Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

Identification of microbiota dynamics using robust parameter estimation methods

The compositions of in-host microbial communities (microbiota) play a significant role in host health, and a better understanding of the microbiota's role in a host's transition from health to disease or vice versa could lead to novel medical treatments. One of the first steps toward this understanding is modeling interaction dynamics of the microbiota, which can be exceedingly challenging given the complexity of the dynamics and difficulties in collecting sufficient data. Methods such as principal differential analysis, dynamic flux estimation, and others have been developed to overcome these challenges. Despite their advantages, these methods are still vastly underutilized in fields such as mathematical biology, and one potential reason for this is their sophisticated implementation. While this paper focuses on applying principal differential analysis to microbiota data, we also provide comprehensive details regarding the derivation and numerics of this method and include a functional implementation for readers' benefit. For further validation of these methods, we demonstrate the feasibility of principal differential analysis using simulation studies and then apply the method to intestinal and vaginal microbiota data. In working with these data, we capture experimentally confirmed dynamics while also revealing potential new insights into the system dynamics.

Myriophyllum aquaticum Constructed Wetland Effectively Removes Nitrogen in Swine Wastewater

Removal of nitrogen (N) is a critical aspect in the functioning of constructed wetlands (CWs), and the N treatment in CWs depends largely on the presence and activity of macrophytes and microorganisms. However, the effects of plants on microorganisms responsible for N removal are poorly understood. In this study, a three-stage surface flow CW was constructed in a pilot-scale within monospecies stands of Myriophyllum aquaticum to treat swine wastewater. Steady-state conditions were achieved throughout the 600-day operating period, and a high (98.3%) average ammonia removal efficiency under a N loading rate of 9 kg ha^-1 d^-1 was observed. To determine whether this high efficiency was associated with the performance of active microbes, the abundance, structure, and interactions of microbial community were compared in the unvegetated and vegetated samples. Real-time quantitative polymerase chain reactions showed the abundances of nitrifying genes (archaeal and bacterial amoA) and denitrifying genes (nirS, nirK, and nosZ) were increased significantly by M. aquaticum in the sediments, and the strongest effects were observed for the archaeal amoA (218-fold) and nirS genes (4620-fold). High-throughput sequencing of microbial 16S rRNA gene amplicons showed that M. aquaticum greatly changed the microbial community, and ammonium oxidizers (Nitrosospira and Nitrososphaera), nitrite-oxidizing bacteria (Nitrospira), and abundant denitrifiers including Rhodoplanes, Bradyrhizobium, and Hyphomicrobium, were enriched significantly in the sediments. The results of a canonical correspondence analysis and Mantle tests indicated that M. aquaticum may shift the sediment microbial community by changing the sediment chemical properties. The enriched nitrifiers and denitrifiers were distributed widely in the vegetated sediments, showing positive ecological associations among themselves and other bacteria based on phylogenetic molecular ecological networks.

Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing

Microbial ecology as a scientific field is fundamentally driven by technological advance. The past decade's revolution in DNA sequencing cost and throughput has made it possible for most research groups to map microbial community composition in environments of interest. However, the computational and statistical methodology required to analyse this kind of data is often not part of the biologist training. In this review, we give a historical perspective on the use of sequencing data in microbial ecology and restate the current need for this method; but also highlight the major caveats with standard practices for handling these data, from sample collection and library preparation to statistical analysis. Further, we outline the main new analytical tools that have been developed in the past few years to bypass these caveats, as well as highlight the major requirements of common statistical practices and the extent to which they are applicable to microbial data. Besides delving into the meaning of select alpha- and beta-diversity measures, we give special consideration to techniques for finding the main drivers of community dissimilarity and for interaction network construction. While every project design has specific needs, this review should serve as a starting point for considering what options are available.

Common gene-network signature of different neurological disorders and their potential implications to neuroAIDS

The neurological complications of AIDS (neuroAIDS) during the infection of human immunodeficiency virus (HIV) are symptomized by non-specific, multifaceted neurological conditions and therefore, defining a specific diagnosis/treatment mechanism(s) for this neuro-complexity at the molecular level remains elusive. Using an in silico based integrated gene network analysis we discovered that HIV infection shares convergent gene networks with each of twelve neurological disorders selected in this study. Importantly, a common gene network was identified among HIV infection, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and age macular degeneration. An mRNA microarray analysis in HIV-infected monocytes showed significant changes in the expression of several genes of this in silico derived common pathway which suggests the possible physiological relevance of this gene-circuit in driving neuroAIDS condition. Further, this unique gene network was compared with another in silico derived novel, convergent gene network which is shared by seven major neurological disorders (Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Age Macular Degeneration, Amyotrophic Lateral Sclerosis, Vascular Dementia, and Restless Leg Syndrome). These networks differed in their gene circuits; however, in large, they involved innate immunity signaling pathways, which suggests commonalities in the immunological basis of different neuropathogenesis. The common gene circuits reported here can provide a prospective platform to understand how gene-circuits belonging to other neuro-disorders may be convoluted during real-time neuroAIDS condition and it may elucidate the underlying-and so far unknown-genetic overlap between HIV infection and neuroAIDS risk. Also, it may lead to a new paradigm in understanding disease progression, identifying biomarkers, and developing therapies.

Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor

Marine archaea are critical contributors to global carbon and nitrogen redox cycles, but their temporal variability and microbial associations across the water column are poorly known. We evaluated seasonal variability of free living (0.2-1 μm size fraction) Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) communities and their associations with the microbial community from surface to seafloor (890 m) over 5 years by 16S rRNA V4-V5 gene sequencing. MGI and MGII communities demonstrated distinct compositions at different depths, and seasonality at all depths. Microbial association networks at 150 m, 500 m and 890 m, revealed diverse assemblages of MGI (presumed ammonia oxidizers) and Nitrospina taxa (presumed dominant nitrite oxidizers, completing the nitrification process), suggesting distinct MGI-Nitrospina OTUs are responsible for nitrification at different depths and seasons, and depth- related and seasonal variability in nitrification could be affected by alternating MGI-Nitrospina assemblages. MGII taxa also showed distinct correlations to possibly heterotrophic bacteria, most commonly to members of Marine Group A, Chloroflexi, Marine Group B, and SAR86. Thus, both MGI and MGII likely have dynamic associations with bacteria based on similarities in activity or other interactions that select for distinct microbial assemblages over time. The importance of MGII taxa as members of the heterotrophic community previously reported for photic zone appears to apply throughout the water column.

The response of archaeal species to seasonal variables in a subtropical aerated soil: insight into the low abundant methanogens

Archaea are cosmopolitan in aerated soils around the world. While the dominance of Thaumarchaeota has been reported in most soils, the methanogens are recently found to be ubiquitous but with low abundances in the aerated soil globally. However, the seasonal changes of Archaea community in the aerated soils are still in the mist. In this study, we investigated the change of Archaea in the context of environmental variables over a period of 12 months in a subtropical soil on the Chongming Island, China. The results showed that Nitrososphaera spp. were the dominant archaeal population while the methanogens were in low proportions but highly diverse (including five genera: Methanobacterium, Methanocella, Methanosaeta, Methanosarcina, and Methanomassiliicoccus) in the aerated soil samples determined by high throughput sequencing. A total of 126 LSA correlations were found in the dataset including all the 72 archaeal OTUs and 8 environmental factors. A significance index defined as the pagerank score of each OTU divided by its relative abundance was used to evaluate the significance of each OTU. The results showed that five out of 17 methanogen OTUs were significantly positively correlated with temperature, suggesting those methanogens might increase with temperature rather than being dormant in the aerated soils. Given the metabolic response of methanogens to temperature under aerated soil conditions, their contribution to the global methane cycle warrants evaluation.

Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm

Background

Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges.

Results

This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN.

Conclusions

Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.

Disentangling Interactions in the Microbiome: A Network Perspective

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

Phytoremediation: State-of-the-art and a key role for the plant microbiome in future trends and research prospects

Phytoremediation is increasingly adopted as a more sustainable approach for soil remediation. However, significant advances in efficiency are still necessary to attain higher levels of environmental and economic sustainability. Current interventions do not always give the expected outcomes in field settings due to an incomplete understanding of the multicomponent biological interactions. New advances in -omics are gradually implemented for studying microbial communities of polluted land in situ. This opens new perspectives for the discovery of biodegradative strains and provides us new ways of interfering with microbial communities to enhance bioremediation rates. This review presents retrospectives and future perspectives for plant microbiome studies relevant to phytoremediation, as well as some knowledge gaps in this promising research field. The implementation of phytoremediation in soil clean-up management systems is discussed, and an overview of the promoting factors that determine the growth of the phytoremediation market is given. Continuous growth is expected since elimination of contaminants from the environment is demanded. The evolution of scientific thought from a reductionist view to a more holistic approach will boost phytoremediation as an efficient and reliable phytotechnology. It is anticipated that phytoremediation will prove the most promising for organic contaminant degradation and bioenergy crop production on marginal land.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally.

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

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

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Predicting microbial interactions through computational approaches

Microorganisms play a vital role in various ecosystems and characterizing interactions between them is an essential step towards understanding the organization and function of microbial communities. Computational prediction has recently become a widely used approach to investigate microbial interactions. We provide a thorough review of emerging computational methods organized by the type of data they employ. We highlight three major challenges in inferring interactions using metagenomic survey data and discuss the underlying assumptions and mathematics of interaction inference algorithms. In addition, we review interaction prediction methods relying on metabolic pathways, which are increasingly used to reveal mechanisms of interactions. Furthermore, we also emphasize the importance of mining the scientific literature for microbial interactions - a largely overlooked data source for experimentally validated interactions.

Dynamic models of the complex microbial metapopulation of lake mendota

Like many other environments, Lake Mendota, WI, USA, is populated by many thousand microbial species. Only about 1,000 of these constitute between 80 and 99% of the total microbial community, depending on the season, whereas the remaining species are rare. The functioning and resilience of the lake ecosystem depend on these microorganisms, and it is therefore important to understand their dynamics throughout the year. We propose a two-layered set of dynamic mathematical models that capture and interpret the yearly abundance patterns of the species within the metapopulation. The first layer analyzes the interactions between 14 subcommunities (SCs) that peak at different times of the year and together contain all species whereas the second layer focuses on interactions between individual species and SCs. Each SC contains species from numerous families, genera, and phyla in strikingly different abundances. The dynamic models quantify the importance of environmental factors in shaping the dynamics of the lake’s metapopulation and reveal positive or negative interactions between species and SCs. Three environmental factors, namely temperature, ammonia/phosphorus, and nitrate+nitrite, positively affect almost all SCs, whereas by far the most interactions between SCs are inhibitory. As far as the interactions can be independently validated, they are supported by literature information. The models are quite robust and permit predictions of species abundances over many years both, under the assumption that conditions do not change drastically, or in response to environmental perturbations.

Microbial community modeling using reliability theory

Linking microbial community composition with the corresponding ecosystem functions remains challenging. Because microbial communities can differ in their functional responses, this knowledge gap limits ecosystem assessment, design and management. To develop models that explicitly incorporate microbial populations and guide efforts to characterize their functional differences, we propose a novel approach derived from reliability engineering. This reliability modeling approach is illustrated here using a microbial ecology dataset from denitrifying bioreactors. Reliability modeling is well-suited for analyzing the stability of complex networks composed of many microbial populations. It could also be applied to evaluate the redundancy within a particular biochemical pathway in a microbial community. Reliability modeling allows characterization of the system's resilience and identification of failure-prone functional groups or biochemical steps, which can then be targeted for monitoring or enhancement. The reliability engineering approach provides a new perspective for unraveling the interactions between microbial community diversity, functional redundancy and ecosystem services, as well as practical tools for the design and management of engineered ecosystems.

Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China

Soil microbiota play a critical role in soil biogeochemical processes and have a profound effect on soil functions. Recent studies have revealed microbial co-occurrence patterns in soil microbial communities, yet the geographic pattern of topological features in soil microbial co-occurrence networks at the continental scale are largely unknown. Here, we investigated the shifts of topological features in co-occurrence networks inferred from soil microbiota along a continental scale in eastern China. Integrating archaeal, bacterial and fungal community datasets, we inferred a meta-community co-occurrence network and analyzed node-level and network-level topological shifts associated with five climatic regions. Both node-level and network-level topological features revealed geographic patterns wherein microorganisms in the northern regions had closer relationships but had a lower interaction influence than those in the southern regions. We further identified topological differences associated with taxonomic groups and demonstrated that co-occurrence patterns were random for archaea and non-random for bacteria and fungi. Given that microbial interactions may contribute to soil functions more than species diversity, this geographic shift of topological features provides new insight into studying microbial biogeographic patterns, their organization and impacts on soil-associated function.

Molecular Ecology of Hypersaline Microbial Mats: Current Insights and New Directions

Microbial mats are unique geobiological ecosystems that form as a result of complex communities of microorganisms interacting with each other and their physical environment. Both the microorganisms present and the network of metabolic interactions govern ecosystem function therein. These systems are often found in a range of extreme environments, and those found in elevated salinity have been particularly well studied. The purpose of this review is to briefly describe the molecular ecology of select model hypersaline mat systems (Guerrero Negro, Shark Bay, S’Avall, and Kiritimati Atoll), and any potentially modulating effects caused by salinity to community structure. In addition, we discuss several emerging issues in the field (linking function to newly discovered phyla and microbial dark matter), which illustrate the changing paradigm that is seen as technology has rapidly advanced in the study of these extreme and evolutionally significant ecosystems.

Putative bacterial interactions from metagenomic knowledge with an integrative systems ecology approach

Following the trend of studies that investigate microbial ecosystems using different metagenomic techniques, we propose a new integrative systems ecology approach that aims to decipher functional roles within a consortium through the integration of genomic and metabolic knowledge at genome scale. For the sake of application, using public genomes of five bacterial strains involved in copper bioleaching: Acidiphilium cryptum, Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans, we first reconstructed a global metabolic network. Next, using a parsimony assumption, we deciphered sets of genes, called Sets from Genome Segments (SGS), that (1) are close on their respective genomes, (2) take an active part in metabolic pathways and (3) whose associated metabolic reactions are also closely connected within metabolic networks. Overall, this SGS paradigm depicts genomic functional units that emphasize respective roles of bacterial strains to catalyze metabolic pathways and environmental processes. Our analysis suggested that only few functional metabolic genes are horizontally transferred within the consortium and that no single bacterial strain can accomplish by itself the whole copper bioleaching. The use of SGS pinpoints a functional compartmentalization among the investigated species and exhibits putative bacterial interactions necessary for promoting these pathways.

Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells

The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN). CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.

Network of Interactions Between Ciliates and Phytoplankton During Spring

The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile) as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic/omnivorous species, and highlighted the role of Halteria/Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA) proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species.

Cross-biome comparison of microbial association networks

Clinical and environmental meta-omics studies are accumulating an ever-growing amount of microbial abundance data over a wide range of ecosystems. With a sufficiently large sample number, these microbial communities can be explored by constructing and analyzing co-occurrence networks, which detect taxon associations from abundance data and can give insights into community structure. Here, we investigate how co-occurrence networks differ across biomes and which other factors influence their properties. For this, we inferred microbial association networks from 20 different 16S rDNA sequencing data sets and observed that soil microbial networks harbor proportionally fewer positive associations and are less densely interconnected than host-associated networks. After excluding sample number, sequencing depth and beta-diversity as possible drivers, we found a negative correlation between community evenness and positive edge percentage. This correlation likely results from a skewed distribution of negative interactions, which take place preferentially between less prevalent taxa. Overall, our results suggest an under-appreciated role of evenness in shaping microbial association networks.

A network-based approach to disturbance transmission through microbial interactions

Microbes numerically dominate aquatic ecosystems and play key roles in the biogeochemistry and the health of these environments. Due to their short generations times and high diversity, microbial communities are among the first responders to environmental changes, including natural and anthropogenic disturbances such as storms, pollutant releases, and upwelling. These disturbances affect members of the microbial communities both directly and indirectly through interactions with impacted community members. Thus, interactions can influence disturbance propagation through the microbial community by either expanding the range of organisms affected or buffering the influence of disturbance. For example, interactions may expand the number of disturbance-affected taxa by favoring a competitor or buffer the impacts of disturbance when a potentially disturbance-responsive clade’s growth is limited by an essential microbial partner. Here, we discuss the potential to use inferred ecological association networks to examine how disturbances propagate through microbial communities focusing on a case study of a coastal community’s response to a storm. This approach will offer greater insight into how disturbances can produce community-wide impacts on aquatic environments following transient changes in environmental parameters.

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains

BACKGROUND

Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies.

RESULTS

By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns.

AVAILABILITY

The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Rehabilitating the cyanobacteria - niche partitioning, resource use efficiency and phytoplankton community structure during diazotrophic cyanobacterial blooms

Blooms of nitrogen-fixing cyanobacteria are recurrent phenomena in marine and freshwater habitats, and their supplying role in aquatic biogeochemical cycles is generally considered vital. The objective of this study was to analyse whether an increasing proportion of nitrogen-fixing cyanobacteria affects (i) the composition of the non-diazotrophic component of ambient phytoplankton communities and (ii) resource use efficiency (RUE; ratio of Chl a to total nutrients) - an important ecosystem function. We hypothesize that diazotrophs increase community P use and decrease N use efficiencies, as new N is brought into the system, relaxing N, and concomitantly aggravating P limitation. We test this by analysing an extensive data set from the Baltic Sea (> 3700 quantitative phytoplankton samples), known to harbour conspicuous and recurrent blooms of Nodularia spumigena and Aphanizomenon sp.System-level phosphorus use efficiency (RUEP) was positively related to high proportion of diazotrophic cyanobacteria, suggesting aggravation of phosphorus limitation. However, concomitant decrease of nitrogen use efficiency (RUEN) was not observed. Nodularia spumigena, a dominant diazotroph and a notorious toxin producer, had a significantly stronger relationship with RUEP, compared to the competing non-toxic Aphanizomenon sp., confirming niche differentiation in P acquisition strategies between the major bloom-forming cyanobacterial species in the Baltic Sea. Nodularia occurrences were associated with stronger temperature stratification in more offshore environments, indicating higher reliance on in situ P regeneration.By using constrained and unconstrained ordination, permutational multivariate analysis of variance and local similarity analysis, we show that diazotrophic cyanobacteria explained no more than a few percentage of the ambient phytoplankton community variation. The analyses furthermore yielded rather evenly distributed negative and positive effects on individual co-occurring phytoplankton taxa, with no obvious phylogenetic or functional trait-based patterns. Synthesis. Our study reveals that despite the widely acknowledged noxious impacts of cyanobacterial blooms, the overall effect on phytoplankton community structure is minor. There are no predominantly positive or negative associations with ambient phytoplankton species. Species-specific niche differences in cyanobacterial resource acquisition affect important ecosystem functions, such as biomass production per unit limiting resource.

Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron

Marine picocyanobacteria, comprised of the genera Synechococcus and Prochlorococcus, are the most abundant and widespread primary producers in the ocean. More than 20 genetically distinct clades of marine Synechococcus have been identified, but their physiology and biogeography are not as thoroughly characterized as those of Prochlorococcus. Using clade-specific qPCR primers, we measured the abundance of 10 Synechococcus clades at 92 locations in surface waters of the Atlantic and Pacific Oceans. We found that Synechococcus partition the ocean into four distinct regimes distinguished by temperature, macronutrients and iron availability. Clades I and IV were prevalent in colder, mesotrophic waters; clades II, III and X dominated in the warm, oligotrophic open ocean; clades CRD1 and CRD2 were restricted to sites with low iron availability; and clades XV and XVI were only found in transitional waters at the edges of the other biomes. Overall, clade II was the most ubiquitous clade investigated and was the dominant clade in the largest biome, the oligotrophic open ocean. Co-occurring clades that occupy the same regime belong to distinct evolutionary lineages within Synechococcus, indicating that multiple ecotypes have evolved independently to occupy similar niches and represent examples of parallel evolution. We speculate that parallel evolution of ecotypes may be a common feature of diverse marine microbial communities that contributes to functional redundancy and the potential for resiliency.

Niche distribution and influence of environmental parameters in marine microbial communities: a systematic review

Associations between microorganisms occur extensively throughout Earth’s oceans. Understanding how microbial communities are assembled and how the presence or absence of species is related to that of others are central goals of microbial ecology. Here, we investigate co-occurrence associations between marine prokaryotes by combining 180 new and publicly available metagenomic datasets from different oceans in a large-scale meta-analysis. A co-occurrence network was created by calculating correlation scores between the abundances of microorganisms in metagenomes. A total of 1,906 correlations amongst 297 organisms were detected, segregating them into 11 major groups that occupy distinct ecological niches. Additionally, by analyzing the oceanographic parameters measured for a selected number of sampling sites, we characterized the influence of environmental variables over each of these 11 groups. Clustering organisms into groups of taxa that have similar ecology, allowed the detection of several significant correlations that could not be observed for the taxa individually.

Metagenomics meets time series analysis: unraveling microbial community dynamics

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research.

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes

Interactions among microbes and stratification across depths are both believed to be important drivers of microbial communities, though little is known about how microbial associations differ between and across depths. We have monitored the free-living microbial community at the San Pedro Ocean Time-series station, monthly, for a decade, at five different depths: 5 m, the deep chlorophyll maximum layer, 150 m, 500 m and 890 m (just above the sea floor). Here, we introduce microbial association networks that combine data from multiple ocean depths to investigate both within- and between-depth relationships, sometimes time-lagged, among microbes and environmental parameters. The euphotic zone, deep chlorophyll maximum and 890 m depth each contain two negatively correlated 'modules' (groups of many inter-correlated bacteria and environmental conditions) suggesting regular transitions between two contrasting environmental states. Two-thirds of pairwise correlations of bacterial taxa between depths lagged such that changes in the abundance of deeper organisms followed changes in shallower organisms. Taken in conjunction with previous observations of seasonality at 890 m, these trends suggest that planktonic microbial communities throughout the water column are linked to environmental conditions and/or microbial communities in overlying waters. Poorly understood groups including Marine Group A, Nitrospina and AEGEAN-169 clades contained taxa that showed diverse association patterns, suggesting these groups contain multiple ecological species, each shaped by different factors, which we have started to delineate. These observations build upon previous work at this location, lending further credence to the hypothesis that sinking particles and vertically migrating animals transport materials that significantly shape the time-varying patterns of microbial community composition.

16S rRNA gene high-throughput sequencing data mining of microbial diversity and interactions

The ubiquitous occurrence of microorganisms gives rise to continuous public concerns regarding their pathogenicity and threats to human environment, as well as potential engineering benefits in biotechnology. The development and wide application of environmental biotechnology, for example in bioenergy production, wastewater treatment, bioremediation, and drinking water disinfection, have been bringing us with both environmental and economic benefits. Strikingly, extensive applications of microscopic and molecular techniques since 1990s have allowed engineers to peep into the microbiology in "black box" of engineered microbial communities in biotechnological processes, providing guidelines for process design and optimization. Recently, revolutionary advances in DNA sequencing technologies and rapidly decreasing costs are altering conventional ways of microbiology and ecology research, as it launches an era of next-generation sequencing (NGS). The principal research burdens are now transforming from traditional labor-intensive wet-lab experiments to dealing with analysis of huge and informative NGS data, which is computationally expensive and bioinformatically challenging. This study discusses state-of-the-art bioinformatics and statistical analyses of 16S ribosomal RNA (rRNA) gene high-throughput sequencing (HTS) data from prevalent NGS platforms to promote its applications in exploring microbial diversity of functional and pathogenic microorganisms, as well as their interactions in biotechnological processes.

Marine microbial community dynamics and their ecological interpretation

Recent advances in studying the dynamics of marine microbial communities have shown that the composition of these communities follows predictable patterns and involves complex network interactions, which shed light on the underlying processes regulating these globally important organisms. Such 'holistic' (or organism- and system-based) studies of these communities complement popular reductionist, often culture-based, approaches for understanding organism function one gene or protein at a time. In this Review, we summarize our current understanding of marine microbial community dynamics at various scales, from hours to decades. We also explain how the data illustrate community resilience and seasonality, and reveal interactions among microorganisms.

An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics

Algal blooms are a worldwide phenomenon and the biological interactions that underlie their regulation are only just beginning to be understood. It is established that algal microorganisms associate with many other ubiquitous, oceanic organisms, but the interactions that lead to the dynamics of bloom formation are currently unknown. To address this gap, we used network approaches to investigate the association patterns among microeukaryotes and bacterioplankton in response to a natural Scrippsiella trochoidea bloom. This is the first study to apply network approaches to bloom dynamics. To this end, terminal restriction fragment (T-RF) length polymorphism analysis showed dramatic changes in community compositions of microeukaryotes and bacterioplankton over the blooming period. A variance ratio test revealed significant positive overall associations both within and between microeukaryotic and bacterioplankton communities. An association network generated from significant correlations between T-RFs revealed that S. trochoidea had few connections to other microeukaryotes and bacterioplankton and was placed on the edge. This lack of connectivity allowed for the S. trochoidea sub-network to break off from the overall network. These results allowed us to propose a conceptual model for explaining how changes in microbial associations regulate the dynamics of an algal bloom. In addition, key T-RFs were screened by principal components analysis, correlation coefficients, and network analysis. Dominant T-RFs were then identified through 18S and 16S rRNA gene clone libraries. Results showed that microeukaryotes clustered predominantly with Dinophyceae and Perkinsea while the majority of bacterioplankton identified were Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes. The ecologi-cal roles of both were discussed in the context of these findings.

Dynamics in microbial communities: unraveling mechanisms to identify principles

Diversity begets higher-order properties such as functional stability and robustness in microbial communities, but principles that inform conceptual (and eventually predictive) models of community dynamics are lacking. Recent work has shown that selection as well as dispersal and drift shape communities, but the mechanistic bases for assembly of communities and the forces that maintain their function in the face of environmental perturbation are not well understood. Conceptually, some interactions among community members could generate endogenous dynamics in composition, even in the absence of environmental changes. These endogenous dynamics are further perturbed by exogenous forcing factors to produce a richer network of community interactions and it is this 'system' that is the basis for higher-order community properties. Elucidation of principles that follow from this conceptual model requires identifying the mechanisms that (a) optimize diversity within a community and (b) impart community stability. The network of interactions between organisms can be an important element by providing a buffer against disturbance beyond the effect of functional redundancy, as alternative pathways with different combinations of microbes can be recruited to fulfill specific functions.

Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data

MOTIVATION

Identifying disease associated taxa and constructing networks for bacteria interactions are two important tasks usually studied separately. In reality, differentiation of disease associated taxa and correlation among taxa may affect each other. One genus can be differentiated because it is highly correlated with another highly differentiated one. In addition, network structures may vary under different clinical conditions. Permutation tests are commonly used to detect differences between networks in distinct phenotypes, and they are time-consuming.

RESULTS

In this manuscript, we propose a multilevel regularized regression method to simultaneously identify taxa and construct networks. We also extend the framework to allow construction of a common network and differentiated network together. An efficient algorithm with dual formulation is developed to deal with the large-scale n ≪ m problem with a large number of taxa (m) and a small number of samples (n) efficiently. The proposed method is regularized with a general Lp (p ∈ [0, 2]) penalty and models the effects of taxa abundance differentiation and correlation jointly. We demonstrate that it can identify both true and biologically significant genera and network structures.

AVAILABILITY AND IMPLEMENTATION

Software MLRR in MATLAB is available at http://biostatistics.csmc.edu/mlrr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bacterial community structure is indicative of chemical inputs in the Upper Mississippi River

Local and regional associations between bacterial communities and nutrient and chemical concentrations were assessed in the Upper Mississippi River in Minnesota to determine if community structure was associated with discrete types of chemical inputs associated with different land cover. Bacterial communities were characterized by Illumina sequencing of the V6 region of 16S rDNA and compared to >40 chemical and nutrient concentrations. Local bacterial community structure was shaped primarily by associations among bacterial orders. However, order abundances were correlated regionally with nutrient and chemical concentrations, and were also related to major land coverage types. Total organic carbon and total dissolved solids were among the primary abiotic factors associated with local community composition and co-varied with land cover. Escherichia coli concentration was poorly related to community composition or nutrient concentrations. Abundances of 14 bacterial orders were related to land coverage type, and seven showed significant differences in abundance (P ≤ 0.046) between forested or anthropogenically-impacted sites. This study identifies specific bacterial orders that were associated with chemicals and nutrients derived from specific land cover types and may be useful in assessing water quality. Results of this study reveal the need to investigate community dynamics at both the local and regional scales and to identify shifts in taxonomic community structure that may be useful in determining sources of pollution in the Upper Mississippi River.

Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant

Understanding environmental and biological influences on the dynamics of microbial communities has received great attention in microbial ecology. Here, utilizing large time-series 16S rRNA gene data, we show that in activated sludge of an environmentally important municipal wastewater treatment plant, 5-year temporal dynamics of bacterial community shows no significant seasonal succession, but is consistent with deterministic assemblage by taxonomic relatedness. Biological interactions are dominant drivers in determining the bacterial community assembly, whereas environmental conditions (mainly sludge retention time and inorganic nitrogen) partially explain phylogenetic and quantitative variances and indirectly influence bacterial assembly. We demonstrate a correlation-based statistical method to integrate bacterial association networks with their taxonomic affiliations to predict community-wide co-occurrence and co-exclusion patterns. The results show that although taxonomically closely related bacteria tend to positively co-occur (for example, out of a cooperative relationship), negative co-excluding correlations are deterministically observed between taxonomically less related species, probably implicating roles of competition in determining bacterial assembly. Overall, disclosures of the positive and negative species-species relations will improve our understanding of ecological niches occupied by unknown species and help to predict their biological functions in ecosystems.

Correlation between viral production and carbon mineralization under nitrate-reducing conditions in aquifer sediment

A variety of microbially mediated metabolic pathways impact biogeochemical cycling in terrestrial subsurface environments. However, the role that viruses have in influencing microbial mortality and microbial community structure is poorly understood. Here we investigated the production of viruses and change in microbial community structure within shallow alluvial aquifer sediment slurries amended with (13)C-labeled acetate and nitrate. Biostimulation resulted in production of viruses concurrent with acetate oxidation, (13)CO2 production and nitrate reduction. Interestingly, change in viral abundance was positively correlated to acetate consumption (r(2)=0.6252, P<0.05) and (13)CO2 production (r(2)=0.6572, P<0.05); whereas change in cell abundance was not correlated to acetate consumption or (13)CO2 production. Viral-mediated cell lysis has implications for microbial community structure. Betaproteobacteria predominated microbial community composition (62% of paired-end reads) upon inoculation but decreased in relative abundance and was negatively correlated to changes in viral abundance (r(2)=0.5036, P<0.05). As members of the Betaproteobacteria decreased, Gammaproteobacteria, specifically Pseudomonas spp., increased in relative abundance (82% of paired-end reads) and was positively correlated with the change in viral abundance (r(2)=0.5368, P<0.05). A nitrate-reducing bacterium, Pseudomonas sp. strain Alda10, was isolated from these sediments and produced viral-like particles with a filamentous morphology that did not result in cell lysis. Together, these results indicate that viruses are linked to carbon biogeochemistry and community structure in terrestrial subsurface sediments. The subsequent cell lysis has the potential to alter available carbon pools in subsurface environments, additionally controlling microbial community structure from the bottom-up.

Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called "errors-in-variables". Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct "keystone species", Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.

Shifts in the microbial community, nitrifiers and denitrifiers in the biofilm in a full-scale rotating biological contactor

The objective of this study was to investigate the microbial community shifts, especially nitrifiers and denitrifiers, in the biofilm of two rotating biological contactor (RBC) trains with different running times along the plug flowpath. The microbial consortia were profiled using multiple approaches, including 454 high-throughput sequencing of the V3-V4 region of 16S rRNA gene, clone libraries, and quantitative polymerase chain reaction (qPCR). The results demonstrated that (1) the overall microbial community at different locations had distinct patterns, that is, there were similar microbial communities at the beginnings of the two RBC trains and completely different populations at the ends of the two RBC trains; (2) nitrifiers, including ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB, Nitrosomonas) and nitrite-oxidizing bacteria (NOB, Nitrospira), increased in relative abundance in the biofilm along the flowpath, whereas denitrifiers (Rhodanobacter, Paracoccus, Thauera, and Azoarcus) markedly decreased; (3) the AOA were subdominant to the AOB in all sampled sections; and (4) strong ecological associations were shown among different bacteria. Overall, the results of this study provided more comprehensive information regarding the biofilm community composition and assemblies in full-scale RBCs.

Phytoplankton succession affects the composition of Polynucleobacter subtypes in humic lakes

Phytoplankton influence the composition of bacterial communities, but the taxonomic specificity of algal-bacterial interactions is unclear due to the aggregation of ecologically distinct bacterial populations by community characterization methods. Here we examine whether phytoplankton seasonal succession affects the composition of subtypes within the cosmopolitan freshwater bacterial genus Polynucleobacter. Changes in the composition of Polynucleobacter subtypes were characterized in samples collected weekly from May to August in 2003 and 2008 from three humic lakes using terminal restriction fragment length polymorphism fingerprinting of the protein-encoding cytochrome c oxidase ccoN gene. Changes in phytoplankton population abundances explained, on average, 30% of temporal variation in the composition of Polynucleobacter subtypes and the interaction between phytoplankton and the environment explained an additional 18% of temporal variation. The effect of phytoplankton on specific Polynucleobacter subtypes was experimentally confirmed by changes in Polynucleobacter subtype composition following incubation with different phytoplankton assemblages or a no-phytoplankton control. Phytoplankton-associated subtypes and differentiation in substrate use among subtypes likely contribute to the effects of phytoplankton on Polynucleobacter subtype composition. Interactions between unique Polynucleobacter populations and phytoplankton highlight the ecological significance and specificity of species interactions in freshwater communities.

Deciphering microbial interactions and detecting keystone species with co-occurrence networks

Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

Specificity and temporal dynamics of complex bacteria--sponge symbiotic interactions

Microbes are known to form intricate and intimate relationships with most animal and plant taxa. Microbe--host symbiotic associations are poorly explored in comparison with other species interaction networks. The current paradigm on symbiosis research stems from species-poor systems where pairwise and reciprocally specialized interactions between a single microbe and a single host that coevolve are the norm. These symbioses involving just a few species are fascinating in their own right, but more diverse and complex host-associated microbial communities are increasingly found, with new emerging questions that require new paradigms and approaches. Here we adopt an intermediate complexity approach to study the specificity, phylogenetic community structure, and temporal variability of the subset of the most abundant bacteria associated with different sponge host species with diverse eco-evolutionary characteristics. We do so by using a monthly resolved annual temporal series of host-associated and free-living bacteria. Bacteria are very abundant and diverse within marine sponges, and these symbiotic interactions are hypothesized to have a very ancient origin. We show that host-bacteria reciprocal specialization depends on the temporal scale and level of taxonomic aggregation considered. Sponge hosts with similar eco-evolutionary characteristics (e.g., volume of tissue corresponding to microbes, water filtering rates, and microbial transmission type) have similar bacterial phylogenetic community structure when looking at interactions aggregated over time. In general, sponge hosts hypothesized to form more intricate relationships with bacteria show a remarkably persistent bacterial community over time. Other hosts, however, show a large turnover similar to that observed for free-living bacterioplankton. Our study highlights the importance of exploring temporal variability in host--microbe interaction networks if we aim to determine how specific and persistent these poorly explored but extremely common interactions are.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease. However, most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystem's structure and its response to external perturbations, therefore not allowing accurate temporal predictions. In this article, we develop a method to analyze temporal community data accounting also for time-dependent external perturbations. In particular, this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics. Using then data from a mouse experiment under antibiotic perturbations, we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. As a result, our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection.