Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections

Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.

Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments

Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

Pleiotropic constraints promote the evolution of cooperation in cellular groups

The evolution of cooperation in cellular groups is threatened by lineages of cheaters that proliferate at the expense of the group. These cell lineages occur within microbial communities, and multicellular organisms in the form of tumours and cancer. In contrast to an earlier study, here we show how the evolution of pleiotropic genetic architectures—which link the expression of cooperative and private traits—can protect against cheater lineages and allow cooperation to evolve. We develop an age-structured model of cellular groups and show that cooperation breaks down more slowly within groups that tie expression to a private trait than in groups that do not. We then show that this results in group selection for pleiotropy, which strongly promotes cooperation by limiting the emergence of cheater lineages. These results predict that pleiotropy will rapidly evolve, so long as groups persist long enough for cheater lineages to threaten cooperation. Our results hold when pleiotropic links can be undermined by mutations, when pleiotropy is itself costly, and in mixed-genotype groups such as those that occur in microbes. Finally, we consider features of multicellular organisms—a germ line and delayed reproductive maturity—and show that pleiotropy is again predicted to be important for maintaining cooperation. The study of cancer in multicellular organisms provides the best evidence for pleiotropic constraints, where abberant cell proliferation is linked to apoptosis, senescence, and terminal differentiation. Alongside development from a single cell, we propose that the evolution of pleiotropic constraints has been critical for cooperation in many cellular groups.

The evolution of cooperation in cellular groups is threatened by lineages of cheaters that proliferate at the expense of the group. In this study, an age-structured model of cellular groups shows that pleiotropy promotes the evolution of cooperation and may have been important for the origins of multicellularity.

A synthetic 'essentialome' for axenic culturing of 'Candidatus Liberibacter asiaticus'

OBJECTIVE

'Candidatus Liberibacter asiaticus' (CLas) is associated with the devastating citrus 'greening' disease. All attempts to achieve axenic growth and complete Koch's postulates with CLas have failed to date, at best yielding complex cocultures with very low CLas titers detectable only by PCR. Reductive genome evolution has rendered all pathogenic 'Ca. Liberibacter' spp. deficient in multiple key biosynthetic, metabolic and structural pathways that are highly unlikely to be rescued in vitro by media supplementation alone. By contrast, Liberibacter crescens (Lcr) is axenically cultured and its genome is both syntenic and highly similar to CLas. Our objective is to achieve replicative axenic growth of CLas via addition of missing culturability-related Lcr genes.

RESULTS

Bioinformatic analyses identified 405 unique ORFs in Lcr but missing (or truncated) in all 24 sequenced CLas strains. Site-directed mutagenesis confirmed and extended published EZ-Tn5 mutagenesis data, allowing elimination of 310 of these 405 genes as nonessential, leaving 95 experimentally validated Lcr genes as essential for CLas growth in axenic culture. Experimental conditions for conjugation of large GFP-expressing plasmids from Escherichia coli to Lcr were successfully established for the first time, providing a practical method for transfer of large groups of 'essential' Lcr genes to CLas.

Search for a Shared Genetic or Biochemical Basis for Biofilm Tolerance to Antibiotics across Bacterial Species

Is there a universal genetically programmed defense providing tolerance to antibiotics when bacteria grow as biofilms? A comparison between biofilms of three different bacterial species by transcriptomic and metabolomic approaches uncovered no evidence of one. Single-species biofilms of three bacterial species (Pseudomonas aeruginosa, Staphylococcus aureus, and Acinetobacter baumannii) were grown in vitro for 3 days and then challenged with respective antibiotics (ciprofloxacin, daptomycin, and tigecycline) for an additional 24 h. All three microorganisms displayed reduced susceptibility in biofilms compared to planktonic cultures. Global transcriptomic profiling of gene expression comparing biofilm to planktonic and antibiotic-treated biofilm to untreated biofilm was performed. Extracellular metabolites were measured to characterize the utilization of carbon sources between biofilms, treated biofilms, and planktonic cells. While all three bacteria exhibited a species-specific signature of stationary phase, no conserved gene, gene set, or common functional pathway could be identified that changed consistently across the three microorganisms. Across the three species, glucose consumption was increased in biofilms compared to planktonic cells, and alanine and aspartic acid utilization were decreased in biofilms compared to planktonic cells. The reasons for these changes were not readily apparent in the transcriptomes. No common shift in the utilization pattern of carbon sources was discerned when comparing untreated to antibiotic-exposed biofilms. Overall, our measurements do not support the existence of a common genetic or biochemical basis for biofilm tolerance against antibiotics. Rather, there are likely myriad genes, proteins, and metabolic pathways that influence the physiological state of individual microorganisms in biofilms and contribute to antibiotic tolerance.

The de novo Purine Biosynthesis Pathway Is the Only Commonly Regulated Cellular Pathway during Biofilm Formation in TSB-Based Medium in Staphylococcus aureus and Enterococcus faecalis

Bacterial biofilms are involved in chronic infections and confer 10 to 1,000 times more resistance to antibiotics compared with planktonic growth, leading to complications and treatment failure. When transitioning from a planktonic lifestyle to biofilms, some Gram-positive bacteria are likely to modulate several cellular pathways, including central carbon metabolism, biosynthesis pathways, and production of secondary metabolites. These metabolic adaptations might play a crucial role in biofilm formation by Gram-positive pathogens such as Staphylococcus aureus and Enterococcus faecalis. Here, we performed a transcriptomic approach to identify cellular pathways that might be similarly regulated during biofilm formation in these bacteria. Different strains and biofilm-inducing media were used to identify a set of regulated genes that are common and independent of the environment or accessory genomes analyzed. Our approach highlighted that the de novo purine biosynthesis pathway was upregulated in biofilms of both species when using a tryptone soy broth-based medium but not so when a brain heart infusion-based medium was used. We did not identify other pathways commonly regulated between both pathogens. Gene deletions and usage of a drug targeting a key enzyme showed the importance of this pathway in biofilm formation of S. aureus. The importance of the de novo purine biosynthesis pathway might reflect an important need for purine during biofilm establishment, and thus could constitute a promising drug target.

IMPORTANCE Biofilms are often involved in nosocomial infections and can cause serious chronic infections if not treated properly. Current anti-biofilm strategies rely on antibiotic usage, but they have a limited impact because of the biofilm intrinsic tolerance to drugs. Metabolism remodeling likely plays a central role during biofilm formation. Using comparative transcriptomics of different strains of Staphylococcus aureus and Enterococcus faecalis, we determined that almost all cellular adaptations are not shared between strains and species. Interestingly, we observed that the de novo purine biosynthesis pathway was upregulated during biofilm formation by both species in a specific medium. The requirement for purine could constitute an interesting new anti-biofilm target with a wide spectrum that could also prevent resistance evolution. These results are also relevant to a better understanding of the physiology of biofilm formation.

The metabolic network of the last bacterial common ancestor

Bacteria are the most abundant cells on Earth. They are generally regarded as ancient, but due to striking diversity in their metabolic capacities and widespread lateral gene transfer, the physiology of the first bacteria is unknown. From 1089 reference genomes of bacterial anaerobes, we identified 146 protein families that trace to the last bacterial common ancestor, LBCA, and form the conserved predicted core of its metabolic network, which requires only nine genes to encompass all universal metabolites. Our results indicate that LBCA performed gluconeogenesis towards cell wall synthesis, and had numerous RNA modifications and multifunctional enzymes that permitted life with low gene content. In accordance with recent findings for LUCA and LACA, analyses of thousands of individual gene trees indicate that LBCA was rod-shaped and the first lineage to diverge from the ancestral bacterial stem was most similar to modern Clostridia, followed by other autotrophs that harbor the acetyl-CoA pathway.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

Antimicrobial resistance (AMR) threatens the effective prevention and treatment of a wide range of infections. Governments around the world are beginning to devote effort for innovative treatment development to treat these resistant bacteria. Systems biology methods have been applied extensively to provide valuable insights into metabolic processes at system level. Genome-scale metabolic models serve as platforms for constraint-based computational techniques which aid in novel drug discovery. Tools for automated reconstruction of metabolic models have been developed to support system level metabolic analysis. We discuss features of such software platforms for potential users to best fit their purpose of research. In this work, we focus to review the development of genome-scale metabolic models of Gram-negative pathogens and also metabolic network approach for identification of antimicrobial drugs targets.

Cofactors as Molecular Fossils To Trace the Origin and Evolution of Proteins

Due to their early origin and extreme conservation, cofactors are valuable molecular fossils for tracing the origin and evolution of proteins. First, as the order of protein folds binding with cofactors roughly coincides with protein-fold chronology, cofactors are considered to have facilitated the origin of primitive proteins by selecting them from pools of random amino acid sequences. Second, in the subsequent evolution of proteins, cofactors still played an important role. More interestingly, as metallic cofactors evolved with geochemical variations, some geochemical events left imprints in the chronology of protein architecture; this provides further evidence supporting the coevolution of biochemistry and geochemistry. In this paper, we attempt to review the molecular fossils used in tracing the origin and evolution of proteins, with a special focus on cofactors.

Flux-based hierarchical organization of Escherichia coli's metabolic network

Biological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

ePath: an online database towards comprehensive essential gene annotation for prokaryotes

Experimental techniques for identification of essential genes (EGs) in prokaryotes are usually expensive, time-consuming and sometimes unrealistic. Emerging in silico methods provide alternative methods for EG prediction, but often possess limitations including heavy computational requirements and lack of biological explanation. Here we propose a new computational algorithm for EG prediction in prokaryotes with an online database (ePath) for quick access to the EG prediction results of over 4,000 prokaryotes ( https://www.pubapps.vcu.edu/epath/ ). In ePath, gene essentiality is linked to biological functions annotated by KEGG Ortholog (KO). Two new scoring systems, namely, E_score and P_score, are proposed for each KO as the EG evaluation criteria. E_score represents appearance and essentiality of a given KO in existing experimental results of gene essentiality, while P_score denotes gene essentiality based on the principle that a gene is essential if it plays a role in genetic information processing, cell envelope maintenance or energy production. The new EG prediction algorithm shows prediction accuracy ranging from 75% to 91% based on validation from five new experimental studies on EG identification. Our overall goal with ePath is to provide a comprehensive and reliable reference for gene essentiality annotation, facilitating the study of those prokaryotes without experimentally derived gene essentiality information.