Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data

BACKGROUND

The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment.

RESULTS

This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B₁₂) to either stimulate or suppress targeted microbial groups was evaluated in a follow-up set of soil microcosm experiments. The results corroborated the stimulant's potential (but not the suppressor) to act as a modulator of plant beneficial bacteria, paving the way for future development of knowledge-based (rather than trial and error) metabolic-defined amendments. Our pipeline for generating predictions for the selective targeting of microbial groups based on processing assembled and annotated metagenomics data is available at https://github.com/ot483/NetCom2 .

CONCLUSIONS

This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

Genetic robustness of let-7 miRNA sequence-structure pairs

Genetic robustness, the preservation of evolved phenotypes against genotypic mutations, is one of the central concepts in evolution. In recent years a large body of work has focused on the origins, mechanisms, and consequences of robustness in a wide range of biological systems. In particular, research on ncRNAs studied the ability of sequences to maintain folded structures against single-point mutations. In these studies, the structure is merely a reference. However, recent work revealed evidence that structure itself contributes to the genetic robustness of ncRNAs. We follow this line of thought and consider sequence-structure pairs as the unit of evolution and introduce the spectrum of extended mutational robustness (EMR spectrum) as a measurement of genetic robustness. Our analysis of the miRNA let-7 family captures key features of structure-modulated evolution and facilitates the study of robustness against multiple-point mutations.

Finding Functional Differences Between Species in a Microbial Community: Case Studies in Wine Fermentation and Kefir Culture

Microbial life usually takes place in a community where individuals interact, by competition for nutrients, cross-feeding, inhibition by end-products, but also by their spatial distribution. Lactic acid bacteria are prominent members of microbial communities responsible for food fermentations. Their niche in a community depends on their own properties as well as those of the other species. Here, we apply a computational approach, which uses only genomic and metagenomic information and functional annotation of genes, to find properties that distinguish a species from others in the community, as well as to follow individual species in a community. We analyzed isolated and sequenced strains from a kefir community, and metagenomes from wine fermentations. We demonstrate how the distinguishing properties of an organism lead to experimentally testable hypotheses concerning the niche and the interactions with other species. We observe, for example, that L. kefiranofaciens, a dominant organism in kefir, stands out among the Lactobacilli because it potentially has more amino acid auxotrophies. Using metagenomic analysis of industrial wine fermentations we investigate the role of an inoculated L. plantarum in malolactic fermentation. We observed that L. plantarum thrives better on white than on red wine fermentations and has the largest number of phosphotransferase system among the bacteria observed in the wine communities. Also, L. plantarum together with Pantoea, Erwinia, Asaia, Gluconobacter, and Komagataeibacter genera had the highest number of genes involved in biosynthesis of amino acids.

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.

Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy

Genetic robustness is a hallmark of cells, occurring through many mechanisms and at many levels. Essential genes lack the common robustness mechanism of genetic redundancy (i.e., existing alongside other genes with the same function), and thus appear at first glance to leave cells highly vulnerable to genetic or environmental perturbations. Here we explore a hypothesis that cells might protect against essential gene loss through mechanisms that occur at various cellular levels aside from the level of the gene. Using Escherichia coli and Saccharomyces cerevisiae as models, we find that essential genes are enriched over non-essential genes for properties we call "coding efficiency" and "coding robustness", denoting respectively a gene's efficiency of translation and robustness to non-synonymous mutations. The coding efficiency levels of essential genes are highly positively correlated with their evolutionary conservation levels, suggesting that this feature plays a key role in protecting conserved, evolutionarily important genes. We then extend our hypothesis into the realm of metabolic networks, showing that essential metabolic reactions are encoded by more "robust" genes than non-essential reactions, and that essential metabolites are produced by more reactions than non-essential metabolites. Taken together, these results testify that robustness at the gene-loss level and at the mutation level (and more generally, at two cellular levels that are usually treated separately) are not decoupled, but rather, that cellular vulnerability exposed due to complete gene loss is compensated by increased mutational robustness. Why some genes are backed up primarily against loss and others against mutations still remains an open question.

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.

Robustness as a non-localizable relational phenomenon

A current challenge in neuroscience, systems and theoretical biology is to understand what properties allow organisms to exhibit and sustain behaviours despite perturbations (behavioural robustness). Indeed, there are still significant theoretical difficulties in this endeavour due to the context-dependent nature of the problem. Contrary to the common view of biological robustness as a phenomenon that emerges internally, this article discusses the hypothesis that behavioural robustness is rooted in dynamical processes that distribute between internal controls, the organism body and the environment. This review highlights the varied perspectives and how they have led to the current focus on robustness as a relational phenomenon. A new perspective is proposed in which robustness is better understood in the context of agent-environment dynamical couplings, in which such couplings are not always the full determinants of robustness. The challenges and limitations of the proposed approach are identified.

Does habitat variability really promote metabolic network modularity?

The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

NetCmpt: a network-based tool for calculating the metabolic competition between bacterial species

NetCmpt is a tool for calculating the competitive potential between pairs of bacterial species. The score describes the effective metabolic overlap (EMO) between two species, derived from analyzing the topology of the corresponding metabolic models. NetCmpt is based on the EMO algorithm, developed and validated in previous studies. It takes as input lists of species-specific enzymatic reactions (EC numbers) and generates a matrix of the potential competition scores between all pairwise combinations.

AVAILABILITY AND IMPLEMENTATION

NetCmpt is provided as both a web tool and a software package, designed for the use of non-computational biologists. The NetCmpt web tool, software, examples, and documentation are freely available online at http://app.agri.gov.il/shiri/NetComp.php.

Hsp90 is important for fecundity, longevity, and buffering of cryptic deleterious variation in wild fly populations

BACKGROUND

In the laboratory, the Drosophila melanogaster heat shock protein Hsp90 can buffer the phenotypic effects of genetic variation. Laboratory experiments either manipulate Hsp90 activity pharmacologically, or they induce mutations with strong effects in the gene Hsp83, the single-copy fly gene encoding Hsp90. It is unknown whether observations from such laboratory experiments are relevant in the wild.

RESULTS

We here study naturally occurring mutations in Hsp83, and their effects on fitness and phenotypic buffering in flies derived from wild populations. We examined more than 4500 flies from 42 Drosophila populations distributed world-wide for insertions or deletions of mobile DNA in or near the Hsp83 gene. The insertions we observed occur at low population frequencies, and reduce Hsp83 gene expression. In competition experiments, mutant flies performed much more poorly than wild-type flies. Mutant flies were also significantly less fecund and shorter-lived than wild-type flies, as well as less well buffered against cryptic deleterious variation, as we show through inbreeding experiments. Specifically, in Hsp83 mutant flies female fecundity dropped to much lower levels after inbreeding than in wild-type flies. At even slightly elevated temperatures, inbred mutant Hsp83 populations went extinct, whereas inbred wild-type populations persisted.

CONCLUSIONS

Our work shows that Hsp90, a regulator of the stress response and of signaling, helps buffer deleterious variation in fruit flies derived from wild population, and that its buffering role becomes even more important under heat stress.

NetSeed: a network-based reverse-ecology tool for calculating the metabolic interface of an organism with its environment

NetSeed is a web tool and Perl module for analyzing the topology of metabolic networks and calculating the set of exogenously acquired compounds. NetSeed is based on the seed detection algorithm, developed and validated in previous studies.

AVAILABILITY

The NetSeed web-based tool, open-source Perl module, examples and documentation are freely available online at: http://depts.washington.edu/elbogs/NetSeed.

Reverse Ecology: from systems to environments and back

The structure of complex biological systems reflects not only their function but also the environments in which they evolved and are adapted to. Reverse Ecology-an emerging new frontier in Evolutionary Systems Biology-aims to extract this information and to obtain novel insights into an organism's ecology. The Reverse Ecology framework facilitates the translation of high-throughput genomic data into large-scale ecological data, and has the potential to transform ecology into a high-throughput field. In this chapter, we describe some of the pioneering work in Reverse Ecology, demonstrating how system-level analysis of complex biological networks can be used to predict the natural habitats of poorly characterized microbial species, their interactions with other species, and universal patterns governing the adaptation of organisms to their environments. We further present several studies that applied Reverse Ecology to elucidate various aspects of microbial ecology, and lay out exciting future directions and potential future applications in biotechnology, biomedicine, and ecological engineering.

Metabolic networks and their evolution

Since the last decade of the twentieth century, systems biology has gained the ability to study the structure and function of genome-scale metabolic networks. These are systems of hundreds to thousands of chemical reactions that sustain life. Most of these reactions are catalyzed by enzymes which are encoded by genes. A metabolic network extracts chemical elements and energy from the environment, and converts them into forms that the organism can use. The function of a whole metabolic network constrains evolutionary changes in its parts. I will discuss here three classes of such changes, and how they are constrained by the function of the whole. These are the accumulation of amino acid changes in enzyme-coding genes, duplication of enzyme-coding genes, and changes in the regulation of enzymes. Conversely, evolutionary change in network parts can alter the function of the whole network. I will discuss here two such changes, namely the elimination of reactions from a metabolic network through loss of function mutations in enzyme-coding genes, and the addition of metabolic reactions, for example through mechanisms such as horizontal gene transfer. Reaction addition also provides a window into the evolution of metabolic innovations, the ability of a metabolism to sustain life on new sources of energy and of chemical elements.

Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease

The human microbiome plays a key role in a wide range of host-related processes and has a profound effect on human health. Comparative analyses of the human microbiome have revealed substantial variation in species and gene composition associated with a variety of disease states but may fall short of providing a comprehensive understanding of the impact of this variation on the community and on the host. Here, we introduce a metagenomic systems biology computational framework, integrating metagenomic data with an in silico systems-level analysis of metabolic networks. Focusing on the gut microbiome, we analyze fecal metagenomic data from 124 unrelated individuals, as well as six monozygotic twin pairs and their mothers, and generate community-level metabolic networks of the microbiome. Placing variations in gene abundance in the context of these networks, we identify both gene-level and network-level topological differences associated with obesity and inflammatory bowel disease (IBD). We show that genes associated with either of these host states tend to be located at the periphery of the metabolic network and are enriched for topologically derived metabolic "inputs." These findings may indicate that lean and obese microbiomes differ primarily in their interface with the host and in the way they interact with host metabolism. We further demonstrate that obese microbiomes are less modular, a hallmark of adaptation to low-diversity environments. We additionally link these topological variations to community species composition. The system-level approach presented here lays the foundation for a unique framework for studying the human microbiome, its organization, and its impact on human health.

A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities

Background

Metabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium E. coli across different single-carbon-source conditions.

Results

A different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures.

Conclusions

This study provides a genome-scale description of the metabolic variability regarding by-production among E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth.

Robustness of metabolic networks: a review of existing definitions

Describing the determinants of robustness of biological systems has become one of the central questions in systems biology. Despite the increasing research efforts, it has proven difficult to arrive at a unifying definition for this important concept. We argue that this is due to the multifaceted nature of the concept of robustness and the possibility to formally capture it at different levels of systemic formalisms (e.g., topology and dynamic behavior). Here we provide a comprehensive review of the existing definitions of robustness pertaining to metabolic networks. As kinetic approaches have been excellently reviewed elsewhere, we focus on definitions of robustness proposed within graph-theoretic and constraint-based formalisms.

The roots of bioinformatics in theoretical biology

From the late 1980s onward, the term "bioinformatics" mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, I will trace this early history (from a personal point of view) and I will argue that the original meaning of the term is re-emerging.

Dispensability of Escherichia coli's latent pathways

Gene-knockout experiments on single-cell organisms have established that expression of a substantial fraction of genes is not needed for optimal growth. This problem acquired a new dimension with the recent discovery that environmental and genetic perturbations of the bacterium Escherichia coli are followed by the temporary activation of a large number of latent metabolic pathways, which suggests the hypothesis that temporarily activated reactions impact growth and hence facilitate adaptation in the presence of perturbations. Here, we test this hypothesis computationally and find, surprisingly, that the availability of latent pathways consistently offers no growth advantage and tends, in fact, to inhibit growth after genetic perturbations. This is shown to be true even for latent pathways with a known function in alternate conditions, thus extending the significance of this adverse effect beyond apparently nonessential genes. These findings raise the possibility that latent pathway activation is in fact derivative of another, potentially suboptimal, adaptive response.

The large-scale organization of the bacterial network of ecological co-occurrence interactions

In their natural environments, microorganisms form complex systems of interactions. Understating the structure and organization of bacterial communities is likely to have broad medical and ecological consequences, yet a comprehensive description of the network of environmental interactions is currently lacking. Here, we mine co-occurrences in the scientific literature to construct such a network and demonstrate an expected pattern of association between the species' lifestyle and the recorded number of co-occurring partners. We further focus on the well-annotated gut community and show that most co-occurrence interactions of typical gut bacteria occur within this community. The network is then clustered into species-groups that significantly correspond with natural occurring communities. The relationships between resource competition, metabolic yield and growth rate within the clusters correspond with the r/K selection theory. Overall, these results support the constructed clusters as a first approximation of a bacterial ecosystem model. This comprehensive collection of predicted communities forms a new data resource for further systematic characterization of the ecological design principals shaping communities. Here, we demonstrate its utility for predicting cooperation and inhibition within communities.

Development of alternative strategies for management of soilborne pathogens currently controlled with methyl bromide

The current standard treatment for management of soilborne pests in some high-value crop production systems is preplant fumigation with mixtures of methyl bromide and chloropicrin. With the impending phase-out of methyl bromide, the agricultural industries that rely on soil fumigation face the need for development of alternative pest management strategies. To maintain farm productivity, immediate term research has focused on evaluation of alternative fumigants, modification of current crop production practices to accommodate their use, and improvement of application technologies to reduce the environmental effects of fumigant applications. Longer-term research goals have focused on developing a more integrated approach for pest management that incorporates the use of cultural practices to reduce pathogen pressure, host resistance to disease, and biological approaches for stimulating plant growth and control of root diseases.