Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Scaling metabolic model reconstruction up to the pan-genome level: A systematic review and prospective applications to photosynthetic organisms

Advances in genomics technologies have generated large data sets that provide tremendous insights into the genetic diversity of taxonomic groups. However, it remains challenging to pinpoint the effect of genetic diversity on different traits without performing resource-intensive phenotyping experiments. Pan-genome-scale metabolic models (panGEMs) extend traditional genome-scale metabolic models by considering the entire reaction repertoire that enables the prediction and comparison of metabolic capabilities within a taxonomic group. Here, we systematically review the state-of-the-art methodologies for constructing panGEMs, focusing on used tools, databases, experimental datasets, and orthology relationships. We highlight the unique advantages of panGEMs compared to single-species GEMs in predicting metabolic phenotypes and in guiding the experimental validation of genome annotations. In addition, we emphasize the disparity between the available (pan-)genomic data on photosynthetic organisms and their under-representation in current (pan)GEMs. Finally, we propose a perspective for tackling the reconstruction of panGEMs for photosynthetic eukaryotes that can help advance our understanding of the metabolic diversity in this taxonomic group.

Tailoring a Functional Synthetic Microbial Community Alleviates Fusobacterium nucleatum-infected Colorectal Cancer via Ecological Control

Polymorphic microbiomes play important roles in colorectal cancer (CRC) occurrence and development. In particular, Fusobacterium nucleatum (F. nucleatum) is prevalent in patients with CRC, and eliminating it is beneficial for treatment. Here, multiple metagenomic sequencing cohorts are combined with multiomics to analyze the microbiome and related functional alterations. Furthermore, local human metagenome and metabolomics are used to discover commensal consortia. A synthetic microbial community (SynCom) is then designed by metabolic network reconstruction, and its performance is validated using coculture experiments and an AOM-DSS induced mouse CRC model. The sequencing result shows that F. nucleatum is more abundant in both the feces and tumor tissues of CRC patients. It causes alterations through various pathways, including microbial dysbiosis, lipid metabolism, amino acid metabolism, and bile acid metabolism disorders. The designed SynCom contains seven species with low competition interrelationship. Furthermore, the SynCom successfully inhibits F. nucleatum growth in vitro and achieves colonization in vivo. Additionally, it promotes F. nucleatum decolonization, and enhances tryptophan metabolism and secondary bile acid conversion, leading to reduced lipid accumulation, decreased inflammatory reaction, and enhanced tumor inhibition effect. Overall, the bottom-up designed SynCom is a controllable and promising approach for treating F. nucleatum-positive CRC.

Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the metabolic capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organism's growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the Metabolic Model Testing suite yields a high score, indicating the model's remarkable semantic quality. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the model's predictive power. Ultimately, this work provides a robust foundation for future research aimed at both exploiting the probiotic potential and mitigating the pathogenic risks posed by S. epidermidis.

IMPORTANCE

Staphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While these infections are typically non-life-threatening, their economic impact is considerable, with annual costs reaching billions of dollars in the United States. To better understand its opportunistic nature, we employed genome-scale metabolic modeling to construct a detailed network of S. epidermidis's metabolic capabilities. This model, comprising over a thousand reactions, metabolites, and genes, adheres to established standards and demonstrates solid benchmarking performance. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, the model provides a valuable resource for future research. Growth simulations and predictions closely match experimental data, underscoring the model's predictive accuracy. Overall, this work lays a solid foundation for future studies aimed at leveraging the beneficial properties of S. epidermidis while mitigating its pathogenic potential.

Unraveling Time-Resolved Transcriptional and Metabolic Shifts in the Mixed Fermentation of Saccharomyces cerevisiae and Hanseniaspora uvarum

Wine fermentation and flavor formation are shaped by complex biochemical reactions driven by a variety of microorganisms. Non-Saccharomyces yeasts, such as Hanseniaspora uvarum (HU), are often used in mixed fermentation with Saccharomyces cerevisiae (SC) to enhance wine aroma. However, the lack of systematic knowledge regarding transcriptional responses and metabolic behaviors during fermentation has hindered the rational control of the mixed fermentation processes. To address this, we investigated transcriptional dynamics and metabolic behavior throughout the entire fermentation process, with a particular focus on the roles of microbial metabolism in flavor formation during mixed fermentation with HU. At the transcriptional level, the addition of HU led to significant changes in SC's gene expression, particularly in pathways related to glyoxylate and dicarboxylate metabolism, pyruvate metabolism, and amino sugar and nucleotide sugar metabolism. Furthermore, using genome-scale metabolic modeling, we uncovered key metabolic strategies employed by the two strains in mixed fermentation. These include distinct sugar utilization patterns, ethanol production, fatty acid metabolism, and central carbon allocation strategies. Notably, we identified two metabolic bypasses, from dihydroxyacetone phosphate to glycerol and from glucose-6-phosphate to the pentose phosphate pathway, which were found to reduce ethanol production and maintain the metabolic balance. Flux distribution analysis also revealed connections among organic acids, amino acids, and fermentation products, highlighting the role of a partial TCA cycle during fermentation. Additionally, metabolic interactions between SC and HU were identified, contributing to the enhanced production of volatile compounds, such as 2-phenylethanol and indole-3-ethanol in mixed fermentation. These findings provide a more comprehensive understanding of transcriptional regulation and metabolic strategies under fermentation conditions. They also offer practical targets for future bioengineering efforts aimed at controlling and optimizing the wine flavor.

MetaBiome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach-encompassing microenvironmental conditions, agent information, and metabolic pathways-we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities.IMPORTANCEOur study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes in E. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health vs IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

Identification of essential genes by transposon insertion sequencing and genome-scale metabolic model construction in Streptococcus suis

Bacterial essential genes are indispensable for the survival of bacteria and therefore are attractive targets for novel anti-microbial drugs. Identifying essential genes provides a roadmap for developing novel antibiotics and anti-microbial therapies. In this study, combining high-throughput transposon sequencing (Tn-seq) and genome-scale metabolic model (GEM) construction, essential genes of Streptococcus suis, an important emerging zoonotic bacterial pathogen, were analyzed. A highly efficient transposon (Tn) mutagenesis system was developed in S. suis. This system facilitated the construction of a high-density library containing over 160,000 Tn mutants. By sequencing the library and data analysis, more than 21,000 insertion sites and 150 essential genes for growth in the rich medium were identified. Subsequently, a GEM of S. suis SC19 strain was constructed, and 165 essential genes were predicted via flux balance analysis (FBA). A total of 244 essential genes were obtained by combining the results of Tn-seq, and FBA performed. Gene identity analysis revealed 101 essential genes as potential anti-bacterial drug targets. Among them, apart from many known antibiotic targets, some interesting essential genes were also identified, including those involved in capsule biosynthesis, aminoacyl-tRNA biosynthesis, lipid biosynthesis, cell division, and cell signaling. This work identified essential genes of S. suis at the whole-genome level, providing a reference for the mining of novel anti-microbial drug targets.

IMPORTANCE

Anti-microbial resistance (AMR) presents an escalating challenge, making anti-microbial drug development an urgent need. Bacterial essential genes represent promising targets for anti-microbial drugs. However, conventional approaches to identifying bacterial essential genes are time and labor intensive. Techniques such as Tn-seq and GEM construction offer a high-throughput approach for this identification. Streptococcus suis is an emerging zoonotic bacterial pathogen, posing a big threat to public health as well as the pig industry, and the levels of AMR are increasing. Our study has successfully identified essential genes in S. suis, providing crucial insights for the discovery of new anti-microbial drug targets.

Moving from genome-scale to community-scale metabolic models for the human gut microbiome

Metabolic models of individual microorganisms or small microbial consortia have become standard research tools in the bioengineering and systems biology fields. However, extending metabolic modelling to diverse microbial communities, such as those in the human gut, remains a practical challenge from both modelling and experimental validation perspectives. In complex communities, metabolic models accounting for community dynamics, or those that consider multiple objectives, may provide optimal predictions over simpler steady-state models, but require a much higher computational cost. Here we describe some of the strengths and limitations of microbial community-scale metabolic models and argue for a robust validation framework for developing personalized, mechanistic and accurate predictions of microbial community metabolic behaviours across environmental contexts. Ultimately, quantitatively accurate microbial community-scale metabolic models could aid in the design and testing of personalized prebiotic, probiotic and dietary interventions that optimize for translationally relevant outcomes.

CarveAdornCurate: a versatile cloud-based platform for constructing multiscale metabolic models

Multiscale modeling is a promising approach for understanding cellular behaviors. However, existing multiscale modeling tools require meticulously curated genome-scale metabolic models (GEMs) as inputs, limiting the broad applications of multiscale models due to complex and time-consuming construction processes. To this end, we developed a novel workflow named CarveAdornCurate (CAC) for de novo multiscale modeling. The Carve module generates an ensemble of GEMs with strong genetic evidence, which is then upgraded to multiscale models using Adorn module. The Curate module was designed to find features important to the generated models. These three modules are integrated into a cloud-based platform to promote broad accessibility. As proof of concept, we constructed CAC-based multiscale models for Corynebacterium glutamicum and Yarrowia lipolytica, demonstrating their potential in guiding metabolic engineering. Overall, CAC is demonstrated to be an efficient and user-friendly tool for constructing multiscale models. It is available online at www.carveadorncurate.com/.

Rhizosphere-triggered viral lysogeny mediates microbial metabolic reprogramming to enhance arsenic oxidation

The rhizosphere is a critical hotspot for metabolic activities involving arsenic (As). While recent studies indicate many functions for soil viruses, much remains overlooked regarding their quantitative impact on rhizosphere processes. Here, we analyze time-series metagenomes of rice (Oryza sativa L.)rhizosphere and bulk soil to explore how viruses mediate rhizosphere As biogeochemistry. We observe the rhizosphere favors lysogeny in viruses associated with As-oxidizing microbes, with a positive correlation between As oxidation and the prevalence of these microbial hosts. Moreover, results demonstrate these lysogenic viruses enrich both As oxidation and phosphorus co-metabolism genes and mediated horizontal gene transfers (HGTs) of As oxidases. In silico simulation with genome-scale metabolic models (GEMs) and in vitro validation with experiments estimate that rhizosphere lysogenic viruses contribute up to 25% of microbial As oxidation. These findings enhance our comprehension of the plant-microbiome-virome interplay and highlight the potential of rhizosphere viruses for improving soil health in sustainable agriculture.

Biogeography and ecological functions of underestimated CPR and DPANN in acid mine drainage sediments

Recent genomic surveys have uncovered candidate phyla radiation (CPR) bacteria and DPANN archaea as major microbial dark matter lineages in various anoxic habitats. Despite their extraordinary diversity, the biogeographic patterns and ecological implications of these ultra-small and putatively symbiotic microorganisms have remained elusive. Here, we performed metagenomic sequencing on 90 geochemically diverse acid mine drainage sediments sampled across southeast China and recovered 282 CPR and 189 DPANN nonredundant metagenome-assembled genomes, which collectively account for up to 28.6% and 31.2% of the indigenous prokaryotic communities, respectively. We found that, remarkably, geographic distance represents the primary factor driving the large-scale ecological distribution of both CPR and DPANN organisms, followed by pH and Fe. Although both groups might be capable of iron reduction through a flavin-based extracellular electron transfer mechanism, significant differences are found in their metabolic capabilities (with complex carbon degradation and chitin degradation being more prevalent in CPR whereas fermentation and acetate production being enriched in DPANN), indicating potential niche differentiation. Predicted hosts are mainly Acidobacteriota, Bacteroidota, and Proteobacteria for CPR and Thermoplasmatota for DPANN, and extensive, unbalanced metabolic exchanges between these symbionts and putative hosts are displayed. Together, our results provide initial insights into the complex interplays between the two lineages and their physicochemical environments and host populations at a large geographic scale.IMPORTANCECandidate phyla radiation (CPR) bacteria and DPANN archaea constitute a significant fraction of Earth's prokaryotic diversity. Despite their ubiquity and abundance, especially in anoxic habitats, we know little about the community patterns and ecological drivers of these ultra-small, putatively episymbiotic microorganisms across geographic ranges. This study is facilitated by a large collection of CPR and DPANN metagenome-assembled genomes recovered from the metagenomes of 90 sediments sampled from geochemically diverse acid mine drainage (AMD) environments across southeast China. Our comprehensive analyses have allowed first insights into the biogeographic patterns and functional differentiation of these major enigmatic prokaryotic groups in the AMD model system.

Unveiling the latitudinal dependency of global patterns in soil prokaryotic gene content

Prokaryotic genomic traits offer insights into their functional roles, evolutionary processes, and ecological interactions, but global patterns in soil microbial genomes remain poorly understood. In this study, we examined 6436 metagenome-assembled genomes (MAGs) from global soil environments to explore the driving factors of prokaryotic gene content. Through random forest analysis, we found that, among numerous potential influencing factors such as climate, soil physicochemical properties, and human activities, geographic latitude was the primary factor affecting prokaryotic gene content. Our results showed a marked decrease in gene content from the tropics to the poles, with polar MAGs containing 10.4 % and 13.3 % fewer genes than those in tropical and temperate zones, respectively. This decline correlates with shifts in key metabolic processes, such as nitrogen fixation and energy conversion. Furthermore, we assessed interspecies metabolic interactions using Metabolic Resource Overlap (MRO) and Metabolic Interaction Potential (MIP) metrics. Our analysis revealed significantly lower MRO in high-latitude microbial communities, yet comparable MIP values to those in lower latitudes, indicating that reduced competition may contribute to genomic streamlining. These findings highlight the significant influence of latitude and interspecies interactions on microbial genomic characteristics, advancing our comprehension of microbial ecological adaptations.

Collaborative metabolic curation of an emerging model marine bacterium, Alteromonas macleodii ATCC 27126

Inferring the metabolic capabilities of an organism from its genome is a challenging process, relying on computationally-derived or manually curated metabolic networks. Manual curation can correct mistakes in the draft network and add missing reactions based on the literature, but requires significant expertise and is often the bottleneck for high-quality metabolic reconstructions. Here, we present a synopsis of a community curation workshop for the model marine bacterium Alteromonas macleodii ATCC 27126 and its genome database in BioCyc, focusing on pathways for utilizing organic carbon and nitrogen sources. Due to the scarcity of biochemical information or gene knock-outs, the curation process relied primarily on published growth phenotypes and bioinformatic analyses, including comparisons with related Alteromonas strains. We report full pathways for the utilization of the algal polysaccharides alginate and pectin in contrast to inconclusive evidence for one-carbon metabolism and mixed acid fermentation, in accordance with the lack of growth on methanol and formate. Pathways for amino acid degradation are ubiquitous across Alteromonas macleodii strains, yet enzymes in the pathways for the degradation of threonine, tryptophan and tyrosine were not identified. Nucleotide degradation pathways are also partial in ATCC 27126. We postulate that demonstrated growth on nitrate as sole nitrogen source proceeds via a nitrate reductase pathway that is a hybrid of known pathways. Our evidence highlights the value of joint and interactive curation efforts, but also shows major knowledge gaps regarding Alteromonas metabolism. The manually-curated metabolic reconstruction is available as a "Tier-2" database on BioCyc.

SARS-CoV-2 infectivity can be modulated through bacterial grooming of the glycocalyx

The gastrointestinal (GI) tract is a site of replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and GI symptoms are often reported by patients. SARS-CoV-2 cell entry depends upon heparan sulfate (HS) proteoglycans, which commensal bacteria that bathe the human mucosa are known to modify. To explore human gut HS-modifying bacterial abundances and how their presence may impact SARS-CoV-2 infection, we developed a task-based analysis of proteoglycan degradation on large-scale shotgun metagenomic data. We observed that gut bacteria with high predicted catabolic capacity for HS differ by age and sex, factors associated with coronavirus disease 2019 (COVID-19) severity, and directly by disease severity during/after infection, but do not vary between subjects with COVID-19 comorbidities or by diet. Gut commensal bacterial HS-modifying enzymes reduce spike protein binding and infection of authentic SARS-CoV-2, suggesting that bacterial grooming of the GI mucosa may impact viral susceptibility.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019, can infect the gastrointestinal (GI) tract, and individuals who exhibit GI symptoms often have more severe disease. The GI tract's glycocalyx, a component of the mucosa covering the large intestine, plays a key role in viral entry by binding SARS-CoV-2's spike protein via heparan sulfate (HS). Here, using metabolic task analysis of multiple large microbiome sequencing data sets of the human gut microbiome, we identify a key commensal human intestinal bacteria capable of grooming glycocalyx HS and modulating SARS-CoV-2 infectivity in vitro. Moreover, we engineered the common probiotic Escherichia coli Nissle 1917 (EcN) to effectively block SARS-CoV-2 binding and infection of human cell cultures. Understanding these microbial interactions could lead to better risk assessments and novel therapies targeting viral entry mechanisms.

MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models

Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.

Comprehensive evaluation of the capacities of microbial cell factories

Systems metabolic engineering is facilitating the development of high-performing microbial cell factories for producing chemicals and materials. However, constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here, we comprehensively evaluate the capacities of various microbial cell factories and propose strategies for systems metabolic engineering steps, including host strain selection, metabolic pathway reconstruction, and metabolic flux optimization. We analyze the metabolic capacities of five representative industrial microorganisms as cell factories for the production of 235 different bio-based chemicals and suggest the most suitable host strain for the corresponding chemical production. To improve the innate metabolic capacity by constructing more efficient metabolic pathways, heterologous metabolic reactions, and cofactor exchanges are systematically analyzed. Additionally, we present metabolic engineering strategies, which include up- and down-regulation target reactions, for the improved production of chemicals. Altogether, this study will serve as a comprehensive resource for the systems metabolic engineering of microorganisms in the bio-based production of chemicals.

UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale

MOTIVATION

Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognizable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, e.g. to the cancer evolution study.

RESULTS

To address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It enables functional studies by combining the analysis of the system's multi-level stable states with its fluctuating conditions. This approach helps to investigate the functional relationships and dependencies among biological entities. This is achieved, thanks to the hybridization of two analysis approaches that capture a system's different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposal's effectiveness was demonstrated by mechanistically simulating the metabolic output of Escherichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.

AVAILABILITY AND IMPLEMENTATION

GreatMod https://qbioturin.github.io/epimod/, epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions, first case study E. coli  https://github.com/qBioTurin/Ec_coli_modelling, second case study C. difficile  https://github.com/qBioTurin/EpiCell_CDifficile.

Interspecies interactions mediated by arginine metabolism enhance the stress tolerance of Fusobacterium nucleatum against Bifidobacterium animalis

Colorectal cancer (CRC) is a common cancer accompanied by microbiome dysbiosis. Exploration of probiotics against oncogenic microorganisms is promising for CRC treatment. Here, differential microorganisms between CRC and healthy control were analyzed. Antibacterial experiments, whole-genome sequencing, and metabolic network reconstruction were combined to reveal the anti-Fusobacterium nucleatum mechanism, which was verified by co-culture assay and mendelian randomization analysis. Sequencing results showed that F. nucleatum was enriched in CRC, yet Bifidobacterium animalis decreased gradually from healthy to CRC. Additionally, F. nucleatum could be inhibited by B. animalis. Whole-genome sequencing of B. animalis showed high phylogenetic similarity with known probiotic strains and highlighted its functions for amino acid and carbohydrate metabolism. Metabolic network reconstruction demonstrated that cross-feeding and specific metabolites (acidic molecules, arginine) had a great influence on the coexistence relationship. Finally, the arginine supplement enhanced the competitive ability of F. nucleatum against B. animalis, and the mendelian randomization and metagenomic sequencing analysis confirmed the positive relationship among F. nucleatum, arginine metabolism, and CRC. Thus, whole-genome sequencing and metabolic network reconstruction are valuable for probiotic mining and patient dietary guidance.IMPORTANCEUsing probiotics to inhibit oncogenic microorganisms (Fusobacterium nucleatum) is promising for colorectal cancer (CRC) treatment. In this study, whole-genome sequencing and metabolic network reconstruction were combined to reveal the anti-F. nucleatum mechanism of Bifidobacterium animalis, which was verified by co-culture assay and mendelian randomization analysis. The result indicated that the arginine supplement enhanced the competitive ability of F. nucleatum, which may be harmful to F. nucleatum-infected CRC patients. B. animalis is a potential probiotic to relieve this dilemma. Thus, using in silico simulation methods based on flux balance analysis, such as genome-scale metabolic reconstruction, provides valuable insights for probiotic mining and dietary guidance for cancer patients.

Disrupted microbial cross-feeding and altered L-phenylalanine consumption in people living with HIV

This work aims to (1) identify microbial and metabolic alterations and (2) reveal a shift in phenylalanine production-consumption equilibrium in individuals with HIV. We conducted extensive searches in multiple databases [MEDLINE, Web of Science (including Cell Press, Oxford, HighWire, Science Direct, IOS Press, Springer Nature, PNAS, and Wiley), Google Scholar, and Embase] and selected two case-control 16S data sets (GenBank IDs: SRP039076 and EBI ID: ERP003611) for analysis. We assessed alpha and beta diversity, performed univariate tests on genus-level relative abundances, and identified significant microbiome features using random forest. We also utilized the MICOM model to simulate growth and metabolic exchanges within the microbiome, focusing on the Metabolite Exchange Score (MES) to determine key metabolic interactions. We found that L-phenylalanine had a higher MES in HIV-uninfected individuals compared with their infected counterparts. The flux of L-phenylalanine consumption was significantly lower in HIV-infected individuals compared with healthy controls, correlating with a decreased number of consuming species in the chronic HIV stage. Prevotella, Roseburia, and Catenibacterium were demonstrated as the most important microbial species involving an increase in L-phenylalanine production in HIV patients, whereas Bacteroides, Faecalibacterium, and Blautia contributed to a decrease in L-phenylalanine consumption. We also found significant alterations in both microbial diversity and metabolic exchanges in people living with HIV. Our findings shed light on why HIV-1 patients have elevated levels of phenylalanine. The impact on essential amino acids like L-phenylalanine underscores the effect of HIV on gut microbiome dynamics. Targeting the restoration of these interactions presents a potential therapeutic avenue for managing HIV-related dysbiosis.

Integration of metagenomic analysis and metabolic modeling reveals microbial interactions in activated sludge systems in response to nanoplastics and plasticizers

Nanoplastics and plasticizers are prevalent in activated sludge and pose a potential threat to microbial communities in wastewater treatment systems. However, studies on the effects of nanoplastics and plasticizers on the interaction mechanisms and metabolic functions of microbial communities in activated sludge systems are still scarce. In this study, the responses of microbial interactions and metabolic functions to PVC nanoplastics (PVCNPs) and bis(2-ethylhexyl) phthalate (DEHP) in activated sludge were investigated via a combination of amplicon sequencing, metagenome sequencing, and metabolic modeling. The results revealed that DEHP had a significant effect on the microbial community under short-term exposure. DEHP contamination may increase vitamin B12 producers to enhance species collaboration in communities. Furthermore, community metabolic modeling revealed that DEHP-degrading bacteria could promote positive interactions among community members. The increased metabolic exchange flux of siderophores and glutathione in microbial communities under PVCNPs and DEHP contamination implied that microbial communities may maintain iron homeostasis in response to PVCNPs and DEHP contamination through interspecies collaboration. However, more data are needed to further validate these results. This study provides vital insights into the response mechanisms of microbial interactions to nanoplastic and plasticizer contamination in activated sludge systems.

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Harnessing microbial division of labor for biomanufacturing: a review of laboratory and formal modeling approaches

Bioprocess industries aim to meet the increasing demand for product complexity by designing enhanced cellular and metabolic capabilities for the host. Monocultures, standard biomanufacturing workhorses, are often restricted in their capability to meet these demands, and the solution often involves the genetic modification of the host. Synthetic microbial communities are a promising alternative to monocultures because they exhibit division of labor, enabling efficient resource utilization and pathway modularity. This specialization minimizes metabolic burden and enhances robustness to perturbations, providing a competitive advantage. Despite this potential, their utilization in biotechnological or bioprocessing applications remains limited. The recent emergence of new and innovative community design tools and strategies, particularly those harnessing the division of labor, holds promise to change this outlook. Understanding the microbial interactions governing natural microbial communities can be used to identify complementary partners, informing synthetic community design. Therefore, we particularly consider engineering division of labor in synthetic microbial communities as a viable solution to accelerate progress in the field. This review presents the current understanding of how microbial interactions enable division of labor and how this information can be used to design synthetic microbial communities to perform tasks otherwise unfeasible to individual organisms. We then evaluate laboratory and formal modeling approaches specifically developed to: elucidate microbial community physiology, guide experimental design, and improve our understanding of complex community interactions assisting synthetic community design. By synthesizing these insights, we aim to present a comprehensive framework that advances the use of microbial communities in biomanufacturing applications.

Ecological dynamics of Enterobacteriaceae in the human gut microbiome across global populations

Gut bacteria from the Enterobacteriaceae family are a major cause of opportunistic infections worldwide. Given their prevalence among healthy human gut microbiomes, interspecies interactions may play a role in modulating infection resistance. Here we uncover global ecological patterns linked to Enterobacteriaceae colonization and abundance by leveraging a large-scale dataset of 12,238 public human gut metagenomes spanning 45 countries. Machine learning analyses identified a robust gut microbiome signature associated with Enterobacteriaceae colonization status, consistent across health states and geographic locations. We classified 172 gut microbial species as co-colonizers and 135 as co-excluders, revealing a genus-wide signal of colonization resistance within Faecalibacterium and strain-specific co-colonization patterns of the underexplored Faecalimonas phoceensis. Co-exclusion is linked to functions involved in short-chain fatty acid production, iron metabolism and quorum sensing, while co-colonization is linked to greater functional diversity and metabolic resemblance to Enterobacteriaceae. Our work underscores the critical role of the intestinal environment in the colonization success of gut-associated opportunistic pathogens with implications for developing non-antibiotic therapeutic strategies.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics

The impact of the gut microbiome on human health is increasingly recognized as dysbiosis has been found to be associated with a spectrum of diseases. Here, we review the databases of genome-scale metabolic models (GEMs), which have paved the way for investigations into the metabolic capabilities of gut microbes and their interspecies dynamics. We further discuss the strategies for developing community-level GEMs, which are crucial for understanding the complex interactions within microbial communities and between the microbiome and its host. Such GEMs can guide the design of synthetic microbial communities for disease treatment. Finally, we explore advances in personalized gut microbiome modeling. These advancements broaden our mechanistic understanding and hold promise for applications in precision medicine and therapeutic interventions.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease

BACKGROUND

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

RESULTS

Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845-0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively.

CONCLUSION

Our study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstract.

Uncovering a cryptic Streptococcus suis endemic post-outbreak: Evidence of host switching to humans

Streptococcus suis (S. suis) is a neglected and emerging pathogen that leads to severe economic losses in swine industry. Despite its epidemic potential, the zoonotic threat posed by S. suis remains underappreciated, even after the unprecedented Sichuan outbreak, which highlighted its ability to cause fatal human infections. Understanding of the dynamics and evolution of this pathogen in human populations is crucial for preventing future outbreaks. Our study revealed the emergence of highly pathogenic S. suis lineages in Zhejiang Province following the Sichuan outbreak, showing an increasingly specialized lifestyle that has persisted for nearly two decades. Phylogenetic analysis traced the zoonotic transmission of this pathogen back to a livestock lineage in the Netherlands prior to 1990, which eventually led to the Sichuan outbreak lineage in 2005 and its subsequent spread to Zhejiang the same year. Two independent evolved sub-lineages were identified in Zhejiang, suggesting a cryptic, regional endemicity following the Sichuan outbreak. Furthermore, the accumulation of lineage-specific resistance and metabolic acclimation after divergence from the Sichuan population suggested potential regional evolutionary shifts in S. suis. These new findings could help inform future intervention strategies and guide public health policies.

gutMGene v2.0: an updated comprehensive database for target genes of gut microbes and microbial metabolites

The gut microbiota is essential for various physiological functions in the host, primarily through the metabolites it produces. To support researchers in uncovering how gut microbiota contributes to host homeostasis, we launched the gutMGene database in 2022. In this updated version, we conducted an extensive review of previous papers and incorporated new papers to extract associations among gut microbes, their metabolites, and host genes, carefully classifying these as causal or correlational. Additionally, we performed metabolic reconstructions for representative gut microbial genomes from both human and mouse. gutMGene v2.0 features an upgraded web interface, providing users with improved accessibility and functionality. This upgraded version is freely available at http://bio-computing.hrbmu.edu.cn/gutmgene. We believe that this new version will greatly advance research in the gut microbiota field by offering a comprehensive resource.

MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights

The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with >12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.

Within-host competition causes pathogen molecular evolution and perpetual microbiota dysbiosis

Pathogens newly invading a host must compete with resident microbiota. This within-host microbial warfare could lead to more severe disease outcomes or constrain the evolution of virulence. By passaging a widespread pathogen (Staphylococcus aureus) and a natural microbiota community across populations of nematode hosts, we show that the pathogen displaced microbiota and reduced species richness, but maintained its virulence across generations. Conversely, pathogen populations and microbiota passaged in isolation caused more host harm relative to their respective no-host controls. For the evolved pathogens, this increase in virulence was partly mediated by enhanced biofilm formation and expression of the global virulence regulator agr. Whole genome sequencing revealed shifts in the mode of selection from directional (on pathogens evolving in isolation) to fluctuating (on pathogens evolving in host microbiota). This approach also revealed that competitive interactions with the microbiota drove early pathogen genomic diversification. Metagenome sequencing of the passaged microbiota shows that evolution in pathogen-infected hosts caused a significant reduction in community stability (dysbiosis), along with restrictions on the co-existence of some species based on nutrient competition. Our study reveals how microbial competition during novel infection could determine the patterns and processes of evolution with major consequences for host health.

Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data

This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.

Improving genome-scale metabolic models of incomplete genomes with deep learning

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

"Gut" to grips with the science of the microbiome - a symposium report

The latest Yakult Science Study Day was held virtually on 2 November 2023. Aimed at healthcare professionals, researchers, and students, a variety of experts explored the latest gut microbiome research and what it means in practice. The morning sessions discussed the role of the microbiome in health and disease, the rapid advancements in DNA sequencing and implications for personalised nutrition, the current state of evidence on health benefits associated with fermented foods, prebiotics and probiotics and the challenges involved in interpreting research in this area. The afternoon session considered the emerging research on the microbiota-gut-brain axis in mediating effects of food on mood, the bidirectional impact of menopause on the gut microbiota, and the interplay between the gut and skin with implications for the treatment of rare and common skin disorders. The session ended with an update on the use of faecal microbiota transplant in both research and clinical practice. Undoubtedly, the gut microbiome is emerging as a key conductor of human health, both in relation to gastrointestinal and non-gastrointestinal outcomes. As research continues to elucidate mechanisms of action and confirm their effects in human trials, the gut microbiome should be a key consideration within a holistic approach to health moving forward.

A Study of the Community Relationships Between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field, which enables a guide for targeting genetic modifications and the optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Herein, we present a pipeline for community metabolic modeling with a user-friendly GUI, applying it to analyze interactions between Methylococcus capsulatus, a biotechnologically important methanotroph, and Escherichia coli W3110 under oxygen- and nitrogen-limited conditions. We constructed models with unmodified and homoserine-producing E. coli strains using the pipeline implemented in the original BioUML platform. The E. coli strain primarily utilized acetate from M. capsulatus under oxygen limitation. However, homoserine produced by E. coli significantly reduced acetate secretion and the community growth rate. This homoserine was taken up by M. capsulatus, converted to threonine, and further exchanged as amino acids. In nitrogen-limited modeling conditions, nitrate and ammonium exchanges supported the nitrogen needs, while carbon metabolism shifted to fumarate and malate, enhancing E. coli TCA cycle activity in both cases, with and without modifications. The presence of homoserine altered cross-feeding dynamics, boosting amino acid exchanges and increasing pyruvate availability for M. capsulatus. These findings suggest that homoserine production by E. coli optimizes resource use and has potential for enhancing microbial consortia productivity.

Spatiotemporal dynamics during niche remodeling by super-colonizing microbiota in the mammalian gut

While fecal microbiota transplantation (FMT) has been shown to be effective in reversing gut dysbiosis, we lack an understanding of the fundamental processes underlying microbial engraftment in the mammalian gut. Here, we explored a murine gut colonization model leveraging natural inter-individual variations in gut microbiomes to elucidate the spatiotemporal dynamics of FMT. We identified a natural "super-donor" consortium that robustly engrafts into diverse recipients and resists reciprocal colonization. Temporal profiling of the gut microbiome showed an ordered succession of rapid engraftment by early colonizers within 72 h, followed by a slower emergence of late colonizers over 15-30 days. Moreover, engraftment was localized to distinct compartments of the gastrointestinal tract in a species-specific manner. Spatial metagenomic characterization suggested engraftment was mediated by simultaneous transfer of spatially co-localizing species from the super-donor consortia. These results offer a mechanism of super-donor colonization by which nutritional niches are expanded in a spatiotemporally dependent manner. A record of this paper's transparent peer review process is included in the supplemental information.

A temperature-induced metabolic shift in the emerging human pathogen Photorhabdus asymbiotica

Photorhabdus is a bacterial genus containing both insect and emerging human pathogens. Most insect-restricted species display temperature restriction, unable to grow above 34°C, while Photorhabdus asymbiotica can grow at 37°C to infect mammalian hosts and cause Photorhabdosis. Metabolic adaptations have been proposed to facilitate the survival of this pathogen at higher temperatures, yet the biological mechanisms underlying these are poorly understood. We have reconstructed an extensively manually curated genome-scale metabolic model of P. asymbiotica (iEC1073, BioModels ID MODEL2309110001), validated through in silico gene knockout and nutrient utilization experiments with an excellent agreement between experimental data and model predictions. Integration of iEC1073 with transcriptomics data obtained for P. asymbiotica at temperatures of 28°C and 37°C allowed the development of temperature-specific reconstructions representing metabolic adaptations the pathogen undergoes when shifting to a higher temperature in a mammalian compared to insect host. Analysis of these temperature-specific reconstructions reveals that nucleotide metabolism is enriched with predicted upregulated and downregulated reactions. iEC1073 could be used as a powerful tool to study the metabolism of P. asymbiotica, in different genetic or environmental conditions.

IMPORTANCE

Photorhabdus bacterial species contain both human and insect pathogens, and most of these species cannot grow in higher temperatures. However, Photorhabdus asymbiotica, which infects both humans and insects, can grow in higher temperatures and undergoes metabolic adaptations at a temperature of 37°C compared to that of insect body temperature. Therefore, it is important to examine how this bacterial species can metabolically adapt to survive in higher temperatures. In this work, using a mathematical model, we have examined the metabolic shift that takes place when the bacteria switch from growth conditions in 28°C to 37°C. We show that P. asymbiotica potentially experiences predicted temperature-induced metabolic adaptations at 37°C predominantly clustered within the nucleotide metabolism pathway.

Disentangling the microbial genomic traits associated with aromatic hydrocarbon degradation in a jet fuel-contaminated aquifer

Spills of petroleum or its derivatives in the environment lead to an enrichment of microorganisms able to degrade such compounds. The interactions taking place in such microbial communities are complex and poorly understood, since they depend on multiple factors, including diversity and metabolic potential of the microorganisms and a broad range of fluctuating environmental conditions. In our previous study, a complete characterization, based on high-throughput sequencing, was performed in a jet-fuel plume using soil samples and in in-situ microcosms amended with hydrocarbons and exposed for 120 days. Herein, we propose a metabolic model to describe the monoaromatic hydrocarbon degradation process that takes place in such jet-fuel-contaminated sites, by combining genome-centered analysis, functional predictions, and flux balance analysis (FBA). In total, twenty high/medium quality MAGs were recovered; three of them assigned to anaerobic bacteria (Thermincolales, Geobacter and Pelotomaculaceace) and one affiliated to the aerobic bacterium Acinetobacter radioresistens, potentially the main players of hydrocarbon degradation in jet-fuel plumes. Taxonomic assignment of the genes indicated that a putative new species of Geobacteria has the potential for anaerobic degradation pathway, while the Pelotomaculaceae and Thermincolales members probably act via syntrophy oxidizing acetate and hydrogen (fermentation products of oil degradation) via sulfate and/or nitrate reduction.

Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens

Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene, thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics.

Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target

Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti-Xoo target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

Genome-Scale Metabolic Models in Fungal Pathogens: Past, Present, and Future

Fungi are diverse organisms with various characteristics and functions. Some play a role in recycling essential elements, such as nitrogen and carbon, while others are utilized in the food and drink production industry. Some others are known to cause diseases in various organisms, including humans. Fungal pathogens cause superficial, subcutaneous, and systemic infections. Consequently, many scientists have focused on studying the factors contributing to the development of human diseases. Therefore, multiple approaches have been assessed to examine the biology of these intriguing organisms. The genome-scale metabolic models (GEMs) have demonstrated many advantages to microbial metabolism studies and the ability to propose novel therapeutic alternatives. Despite significant advancements, much remains to be elucidated regarding the use of this tool for investigating fungal metabolism. This review aims to compile the data provided by the published GEMs of human fungal pathogens. It gives specific examples of the most significant contributions made by these models, examines the advantages and difficulties associated with using such models, and explores the novel approaches suggested to enhance and refine their development.

Deciphering microbial metabolic interactions and their implications for community dynamics in acid mine drainage sediments

The microbially-mediated reduction processes have potential for the bioremediation of acid mine drainage (AMD), which represents a worldwide environment problem. However, we know little about the microbial interactions in anaerobic AMD sediments. Here we utilized genome-resolved metagenomics to uncover the nature of cooperative and competitive metabolic interactions in 90 AMD sediments across Southern China. Our analyses recovered well-represented prokaryotic communities through the reconstruction of 2625 population genomes. Functional analyses of these genomes revealed extensive metabolic handoffs which occurred more frequently in nitrogen metabolism than in sulfur metabolism, as well as stable functional redundancy across sediments resulting from populations with low genomic relatedness. Genome-scale metabolic modeling showed that metabolic competition promoted microbial co-occurrence relationships, suggesting that community assembly was dominated by habitat filtering in sediments. Notably, communities colonizing more extreme conditions tended to be highly competitive, which was typically accompanied with increased network complexity but decreased stability of the microbiome. Finally, our results demonstrated that heterotrophic Thermoplasmatota associated with ferric iron and sulfate reduction contributed most to the elevated levels of competition. Our study shed light on the cooperative and competitive metabolisms of microbiome in the hazardous AMD sediments, which may provide preliminary clues for the AMD bioremediation in the future.

iNAP 2.0: Harnessing metabolic complementarity in microbial network analysis

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments

Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.

Comparative evaluation of soil DNA extraction kits for long read metagenomic sequencing

Metagenomics has been transformative in our understanding of the diversity and function of soil microbial communities. Applying long read sequencing to whole genome shotgun metagenomics has the potential to revolutionise soil microbial ecology through improved taxonomic classification, functional characterisation and metagenome assembly. However, optimisation of robust methods for long read metagenomics of environmental samples remains undeveloped. In this study, Oxford Nanopore sequencing using samples from five commercially available soil DNA extraction kits was compared across four soil types, in order to optimise read length and reproducibility for comparative long read soil metagenomics. Average extracted DNA lengths varied considerably between kits, but longer DNA fragments did not translate consistently into read lengths. Highly variable decreases in the length of resulting reads from some kits were associated with poor classification rate and low reproducibility in microbial communities identified between technical repeats. Replicate samples from other kits showed more consistent conversion of extracted DNA fragment size into read length and resulted in more congruous microbial community representation. Furthermore, extraction kits showed significant differences in the community representation and structure they identified across all soil types. Overall, the QIAGEN DNeasy PowerSoil Pro Kit displayed the best suitability for reproducible long-read WGS metagenomic sequencing, although further optimisation of DNA purification and library preparation may enable translation of higher molecular weight DNA from other kits into longer read lengths. These findings provide a novel insight into the importance of optimising DNA extraction for achieving replicable results from long read metagenomic sequencing of environmental samples.