Mechanistic models of microbial community metabolism

Metabolic interplay between Proteus mirabilis and Enterococcus faecalis facilitates polymicrobial biofilm formation and invasive disease

Biofilms play an important role in the development and pathogenesis of catheter-associated urinary tract infection (CAUTI). Proteus mirabilis and Enterococcus faecalis are common CAUTI pathogens that persistently co-colonize the catheterized urinary tract and form biofilms with increased biomass and antibiotic resistance. In this study, we uncover the metabolic interplay that drives biofilm enhancement and examine the contribution to CAUTI severity. Through compositional and proteomic biofilm analyses, we determined that the increase in biofilm biomass stems from an increase in the protein fraction of the polymicrobial biofilm. We further observed an enrichment in proteins associated with ornithine and arginine metabolism in polymicrobial biofilms compared with single-species biofilms. We show that arginine/ornithine antiport by E. faecalis promotes arginine biosynthesis and metabolism in P. mirabilis, ultimately driving the increase in polymicrobial biofilm protein content without affecting viability of either species. We further show that disrupting E. faecalis ornithine antiport alters the metabolic profile of polymicrobial biofilms and prevents enhancement, and this defect was complemented by supplementation with exogenous ornithine. In a murine model of CAUTI, ornithine antiport did not contribute to E. faecalis colonization but was required for the increased incidence of urinary stone formation and bacteremia that occurs during polymicrobial CAUTI with P. mirabilis. Thus, disrupting metabolic interplay between common co-colonizing species may represent a viable strategy for reducing risk of bacteremia.IMPORTANCEChronic infections often involve the formation of antibiotic-resistant biofilm communities that include multiple different microbes, which pose a challenge for effective treatment. In the catheterized urinary tract, potential pathogens persistently co-colonize for long periods of time and the interactions between them can lead to more severe disease outcomes. In this study, we identified the metabolite L-ornithine as a key mediator of disease-enhancing interactions between two common and challenging pathogens, Enterococcus faecalis and Proteus mirabilis. Disrupting ornithine-mediated interactions may therefore represent a strategy to prevent polymicrobial biofilm formation and decrease risk of severe disease.

Genomic insights into fish pathogenic bacteria: A systems biology perspective for sustainable aquaculture

Fish diseases significantly challenge global aquaculture, causing substantial financial losses and impacting sustainability, trade, and socioeconomic conditions. Understanding microbial pathogenesis and virulence at the molecular level is crucial for disease prevention in commercial fish. This review provides genomic insights into fish pathogenic bacteria from a systems biology perspective, aiming to promote sustainable aquaculture. It covers the genomic characteristics of various fish pathogens and their industry impact. The review also explores the systems biology of zebrafish, fish bacterial pathogens, and probiotic bacteria, offering insights into fish production, potential vaccines, and therapeutic drugs. Genome-scale metabolic models aid in studying pathogenic bacteria, contributing to disease management and antimicrobial development. Researchers have also investigated probiotic strains to improve aquaculture health. Additionally, the review highlights bioinformatics resources for fish and fish pathogens, which are essential for researchers. Systems biology approaches enhance understanding of bacterial fish pathogens by revealing virulence factors and host interactions. Despite challenges from the adaptability and pathogenicity of bacterial infections, sustainable alternatives are necessary to meet seafood demand. This review underscores the potential of systems biology in understanding fish pathogen biology, improving production, and promoting sustainable aquaculture.

Virus ecology and 7-year temporal dynamics across a permafrost thaw gradient

Soil microorganisms are pivotal in the global carbon cycle, but the viruses that affect them and their impact on ecosystems are less understood. In this study, we explored the diversity, dynamics, and ecology of soil viruses through 379 metagenomes collected annually from 2010 to 2017. These samples spanned the seasonally thawed active layer of a permafrost thaw gradient, which included palsa, bog, and fen habitats. We identified 5051 virus operational taxonomic units (vOTUs), doubling the known viruses for this site. These vOTUs were largely ephemeral within habitats, suggesting a turnover at the vOTU level from year to year. While the diversity varied by thaw stage and depth-related patterns were specific to each habitat, the virus communities did not significantly change over time. The abundance ratios of virus to host at the phylum level did not show consistent trends across the thaw gradient, depth, or time. To assess potential ecosystem impacts, we predicted hosts in silico and found viruses linked to microbial lineages involved in the carbon cycle, such as methanotrophy and methanogenesis. This included the identification of viruses of Candidatus Methanoflorens, a significant global methane contributor. We also detected a variety of potential auxiliary metabolic genes, including 24 carbon-degrading glycoside hydrolases, six of which are uniquely terrestrial. In conclusion, these long-term observations enhance our understanding of soil viruses in the context of climate-relevant processes and provide opportunities to explore their role in terrestrial carbon cycling.

Metabolic interplay between Proteus mirabilis and Enterococcus faecalis facilitates polymicrobial biofilm formation and invasive disease

Polymicrobial biofilms play an important role in the development and pathogenesis of CAUTI. Proteus mirabilis and Enterococcus faecalis are common CAUTI pathogens that persistently co-colonize the catheterized urinary tract and form biofilms with increased biomass and antibiotic resistance. In this study, we uncover the metabolic interplay that drives biofilm enhancement and examine the contribution to CAUTI severity. Through compositional and proteomic biofilm analyses, we determined that the increase in biofilm biomass stems from an increase in the protein fraction of the polymicrobial biofilm matrix. We further observed an enrichment in proteins associated with ornithine and arginine metabolism in polymicrobial biofilms compared to single-species biofilms. We show that L-ornithine secretion by E. faecalis promotes arginine biosynthesis in P. mirabilis, and that disruption of this metabolic interplay abrogates the biofilm enhancement we see in vitro and leads to significant decreases in infection severity and dissemination in a murine CAUTI model.

Emerging methods for genome-scale metabolic modeling of microbial communities

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

From metagenomes to metabolism: Systematically assessing the metabolic flux feasibilities for "Candidatus Accumulibacter" species during anaerobic substrate uptake

With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for "Ca. Accumulibacter" species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among "Ca. Accumulibacter" MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of "Ca. Accumulibacter". However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

Flux sampling in genome-scale metabolic modeling of microbial communities

BACKGROUND

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates.

RESULTS

In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux.

CONCLUSIONS

Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.

Agent-Based Modeling of Microbial Communities

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

Toward merging bottom-up and top-down model-based designing of synthetic microbial communities

The increasing interest of microbial communities as promising biocatalyst is leading an intense effort into the development of computational frameworks assisting the analysis and rational engineering of such complex ecosystems. Here, we critically review the recent computational and model-guided advances in the system-level engineering of microbiome, including both the rational bottom-up and the evolutionary top-down approaches. Furthermore, we highlight modeling and computational methods supporting both engineering paradigms. Finally, we discuss the advantages of combining both strategies into a hybrid top-down/bottom-up (middle-out) strategy to engineer synthetic microbial communities with improved performance and scope.

A Survey of Statistical Methods for Microbiome Data Analysis

In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.

Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions

Anaerobic digestion is a promising method for energy recovery through conversion of organic waste to biogas and other industrial valuables. However, to tap the full potential of anaerobic digestion, deciphering the microbial metabolic pathway activities and their underlying bioenergetics is required. In addition, the behavior of organisms in consortia along with the analytical abilities to kinetically measure their metabolic interactions will allow rational optimization of the process. This review aims to explore the metabolic bottlenecks of the microbial communities adopting latest advances of profiling and ¹³C tracer-based analysis using state of the art analytical platforms (GC, GC-MS, LC-MS, NMR). The review summarizes the phases of anaerobic digestion, the role of microbial communities, key process parameters of significance, syntrophic microbial interactions and the bottlenecks that are critical for optimal bioenergetics and enhanced production of valuables. Considerations into the designing of efficient synthetic microbial communities as well as the latest advances in capturing their metabolic cross talk will be highlighted. The review further explores how the presence of additives and inhibiting factors affect the metabolic pathways. The critical insight into the reaction mechanism covered in this review may be helpful to optimize and upgrade the anaerobic digestion system.