Difficulty in inferring microbial community structure based on co-occurrence network approaches

Alteration of bacterial communities and co-occurrence networks as a legacy effect upon exposure to polyethylene residues under field environment

The use of plastic film mulch threatens the sustainability of the terrestrial environment because of the persistence of plastic residue. Identification of the potential long-term impacts of polyethylene (PE) residue on the soil microbiome has been overlooked in most studies. A long-term field experiment was conducted to expand this understanding by performing a co-occurrence network analysis of bacterial communities among different compartment niches (i.e. plastisphere, rhizosphere, and bulk soil) and three PE residue concentrations to determine the differential operational taxonomic units (OTUs) and keystone taxa. The specific set of bacterial microbes in the plastisphere was different from that of bulk soil and rhizosphere (R² = 0.372, P < 0.001, PERMANOVA). Totally, 215 and 257 differential OTUs were identified in response to the different compartment niches and PE residue concentrations, respectively. Among these, several hubs or keystone taxa responsible for the exposure to PE residues were further identified, most of which have potential biodegradation functions. Exposure to PE residues led to a reduced network complexity and microbiome stability in the soil ecosystem. This study provides a comprehensive evidence on the alteration of bacterial communities and co-occurrence networks in the terrestrial environment as a legacy effect when exposed to PE residues, and has potential implications for predicting the ecological functions of the soil ecosystem.

Effects of Waterlogging on Soybean Rhizosphere Bacterial Community Using V4, LoopSeq, and PacBio 16S rRNA Sequence

Waterlogging causes a significant reduction in soil oxygen levels, which in turn negatively affects soil nutrient use efficiency and crop yields. Rhizosphere microbes can help plants to better use nutrients and thus better adapt to this stress, while it is not clear how the plant-associated microbes respond to waterlogging stress. There are also few reports on whether this response is influenced by different sequencing methods and by different soils. In this study, using partial 16S rRNA sequencing targeting the V4 region and two full-length 16S rRNA sequencing approaches targeting the V1 to V9 regions, the effects of waterlogging on soybean rhizosphere bacterial structure in two types of soil were examined. Our results showed that, compared with the partial 16S sequencing, full-length sequencing, both LoopSeq and Pacific Bioscience (PacBio) 16S sequencing, had a higher resolution. On both types of soil, all the sequencing methods showed that waterlogging significantly affected the bacterial community structure of the soybean rhizosphere and increased the relative abundance of Geobacter. Furthermore, modular analysis of the cooccurrence network showed that waterlogging increased the relative abundance of some microorganisms related to nitrogen cycling when using V4 sequencing and increased the microorganisms related to phosphorus cycling when using LoopSeq and PacBio 16S sequencing methods. Core microorganism analysis further revealed that the enriched members of different species might play a central role in maintaining the stability of bacterial community structure and ecological functions. Together, our study explored the role of microorganisms enriched at the rhizosphere under waterlogging in assisting soybeans to resist stress. Furthermore, compared to partial and PacBio 16S sequencing, LoopSeq offers improved accuracy and reduced sequencing prices, respectively, and enables accurate species-level and strain identification from complex environmental microbiome samples. IMPORTANCE Soybeans are important oil-bearing crops, and waterlogging has caused substantial decreases in soybean production all over the world. The microbes associated with the host have shown the ability to promote plant growth, nutrient absorption, and abiotic resistance. High-throughput sequencing of partial 16S rRNA is the most commonly used method to analyze the microbial community. However, partial sequencing cannot provide correct classification information below the genus level, which greatly limits our research on microbial ecology. In this study, the effects of waterlogging on soybean rhizosphere microbial structure in two soil types were explored using partial 16S rRNA and full-length 16S gene sequencing by LoopSeq and Pacific Bioscience (PacBio). The results showed that full-length sequencing had higher classification resolution than partial sequencing. Three sequencing methods all indicated that rhizosphere bacterial community structure was significantly impacted by waterlogging, and the relative abundance of Geobacter was increased in the rhizosphere in both soil types after suffering waterlogging. Moreover, the core microorganisms obtained by different sequencing methods all contain species related to nitrogen cycling. Together, our study not only explored the role of microorganisms enriched at the rhizosphere level under waterlogging in assisting soybean to resist stress but also showed that LoopSeq sequencing is a less expensive and more convenient method for full-length sequencing by comparing different sequencing methods.

Conceptual strategies for characterizing interactions in microbial communities

Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.

Stochastic microbiome assembly depends on context

Individuals are constantly exposed to microbial organisms that may or may not colonize their gut microbiome, and newborn individuals assemble their microbiomes through a number of these acquisition events. Since microbiome composition has been shown to influence host physiology, a mechanistic understanding of community assembly has potentially therapeutic applications. In this paper we study microbiome acquisition in a highly controlled setting using germ-free fruit flies inoculated with specific bacterial species at known abundances. Our approach revealed that acquisition events are stochastic, and the colonization odds of different species in different contexts encode ecological information about interactions. These findings have consequences for microbiome-based therapies like fecal microbiota transplantation that attempt to modify a person’s gut microbiome by deliberately introducing foreign microbes.

Observational studies reveal substantial variability in microbiome composition across individuals. Targeted studies in gnotobiotic animals underscore this variability by showing that some bacterial strains colonize deterministically, while others colonize stochastically. While some of this variability can be explained by external factors like environmental, dietary, and genetic differences between individuals, in this paper we show that for the model organism Drosophila melanogaster, interactions between bacteria can affect the microbiome assembly process, contributing to a baseline level of microbiome variability even among isogenic organisms that are identically reared, housed, and fed. In germ-free flies fed known combinations of bacterial species, we find that some species colonize more frequently than others even when fed at the same high concentration. We develop an ecological technique that infers the presence of interactions between bacterial species based on their colonization odds in different contexts, requiring only presence/absence data from two-species experiments. We use a progressive sequence of probabilistic models, in which the colonization of each bacterial species is treated as an independent stochastic process, to reproduce the empirical distributions of colonization outcomes across experiments. We find that incorporating context-dependent interactions substantially improves the performance of the models. Stochastic, context-dependent microbiome assembly underlies clinical therapies like fecal microbiota transplantation and probiotic administration and should inform the design of synthetic fecal transplants and dosing regimes.

Bacterioplankton Diversity and Distribution in Relation to Phytoplankton Community Structure in the Ross Sea Surface Waters

Primary productivity in the Ross Sea region is characterized by intense phytoplankton blooms whose temporal and spatial distribution are driven by changes in environmental conditions as well as interactions with the bacterioplankton community. However, the number of studies reporting the simultaneous diversity of the phytoplankton and bacterioplankton in Antarctic waters are limited. Here, we report data on the bacterial diversity in relation to phytoplankton community structure in the surface waters of the Ross Sea during the Austral summer 2017. Our results show partially overlapping bacterioplankton communities between the stations located in the Terra Nova Bay (TNB) coastal waters and the Ross Sea Open Waters (RSOWs), with a dominance of members belonging to the bacterial phyla Bacteroidetes and Proteobacteria. In the TNB coastal area, microbial communities were characterized by a higher abundance of sequences related to heterotrophic bacterial genera such as Polaribacter spp., together with higher phytoplankton biomass and higher relative abundance of diatoms. On the contrary, the phytoplankton biomass in the RSOW were lower, with relatively higher contribution of haptophytes and a higher abundance of sequences related to oligotrophic and mixothrophic bacterial groups like the Oligotrophic Marine Gammaproteobacteria (OMG) group and SAR11. We show that the rate of diversity change between the two locations is influenced by both abiotic (salinity and the nitrogen to phosphorus ratio) and biotic (phytoplankton community structure) factors. Our data provide new insight into the coexistence of the bacterioplankton and phytoplankton in Antarctic waters, suggesting that specific rather than random interaction contribute to the organic matter cycling in the Southern Ocean.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

Network-based assessments are important for disentangling complex microbial and microbial–host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka–Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

Depth-structuring of multi-kingdom soil communities in agricultural pastures

The biodiversity and structure of deep agricultural soil communities are poorly understood, especially for eukaryotes. Using DNA metabarcoding and co-occurrence networks, we tested whether prokaryote, fungal, protist, and nematode biodiversity declines with increasing depth (0-0.1,  0.3-0.5,  and 1.1-1.7m) in pastoral soil; whether deep soil organisms are subsets of those at the surface; and whether multi-kingdom networks become more interconnected with increasing depth. Depth-related richness declines were observed for almost all detected fungal classes, protist phyla, and nematode orders, but only 13 of 25 prokaryote phyla, of which nine had increasing richness with depth. Deep soil communities were not simply subsets of surface communities, with 3.8%-12.2% of eukaryotes and 13.2% of prokaryotes detected only in the deepest samples. Eukaryotes mainly occurred in the upper soil layers whereas prokaryotes were more evenly distributed across depths. Plant-feeding nematodes were most abundant in top soil, whereas bacteria feeders were more abundant in deep soil. Co-occurrence network structure differences suggested that deep soil communities are concentrated around scarce niches of resource availability, in contrast to more spatially homogenous and abundant resources at the surface. Together, these results demonstrate effects of depth on the composition, distribution, and structure of prokaryote and eukaryote soil communities.

Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity

BACKGROUND

Obesity and related comorbidities are major health concerns among many US immigrant populations. Emerging evidence suggests a potential involvement of the gut microbiome. Here, we evaluated gut microbiome features and their associations with immigration, dietary intake, and obesity in 2640 individuals from a population-based study of US Hispanics/Latinos.

RESULTS

The fecal shotgun metagenomics data indicate that greater US exposure is associated with reduced ɑ-diversity, reduced functions of fiber degradation, and alterations in individual taxa, potentially related to a westernized diet. However, a majority of gut bacterial genera show paradoxical associations, being reduced with US exposure and increased with fiber intake, but increased with obesity. The observed paradoxical associations are not explained by host characteristics or variation in bacterial species but might be related to potential microbial co-occurrence, as seen by positive correlations among Roseburia, Prevotella, Dorea, and Coprococcus. In the conditional analysis with mutual adjustment, including all genera associated with both obesity and US exposure in the same model, the positive associations of Roseburia and Prevotella with obesity did not persist, suggesting that their positive associations with obesity might be due to their co-occurrence and correlations with obesity-related taxa, such as Dorea and Coprococcus.

CONCLUSIONS

Among US Hispanics/Latinos, US exposure is associated with unfavorable gut microbiome profiles for obesity risk, potentially related to westernized diet during acculturation. Microbial co-occurrence could be an important factor to consider in future studies relating individual gut microbiome taxa to environmental factors and host health and disease.

Robust bacterial co-occurence community structures are independent of r- and K-selection history

Selection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.

Microbial interaction-driven community differences as revealed by network analysis

Diversity and compositional analysis are the most common approaches in deciphering microbial community differences. However, these approaches neglect microbial structural differences driven by microbial interactions. In this study, the microbiota data were generated from 12 rectal digesta samples collected from steers in which the Shiga toxin 2 gene (stx2) was not expressed (defined as Stx2- group) in the bacteria, and those with stx2 expressed (defined as Stx2+ group) and used to explore whether microbial networks affect gut microbiota and foodborne pathogen virulence in cattle. Although the Shannon and Chao1 indices of rectal digesta microbial communities did not differ between the two groups (P > 0.05), 24 and 13 taxa were identified to be group-specific genera for Stx2- and Stx2+ microbial communities, respectively. The network analysis indicated 12 and 14 generalists (microbes that were densely connected with other taxa) in microbial communities for Stx2- and Stx2+ groups, and 8 out of 12 generalists and 6 out of 14 generalists were designated to Stx2- and Stx2+ group-specific genera, respectively. However, the 66 core genera were not classified as network generalists. Natural connectivity measurements revealed that the higher stability of the Stx2- microbial network in comparison to the Stx2+ network, suggesting that the structure of each microbial community was inherently different even when their diversity and composition were comparable. Group-specific genera intensely interacted with other taxa in the co-occurrence network, indicating that characterizing microbial networks together with group-specific genera could be an alternative approach to identify variation in microbial communities.

Open challenges for microbial network construction and analysis

Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis.

A call to arms for cell-cell interactions between bacteria in the plant microbiome

Next-generation sequencing and computational biology has unravelled the different bacterial groups populating plant microbiomes. In addition, microbiologists have discovered many different mechanisms of cell-cell interactions that take place between bacteria. Bacteria use four prevalent mechanisms for intercellular interactions; however, their pertinent role in the formation and maintenance of plant microbiomes is currently unknown. We argue that it is overdue to speed up research on the biotic cell-cell interactions that take place between bacteria in plant microbiomes. This research will have a major impact on both fundamental sciences and translational agriculture via the development of bacterial prebiotic compounds as well probiotics competence, resulting in a more sustainable agriculture of economically important crops.

Cropping systems impact changes in soil fungal, but not prokaryote, alpha-diversity and community composition stability over a growing season in a long-term field trial

Crop harvest followed by a fallow period can act as a disturbance on soil microbial communities. Cropping systems intended to improve alpha-diversity of communities may also confer increased compositional stability during succeeding growing seasons. Over a single growing season in a long-term (18 year) agricultural field experiment incorporating conventional (CON), conservation (CA), organic (ORG) and integrated (INT) cropping systems, temporal changes in prokaryote, fungal and arbuscular mycorrhizal fungi (AMF) communities were investigated overwinter, during crop growth and at harvest. While certain prokaryote phyla were influenced by cropping system (e.g. Acidobacteria), the community as a whole was primarily driven by temporal changes over the growing season as distinct overwinter and crop-associated communities, with the same trend observed regardless of cropping system. Species-rich prokaryote communities were most stable over the growing season. Cropping system exerted a greater effect on fungal communities, with alpha-diversity highest and temporal changes most stable under CA. CON was particularly detrimental for alpha-diversity in AMF communities, with AMF alpha-diversity and stability improved under all other cropping systems. Practices that promoted alpha-diversity tended to also increase the similarity and temporal stability of soil fungal (and AMF) communities during a growing season, while prokaryote communities were largely insensitive to management.

Keystone Species and Niche Differentiation Promote Microbial N, P, and COD Removal in Pilot Scale Constructed Wetlands Treating Domestic Sewage

The microbial characteristics related to nitrogen (N), phosphorus (P), and chemical oxygen demand (COD) removal were investigated in three pilot scale constructed wetlands (CWs). Compared to horizontal subsurface flow (HSSF) and surface flow (SF) CWs, the aerobic vertical flow (VF) CW enriched more functional bacteria carrying genes for nitrification (nxrA, amoA), denitrification (nosZ), dephosphorization (phoD), and methane oxidation (mmoX), while the removal of COD, total P, and total N increased by 33.28%, 255.28%, and 299.06%, respectively. The co-occurrence network of functional bacteria in the HSSF CW was complex, with equivalent bacterial cooperation and competition. Both the VF and SF CWs exhibited a simple functional topological structure. The VF CW reduced functional redundancy by forming niche differentiation, which filtered out keystone species that were closely related to each other, thus achieving effective sewage purification. Alternatively, bacterial niche overlap protected a single function in the SF CW. Compared with the construction type, temperature, and plants had less effect on nutrient removal in the CWs from this subtropical region. Partial least-squares path modeling (PLS-PM) suggests that high dissolved oxygen and oxidation-reduction potential promoted a diverse bacterial community and that the nonkeystone bacteria reduced external stress for functional bacteria, thereby indirectly promoting nutrient removal.

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.

Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.

NetCoMi: network construction and comparison for microbiome data in R

MOTIVATION

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.

RESULTS

Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).

AVAILABILITY

R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.

CONTACT

Tel:+49 89 3187 43258; stefanie.peschel@mail.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Null-model-based network comparison reveals core associations

Microbial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Changes in Bacterial Diversity, Composition and Interactions During the Development of the Seabird Tick Ornithodoros maritimus (Argasidae)

Characterising within-host microbial interactions is essential to understand the drivers that shape these interactions and their consequences for host ecology and evolution. Here, we examined the bacterial microbiota hosted by the seabird soft tick Ornithodoros maritimus (Argasidae) in order to uncover bacterial interactions within ticks and how these interactions change over tick development. Bacterial communities were characterised through next-generation sequencing of the V3-V4 hypervariable region of the bacterial 16S ribosomal RNA gene. Bacterial co-occurrence and co-exclusion were determined by analysing networks generated from the metagenomic data obtained at each life stage. Overall, the microbiota of O. maritimus was dominated by four bacterial genera, namely Coxiella, Rickettsia, Brevibacterium and Arsenophonus, representing almost 60% of the reads. Bacterial diversity increased over tick development, and adult male ticks showed higher diversity than did adult female ticks. Bacterial networks showed that co-occurrence was more frequent than co-exclusion and highlighted substantial shifts across tick life stages; interaction networks changed from one stage to the next with a steady increase in the number of interactions through development. Although many bacterial interactions appeared unstable across life stages, some were maintained throughout development and were found in both sexes, such as Coxiella and Arsenophonus. Our data support the existence of a few stable interactions in O. maritimus ticks, on top of which bacterial taxa accumulate from hosts and/or the environment during development. We propose that stable associations delineate core microbial interactions, which are likely to be responsible for key biological functions.

Network analyses in microbiome based on high-throughput multi-omics data

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Diversity of Dominant Soil Bacteria Increases with Warming Velocity at the Global Scale

Understanding global soil bacterial diversity is important because of its role in maintaining a healthy global ecosystem. Given the effects of environmental changes (e.g., warming and human impact) on the diversity of animals and plants, effects on soil bacterial diversity are expected; however, they have been poorly evaluated at the global scale to date. Thus, in this study, we focused on the dominant soil bacteria, which are likely critical drivers of key soil processes worldwide, and investigated the effects of warming velocity and human activities on their diversity. Using a global dataset of bacteria, we performed spatial analysis to evaluate the effects of warming velocity and human activities, while statistically controlling for the potentially confounding effects of current climate and geographic parameters with global climate and geographic data. We demonstrated that the diversity of the dominant soil bacteria was influenced globally, not only by the aridity index (dryness) and pH but also by warming velocity from the Last Glacial Maximum (21,000 years ago) to the present, showing significant increases. The increase in bacterial diversity with warming velocity was particularly significant in forests and grasslands. An effect of human activity was also observed, but it was secondary to warming velocity. These findings provide robust evidence and advance our understanding of the effects of environmental changes (particularly global warming) on soil bacterial diversity at the global scale.

Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series

Resource-consumer interactions are considered a major driving force of population and community dynamics. However, species also interact in many non-trophic and indirect ways and it is currently not known to what extent the dynamic coupling of species corresponds to the distribution of trophic links. Here, using a 10-year data set of monthly observations of a 40-species tri-trophic insect community and nonlinear time series analysis, we compare the occurrence and strengths of both the trophic and dynamic interactions in the insect community. The matching between observed trophic and dynamic interactions provides evidence that population dynamic interactions reflect resource-consumer interactions in the many-species community. However, the presence of a trophic interaction does not always correspond to a detectable dynamic interaction especially for top-down effects. Moreover a considerable proportion of dynamic interactions are not attributable to direct trophic interactions, suggesting the unignorable role of non-trophic and indirect interactions as co-drivers of community dynamics.

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next-generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network-level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA-based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community- and network-level properties for monitoring change. We selected crop-associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network-level properties, especially β-properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community-level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network-level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.

Experimental and computational approaches to unravel microbial community assembly

Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

Omics and interspecies interaction

Interspecies interactions are key determinants in biofilm behavior, ecology, and architecture. The cellular responses of microorganisms to each other at transcriptional, proteomic, and metabolomic levels ultimately determine the characteristics of biofilm and the corresponding implications for health and disease. Advances in omics technologies have revolutionized our understanding of microbial community composition and their activities as a whole. Large-scale analyses of the complex interaction between the many microbial species residing within a biofilm, however, are currently still hampered by technical and bioinformatics challenges. Thus, studies of interspecies interactions have largely focused on the transcriptional and proteomic changes that occur during the contact of a few prominent species, such as Porphyromonas gingivalis, Streptococcus mutans, Candida albicans, and a few others, with selected partner species. Expansion of available tools is necessary to grow the revealing, albeit limited, insight these studies have provided into a profound understanding of the nature of individual microbial responses to the presence of others. This will allow us to answer important questions including: Which intermicrobial interactions orchestrate the myriad of cooperative, synergistic, antagonistic, manipulative, and other types of relationships and activities in the complex biofilm environment, and what are the implications for oral health and disease?

Microbiome Profiling Reveals Gut Dysbiosis in the Metabotropic Glutamate Receptor 5 Knockout Mouse Model of Schizophrenia

Schizophrenia (SZ) is a psychiatric disorder that constitutes one of the top 10 global causes of disability. More recently, a potential pathogenic role for the gut microbial community (microbiota) has been highlighted, with numerous studies describing dysregulated microbial profiles in SZ patients when compared to healthy controls. However, no animal model of SZ has previously recapitulated the gut dysbiosis observed clinically. Since the metabotropic glutamate receptor 5 (mGlu5) knockout mice provide a preclinical model of SZ with strong face and predictive validity, in the present study we performed gut microbiome profiling of mGlu5 knockout (KO) and wild-type (WT) mice by 16S rRNA sequencing of bacterial genomic DNA from fecal samples, analyzing bacterial diversity and taxonomic composition, as well as gastrointestinal parameters as indicators of gut function. We found a significant genotype difference in microbial beta diversity. Analysis of composition of microbiomes (ANCOM) models were performed to evaluate microbiota compositions, which identified a decreased relative abundance of the Erysipelotrichaceae family and Allobaculum genus in this mouse model of SZ. We also identified a signature of bacteria discriminating between the genotypes (KO and WT), consisting of the Erysipelotrichales, Bacteroidales, and Clostridiales orders and macroscopic gut differences. We thus uncovered global differential community composition in the gut microbiota profile between mGlu5 KO and WT mice, outlining the first evidence for gut dysbiosis in a genetic animal model of SZ. Our findings suggest that this widely used preclinical model of SZ also has substantial utility for investigations of gut dysbiosis and associated signaling via the microbiota-gut-brain axis, as potential modulators of SZ pathogenesis. Our discovery opens up new avenues to explore gut dysbiosis and its proposed links to brain dysfunction in SZ, as well as novel therapeutic approaches to this devastating disorder.

Direct interaction network inference for compositional data via codaloss

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss.

Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species

Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut associated bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.

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.

From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

A Primer for Microbiome Time-Series Analysis

Time-series can provide critical insights into the structure and function of microbial communities. The analysis of temporal data warrants statistical considerations, distinct from comparative microbiome studies, to address ecological questions. This primer identifies unique challenges and approaches for analyzing microbiome time-series. In doing so, we focus on (1) identifying compositionally similar samples, (2) inferring putative interactions among populations, and (3) detecting periodic signals. We connect theory, code and data via a series of hands-on modules with a motivating biological question centered on marine microbial ecology. The topics of the modules include characterizing shifts in community structure and activity, identifying expression levels with a diel periodic signal, and identifying putative interactions within a complex community. Modules are presented as self-contained, open-access, interactive tutorials in R and Matlab. Throughout, we highlight statistical considerations for dealing with autocorrelated and compositional data, with an eye to improving the robustness of inferences from microbiome time-series. In doing so, we hope that this primer helps to broaden the use of time-series analytic methods within the microbial ecology research community.

Co-occurrence patterns of bacteria within microbiome of Moscow subway

Microbial ecosystems of the built environments have become key mediators of health as people worldwide tend to spend large amount of time indoors. Underexposure to microbes at an early age is linked to increased risks of allergic and autoimmune diseases. Transportation systems are of particular interest, as they are globally the largest space for interactions between city-dwellers. Here we performed the first pilot study of the Moscow subway microbiome by analyzing swabs collected from 5 types of surfaces at 4 stations using high-throughput 16S rRNA gene sequencing. The study was conducted as a part of The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) project. The most abundant microbial taxa comprising the subway microbiome originated from soil and human skin. Microbiome diversity was positively correlated with passenger traffic. No substantial evidence of major human pathogens presence was found. Co-occurrence analysis revealed clusters of microbial genera including combinations of microbes likely originating from different niches. The clusters as well as the most abundant microbes were similar to ones obtained for the published data on New-York City subway microbiome. Our results suggest that people are the main source and driving force of diversity in subway-associated microbiome. The data form a basis for a wider survey of Moscow subway microbiome to explore its longitudinal dynamics by analyzing an extended set of sample types and stations. Complementation of methods with viability testing, "shotgun" metagenomics, sequencing of bacterial isolates and culturomics will provide insights for public health, biosafety, microbial ecology and urban design.