Rewiring of peatland plant–microbe networks outpaces species turnover

Enviro–climatic changes are thought to be causing alterations in ecosystem processes through shifts in plant and microbial communities; however, how links between plant and microbial communities change with enviro–climatic change is likely to be less straightforward but may be fundamental for many ecological processes. To address this, we assessed the composition of the plant community and the prokaryotic community – using amplicon-based sequencing – of three European peatlands that were distinct in enviro–climatic conditions. Bipartite networks were used to construct site-specific plant–prokaryote co-occurrence networks. Our data show that between sites, plant and prokaryotic communities differ and that turnover in interactions between the communities was complex. Essentially, turnover in plant–microbial interactions is much faster than turnover in the respective communities. Our findings suggest that network rewiring does largely result from novel or different interactions between species common to all realised networks. Hence, turnover in network composition is largely driven by the establishment of new interactions between a core community of plants and microorganisms that are shared among all sites. Taken together our results indicate that plant–microbe associations are context dependent, and that changes in enviro–climatic conditions will likely lead to network rewiring. Integrating turnover in plant–microbe interactions into studies that assess the impact of enviro–climatic change on peatland ecosystems is essential to understand ecosystem dynamics and must be combined with studies on the impact of these changes on ecosystem processes.     

基于分子生态学网络探究西藏草地放牧对土壤微生物群落的影响

【目的】揭示西藏地区放牧对在功能基因层面上的微生物相互作用的影响。【方法】利用最近发明的网络工具(基于随机矩阵理论的分子生态学网络)分别在对照和放牧条件下分析基因芯片中碳循环和氮循环基因。【结果】碳和氮循环基因网络在对照和放牧条件下都具有无标度、小世界、模块性和层次性的拓扑学特征。放牧条件下的网络关键基因(模块枢纽和连通者)与对照显著不同。放牧导致网络变得小而紧密,暗示环境压力的存在。地上植物生物量与微生物基因网络显著相关(P=0.001),证实了研究样地地上植物与地下微生物紧密相连。【结论】放牧显著改变了西藏草地在功能基因层面上的微生物相互作用关系。     

LNRLMI: Linear neighbour representation for predicting lncRNA‐miRNA interactions

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.     

What Can Interaction Webs Tell Us About Species Roles?

The group model is a useful tool to understand broad-scale patterns of interaction in a network, but it has previously been limited in use to food webs, which contain only predator-prey interactions. Natural populations interact with each other in a variety of ways and, although most published ecological networks only include information about a single interaction type (e.g., feeding, pollination), ecologists are beginning to consider networks which combine multiple interaction types. Here we extend the group model to signed directed networks such as ecological interaction webs. As a specific application of this method, we examine the effects of including or excluding specific interaction types on our understanding of species roles in ecological networks. We consider all three currently available interaction webs, two of which are extended plant-mutualist networks with herbivores and parasitoids added, and one of which is an extended intertidal food web with interactions of all possible sign structures (+/+, -/0, etc.). Species in the extended food web grouped similarly with all interactions, only trophic links, and only nontrophic links. However, removing mutualism or herbivory had a much larger effect in the extended plant-pollinator webs. Species removal even affected groups that were not directly connected to those that were removed, as we found by excluding a small number of parasitoids. These results suggest that including additional species in the network provides far more information than additional interactions for this aspect of network structure. Our methods provide a useful framework for simplifying networks to their essential structure, allowing us to identify generalities in network structure and better understand the roles species play in their communities.     

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Background

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16?S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

Conclusions

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.     

Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso

With the increasing availability of microbiome 16S data, network estimation has become a useful approach to studying the interactions between microbial taxa. Network estimation on a set of variables is frequently explored using graphical models, in which the relationship between two variables is modeled via their conditional dependency given the other variables. Various methods for sparse inverse covariance estimation have been proposed to estimate graphical models in the high-dimensional setting, including graphical lasso. However, current methods do not address the compositional count nature of microbiome data, where abundances of microbial taxa are not directly measured, but are reflected by the observed counts in an error-prone manner. Adding to the challenge is that the sum of the counts within each sample, termed “sequencing depth,” is an experimental technicality that carries no biological information but can vary drastically across samples. To address these issues, we develop a new approach to network estimation, called BC-GLASSO (bias-corrected graphical lasso), which models the microbiome data using a logistic normal multinomial distribution with the sequencing depths explicitly incorporated, corrects the bias of the naive empirical covariance estimator arising from the heterogeneity in sequencing depths, and builds the inverse covariance estimator via graphical lasso. We demonstrate the advantage of BC-GLASSO over current approaches to microbial interaction network estimation under a variety of simulation scenarios. We also illustrate the efficacy of our method in an application to a human microbiome data set.

     

镉离子污染条件下微生物群落中细菌与藻类的相互作用

【背景】水体微生物有着丰富的多样性,不同种类的微生物之间的相互作用对水体生态系统的组成结构与功能具有重要影响。水体内的藻类与某些微生物可以发生多种相互作用,然而人们对逆境条件下的菌藻有益相互作用尚缺乏深入研究。【目的】为了研究镉对水体微生物群落的影响以及镉胁迫下菌藻之间可能的相互作用。【方法】本研究运用了基于16S rRNA基因的高通量测序技术,分析在不同Cd~(2+)条件下微生物群落结构的变化,利用微生物相互作用网络分析菌藻之间可能发生的相互作用。【结果】通过分离培养筛选出了与集胞藻PCC6803互作抗Cd~(2+)的关键细菌Y9菌株。【结论】研究结果表明Y9菌株属于Phyllobacteriaceae科,与微生物群落组成和微生物互作网络的分析结果相符。本研究为探索水体环境中微生物种间相互作用、菌藻互作抗Cd~(2+)的生态效应提供参考依据。     

Functional potential and assembly of microbes from sediments in a lake bay and adjoining river ecosystem for polycyclic aromatic hydrocarbon biodegradation

Lake and adjoining river ecosystems are ecologically and economically valuable and are heavily threatened by anthropogenic activities. Determining the inherent capacity of ecosystems for polycyclic aromatic hydrocarbon (PAH) biodegradation can help quantify environmental impacts on the functioning of ecosystems, especially on that of the microbial community. Here, PAH biodegradation potential was compared between sediments collected from a lake bay (LS) and an adjoining river (RS) ecosystem. Microbial community composition, function, and their co-occurrence patterns were also explored. In the RS, the biodegradation rates (K_D) of pyrene or PAH were almost two orders of magnitude higher than those in the LS. Sediment functional community structure and network interactions were dramatically different between the LS and RS. Although PAH degradation genes (p450aro, quinoline, and qorl) were detected in the LS, the community activity of these genes needed to be biostimulated for accelerated bioremediation. In contrast, functional communities in the RS were capable of spontaneous natural attenuation of PAH. The degradation of PAH in the RS also required coordinated response of the complex functional community. Taken together, elucidating functions and network interactions in sediment microbial communities and their responses to environmental changes are very important for the bioremediation of anthropogenic toxic contaminants.     

Microbial trophic interactions in aquatic microcosms designed for testing genetically engineered microorganisms: A field comparison

Microcosms may potentially be used as tools for evaluating the fate and effects of genetically engineered microorganisms released into the environment. Extrapolation of data to the field, however, requires that the correspondence between microcosm and field is known. Microbial trophic interactions within the microbial loop were compared quantitatively and qualitatively between field and microcosms containing estuarine water with and without intact sediment cores. The comparison showed that whereas proportions between trophic levels in microcosms were qualitatively similar to those in the field, rates of microbial processes were from 25 to 40% lower in microcosms. Nitrogen cycling was disrupted in microcosms incubated in the dark to eliminate primary production. Examination of the microbial parameters further suggests that sediment in microcosms may be an important factor regulating the bacterial trophic level. These results demonstrate that analysis of microbial trophic interactions is a sensitive method for the field comparison of aquatic microcosms and a potentially useful tool in the risk assessment of genetically engineered microorganisms. Offprint requests to: N. Kroer.     

Host‐derived population genomics data provides insights into bacterial and diatom composition of the killer whale skin

Recent exploration into the interactions and relationship between hosts and their microbiota has revealed a connection between many aspects of the host's biology, health and associated micro‐organisms. Whereas amplicon sequencing has traditionally been used to characterize the microbiome, the increasing number of published population genomics data sets offers an underexploited opportunity to study microbial profiles from the host shotgun sequencing data. Here, we use sequence data originally generated from killer whale Orcinus orca skin biopsies for population genomics, to characterize the skin microbiome and investigate how host social and geographical factors influence the microbial community composition. Having identified 845 microbial taxa from 2.4 million reads that did not map to the killer whale reference genome, we found that both ecotypic and geographical factors influence community composition of killer whale skin microbiomes. Furthermore, we uncovered key taxa that drive the microbiome community composition and showed that they are embedded in unique networks, one of which is tentatively linked to diatom presence and poor skin condition. Community composition differed between Antarctic killer whales with and without diatom coverage, suggesting that the previously reported episodic migrations of Antarctic killer whales to warmer waters associated with skin turnover may control the effects of potentially pathogenic bacteria such as Tenacibaculum dicentrarchi. Our work demonstrates the feasibility of microbiome studies from host shotgun sequencing data and highlights the importance of metagenomics in understanding the relationship between host and microbial ecology.     

Meta-network: optimized species-species network analysis for microbial communities

Background

The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need.

Results

Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining.

Conclusions

Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks.

     

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.     

GI-POP: A combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project.     

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

Background

Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities.

Methods

We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third “mediator” variable. These “mediator” variables appear to modulate the associations between pairs of bacteria.

Results

Using this analysis, we were able to assess the OTUs’ ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors.

Conclusions

By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download (http://bitbucket.org/charade/elsa).

     

Ecological control analysis: being(s) in control of mass flux and metabolite concentrations in anaerobic degradation processes

Identification of the functional groups of microorganisms that are predominantly in control of fluxes through, and concentrations in, microbial networks would benefit microbial ecology and environmental biotechnology: the properties of those controlling microorganisms could be studied or monitored specifically or their activity could be modulated in attempts to manipulate the behaviour of such networks. Herein we present ecological control analysis (ECA) as a versatile mathematical framework that allows for the quantification of the control of each functional group in a microbial network on its process rates and concentrations of intermediates. In contrast to current views, we show that rates of flow of matter are not always limited by a single functional group; rather flux control can be distributed over several groups. Also, control over intermediate concentrations is always shared. Because of indirect interactions, through other functional groups, the concentration of an intermediate can also be controlled by functional groups not producing or consuming it. Ecological control analysis is illustrated by a case study on the anaerobic degradation of organic matter, using experimental data obtained from the literature. During anaerobic degradation, fermenting microorganisms interact with terminal electron-accepting microorganisms (e.g. halorespirers, methanogens). The analysis indicates that flux control mainly resides with fermenting microorganisms, but can shift to the terminal electron-accepting microorganisms under less favourable redox conditions. Paradoxically, halorespiring microorganisms do not control the rate of perchloroethylene and trichloroethylene degradation even though they catalyse those processes themselves.     

Headwaters are critical reservoirs of microbial diversity for fluvial networks

Streams and rivers form conspicuous networks on the Earth and are among nature''s most effective integrators. Their dendritic structure reaches into the terrestrial landscape and accumulates water and sediment en route from abundant headwater streams to a single river mouth. The prevailing view over the last decades has been that biological diversity also accumulates downstream. Here, we show that this pattern does not hold for fluvial biofilms, which are the dominant mode of microbial life in streams and rivers and which fulfil critical ecosystem functions therein. Using 454 pyrosequencing on benthic biofilms from 114 streams, we found that microbial diversity decreased from headwaters downstream and especially at confluences. We suggest that the local environment and biotic interactions may modify the influence of metacommunity connectivity on local biofilm biodiversity throughout the network. In addition, there was a high degree of variability in species composition among headwater streams that could not be explained by geographical distance between catchments. This suggests that the dendritic nature of fluvial networks constrains the distributional patterns of microbial diversity similar to that of animals. Our observations highlight the contributions that headwaters make in the maintenance of microbial biodiversity in fluvial networks.     

A road map for the development of community systems (CoSy) biology

Microbial interactions are essential for all global geochemical cycles and have an important role in human health and disease. Although we possess general knowledge about the major processes within a microbial community, we are presently unable to decipher what role individual microorganisms have and how their individual actions influence others in the community. We also have limited knowledge with which to predict the effects of microbial interactions and community composition on the environment and vice versa. In this Opinion article, we describe how community systems (CoSy) biology will enable us to decode these complex relationships and will therefore improve our understanding of individual members of the community and the modes of interactions in which they engage.     

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, includ-ing KEGG orthologous groups and the evolutionary genealogy of genes:Non-supervised Ortholo-gous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 dia-betes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D.