Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization. Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

XomicsToModel: Multiomics data integration and generation of thermodynamically consistent metabolic models

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introduces XomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables multi-omic data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

Culture-Independent Exploration of the Hypersaline Ecosystem Indicates the Environment-Specific Microbiome Evolution

Sambhar Salt Lake, situated in the state of Rajasthan, India is a unique temperate hypersaline ecosystem. Exploration of the salt lake microbiome will enable us to understand microbiome functioning in nutrient-deprived extreme conditions, as well as enrich our understanding of the environment-specific microbiome evolution. The current study has been designed to explore the Sambhar Salt Lake microbiome with a culture-independent multi-omics approach to define its metagenomic features and prevalent metabolic functionaries. The rRNA feature and protein feature-based phylogenetic reconstruction synchronously (R = 0.908) indicated the dominance of the archaea (Euryarchaeota) and bacteria (Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria). Metabolic reconstruction identified selective enrichment of the protein features associated with energy harvesting and stress tolerance (osmotic, oxidative, metal/metalloid, heat/cold, antibiotic, and desiccation). Metabolites identified with metabolome analysis confirmed physiological adaptation of the lake microbiome within a hypersaline and nutrient-deprived environment. Comparative metagenomics of the 212 metagenomes representing freshwater, alkaline, and saline ecosystem microbiome indicated the selective enrichment of the microbial groups and genetic features. The current study elucidates microbiome functioning within the nutrient-deprived harsh ecosystems. In summary, the current study harnessing the strength of multi-omics and comparative metagenomics indicates the environment-specific microbiome evolution.

Comammox Nitrospira bacteria outnumber canonical nitrifiers irrespective of electron donor mode and availability

Complete ammonia oxidizing bacteria coexist with canonical ammonia and nitrite oxidizing bacteria in a wide range of environments. Whether this coexistence is due to competitive or cooperative interactions between the three guilds, or a result of niche separation is not yet clear. Understanding the factors driving coexistence of nitrifying guilds is critical to effectively manage nitrification processes occurring in engineered and natural ecosystems. In this study, microcosms-based experiments were used to investigate the impact of electron donor mode (i.e., ammonia and urea) and loading on the population dynamics of nitrifying guilds in drinking water biofilter media. Shotgun sequencing of DNA from select time points followed by co-assembly and re-construction of metagenome assembled genomes (MAGs) revealed multiple clade A2 and one clade A1 comammox bacterial populations coexisted in the microcosms alongside Nitrosomonas-like ammonia oxidizers and Nitrospira-like nitrite oxidizer populations. Clade A2 comammox bacteria were likely the primary nitrifiers within the microcosms and increased in abundance over canonical ammonia and nitrite oxidizing bacteria irrespective of electron donor mode or nitrogen loading rates. This suggests that comammox bacteria will outnumber nitrifying communities sourced from oligotrophic environments irrespective of variable nitrogen regimes. Changes in comammox bacterial abundance were not correlated with either ammonia or nitrite oxidizing bacterial abundance in urea amended systems where metabolic reconstruction indicated potential cross feeding between ammonia and nitrite oxidizing bacteria. In contrast, comammox bacterial abundance demonstrated a negative correlation with that of nitrite oxidizers in ammonia amended systems. This suggests that potentially weaker synergistic relationships between ammonia and nitrite oxidizers might enable comammox bacteria to displace nitrite oxidizers from complex nitrifying communities.

Genome Scale Constraint-Based Metabolic Reconstruction of Propionibacterium Freudenreichii DSM 20271

Propionibacterium is an anaerobic bacterium with a history of use in the production of Swiss cheese and, more recently, several industrial bioproducts. While the use of this strain for the production of organic acids and secondary metabolites has gained growing interest, the industrial application of the strain requires further improvement in the yield and productivity of the target products. Systems modeling and analysis of metabolic networks are widely leveraged to gain holistic insights into the metabolic features of biotechnologically important strains and to devise metabolic engineering and culture optimization strategies for economically viable bioprocess development. In the present study, a high-quality genome-scale metabolic model of P. freudenreichii ssp. freudenreichii strain DSM 20271 was developed based on the strain’s genome annotation and biochemical and physiological data. The model covers the functions of 23% of the strain’s ORFs and accounts for 711 metabolic reactions and 647 unique metabolites. Literature-based reconstruction of the central metabolism and rigorous refinement of annotation data for establishing gene-protein-reaction associations renders the model a curated omic-scale knowledge base of the organism. Validation of the model against experimental data indicates that the reconstruction can capture the key structural and functional features of P. freudenreichii metabolism, including the growth rate, the pattern of flux distribution, the strain’s aerotolerance behavior, and the change in the mode of metabolic activity during the transition from an anaerobic to an aerobic growth regime. The model also includes an accurately curated pathway of cobalamin biosynthesis, which was used to examine the capacity of the strain to produce vitamin B12 precursors. Constraint-based reconstruction and analysis of the P. freudenreichii metabolic network also provided novel insights into the complexity and robustness of P. freudenreichii energy metabolism. The developed reconstruction, hence, may be used as a platform for the development of P. freudenreichii-based microbial cell factories and bioprocesses.

Genomic insights into the ecological role and evolution of a novel Thermoplasmata order, “ Candidatus Sysuiplasmatales”

Recent omics studies have provided invaluable insights into the metabolic potential, adaptation and evolution of novel archaeal lineages from a variety of extreme environments. We have utilized a genome-resolved metagenomic approach to recover eight medium- to high-quality metagenome-assembled genomes (MAGs) that likely represent a new order (“ Candidatus Sysuiplasmatales”) within Thermoplasmata from mine tailings and acid mine drainage (AMD) sediments sampled from two copper mines in South China. 16S rRNA gene based analyses revealed a narrow habitat range for these uncultured archaea limiting to AMD and hot spring-related environments. Metabolic reconstruction indicated a facultatively anaerobic heterotrophic lifestyle. This may allow the archaea to adapt to oxygen fluctuations and is thus in marked contrast to the majority of lineages in the domain Archaea which typically show obligately anaerobic metabolisms. Notably, “ Ca. Sysuiplasmatales” could conserve energy through degradation of fatty acids, amino acid metabolism and oxidation of reduced inorganic sulfur compounds (RISCs), suggesting that they may contribute to acid generation in the extreme mine environments. Unlike its closely related Methanomassiliicoccales and “ Ca. Gimiplasmatales”, “ Ca. Sysuiplasmatales” lack the capacity to perform methanogenesis and carbon fixation. Ancestral state reconstruction indicated that “ Ca. Sysuiplasmatales” and its closely related Methanomassiliicoccales, “ Ca. Gimiplasmatales”, and the SG8-5 and the RBG-16-68-12 orders originated from a facultatively anaerobic ancestor capable of carbon fixation via the bacterial-type H 4 F Wood–Ljungdahl pathway (WLP). Their metabolic divergence might be attributed to different evolutionary paths. Importance A wide array of archaea populate Earth’s extreme environments thereby they may play important roles in mediating biogeochemical processes such as iron and sulfur cycling. However, our knowledge of archaeal biology and evolution is still limited considering the uncultured majority of archaeal diversity. For instance, most order-level lineages except Thermoplasmatales, Aciduliprofundales and Methanomassiliicoccales within Thermoplasmata do not have cultured representatives. Here, we report the discovery and genomic characterization of a novel order, namely “ Ca . Sysuiplasmatales”, within Thermoplasmata in the extremely acidic mine environments. “ Ca . Sysuiplasmatales” are inferred to be facultatively anaerobic heterotrophs and likely contribute to acid generation through the oxidation of RISCs. The physiological divergence between “ Ca . Sysuiplasmatales” and its closely related Thermoplasmata lineages may be attributed to different evolutionary paths. These results expand our knowledge of archaea in the extreme mine ecosystem.

Microbial community of recently discovered Auka vent field sheds light on vent biogeography and evolutionary history of thermophily

Hydrothermal vents have been key to our understanding of the limits of life, and the metabolic and phylogenetic diversity of thermophilic organisms. Here we used environmental metagenomics combined with analysis of physico-chemical data and 16S rRNA amplicons to characterize the diversity, temperature optima, and biogeographic distribution of sediment-hosted microorganisms at the recently discovered Auka vents in the Gulf of California, the deepest known hydrothermal vent field in the Pacific Ocean. We recovered 325 metagenome assembled genomes (MAGs) representing 54 phyla, over 1/3 of the currently known phylum diversity, showing the microbial community in Auka hydrothermal sediments is highly diverse. Large scale 16S rRNA amplicon screening of 227 sediment samples across the vent field indicates that the MAGs are largely representative of the microbial community. Metabolic reconstruction of a vent-specific, deeply branching clade within the Desulfobacterota (Tharpobacteria) suggests these organisms metabolize sulfur using novel octaheme cytochrome-c proteins related to hydroxylamine oxidoreductase. Community-wide comparison of the average nucleotide identity of the Auka MAGs with MAGs from the Guaymas Basin vent field, found 400 km to the Northwest, revealed a remarkable 20% species-level overlap between vent sites, suggestive of long-distance species transfer and sediment colonization. An adapted version of a recently developed model for predicting optimal growth temperature to the Auka and Guaymas MAGs indicates several of these uncultured microorganisms could grow at temperatures exceeding the currently known upper limit of life. Extending this analysis to reference data shows that thermophily is a trait that has evolved frequently among Bacteria and Archaea. Combined, our results show that Auka vent field offers new perspectives on our understanding of hydrothermal vent microbiology.

Novel Chloroflexi Genomes From The Deepest Ocean Reveal Metabolic Strategies For The Adaptation To Deep-Sea Habitats

Background: The deep-sea harbors the majority of the microbial biomass in the Ocean, and it is a key site for organic matter (OM) remineralization and storage in the biosphere. Microbial metabolism in the deep ocean is greatly controlled by the generally depleted but periodically fluctuating supply of OM. Currently, little is known about metabolic potentials of dominant deep-sea microbes to cope with the variable OM inputs, especially for those living in the hadal trenches - the deepest part of the ocean. Results: In this study, we report the first extensive examination of the metabolic potentials of hadal sediment Chloroflexi, a dominant phylum in hadal trenches and the global deep ocean. Sixty-two metagenome-assembled-genomes (MAGs) were reconstructed from nine metagenomic datasets derived from sediments of the Mariana Trench. These MAGs represent six novel species, four novel genera, one novel family and one novel order within the classes Anaerolineae and Dehalococcoidia. Fragment recruitment showed that these MAGs are globally distributed in deep-sea waters and surface sediments, and transcriptomic analysis indicated their in-situ activities. Metabolic reconstruction showed that hadal Chloroflexi mainly had a heterotrophic lifestyle, with the potential to degrade a wide range of organic carbon, sulfur, and halogenated compounds. Our results revealed for the first time that hadal Chloroflexi harbor pathways for the complete hydrolytic or oxidative degradation of various recalcitrant OM, including aromatic compounds (e.g. benzoate), polyaromatic hydrocarbons (e.g. fluorene), polychlorobiphenyl (e.g. 4-chlorobiphenyl) and organochlorine compounds (e.g. chloroalkanes, chlorocyclohexane). Moreover, these organisms showed the potential to synthesize energy storage compounds (e.g. trehalose), and had regulatory modules to respond to changes in nutrient conditions. These metabolic traits lead us to postulate that the Chloroflexi may follow a “feast and famine” metabolic strategy, allowing them to efficiently consume labile OM and store the energy under OM rich conditions, and to survive under OM limitations by utilizing stored energy and degrading recalcitrant OM. Conclusion: This study expands the current knowledge on metabolic strategies in deep-ocean Chlorolfexi, and highlights their significance in deep-sea carbon, sulfur and halogen cycles. The metabolic plasticity likely provides Chloroflexi with advantages for the survival under variable and heterogenic OM inputs in the deep ocean.