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

A comparative whole-genome approach identifies bacterial traits for marine microbial interactions

Microbial interactions shape the structure and function of microbial communities with profound consequences for biogeochemical cycles and ecosystem health. Yet, most interaction mechanisms are studied only in model systems and their prevalence is unknown. To systematically explore the functional and interaction potential of sequenced marine bacteria, we developed a trait-based approach, and applied it to 473 complete genomes (248 genera), representing a substantial fraction of marine microbial communities. We identified genome functional clusters (GFCs) which group bacterial taxa with common ecology and life history. Most GFCs revealed unique combinations of interaction traits, including the production of siderophores (10% of genomes), phytohormones (3-8%) and different B vitamins (57-70%). Specific GFCs, comprising Alpha- and Gammaproteobacteria, displayed more interaction traits than expected by chance, and are thus predicted to preferentially interact synergistically and/or antagonistically with bacteria and phytoplankton. Linked trait clusters (LTCs) identify traits that may have evolved to act together (e.g., secretion systems, nitrogen metabolism regulation and B vitamin transporters), providing testable hypotheses for complex mechanisms of microbial interactions. Our approach translates multidimensional genomic information into an atlas of marine bacteria and their putative functions, relevant for understanding the fundamental rules that govern community assembly and dynamics.

Co-occurrence Interaction Networks of Extremophile Species Living in a Copper Mining Tailing

Copper mining tailings are characterized by high concentrations of heavy metals and an acidic pH, conditions that require an extreme adaptation for any organism. Currently, several bacterial species have been isolated and characterized from mining environments; however, very little is known about the structure of microbial communities and how their members interact with each other under the extreme conditions where they live. This work generates a co-occurrence network, representing the bacterial soil community from the Cauquenes copper tailing, which is the largest copper waste deposit worldwide. A representative sampling of six zones from the Cauquenes tailing was carried out to determine pH, heavy metal concentration, total DNA extraction, and subsequent assignment of Operational Taxonomic Units (OTUs). According to the elemental concentrations and pH, the six zones could be grouped into two sectors: (1) the "new tailing," characterized by neutral pH and low concentration of elements, and (2) the "old tailing," having extremely low pH (~3.5) and a high concentration of heavy metals (mainly copper). Even though the abundance and diversity of species were low in both sectors, the Pseudomonadaceae and Flavobacteriaceae families were over-represented. Additionally, the OTU identifications allowed us to identify a series of bacterial species with diverse biotechnological potentials, such as copper bioleaching and drought stress alleviation in plants. Using the OTU information as a template, we generated co-occurrence networks for the old and new tailings. The resulting models revealed a rearrangement between the interactions of members living in the old and new tailings, and highlighted conserved bacterial drivers as key nodes, with positive interactions in the network of the old tailings, compared to the new tailings. These results provide insights into the structure of the soil bacterial communities growing under extreme environmental conditions in mines.

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.

A community perspective on the concept of marine holobionts: current status, challenges, and future directions

Host-microbe interactions play crucial roles in marine ecosystems. However, we still have very little understanding of the mechanisms that govern these relationships, the evolutionary processes that shape them, and their ecological consequences. The holobiont concept is a renewed paradigm in biology that can help to describe and understand these complex systems. It posits that a host and its associated microbiota with which it interacts, form a holobiont, and have to be studied together as a coherent biological and functional unit to understand its biology, ecology, and evolution. Here we discuss critical concepts and opportunities in marine holobiont research and identify key challenges in the field. We highlight the potential economic, sociological, and environmental impacts of the holobiont concept in marine biological, evolutionary, and environmental sciences. Given the connectivity and the unexplored biodiversity specific to marine ecosystems, a deeper understanding of such complex systems requires further technological and conceptual advances, e.g., the development of controlled experimental model systems for holobionts from all major lineages and the modeling of (info)chemical-mediated interactions between organisms. Here we propose that one significant challenge is to bridge cross-disciplinary research on tractable model systems in order to address key ecological and evolutionary questions. This first step is crucial to decipher the main drivers of the dynamics and evolution of holobionts and to account for the holobiont concept in applied areas, such as the conservation, management, and exploitation of marine ecosystems and resources, where practical solutions to predict and mitigate the impact of human activities are more important than ever.

An integrated insight into bioleaching performance of chalcopyrite mediated by microbial factors: Functional types and biodiversity

Six artificial communities with different function or biodiversity were reconstructed by six typical bioleaching species for chalcopyrite leaching. Absence of sulfur oxidizers in communities significantly reduced copper extraction rates, and low diversity communities also exhibited slightly poor bioleaching performances. The variations of pH, redox potential, ferrous and copper ions indicated that the community with both sulfur oxidizers and high diversity showed fast adaptation to the environment and rapid dissolution of chalcopyrite. Integrated analysis of mineralogical and microbial parameters demonstrated that functional types of microorganisms made more contributions in mediating chalcopyrite dissolution than microbial diversity. Further correlation analysis between microbial types and chalcopyrite dissolution performances showed that sulfur oxidizers, especially Acidithiobacillus caldus, could greatly accelerate chalcopyrite dissolution by regulating solution physicochemical factors, such as redox potential and pH. This study provided a theoretical basis for improving bioleaching efficiency by balancing microbial functional types and biodiversity.

Metatranscriptomics reveals microbial adaptation and resistance to extreme environment coupling with bioleaching performance

Chalcopyrite bioleaching by 2, 4 and 6 acidophilic strains with the same inoculation density were studied, respectively. The results indicated that the 6-strain community firstly adapted to bioleaching environment, dissolved the chalcopyrite rapidly and maintained an efficient work until late stage. Transcriptome profiles of the 6-strain community at 6th and 30th day during bioleaching process were investigated by RNA-seq. Comparative transcriptomics identified 226 and 737 significantly up-regulated genes at early and late stage, respectively. Gene annotation results revealed that microorganisms adapted to the oligotrophic environment by enhancing cell proliferation, catalytic activation and binding action to maintain their life activities at early stage, and genes related to signal transduction, localization and transporter were highly expressed as an effective response to the stressful late stage. A graphical representation was presented to show how microorganisms adapted and resisted to the extreme environment by their inner functional properties and promoted the bioleaching efficiency.

Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.

Gene Expression Analysis of Zobellia galactanivorans during the Degradation of Algal Polysaccharides Reveals both Substrate-Specific and Shared Transcriptome-Wide Responses

Flavobacteriia are recognized as key players in the marine carbon cycle, due to their ability to efficiently degrade algal polysaccharides both in the open ocean and in coastal regions. The chemical complexity of algal polysaccharides, their differences between algal groups and variations through time and space, imply that marine flavobacteria have evolved dedicated degradation mechanisms and regulation of their metabolism during interactions with algae. In the present study, we report the first transcriptome-wide gene expression analysis for an alga-associated flavobacterium during polysaccharide degradation. Zobellia galactanivorans Dsij^T, originally isolated from a red alga, was grown in minimal medium with either glucose (used as a reference monosaccharide) or one selected algal polysaccharide from brown (alginate, laminarin) or red algae (agar, porphyran, ι- or κ-carrageenan) as sole carbon source. Expression profiles were determined using whole-genome microarrays. Integration of genomic knowledge with the automatic building of a co-expression network allowed the experimental validation of operon-like transcription units. Differential expression analysis revealed large transcriptomic shifts depending on the carbon source. Unexpectedly, transcriptomes shared common signatures when growing on chemically divergent polysaccharides from the same algal phylum. Together with the induction of numerous transcription factors, this hints at complex regulation events that fine-tune the cell behavior during interactions with algal biomass in the marine environment. The results further highlight genes and loci that may participate in polysaccharide utilization, notably encoding Carbohydrate Active enZymes (CAZymes) and glycan binding proteins together with a number of proteins of unknown function. This constitutes a set of candidate genes potentially representing new substrate specificities. By providing an unprecedented view of global transcriptomic responses during polysaccharide utilization in an alga-associated model flavobacterium, this study expands the current knowledge on the functional role of flavobacteria in the marine carbon cycle and on their interactions with algae.

Analysis of Microbial Functions in the Rhizosphere Using a Metabolic-Network Based Framework for Metagenomics Interpretation

Advances in metagenomics enable high resolution description of complex bacterial communities in their natural environments. Consequently, conceptual approaches for community level functional analysis are in high need. Here, we introduce a framework for a metagenomics-based analysis of community functions. Environment-specific gene catalogs, derived from metagenomes, are processed into metabolic-network representation. By applying established ecological conventions, network-edges (metabolic functions) are assigned with taxonomic annotations according to the dominance level of specific groups. Once a function-taxonomy link is established, prediction of the impact of dominant taxa on the overall community performances is assessed by simulating removal or addition of edges (taxa associated functions). This approach is demonstrated on metagenomic data describing the microbial communities from the root environment of two crop plants – wheat and cucumber. Predictions for environment-dependent effects revealed differences between treatments (root vs. soil), corresponding to documented observations. Metabolism of specific plant exudates (e.g., organic acids, flavonoids) was linked with distinct taxonomic groups in simulated root, but not soil, environments. These dependencies point to the impact of these metabolite families as determinants of community structure. Simulations of the activity of pairwise combinations of taxonomic groups (order level) predicted the possible production of complementary metabolites. Complementation profiles allow formulating a possible metabolic role for observed co-occurrence patterns. For example, production of tryptophan-associated metabolites through complementary interactions is unique to the tryptophan-deficient cucumber root environment. Our approach enables formulation of testable predictions for species contribution to community activity and exploration of the functional outcome of structural shifts in complex bacterial communities. Understanding community-level metabolism is an essential step toward the manipulation and optimization of microbial function. Here, we introduce an analysis framework addressing three key challenges of such data: producing quantified links between taxonomy and function; contextualizing discrete functions into communal networks; and simulating environmental impact on community performances. New technologies will soon provide a high-coverage description of biotic and a-biotic aspects of complex microbial communities such as these found in gut and soil. This framework was designed to allow the integration of high-throughput metabolomic and metagenomic data toward tackling the intricate associations between community structure, community function, and metabolic inputs.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

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.