Inferring microbial interaction network from microbiome data using RMN algorithm

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks

Co-occurrence networks inferred from the abundance data of microbial communities are widely applied to predict microbial interactions. However, the high workloads of bacterial isolation and the complexity of the networks themselves constrained experimental demonstrations of the predicted microbial associations and interactions. Here, we integrate droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from environmental samples, and experimentally investigate the relationships between taxon pairs inferred from microbial co-occurrence networks. We collected Potamogeton perfoliatus plants (including roots) and associated sediments from Beijing Olympic Park wetland. Droplets of series diluted homogenates of wetland samples were inoculated into 126 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates with > 30% wells showed microbial growth were selected for the inference of microbial co-occurrence networks. We cultivated 129 bacterial isolates belonging to 15 species that could represent the zero-level OTUs (Zotus) in the inferred co-occurrence networks. The co-cultivations of bacterial isolates corresponding to the prevalent Zotus pairs in networks were performed on agar plates and in broth. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism, depending on bacterial isolate combination and cultivation time.

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.

INFORMATION CONTENT OF HIGH-ORDER ASSOCIATIONS OF THE HUMAN GUT MICROBIOTA NETWORK

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

Identifying prognostic pairwise relationships among bacterial species in microbiome studies

In recent literature, the human microbiome has been shown to have a major influence on human health. To investigate this impact, scientists study the composition and abundance of bacterial species, commonly using 16S rRNA gene sequencing, among patients with and without a disease or condition. Methods for such investigations to date have focused on the association between individual bacterium and an outcome, and higher-order pairwise relationships or interactions among bacteria are often avoided due to the substantial increase in dimension and the potential for spurious correlations. However, overlooking such relationships ignores the environment of the microbiome, where there is dynamic cooperation and competition among bacteria. We present a method for identifying and ranking pairs of bacteria that have a differential dichotomized relationship across outcomes. Our approach, implemented in an R package PairSeek, uses the stability selection framework with data-driven dichotomized forms of the pairwise relationships. We illustrate the properties of the proposed method using a published oral cancer data set and a simulation study.

Within an ecological system, microbial communities represent complex relationships between bacteria, where they co-exist and interact with each other in multiple ways including cooperation and competition. Most existing statistical tools for examining the association between microbiota and a disease state, such as individuals with and without cancer, focus on individual bacterium in isolation, ignoring the dynamic environment in which it lives. In this manuscript, we propose an algorithm for assessing the association between pairs of bacteria and a disease state. The approach provides a mechanism to rank pairs of bacteria, from pairs with the most evidence of an association with the disease state to the least amount of evidence. This ranking helps generate hypotheses and prioritize bacteria for further investigation. We illustrate the algorithm using a publicly available data set of oral cancer patients.

TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction

MOTIVATION

Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations.

RESULTS

We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model's effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest.

AVAILABILITY AND IMPLEMENTATION

https://github.com/divya031090/TaxoNN_OTU.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

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.

Probiotic Cocktail Identified by Microbial Network Analysis Inhibits Growth, Virulence Gene Expression, and Host Cell Colonization of Vancomycin-Resistant Enterococci

The prevalence of vancomycin resistant enterococcus (VRE) carrier-state has been increasing in patients of intensive care unit and it would be a public health threat. Different research groups conducted decolonizing VRE with probiotic and the results were controversial. Therefore, a systemic approach to search for the probiotic species capable of decolonizing VRE is necessary. Thus, VRE was co-cultured with ten probiotic species. The fluctuations of each bacterial population were analyzed by 16S rRNA sequencing. Microbial network analysis (MNA) was exploited to identify the most critical species in inhibiting the VRE population. The MNA-selected probiotic cocktail was then validated for its efficacy in inhibiting VRE, decolonizing VRE from Caco-2 cells via three approaches: exclusion, competition, and displacement. Finally, the expression of VRE virulence genes after co-incubation with the probiotic cocktail were analyzed with quantitative real-time PCR (qRT-PCR). The MNA-selected probiotic cocktail includes Bacillus coagulans, Lactobacillus rhamnosus GG, Lactobacillus reuteri, and Lactobacillus acidophilus. This probiotic combination significantly reduces the population of co-cultured VRE and prevents VRE from binding to Caco-2 cells by down-regulating several host-adhesion genes of VRE. Our results suggested the potential of this four-strain probiotic cocktail in clinical application for the decolonization of VRE in human gut.

Fine-Scale Biogeography and the Inference of Ecological Interactions Among Neutrophilic Iron-Oxidizing Zetaproteobacteria as Determined by a Rule-Based Microbial Network

Hydrothermal vents, such as those at Lō‘ihi Seamount and the Mariana Arc and back-arc, release iron required to support life from the Earth’s crust. In these ecosystems, bacteria and archaea can oxidize the released iron and therefore play an important role in the biogeochemical cycles of essential nutrients. These organisms often form microbial mats, and the primary producers in these communities can support diverse higher trophic levels. One such class of bacteria are the Zetaproteobacteria. This class of bacteria oxidize iron and commonly produce extracellular iron oxyhydroxide matrices that provide architecture to the microbial mats, so they are considered foundational members of the community and ecosystem engineers. Zetaproteobacteria are responsible for the majority of iron-oxidation in circumneutral, marine, low-oxygen environments. To study the composition of these communities, microbial mats were collected using a biomat sampler, which allows for fine-scale collection of microbial mats. DNA was then extracted and amplified for analysis of the SSU rRNA gene. After quality control and filtering, the SSU rRNA genes from Mariana Arc and Lō‘ihi Seamount microbial mat communities were compared pairwise to determine which site exhibits a greater microbial diversity and how much community overlap exists between the two sites. In-depth analysis was performed with the rule-based microbial network (RMN) algorithm, which identified a possible competitive relationship across oligotypes of a cosmopolitan Zetaproteobacteria operational taxonomic unit (OTU). This result demonstrated the ecological relevance of oligotypes, or fine-scale OTU variants. The oligotype distributions of the cosmopolitan ZetaOTUs varied greatly across the Pacific Ocean. The competitive relationship between dominant oligotypes at Lō‘ihi Seamount and the Mariana Arc and back-arc may be driving their differential distributions across the two regions and may result in species divergence within a cosmopolitan ZetaOTU. This implementation of the RMN algorithm can both predict directional relationships within a community and provide insight to the level at which evolution is occurring across ecosystems.

Massively parallel screening of synthetic microbial communities

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.

Comparison of Microbiota Variation in Korean Healthy Adolescents with Adults Suggests Notable Maturity Differences

Comparative studies of microbiome variation in world populations and different developmental stages of organisms are essential to decipher the linkages among microbiome, health, and disease. Notably, the gut microbiota are believed to mature in early life. In this context, we compared the gut microbiota diversity in Korean adolescent healthy samples (KAHSs) to healthy Korean adults (HKAs) as well as the Human Microbiome Project healthy samples (HMPHSs), the latter being one of the largest adult cohorts, based on organismal composition, alpha- and beta-diversities, function/pathway prediction analysis, and co-occurrence networks. We found that the gut microbiota compositions, including the ratios of firmicutes to bacteroidetes, between KAHSs and HMPHSs were different, and the diversities of KAHSs were less than those of HMPHSs. The predicted functions, for example, secondary bile acid synthesis and insulin signaling of KAHSs and HMPHSs, were also significantly different. Genus-level networks showed that co-occurrences among different taxa more frequently happened in HMPHSs than in KAHSs. Even though both KAHSs and HMPHSs represent healthy microbiomes, comparisons showed substantial differences, likely implicating different diets, environments, and demographics. Interestingly, we observed lower microbial diversities and less frequent co-occurrences among different taxa in KAHSs than adult HMPHSs and HKAs. These new findings collectively suggest that the adolescent gut microbiota in the present Korean sample did not reach the extent of maturity of adult microbiota diversity. In all, further population studies of microbiome variation across geographies and developmental stages are warranted, and should usefully inform future diagnostics and therapeutics innovation targeting the microbiome.

Interaction of genotype and diet on small intestine microbiota of Japanese quail fed a cholesterol enriched diet

Our previous study has shown that genetic selection for susceptibility/resistance to diet-induced atherosclerosis has affected the Japanese quail's cecal environment to accommodate distinctly different cecal microbiota. In this study, we fed the Atherosclerosis-resistant (RES) and -susceptable (SUS) quail a regular and a cholesterol enriched diet to examine the interaction of host genotype and diet on the diversity, composition, and metabolic functions of the duodenal and ileal microbiota with relations to atherosclerosis development. In the duodenal content, 9 OTUs (operational taxonomic units) were identified whose abundance had significant positive correlations with plasma total cholesterol, LDL level and/or LDL/HDL ratio. In the ileal content, 7 OTUs have significant correlation with plasma HDL. Cholesterol fed RES hosted significantly less Escherichia and unclassified Enterobacteriaceae (possibly pathogenic) in their duodenum than SUS fed the same diet. Dietary cholesterol significantly decreased the duodenal microbiome of SUS's biosynthesis of Ubiquinone and other terpenoid-quinone. Cholesterol fed RES had significantly more microbiome genes for Vitamin B6, selenocompound, taurine and hypotaurine, and Linoleic acid metabolism; Bisphenol degradation; primary bile acid, and butirosin and neomycin biosynthesis than SUS on the same diet. Microbiome in the ileum and ceca of RES contributed significantly towards the resistance to diet induced atherosclerosis.

Competition and niche separation of pelagic bacteria in freshwater habitats

Freshwater bacterioplankton assemblages are composed of sympatric populations that can be delineated, for example, by ribosomal RNA gene relatedness and that differ in key ecophysiological properties. They may be free-living or attached, specialized for particular concentrations or subsets of substrates, or invest a variable amount of their resources in defence traits against protistan predators and viruses. Some may be motile and tactic whereas others are not, with far-reaching implications for their respective life styles and niche partitioning. The co-occurrence of competitors with overlapping growth requirements has profound consequences for the stability of community functions; it can to some extent be explained by habitat factors such as the microscale complexity and spatiotemporal variability of the lacustrine environments. On the other hand, the composition and diversity of freshwater microbial assemblages also reflects non-equilibrium states, dispersal and the stochasticity of community assembly processes. This review synoptically discusses the competition and niche separation of heterotrophic bacterial populations (defined at various levels of phylogenetic resolution) in the pelagic zone of inland surface waters from a variety of angles, focusing on habitat heterogeneity and the resulting biogeographic distribution patterns, the ecophysiological adaptations to the substrate field and the interactions of prokaryotes with predators and viruses.

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.