination of trichloroethylene to ethylene. However, the mechanism of the effect of pH conditions on the assembly of dechlorinating populations and their relations to the structure, function, and dynamics of the microbiome are unclear. In this study, we evaluated the effects of pH on microbiomes assembled in anaerobic trichloroethylene-dechlorinating reactors under neutral (pH 7.2), acidic (pH 6.2), and alkaline (pH 8.2) conditions. The results revealed that among the reactors, the acidic reactor had the highest efficiency for dechlorination without accumulation of dechlorinated metabolites, even at high loading rates. The results of high-throughput sequencing of the 16S rRNA gene indicated that the microbiomes in the 3 reactors underwent varied dynamic succession. The acidic reactor harbored a higher degree of complex microbes, dechlorinator diversity, and abundance of the Victoria subgroup of Dehalococcoides (1.2 ± 0.1 × 10⁶ cell/mL), which were approximately 10-10²-fold higher than those at neutral and alkaline conditions. The pH settings altered species-species connectivity and complexity of microbial interaction networks, with more commensal interactions in the dechlorinators of the acidic reactor. As predicted, abundances of several functional gene categories were in strong linearity with pH values, and the microbiome possessed significantly more abundant functions in the acidic reactor (P < 0.001), such as potentially stimulating hydrogen production, cobalamin synthesis, cobalt transport, transport and metabolism of amino acids and secondary metabolites, cell motility, and transcription. All results of microbiomic analyses consistently revealed the observed superior dechlorination process and suggested an association of the reductive dechlorination process with the pH-dependent microbiome. The results of this study provide a new insight into the trichloroethylene dechlorination with regards to pH, and they will be useful for improving bioremediation and management of trichloroethylene-contaminated sites.

Bacterial Diversity and Interaction Networks of Agave lechuguilla Rhizosphere Differ Significantly From Bulk Soil in the Oligotrophic Basin of Cuatro Cienegas

Due to the environmental conditions presented in arid zones, it is expected to have a high influence of deterministic processes over the community assemblages. Symbiotic interactions with microorganisms could increase colonization and survival of plants in difficult conditions, independent of the plants physiological and morphological characteristics. In this context, the microbial communities associated to plants that inhabit these types of areas can be a good model to understand the community assembly processes. We investigated the influence of stochastic and deterministic processes in the assemblage of rhizosphere microbial communities of Agave lechuguilla and bulk soil on the Cuatro Cienegas Basin, a site known for its oligotrophic conditions. We hypothesize that rhizospheric microbial communities of A. lechuguilla differ from those of bulk soil as they differ in physicochemical properties of soil and biotic interactions, including not only the plant, but also their microbial co-occurrence networks, it is expected that microbial species usually critical for plant growth and health are more common in the rhizosphere, whereas in the bulk soil microbial species related to the resistance to abiotic stress are more abundant. In order to confirm this hypothesis, 16S rRNA gene was sequenced by Illumina from rhizospheric and bulk soil samples in two seasons, also the physicochemical properties of the soil were determined. Our results showed differences in bacterial diversity, community composition, potential functions, and interaction networks between the rhizosphere samples and the ones from bulk soil. Although community structure arises from a complex interplay between deterministic and stochastic forces, our results suggest that A. lechuguilla recruits specific rhizospheric microbes with functional traits that benefits the plant through growth promotion and nutrition. This selection follows principally a deterministic process that shapes the rhizospheric microbial communities, directed by the plant modifications around the roots but also subjected to the influence of other environmental variables, such as seasonality and soil properties. Interestingly, keystone taxa in the interactions networks, not necessarily belong to the most abundant taxonomic groups, but they have an important role by their functional traits and keeping the connections on the community network.

Characterization of the Rat Gut Microbiota via 16S rRNA Amplicon Library Sequencing

It is becoming increasingly apparent that microbiota have measurable effects on numerous phenotypes in laboratory animals. This "second genome" has often been disregarded or ignored due to its commensal nonpathogenic nature, and the difficulty, expense, and analysis of sequence. Recent advances in sequencing methods and analyses of large datasets have made characterization of microbiota populations routine and have uncovered previously unknown relationships of microbial communities and host biological systems. The largest and most diverse microbial community in the laboratory rat is in the gut, and has been shown to affect the physiology of the whole animal, and genetic disease penetrance. We present here a cost-effective method for the characterization of the rat fecal microbiota through multiplexed 16S ribosomal sequencing and freely available software.

Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species-species modelling

Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species-metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species-species modelling. Using publicly available data, we examine the ability of species-species models and species-metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species-species interaction models and quadratic SMI models and conclude that only species-metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species-species interaction models cannot match the patterns observed in community growth dynamics, whereas species-metabolite models can. We conclude that species-metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

A systematic approach re-analyzing the effects of temperature disturbance on the microbial community of mesophilic anaerobic digestion

Microbial communities are key drivers of ecosystem processes, but their behavior in disturbed environments is difficult to measure. How microbial community composition and function respond disturbances is a common challenge in biomedical, environmental, agricultural, and bioenergy research. A novel way to solve this problem is to use a systems-level perspective and describe microbial communities as networks. Based on a mesophilic anaerobic digestion system of swine manure as a tool, we propose a simple framework to investigate changes in microbial communities via compositions, metabolic pathways, genomic properties and interspecies relationships in response to a long-term temperature disturbance. After temperature disturbance, microbial communities tend towards a competitive interaction network with higher GC content and larger genome size. Based on microbial interaction networks, communities responded to the disturbance by showing a transition from acetotrophic (Methanotrichaceae and Methanosarcinaceae) to methylotrophic methanogens (Methanomassiliicoccaceae and Methanobacteriaceae) and a fluctuation in rare biosphere taxa. To conclude, this study may be important for exploring the dynamic relationships between disturbance and microbial communities as a whole, as well as for providing researchers with a better understanding of how changes in microbial communities relate to ecological processes.

Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

IMPACT STATEMENT

This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper, we present Web-gLV-a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

Understanding the Mechanisms Behind the Response to Environmental Perturbation in Microbial Mats: A Metagenomic-Network Based Approach

To date, it remains unclear how anthropogenic perturbations influence the dynamics of microbial communities, what general patterns arise in response to disturbance, and whether it is possible to predict them. Here, we suggest the use of microbial mats as a model of study to reveal patterns that can illuminate the ecological processes underlying microbial dynamics in response to stress. We traced the responses to anthropogenic perturbation caused by water depletion in microbial mats from Cuatro Cienegas Basin (CCB), Mexico, by using a time-series spatially resolved analysis in a novel combination of three computational approaches. First, we implemented MEBS (Multi-genomic Entropy-Based Score) to evaluate the dynamics of major biogeochemical cycles across spatio-temporal scales with a single informative value. Second, we used robust Time Series-Ecological Networks (TS-ENs) to evaluate the total percentage of interactions at different taxonomic levels. Lastly, we utilized network motifs to characterize specific interaction patterns. Our results indicate that microbial mats from CCB contain an enormous taxonomic diversity with at least 100 phyla, mainly represented by members of the rare biosphere (RB). Statistical ecological analyses point out a clear involvement of anaerobic guilds related to sulfur and methane cycles during wet versus dry conditions, where we find an increase in fungi, photosynthetic, and halotolerant taxa. TS-ENs indicate that in wet conditions, there was an equilibrium between cooperation and competition (positive and negative relationships, respectively), while under dry conditions there is an over-representation of negative relationships. Furthermore, most of the keystone taxa of the TS-ENs at family level are members of the RB and the microbial mat core highlighting their crucial role within the community. Our results indicate that microbial mats are more robust to perturbation due to redundant functions that are likely shared among community members in the highly connected TS-ENs with density values close to one (≈0.9). Finally, we provide evidence that suggests that a large taxonomic diversity where all community members interact with each other (low modularity), the presence of permanent of low-abundant taxa, and an increase in competition can be potential buffers against environmental disturbance in microbial mats.

Inference of Significant Microbial Interactions From Longitudinal Metagenomics Data

Data of next-generation sequencing (NGS) and their analysis have been facilitating advances in our understanding of microbial ecosystems such as human gut microbiota. However, inference of microbial interactions occurring within an ecosystem is still a challenge mainly due to sequencing data (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe growtth dynamics of microbial communities and estimate the involved microbial interactions, we introduce a procedure by integrating generalized Lotka-Volterra equations, forward stepwise regression and bootstrap aggregation. First, we successfully identify experimentally confirmed microbial interactions based on relative abundance data of a cheese microbial community. Then, we apply the procedure to time-series of 16S rDNA sequences of gut microbiomes of children who were progressing to Type 1 diabetes (T1D progressors), and compare their gut microbial interactions to a healthy control group. Our results suggest that the number of inferred microbial interactions increased over time during the first 3 years of life. More microbial interactions are found in the gut flora of healthy children than that of T1D progressors. The inhibitory effects from Actinobacteria and Bacilli to Bacteroidia, from Bacteroidia to Clostridia, and the beneficial effect from Clostridia to Bacteroidia are shared between healthy children and T1D progressors. An inhibition of Clostridia by Gammaproteobacteria is found in healthy children that maintains through their first 3 years of life. This suppression appears in T1D progressors during the first year of life, which transforms to a commensalism relationship at the age of 3 years old. Gammaproteobacteria is found exerting an inhibition on Bacteroidia in the T1D progressors, which is not identified in the healthy controls.

Moody microbiome: Challenges and chances

Growing evidence link gut microbiome to the development and maturation of the central nervous system, which are regulated by microbiota potentially through stress response, neurotransmitter, neuroimmune, and endocrine pathways. The dysfunction of such microbiota-gut-brain axis is implicated in neuropsychiatric disorders, depression, and other stress-related conditions. Using affective disorders as our primary outcomes, we inspect the current evidence of microbiota studies mainly in human clinical samples. Additionally, to restore microbiome equilibrium in bacteria diversity and abundance might represent a novel strategy to prevent or treat mood symptoms. We reviewed findings from clinical trials regarding efficacy of probiotics supplement with or without antidepressant treatment, and adjuvant antimicrobiotics treatment. In microbiota studies, the considerations of host-microbiota interaction and bacteria-bacteria interaction are discussed. In conclusion, the roles of microbiota in depression and mania state are not fully elucidated. One of the challenges is to find reliable targets for functional analyses and experiments. Notwithstanding some inconsistencies and methodological limitations across studies, results from recent clinical trials support for the beneficial effects of probiotics on alleviating depressive symptoms and increasing well-beings. Moreover, modifying the composition of gut microbiota via antibiotics can be a viable adjuvant treatment option for individuals with depressive symptoms.

Integrating molecular and ecological approaches to identify potential polymicrobial pathogens over a shrimp disease progression

It is now recognized that some gut diseases attribute to polymicrobial pathogens infections. Thus, traditional isolation of single pathogen from disease subjects could bias the identification of causal agents. To fill this gap, using Illumina sequencing of the bacterial 16S rRNA gene, we explored the dynamics of gut bacterial communities over a shrimp disease progression. The results showed significant differences in the gut bacterial communities between healthy and diseased shrimp. Potential pathogens were inferred by a local pathogens database, of which two OTUs (affiliated with Vibrio tubiashii and Vibrio harveyi) exhibited significantly higher abundances in diseased shrimp as compared to healthy subjects. The two OTUs cumulatively contributed 64.5% dissimilarity in the gut microbiotas between shrimp health status. Notably, the random Forest model depicted that profiles of the two OTUs contributed 78.5% predicted accuracy of shrimp health status. Removal of the two OTUs from co-occurrence networks led to network fragmentation, suggesting their gatekeeper features. For these evidences, the two OTUs were inferred as candidate pathogens. Three virulence genes (bca, tlpA, and fdeC) that were coded by the two candidate pathogens were inferred by a virulence factor database, which were enriched significantly (P < 0.05 in the three cases, as validated by qPCR) in diseased shrimp as compared to healthy ones. The two candidate pathogens were repressed by Flavobacteriaceae, Garvieae, and Photobacrerium species in healthy shrimp, while these interactions shifted into synergy in disease cohorts. Collectively, our findings offer a frame to identify potential polymicrobial pathogen infections from an ecological perspective.

Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota

One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

Experimental design and quantitative analysis of microbial community multiomics

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.

Microbial ecology of cereal vinegar fermentation: insights for driving the ecosystem function

Over thousands of years, humans have mastered the natural vinegar fermentation technique of cultivating functional microbiota on different raw materials. Functional microbial communities that form reproducibly on non-autoclaved raw materials through repeated batch acetic acid fermentation underpin the flavour development of traditional cereal vinegars. However, how to modulate rationally and optimise the metabolic function of these naturally engineered acidic ecosystems remains unclear. Exploring two key minorities in a vinegar ecosystem, including microbial functions (e.g., flavour and aroma synthesis) and microbial strains, is a crucial step for the vinegar industry to modulate the metabolic function of vinegar microbiota, to monitor the fermentation process, and to maintain the flavour quality of final product.

Microbiome Dynamics in a Shrimp Grow-out Pond with Possible Outbreak of Acute Hepatopancreatic Necrosis Disease

Acute hepatopancreatic necrosis disease (AHPND) (formerly, early mortality syndrome) is a high-mortality-rate shrimp disease prevalent in shrimp farming areas. Although AHPND is known to be caused by pathogenic Vibrio parahaemolyticus hosting the plasmid-related PirAB^vp toxin gene, the effects of disturbances in microbiome have not yet been studied. We took 62 samples from a grow-out pond during an AHPND developing period from Days 23 to 37 after stocking white postlarvae shrimp and sequenced the 16S rRNA genes with Illumina sequencing technology. The microbiomes of pond seawater and shrimp stomachs underwent varied dynamic succession during the period. Despite copies of PirAB^vp, principal co-ordinates analysis revealed two distinctive stages of change in stomach microbiomes associated with AHPND. AHPND markedly changed the bacterial diversity in the stomachs; it decreased the Shannon index by 53.6% within approximately 7 days, shifted the microbiome with Vibrio and Candidatus Bacilloplasma as predominant populations, and altered the species-to-species connectivity and complexity of the interaction network. The AHPND-causing Vibrio species were predicted to develop a co-occurrence pattern with several resident and transit members within Candidatus Bacilloplasma and Cyanobacteria. This study’s insights into microbiome dynamics during AHPND infection can be valuable for minimising this disease in shrimp farming ponds.

Inferring microbial interactions in thermophilic and mesophilic anaerobic digestion of hog waste

Anaerobic digestion (AnD) is a microbiological process that converts organic waste materials into biogas. Because of its high methane content, biogas is a combustible energy source and serves as an important environmental technology commonly used in the management of animal waste generated on large animal farms. Much work has been done on hardware design and process engineering for the generation of biogas. However, little is known about the complexity of the microbiology in this process. In particular, how microbes interact in the digester and eventually breakdown and convert organic matter into biogas is still regarded as a "black box." We used 16S rRNA sequencing as a tool to study the microbial community in laboratory hog waste digesters under tightly controlled conditions, and systematically unraveled the distinct interaction networks of two microbial communities from mesophilic (MAnD) and thermophilic anaerobic digestion (TAnD). Under thermophilic conditions, the well-known association between hydrogen-producing bacteria, e.g., Ruminococcaceae and Prevotellaceae, and hydrotrophic methanogens, Methanomicrobiaceae, was reverse engineered by their interactive topological niches. The inferred interaction network provides a sketch enabling the determination of microbial interactive relationships that conventional strategy of finding differential taxa was hard to achieve. This research is still in its infancy, but it can help to depict the dynamics of microbial ecosystems and to lay the groundwork for understanding how microorganisms cohabit in the anaerobic digester.

Inferring Microbial Interactions in the Gut of the Hong Kong Whipping Frog (Polypedates megacephalus) and a Validation Using Probiotics

The concerted activity of intestinal microbes is crucial to the health and development of their host organisms. Investigation of microbial interactions in the gut should deepen our understanding of how these micro-ecosystems function. Due to advances in Next Generation Sequencing (NGS) technologies, various bioinformatic strategies have been proposed to investigate these microbial interactions. However, due to the complexity of the intestinal microbial community and difficulties in monitoring their interactions, at present there is a gap between the theory and biological application. In order to construct and validate microbial relationships, we first induce a community shift from simple to complex by manipulating artificial hibernation (AH) in the treefrog Polypedates megacephalus. To monitor community growth and microbial interactions, we further performed a time-course screen using a 16S rRNA amplicon approach and a Lotka-Volterra model. Lotka-Volterra models, also known as predator–prey equations, predict the dynamics of microbial communities and how communities are structured and sustained. An interaction network of gut microbiota at the genus level in the treefrog was constructed using Metagenomic Microbial Interaction Simulator (MetaMIS) package. The interaction network obtained had 1,568 commensal, 1,737 amensal, 3,777 mutual, and 3,232 competitive relationships, e.g., Lactococcus garvieae has a commensal relationship with Corynebacterium variabile. To validate the interacting relationships, the gut microbe composition was analyzed after probiotic trials using single strain (L. garvieae, C. variabile, and Bacillus coagulans, respectively) and a combination of L. garvieae, C. variabile, and B. coagulans, because of the cooperative relationship among their respective genera identified in the interaction network. After a 2 week trial, we found via 16S rRNA amplicon analysis that the combination of cooperative microbes yielded significantly higher probiotic concentrations than single strains, and the immune response (interleukin-10 expression) also significantly changed in a manner consistent with improved probiotic effects. By taking advantage of microbial community shift from simple to complex, we thus constructed a reliable microbial interaction network, and validated it using probiotic strains as a test system.