Microbial interactions: from networks to models

CompNet: a GUI based tool for comparison of multiple biological interaction networks

Background

Network visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks.

Results

In this paper, we present CompNet - a graphical user interface based network comparison tool, which allows visual comparison of multiple networks based on various network metrics. CompNet allows interactive visualization of the union, intersection and/or complement regions of a selected set of networks. Different visualization features (e.g. pie-nodes, edge-pie matrix, etc.) aid in easy identification of the key nodes/interactions and their significance across the compared networks. The tool also allows one to perform network comparisons on the basis of neighbourhood architecture of constituent nodes and community compositions, a feature particularly useful while analyzing biological networks. To demonstrate the utility of CompNet, we have compared a (time-series) human gene-expression dataset, post-infection by two strains of Mycobacterium tuberculosis, overlaid on the human protein-protein interaction network. Using various functionalities of CompNet not only allowed us to comprehend changes in interaction patterns over the course of infection, but also helped in inferring the probable fates of the host cells upon infection by the two strains.

Conclusions

CompNet is expected to be a valuable visual data mining tool and is freely available for academic use from http://metagenomics.atc.tcs.com/compnet/ or http://121.241.184.233/compnet/

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1013-x) contains supplementary material, which is available to authorized users.

The role of the local microbial ecosystem in respiratory health and disease

Respiratory tract infections are a major global health concern, accounting for high morbidity and mortality, especially in young children and elderly individuals. Traditionally, highly common bacterial respiratory tract infections, including otitis media and pneumonia, were thought to be caused by a limited number of pathogens including Streptococcus pneumoniae and Haemophilus influenzae. However, these pathogens are also frequently observed commensal residents of the upper respiratory tract (URT) and form-together with harmless commensal bacteria, viruses and fungi-intricate ecological networks, collectively known as the 'microbiome'. Analogous to the gut microbiome, the respiratory microbiome at equilibrium is thought to be beneficial to the host by priming the immune system and providing colonization resistance, while an imbalanced ecosystem might predispose to bacterial overgrowth and development of respiratory infections. We postulate that specific ecological perturbations of the bacterial communities in the URT can occur in response to various lifestyle or environmental effectors, leading to diminished colonization resistance, loss of containment of newly acquired or resident pathogens, preluding bacterial overgrowth, ultimately resulting in local or systemic bacterial infections. Here, we review the current body of literature regarding niche-specific upper respiratory microbiota profiles within human hosts and the changes occurring within these profiles that are associated with respiratory infections.

The impact of tropical forest logging and oil palm agriculture on the soil microbiome

Selective logging and forest conversion to oil palm agriculture are rapidly altering tropical forests. However, functional responses of the soil microbiome to these land-use changes are poorly understood. Using 16S rRNA gene and shotgun metagenomic sequencing, we compared composition and functional attributes of soil biota between unlogged, once-logged and twice-logged rainforest, and areas converted to oil palm plantations in Sabah, Borneo. Although there was no significant effect of logging history, we found a significant difference between the taxonomic and functional composition of both primary and logged forests and oil palm. Oil palm had greater abundances of genes associated with DNA, RNA, protein metabolism and other core metabolic functions, but conversely, lower abundance of genes associated with secondary metabolism and cell-cell interactions, indicating less importance of antagonism or mutualism in the more oligotrophic oil palm environment. Overall, these results show a striking difference in taxonomic composition and functional gene diversity of soil microorganisms between oil palm and forest, but no significant difference between primary forest and forest areas with differing logging history. This reinforces the view that logged forest retains most features and functions of the original soil community. However, networks based on strong correlations between taxonomy and functions showed that network complexity is unexpectedly increased due to both logging and oil palm agriculture, which suggests a pervasive effect of both land-use changes on the interaction of soil microbes.

Principles for designing synthetic microbial communities

Advances in synthetic biology to build microbes with defined and controllable properties are enabling new approaches to design and program multispecies communities. This emerging field of synthetic ecology will be important for many areas of biotechnology, bioenergy and bioremediation. This endeavor draws upon knowledge from synthetic biology, systems biology, microbial ecology and evolution. Fully realizing the potential of this discipline requires the development of new strategies to control the intercellular interactions, spatiotemporal coordination, robustness, stability and biocontainment of synthetic microbial communities. Here, we review recent experimental, analytical and computational advances to study and build multi-species microbial communities with defined functions and behavior for various applications. We also highlight outstanding challenges and future directions to advance this field.

Population Dynamics of Bulking and Foaming Bacteria in a Full-scale Wastewater Treatment Plant over Five Years

Bulking and foaming are two notorious problems in activated sludge wastewater treatment plants (WWTPs), which are mainly associated with the excessive growth of bulking and foaming bacteria (BFB). However, studies on affecting factors of BFB in full-scale WWTPs are still limited. In this study, data sets of high-throughput sequencing (HTS) of 16S V3–V4 amplicons of 58 monthly activated sludge samples from a municipal WWTP was re-analyzed to investigate the BFB dynamics and further to study the determinative factors. The population of BFB occupied 0.6~36% (averagely 8.5% ± 7.3%) of the total bacteria and showed seasonal variations with higher abundance in winter-spring than summer-autumn. Pair-wise correlation analysis and canonical correlation analysis (CCA) showed that Gordonia sp. was positively correlated with NO₂-N and negatively correlated with NO₃-N, and Nostocodia limicola II Tetraspharea sp. was negatively correlated with temperature and positively correlated with NH₃-N in activated sludge. Bacteria species correlated with BFB could be clustered into two negatively related modules. Moreover, with intensive time series sampling, the dominant BFB could be accurately modeled with environmental interaction network, i.e. environmental parameters and biotic interactions between BFB and related bacteria, indicating that abiotic and biotic factors were both crucial to the dynamics of BFB.

Network-based metabolic analysis and microbial community modeling

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

It's all relative: analyzing microbiome data as compositions

PURPOSE

The ability to properly analyze and interpret large microbiome data sets has lagged behind our ability to acquire such data sets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing data set is a simplex, which is a part of real space that is restricted to nonnegative values with a constant sum. Such data are compositional and should be analyzed using compositionally appropriate tools and approaches. However, most of the tools for 16S rRNA gene sequencing analysis assume these data are unrestricted.

METHODS

We show that existing tools for compositional data (CoDa) analysis can be readily adapted to analyze high-throughput sequencing data sets.

RESULTS

The Human Microbiome Project tongue versus buccal mucosa data set shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome data set shows that the CoDa approach in concert with multiple hypothesis test corrections prevent false positive identifications.

CONCLUSIONS

The CoDa approach is readily scalable to microbiome-sized analyses. We provide example code and make recommendations to improve the analysis and reporting of microbiome data sets.

Gut Microbiota Diversity and Human Diseases: Should We Reintroduce Key Predators in Our Ecosystem?

Most of the Human diseases affecting westernized countries are associated with dysbiosis and loss of microbial diversity in the gut microbiota. The Western way of life, with a wide use of antibiotics and other environmental triggers, may reduce the number of bacterial predators leading to a decrease in microbial diversity of the Human gut. We argue that this phenomenon is similar to the process of ecosystem impoverishment in macro ecology where human activity decreases ecological niches, the size of predator populations, and finally the biodiversity. Such pauperization is fundamental since it reverses the evolution processes, drives life backward into diminished complexity, stability, and adaptability. A simple therapeutic approach could thus be to reintroduce bacterial predators and restore a bacterial diversity of the host microbiota.

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.

The human gut microbiome and its dysfunctions through the meta-omics prism

The microorganisms inhabiting the human gut are abundant (10(14) cells) and diverse (approximately 500 species per individual). It is now acknowledged that the microbiota has coevolved with its host to achieve a symbiotic relationship, leading to physiological homeostasis. The gut microbiota ensures vital functions, such as food digestibility, maturation of the host immune system, and protection against pathogens. Over the last few decades, the gut microbiota has also been associated with numerous diseases, such as inflammatory bowel disease, irritable bowel syndrome, obesity, and metabolic diseases. In most of these pathologies, a microbial dysbiosis has been found, indicating shifts in the taxonomic composition of the gut microbiota and changes in its functionality. Our understanding of the influence of the gut microbiota on human health is still growing. Working with microorganisms residing in the gut is challenging since most of them are anaerobic and a vast majority (approximately 75%) are uncultivable to date. Recently, a wide range of new approaches (meta-omics) has been developed to bypass the uncultivability and reveal the intricate mechanisms that sustain gut microbial homeostasis. After a brief description of these approaches (metagenomics, metatranscriptomics, metaproteomics, and metabolomics), this review will discuss the importance of considering the gut microbiome as a structured ecosystem and the use of meta-omics to decipher dysfunctions of the gut microbiome in diseases.

Evaluation of next generation sequencing for the analysis of Eimeria communities in wildlife

Next-generation sequencing (NGS) techniques are well-established for studying bacterial communities but not yet for microbial eukaryotes. Parasite communities remain poorly studied, due in part to the lack of reliable and accessible molecular methods to analyse eukaryotic communities. We aimed to develop and evaluate a methodology to analyse communities of the protozoan parasite Eimeria from populations of the Australian marsupial Petrogale penicillata (brush-tailed rock-wallaby) using NGS. An oocyst purification method for small sample sizes and polymerase chain reaction (PCR) protocol for the 18S rRNA locus targeting Eimeria was developed and optimised prior to sequencing on the Illumina MiSeq platform. A data analysis approach was developed by modifying methods from bacterial metagenomics and utilising existing Eimeria sequences in GenBank. Operational taxonomic unit (OTU) assignment at a high similarity threshold (97%) was more accurate at assigning Eimeria contigs into Eimeria OTUs but at a lower threshold (95%) there was greater resolution between OTU consensus sequences. The assessment of two amplification PCR methods prior to Illumina MiSeq, single and nested PCR, determined that single PCR was more sensitive to Eimeria as more Eimeria OTUs were detected in single amplicons. We have developed a simple and cost-effective approach to a data analysis pipeline for community analysis of eukaryotic organisms using Eimeria communities as a model. The pipeline provides a basis for evaluation using other eukaryotic organisms and potential for diverse community analysis studies.

Network modules and hubs in plant-root fungal biomes

Terrestrial plants host phylogenetically and functionally diverse groups of below-ground microbes, whose community structure controls plant growth/survival in both natural and agricultural ecosystems. Therefore, understanding the processes by which whole root-associated microbiomes are organized is one of the major challenges in ecology and plant science. We here report that diverse root-associated fungi can form highly compartmentalized networks of coexistence within host roots and that the structure of the fungal symbiont communities can be partitioned into semi-discrete types even within a single host plant population. Illumina sequencing of root-associated fungi in a monodominant south beech forest revealed that the network representing symbiont-symbiont co-occurrence patterns was compartmentalized into clear modules, which consisted of diverse functional groups of mycorrhizal and endophytic fungi. Consequently, terminal roots of the plant were colonized by either of the two largest fungal species sets (represented by Oidiodendron or Cenococcum). Thus, species-rich root microbiomes can have alternative community structures, as recently shown in the relationships between human gut microbiome type (i.e., 'enterotype') and host individual health. This study also shows an analytical framework for pinpointing network hubs in symbiont-symbiont networks, leading to the working hypothesis that a small number of microbial species organize the overall root-microbiome dynamics.

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

BACKGROUND

Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.

METHODOLOGY/PRINCIPAL FINDINGS

In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.

CONCLUSION/SIGNIFICANCE

The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Plankton networks driving carbon export in the oligotrophic ocean

The biological carbon pump is the process by which CO₂ is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterised. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria, alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of just a few bacterial and viral genes can predict most of the variability in carbon export in these regions.

Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks

Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p < 0.001), but showed remarkable insensitivity to initial conditions and predicted convergence to a mix of Clostridia, Gammaproteobacteria, and Bacilli. DBNs were able to identify important relationships between microbiome taxa and predict future changes in microbiome composition from measured or synthetic initial conditions. DBNs also provided likelihood estimates for sudden, dramatic shifts in microbiome composition, which may be useful in guiding further analysis of those samples.

Nanoporous microscale microbial incubators

Reconstruction of phylogenetic trees based on 16S rRNA gene sequencing reveals abundant microbial diversity that has not been cultured in the laboratory. Many attribute this so-called 'great plate count anomaly' to traditional microbial cultivation techniques, which largely facilitate the growth of a single species. Yet, it is widely recognized that bacteria in nature exist in complex communities. One technique to increase the pool of cultivated bacterial species is to co-culture multiple species in a simulated natural environment. Here, we present nanoporous microscale microbial incubators (NMMI) that enable high-throughput screening and real-time observation of multi-species co-culture. The key innovation in NMMI is that they facilitate inter-species communication while maintaining physical isolation between species, which is ideal for genomic analysis. Co-culture of a quorum sensing pair demonstrates that the NMMI can be used to culture multiple species in chemical communication while monitoring the growth dynamics of individual species.

Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential "keystone" genes, defined as either "hubs" or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

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.

On the Origins and Control of Community Types in the Human Microbiome

Microbiome-based stratification of healthy individuals into compositional categories, referred to as "enterotypes" or "community types", holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configuration.

Reorganization of a synthetic microbial consortium for one-step vitamin C fermentation

Background

In the industry, the conventional two-step fermentation method was used to produce 2-keto-l-gulonic acid (2-KGA), the precursor of vitamin C, by three strains, namely, Gluconobacter oxydans, Bacillus spp. and Ketogulonicigenium vulgare. Despite its high production efficiency, the long incubation period and an additional second sterilization process inhibit the further development. Therefore, we aimed to reorganize a synthetic consortium of G. oxydans and K. vulgare for one-step fermentation of 2-KGA and enhance the symbiotic interaction between microorganisms to perform better.

Results

During the fermentation, competition for sorbose of G. oxydans arose when co-cultured with K. vulgare. In this study, the competition between the two microbes was alleviated and their mutualism was enhanced by deleting genes involved in sorbose metabolism of G. oxydans. In the engineered synthetic consortium (H₆ + Kv), the yield of 2-KGA (mol/mol) against d-sorbitol reached 89.7 % within 36 h, increased by 29.6 %. Furthermore, metabolomic analysis was used to verify the enhancement of the symbiotic relationship and to provide us potential strategies for improving the synthetic consortium. Additionally, a significant redistribution of metabolism occurred by co-culturing the K. vulgare with the engineered G. oxydans, mainly reflected in the increased TCA cycle, purine, and fatty acid metabolism.

Conclusions

We reorganized and optimized a synthetic consortium of G. oxydans and K. vulgare to produce 2-KGA directly from d-sorbitol. The yield of 2-KGA was comparable to that of the conventional two-step fermentation. The metabolic interaction between the strains was further investigated by metabolomics, which verified the enhancement of the mutualism between the microbes and gave us a better understanding of the synthetic consortium.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0418-6) contains supplementary material, which is available to authorized users.

Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process

BACKGROUND

Microbial communities are ubiquitous in nature and play a major role in ecology, medicine, and various industrial processes. In this study, we used stoichiometric metabolic modeling to investigate a community of three species, Desulfovibrio vulgaris, Methanococcus maripaludis, and Methanosarcina barkeri, which are involved in acetogenesis and methanogenesis in anaerobic digestion for biogas production.

RESULTS

We first constructed and validated stoichiometric models of the core metabolism of the three species which were then assembled to community models. The community was simulated by applying the previously described concept of balanced growth demanding that all organisms of the community grow with equal specific growth rate. For predicting community compositions, we propose a novel hierarchical optimization approach: first, similar to other studies, a maximization of the specific community growth rate is performed which, however, often leads to a wide range of optimal community compositions. In a secondary optimization, we therefore also demand that all organisms must grow with maximum biomass yield (optimal substrate usage) reducing the range of predicted optimal community compositions. Simulating two-species as well as three-species communities of the three representative organisms, we gained several important insights. First, using our new optimization approach we obtained predictions on optimal community compositions for different substrates which agree well with measured data. Second, we found that the ATP maintenance coefficient influences significantly the predicted community composition, especially for small growth rates. Third, we observed that maximum methane production rates are reached under high-specific community growth rates and if at least one of the organisms converts its substrate(s) with suboptimal biomass yield. On the other hand, the maximum methane yield is obtained at low community growth rates and, again, when one of the organisms converts its substrates suboptimally and thus wastes energy. Finally, simulations in the three-species community clarify exchangeability and essentiality of the methanogens in case of alternative substrate usage and competition scenarios.

CONCLUSIONS

In summary, our study presents new methods for stoichiometric modeling of microbial communities in general and provides valuable insights in interdependencies of bacterial species involved in the biogas process.

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.

Elevated nutrients change bacterial community composition and connectivity: high throughput sequencing of young marine biofilms

Biofilms are integral to many marine processes but their formation and function may be affected by anthropogenic inputs that alter environmental conditions, including fertilisers that increase nutrients. Density composition and connectivity of biofilms developed in situ (under ambient and elevated nutrients) were compared using 454-pyrosequencing of the 16S gene. Elevated nutrients shifted community composition from bacteria involved in higher processes (eg Pseudoalteromonas spp. invertebrate recruitment) towards more nutrient-tolerant bacterial species (eg Terendinibacter sp.). This may enable the persistence of biofilm communities by increasing resistance to nutrient inputs. A core biofilm microbiome was identified (predominantly Alteromonadales and Oceanospirillales) and revealed shifts in abundances of core microbes that could indicate enrichment by fertilisers. Fertiliser decreased density and connectivity within biofilms indicating that associations were disrupted perhaps via changes to energetic allocations within the core microbiome. Density composition and connectivity changes suggest nutrients can affect the stability and function of these important marine communities.

Biogeographic variation in the microbiome of the ecologically important sponge, Carteriospongia foliascens

Sponges are well known for hosting dense and diverse microbial communities, but how these associations vary with biogeography and environment is less clear. Here we compared the microbiome of an ecologically important sponge species, Carteriospongia foliascens, over a large geographic area and identified environmental factors likely responsible for driving microbial community differences between inshore and offshore locations using co-occurrence networks (NWs). The microbiome of C. foliascens exhibited exceptionally high microbial richness, with more than 9,000 OTUs identified at 97% sequence similarity. A large biogeographic signal was evident at the OTU level despite similar phyla level diversity being observed across all geographic locations. The C. foliascens bacterial community was primarily comprised of Gammaproteobacteria (34.2% ± 3.4%) and Cyanobacteria (32.2% ± 3.5%), with lower abundances of Alphaproteobacteria, Bacteroidetes, unidentified Proteobacteria, Actinobacteria, Acidobacteria and Deltaproteobacteria. Co-occurrence NWs revealed a consistent increase in the proportion of Cyanobacteria over Bacteroidetes between turbid inshore and oligotrophic offshore locations, suggesting that the specialist microbiome of C. foliascens is driven by environmental factors.

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.

Network construction and structure detection with metagenomic count data

Background

The human microbiome plays a critical role in human health. Massive amounts of metagenomic data have been generated with advances in next-generation sequencing technologies that characterize microbial communities via direct isolation and sequencing. How to extract, analyze, and transform these vast amounts of data into useful knowledge is a great challenge to bioinformaticians. Microbial biodiversity research has focused primarily on taxa composition and abundance and less on the co-occurrences among different taxa. However, taxa co-occurrences and their relationships to environmental and clinical conditions are important because network structure may help to understand how microbial taxa function together.

Results

We propose a systematic robust approach for bacteria network construction and structure detection using metagenomic count data. Pairwise similarity/distance measures between taxa are proposed by adapting distance measures for samples in ecology. We also extend the sparse inverse covariance approach to a sparse inverse of a similarity matrix from count data for network construction. Our approach is efficient for large metagenomic count data with thousands of bacterial taxa. We evaluate our method with real and simulated data. Our method identifies true and biologically significant network structures efficiently.

Conclusions

Network analysis is crucial for detecting subnetwork structures with metagenomic count data. We developed a software tool in MATLAB for network construction and biologically significant module detection. Software MetaNet can be downloaded from http://biostatistics.csmc.edu/MetaNet/.

FoodMicrobionet: A database for the visualisation and exploration of food bacterial communities based on network analysis

Amplicon targeted high-throughput sequencing has become a popular tool for the culture-independent analysis of microbial communities. Although the data obtained with this approach are portable and the number of sequences available in public databases is increasing, no tool has been developed yet for the analysis and presentation of data obtained in different studies. This work describes an approach for the development of a database for the rapid exploration and analysis of data on food microbial communities. Data from seventeen studies investigating the structure of bacterial communities in dairy, meat, sourdough and fermented vegetable products, obtained by 16S rRNA gene targeted high-throughput sequencing, were collated and analysed using Gephi, a network analysis software. The resulting database, which we named FoodMicrobionet, was used to analyse nodes and network properties and to build an interactive web-based visualisation. The latter allows the visual exploration of the relationships between Operational Taxonomic Units (OTUs) and samples and the identification of core- and sample-specific bacterial communities. It also provides additional search tools and hyperlinks for the rapid selection of food groups and OTUs and for rapid access to external resources (NCBI taxonomy, digital versions of the original articles). Microbial interaction network analysis was carried out using CoNet on datasets extracted from FoodMicrobionet: the complexity of interaction networks was much lower than that found for other bacterial communities (human microbiome, soil and other environments). This may reflect both a bias in the dataset (which was dominated by fermented foods and starter cultures) and the lower complexity of food bacterial communities. Although some technical challenges exist, and are discussed here, the net result is a valuable tool for the exploration of food bacterial communities by the scientific community and food industry.

What can ecosystems learn? Expanding evolutionary ecology with learning theory

Background

The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole?

Results

Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, ‘unsupervised learning’, well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community’s response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts.

Conclusions

This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.

Reviewers

This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

Bacterial predation in a marine host-associated microbiome

In many ecological communities, predation has a key role in regulating community structure or function. Although predation has been extensively explored in animals and microbial eukaryotes, predation by bacteria is less well understood. Here we show that predatory bacteria of the genus Halobacteriovorax are prevalent and active predators on the surface of several genera of reef-building corals. Across a library of 198 16S rRNA samples spanning three coral genera, 79% were positive for carriage of Halobacteriovorax. Cultured Halobacteriovorax from Porites asteroides corals tested positive for predation on the putative coral pathogens Vibrio corallyticus and Vibrio harveyii. Co-occurrence network analysis showed that Halobacteriovorax's interactions with other bacteria are influenced by temperature and inorganic nutrient concentration, and further suggested that this bacterial predator's abundance may be driven by prey availability. Thus, animal microbiomes can harbor active bacterial predators, which may regulate microbiome structure and protect the host by consuming potential pathogens.

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.

Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas)

Increasingly, host-associated microbiota are recognized to mediate pathogen establishment, providing new ecological perspectives on health and disease. Amphibian skin-associated microbiota interact with the fungal pathogen, Batrachochytrium dendrobatidis (Bd), but little is known about microbial turnover during host development and associations with host immune function. We surveyed skin microbiota of Colorado's endangered boreal toads (Anaxyrus boreas), sampling 181 toads across four life stages (tadpoles, metamorphs, subadults and adults). Our goals were to (1) understand variation in microbial community structure among individuals and sites, (2) characterize shifts in communities during development and (3) examine the prevalence and abundance of known Bd-inhibitory bacteria. We used high-throughput 16S and 18S rRNA gene sequencing (Illumina MiSeq) to characterize bacteria and microeukaryotes, respectively. Life stage had the largest effect on the toad skin microbial community, and site and Bd presence also contributed. Proteobacteria dominated tadpole microbial communities, but were later replaced by Actinobacteria. Microeukaryotes on tadpoles were dominated by the classes Alveolata and Stramenopiles, while fungal groups replaced these groups after metamorphosis. Using a novel database of Bd-inhibitory bacteria, we found fewer Bd-inhibitory bacteria in post-metamorphic stages correlated with increased skin fungi, suggesting that bacteria have a strong role in early developmental stages and reduce skin-associated fungi.

Synthetic Ecology of Microbes: Mathematical Models and Applications

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

So Many Variables: Joint Modeling in Community Ecology

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions.

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.

Annual periodicity in planktonic bacterial and archaeal community composition of eutrophic Lake Taihu

Bacterioplankton plays a key role in nutrient cycling and is closely related to water eutrophication and algal bloom. We used high-throughput 16S rRNA gene sequencing to profile archaeal and bacterial community compositions in the surface water of Lake Taihu. It is one of the largest lakes in China and has suffered from recurring cyanobacterial bloom. A total of 81 water samples were collected from 9 different sites in 9 different months of 2012. We found that temporal variation of the microbial community was significantly greater than spatial variation (adonis, n = 9999, P < 1e−4). The composition of bacterial community in December was similar to that in January, and so was the archaeal community, suggesting potential annual periodicity. Unsupervised K-means clustering was used to identify the synchrony of abundance variations between different taxa. We found that the cluster consisting mostly of ACK-M1, C111 (members of acIV), Pelagibacteraceae (alfV-A) and Synechococcaceae showed relatively higher abundance in autumn. On the contrary, the cluster of Comamonadaceae and Methylophilaceae (members of lineage betI and betIV) had higher abundance in spring. The co-occurrence relationships between taxa were greatly altered during the cyanobacterial bloom according to our further network module analysis.

No apparent correlation between honey bee forager gut microbiota and honey production

One of the best indicators of colony health for the European honey bee (Apis mellifera) is its performance in the production of honey. Recent research into the microbial communities naturally populating the bee gut raise the question as to whether there is a correlation between microbial community structure and colony productivity. In this work, we used 16S rRNA amplicon sequencing to explore the microbial composition associated with forager bees from honey bee colonies producing large amounts of surplus honey (productive) and compared them to colonies producing less (unproductive). As supported by previous work, the honey bee microbiome was found to be dominated by three major phyla: the Proteobacteria, Bacilli and Actinobacteria, within which we found a total of 23 different bacterial genera, including known “core” honey bee microbiome members. Using discriminant function analysis and correlation-based network analysis, we identified highly abundant members (such as Frischella and Gilliamella) as important in shaping the bacterial community; libraries from colonies with high quantities of these Orbaceae members were also likely to contain fewer Bifidobacteria and Lactobacillus species (such as Firm-4). However, co-culture assays, using isolates from these major clades, were unable to confirm any antagonistic interaction between Gilliamella and honey bee gut bacteria. Our results suggest that honey bee colony productivity is associated with increased bacterial diversity, although this mechanism behind this correlation has yet to be determined. Our results also suggest researchers should not base inferences of bacterial interactions solely on correlations found using sequencing. Instead, we suggest that depth of sequencing and library size can dramatically influence statistically significant results from sequence analysis of amplicons and should be cautiously interpreted.

Ecology of bacteria in the human gastrointestinal tract--identification of keystone and foundation taxa

BACKGROUND

Determining ecological roles of community members and the impact of specific taxa on overall biodiversity in the gastrointestinal (GI) microbiota is of fundamental importance. A step towards a systems-level understanding of the GI microbiota is characterization of biotic interactions. Community time series analysis, an approach based on statistical analysis of changing population abundances within a single system over time, is needed in order to say with confidence that one population is affecting the dynamics of another.

RESULTS

Here, we characterize biotic interaction structures and define ecological roles of major bacterial groups in four healthy individuals by analysing high-resolution, long-term (>180 days) GI bacterial community time series. Actinobacteria fit the description of a keystone taxon since they are relatively rare, but have a high degree of ecological connectedness, and are positively correlated with diversity both within and between individuals. Bacteriodetes were found to be a foundation taxon in that they are numerically dominant and interact extensively, in particular through positive interactions, with other taxa. Although community structure, diversity and biotic interaction patterns were specific to each individual, we observed a strong tendency towards more intense competition within than between phyla. This is in agreement with Darwin's limiting similarity hypothesis as well as a published biotic interaction model of the GI microbiota based on reverse ecology. Finally, we link temporal enterotype switching to a reciprocal positive interaction between two key genera.

CONCLUSIONS

In this study, we identified ecological roles of key taxa in the human GI microbiota and compared our time series analysis results with those obtained through a reverse ecology approach, providing further evidence in favour of the limiting similarity hypothesis first put forth by Darwin. Larger longitudinal studies are warranted in order to evaluate the generality of basic ecological concepts as applied to the GI microbiota, but our results provide a starting point for achieving a more profound understanding of the GI microbiota as an ecological system.

Microbial metabolic networks in a complex electrogenic biofilm recovered from a stimulus-induced metatranscriptomics approach

Microorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

Quantitative Analysis of Lysobacter Predation

Bacteria of the genus Lysobacter are considered to be facultative predators that use a feeding strategy similar to that of myxobacteria. Experimental data supporting this assumption, however, are scarce. Therefore, the predatory activities of three Lysobacter species were tested in the prey spot plate assay and in the lawn predation assay, which are commonly used to analyze myxobacterial predation. Surprisingly, only one of the tested Lysobacter species showed predatory behavior in the two assays. This result suggested that not all Lysobacter strains are predatory or, alternatively, that the assays were not appropriate for determining the predatory potential of this bacterial group. To differentiate between the two scenarios, predation was tested in a CFU-based bioassay. For this purpose, defined numbers of Lysobacter cells were mixed together with potential prey bacteria featuring phenotypic markers, such as distinctive pigmentation or antibiotic resistance. After 24 h, cocultivated cells were streaked out on agar plates and sizes of bacterial populations were individually determined by counting the respective colonies. Using the CFU-based predation assay, we observed that Lysobacter spp. strongly antagonized other bacteria under nutrient-deficient conditions. Simultaneously, the Lysobacter population was increasing, which together with the killing of the cocultured bacteria indicated predation. Variation of the predator/prey ratio revealed that all three Lysobacter species tested needed to outnumber their prey for efficient predation, suggesting that they exclusively practiced group predation. In summary, the CFU-based predation assay not only enabled the quantification of prey killing and consumption by Lysobacter spp. but also provided insights into their mode of predation.

How informative is the mouse for human gut microbiota research?

The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research.

Ocean plankton. Determinants of community structure in the global plankton interactome

Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.

Bacterial social interactions drive the emergence of differential spatial colony structures

Background

Social interactions have been increasingly recognized as one of the major factors that contribute to the dynamics and function of bacterial communities. To understand their functional roles and enable the design of robust synthetic consortia, one fundamental step is to determine the relationship between the social interactions of individuals and the spatiotemporal structures of communities.

Results

We present a systematic computational survey on this relationship for two-species communities by developing and utilizing a hybrid computational framework that combines discrete element techniques with reaction-diffusion equations. We found that deleterious interactions cause an increased variance in relative abundance, a drastic decrease in surviving lineages, and a rough expanding front. In contrast, beneficial interactions contribute to a reduced variance in relative abundance, an enhancement in lineage number, and a smooth expanding front. We also found that mutualism promotes spatial homogeneity and population robustness while competition increases spatial segregation and population fluctuations. To examine the generality of these findings, a large set of initial conditions with varying density and species abundance was tested and analyzed. In addition, a simplified mathematical model was developed to provide an analytical interpretation of the findings.

Conclusions

This work advances our fundamental understanding of bacterial social interactions and population structures and, simultaneously, benefits synthetic biology for facilitated engineering of artificial microbial consortia.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0188-5) contains supplementary material, which is available to authorized users.