The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

Maternal consumption of urbanized diet compromises early-life health in association with gut microbiota

Urbanization has significantly transformed dietary habits worldwide, contributing to a globally increased burden of non-communicable diseases and altered gut microbiota landscape. However, it is often overlooked that the adverse effects of these dietary changes can be transmitted from the mother to offspring during early developmental stages, subsequently influencing the predisposition to various diseases later in life. This review aims to delineate the detrimental effects of maternal urban-lifestyle diet (urbanized diet) on early-life health and gut microbiota assembly, provide mechanistic insights on how urbanized diet mediates mother-to-offspring transfer of bioactive substances in both intrauterine and extrauterine and thus affects fetal and neonatal development. Moreover, we also further propose a framework for developing microbiome-targeted precision nutrition and diet strategies specifically for pregnant and lactating women. The establishment of such knowledge can help develop proactive preventive measures from the beginning of life, ultimately reducing the long-term risk of disease and improving public health outcomes.

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.

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.

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.

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.

Causal effects of metabolites on malignant neoplasm of bone and articular cartilage: a mendelian randomization study

Objective

Previous research has demonstrated that metabolites play a significant role in modulating disease phenotypes; nevertheless, the causal association between metabolites and malignant malignancies of bones and joint cartilage (MNBAC)has not been fully elucidated.

Methods

This study used two-sample Mendelian randomization (MR) to explore the causal correlation between 1,400 metabolites and MNBAC. Data from recent genome-wide association studies (GWAS) involving 8,299 individuals were summarized. The GWAS summary data for metabolites were acquired from the IEU Open GWAS database, while those for MNBAC were contributed by the Finnish Consortium. We employed eight distinct MR methodologies: simple mode, maximum likelihood estimator, MR robust adjusted profile score, MR-Egger, weighted mode, weighted median, MR-PRESSO and inverse variance weighted to scrutinize the causal association between metabolites engendered by each gene and MNBAC. Consequently, we evaluated outliers, horizontal pleiotropy, heterogeneity, the impact of single nucleotide polymorphisms (SNPs), and adherence to the normal distribution assumption in the MR analysis.

Results

Our findings suggested a plausible causative relationship between N-Formylmethionine (FMet) levels, lignoceroylcarnitine (C24) levels, and MNBAC. We observed a nearly significant causal association between FMet levels and MNBAC within the cohort of 1,400 metabolites (P = 0.024, odds ratio (OR) = 3.22; 95% CI [1.16-8.92]). Moreover, we ascertained a significant causal link between levels of C24 and MNBAC (P = 0.0009; OR = 0.420; 95%CI [0.25-0.70]). These results indicate a potential causative relationship between FMet, C24 level and MNBAC.

Conclusion

The occurrence of MNBAC may be causally related to metabolites. This might unveil new possibilities for investigating early detection and treatment of MNBAC.

Investigating intestinal epithelium metabolic dysfunction in celiac disease using personalized genome-scale models

BACKGROUND

Celiac disease (CeD) is an autoimmune condition characterized by an aberrant immune response triggered by the ingestion of gluten, which damages epithelial cells lining the small intestine. Small intestinal epithelial cells (sIECs) play key roles in the enzymatic digestion and absorption of nutrients, maintaining gut barrier integrity, and regulating immune response. Chronic inflammation and tissue damage associated with CeD disrupt the intricate network of metabolic processes in sIECs that support these functions, impairing their ability to perform their essential roles. However, the specific disrupted metabolic processes underlying sIECs dysfunction in CeD remain largely undefined.

METHODS

To address this knowledge gap, personalized, sex-specific genome-scale models of sIECs metabolism were constructed using transcriptional data from intestinal biopsies of 42 subjects with active CeD, CeD in remission (on a gluten-free diet), and non-CeD controls. These models were computationally simulated under relevant dietary conditions for each group of subjects to assess the activity of several metabolic tasks essential for sIECs function and to profile metabolite secretion into the bloodstream and intestinal lumen.

RESULTS

Significant alterations in the activity of 28 essential metabolic tasks were observed in active CeD and remission CeD, impacting critical processes integral to sIECs function such as oxidative stress regulation, nucleotide synthesis and DNA repair, energy production, and polyamine and amino acid metabolism. Additionally, altered secretion profiles of several metabolites, encompassing amino acids, vitamins, polyamines, lipids, and fatty acids, into the bloodstream were detected in active CeD and remission CeD patients. These findings were partially supported by comparisons with independent external datasets and further corroborated through extensive review of existing literature. Furthermore, a drug target analysis was performed, identifying 22 FDA-approved drugs that target genes encoding impaired sIECs metabolic functions in CeD, potentially helping to restore their normal activity.

CONCLUSIONS

This study unveils new insights into the metabolic reprogramming of sIECs in CeD, highlighting specific dysregulated metabolic processes that compromise cellular functions essential for gut health. These findings offer a foundation for developing therapeutic interventions targeting impaired metabolic processes in CeD.

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.

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.

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.

Systemic regulation of retinal medium-chain fatty acid oxidation repletes TCA cycle flux in oxygen-induced retinopathy

Activation of anaplerosis takes away glutamine from the biosynthetic pathways to the energy-producing TCA cycle. Especially, induction of hyperoxia driven anaplerosis in neurovascular tissues such as the retina during early stages of development could deplete biosynthetic precursors from newly proliferating endothelial cells impeding physiological angiogenesis and leading to vasoobliteration. Using an oxygen-induced retinopathy (OIR) mouse model, we investigated the metabolic differences between OIR-resistant BALB/cByJ and OIR susceptible C57BL/6J strains at system levels to understand the molecular underpinnings that potentially contribute to hyperoxia-induced vascular abnormalities in the neural retina. Our systems level in vivo RNA-seq, proteomics, and lipidomic profiling and ex-vivo retinal explant studies show that the medium-chain fatty acids serves as an alternative source to feed the TCA cycle. Our findings strongly implicate that medium-chain fatty acids could suppress glutamine-fueled anaplerosis and ameliorate hyperoxia-induced vascular abnormalities in conditions such as retinopathy of prematurity.

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.

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.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases.

Personalized modeling of gut microbiome metabolism throughout the first year of life

BACKGROUND

Early-life exposures including diet, and the gut microbiome have been proposed to predispose infants towards multifactorial diseases later in life. Delivery via Cesarian section disrupts the establishment of the gut microbiome and has been associated with negative long-term outcomes. Here, we hypothesize that Cesarian section delivery alters not only the composition of the developing infant gut microbiome but also its metabolic capabilities. To test this, we developed a metabolic modeling workflow targeting the infant gut microbiome.

METHODS

The AGORA2 resource of human microbial genome-scale reconstructions was expanded with a human milk oligosaccharide degradation module. Personalized metabolic modeling of the gut microbiome was performed for a cohort of 20 infants at four time points during the first year of life as well as for 13 maternal gut microbiome samples.

RESULTS

Here we show that at the earliest stages, the gut microbiomes of infants delivered through Cesarian section are depleted in their metabolic capabilities compared with vaginal delivery. Various metabolites such as fermentation products, human milk oligosaccharide degradation products, and amino acids are depleted in Cesarian section delivery gut microbiomes. Compared with maternal gut microbiomes, infant gut microbiomes produce less butyrate but more L-lactate and are enriched in the potential to synthesize B-vitamins.

CONCLUSIONS

Our simulations elucidate the metabolic capabilities of the infant gut microbiome demonstrating they are altered in Cesarian section delivery at the earliest time points. Our workflow can be readily applied to other cohorts to evaluate the effect of feeding type, or maternal factors such as diet on host-gut microbiome inactions in early life.

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn's disease and intestinal tuberculosis: insights from an integrated multi-cohort analysis

BACKGROUND/AIMS

Crohn's disease (CD) and intestinal tuberculosis (ITB) are gastrointestinal (GI) inflammatory disorders with overlapping clinical presentations but diverging etiologies. The study aims to decipher CD and ITB-associated gut dysbiosis signatures and identify disease-associated co-occurring modules to evaluate whether this dysbiosis signature is a disease-specific trait or is a shared feature across diseases of diverging etiologies.

METHODS

Disease-associated gut microbial modules were identified using statistical machine learning and co-abundance network analysis in controls, CD and ITB patients recruited as part of this study. Module reproducibility was reinvestigated through meta-network analysis encompassing >5400 bacteriomes and ~900 mycobiomes. Subsequently, >1600 Indian gut microbiomes were analyzed to identify a central-core gut microbiome of 46 taxa, whose abundances aided in the formulation of an India-specific Core Gut Microbiome Score (CGMS) to measure the degree of core retention.

RESULTS

Both diseases witness similar patterns of alterations in [alpha]-diversity, characterized by a significant reduction in gut bacterial (i.e., bacterial/archaeal) diversity and a concomitant increase in the fungal [alpha]-diversity. Specific bacterial taxa, along with the diverging mycobiome enabled distinction between the diseases. Co-abundance network analysis of these taxa, validated by integrated meta-network analysis, revealed a 'disease-depleted' module, consistent across multiple cohorts, with >75% of this module constituting the central-core Indian gut microbiome. CGMS robustly assessed the core-microbiome loss across different stages of gut inflammatory disorders, in Indian and international cohorts.

CONCLUSIONS

While the disease-specific gain of detrimental bacteria forms an important component of gut dysbiosis, loss of the core microbiome is a shared phenomenon contributing to various GI disorders.

Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health

To ensure sustainable aquaculture, it is essential to understand the path 'from feed to fish', whereby the gut microbiome plays an important role in digestion and metabolism, ultimately influencing host health and growth. Previous work has reported the taxonomic composition of the Atlantic salmon (Salmo salar) gut microbiome; however, functional insights are lacking. Here we present the Salmon Microbial Genome Atlas consisting of 211 high-quality bacterial genomes, recovered by cultivation (n = 131) and gut metagenomics (n = 80) from wild and farmed fish both in freshwater and seawater. Bacterial genomes were taxonomically assigned to 14 different orders, including 35 distinctive genera and 29 previously undescribed species. Using metatranscriptomics, we functionally characterized key bacterial populations, across five phyla, in the salmon gut. This included the ability to degrade diet-derived fibres and release vitamins and other exometabolites with known beneficial effects, which was supported by genome-scale metabolic modelling and in vitro cultivation of selected bacterial species coupled with untargeted metabolomic studies. Together, the Salmon Microbial Genome Atlas provides a genomic and functional resource to enable future studies on salmon nutrition and health.

Game-Theoretic Flux Balance Analysis Model for Predicting Stable Community Composition

Models for microbial interactions attempt to understand and predict the steady state network of inter-species relationships in a community, e.g. competition for shared metabolites, and cooperation through cross-feeding. Flux balance analysis (FBA) is an approach that was introduced to model the interaction of a particular microbial species with its environment. This approach has been extended to analyzing interactions in a community of microbes; however, these approaches have two important drawbacks: first, one has to numerically solve a differential equation to identify the steady state, and second, there are no methods available to analyze the stability of the steady state. We propose a game theory based community FBA model wherein species compete to maximize their individual growth rate, and the state of the community is given by the resulting Nash equilibrium. We develop a computationally efficient method for directly computing the steady state biomasses and fluxes without solving a differential equation. We also develop a method to determine the stability of a steady state to perturbations in the biomasses and to invasion by new species. We report the results of applying our proposed framework to a small community of four E. coli mutants that compete for externally supplied glucose, as well as cooperate since the mutants are auxotrophic for metabolites exported by other mutants, and a more realistic model for a gut microbiome consisting of nine species.

Microbiome-derived metabolites in early to mid-pregnancy and risk of gestational diabetes: a metabolome-wide association study

BACKGROUND

Pre-diagnostic disturbances in the microbiome-derived metabolome have been associated with an increased risk of diabetes in non-pregnant populations. However, the roles of microbiome-derived metabolites, the end-products of microbial metabolism, in gestational diabetes (GDM) remain understudied. We examined the prospective association of microbiome-derived metabolites in early to mid-pregnancy with GDM risk in a diverse population.

METHODS

We conducted a prospective discovery and validation study, including a case-control sample of 91 GDM and 180 non-GDM individuals within the multi-racial/ethnic The Pregnancy Environment and Lifestyle Study (PETALS) as the discovery set, a random sample from the PETALS (42 GDM, 372 non-GDM) as validation set 1, and a case-control sample (35 GDM, 70 non-GDM) from the Gestational Weight Gain and Optimal Wellness randomized controlled trial as validation set 2. We measured untargeted fasting serum metabolomics at gestational weeks (GW) 10-13 and 16-19 by gas chromatography/time-of-flight mass spectrometry (TOF-MS), liquid chromatography (LC)/quadrupole TOF-MS, and hydrophilic interaction LC/quadrupole TOF-MS. GDM was diagnosed using the 3-h, 100-g oral glucose tolerance test according to the Carpenter-Coustan criteria around GW 24-28.

RESULTS

Among 1362 annotated compounds, we identified 140 of gut microbiome metabolism origin. Multivariate enrichment analysis illustrated that carbocyclic acids and branched-chain amino acid clusters at GW 10-13 and the unsaturated fatty acids cluster at GW 16-19 were positively associated with GDM risk (FDR < 0.05). At GW 10-13, the prediction model that combined conventional risk factors and LASSO-selected microbiome-derived metabolites significantly outperformed the model with only conventional risk factors including fasting glucose (discovery AUC: 0.884 vs. 0.691; validation 1: 0.945 vs. 0.731; validation 2: 0.987 vs. 0.717; all P < 0.01). At GW 16-19, similar results were observed (discovery AUC: 0.802 vs. 0.691, P < 0.01; validation 1: 0.826 vs. 0.780; P = 0.10).

CONCLUSIONS

Dysbiosis in microbiome-derived metabolites is present early in pregnancy among individuals progressing to GDM.

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.

Food-breastmilk combinations alter the colonic microbiome of weaning infants: an in silico study

The introduction of solid foods to infants, also known as weaning, is a critical point for the development of the complex microbial community inhabiting the human colon, impacting host physiology in infancy and later in life. This research investigated in silico the impact of food-breastmilk combinations on growth and metabolite production by colonic microbes of New Zealand weaning infants using the metagenome-scale metabolic model named Microbial Community. Eighty-nine foods were individually combined with breastmilk, and the 12 combinations with the strongest influence on the microbial production of short-chain fatty acids (SCFAs) and branched-chain fatty acids (BCFAs) were identified. Fiber-rich and polyphenol-rich foods, like pumpkin and blackcurrant, resulted in the greatest increase in predicted fluxes of total SCFAs and individual fluxes of propionate and acetate when combined, respectively, with breastmilk. Identified foods were further combined with other foods and breastmilk, resulting in 66 multiple food-breastmilk combinations. These combinations altered in silico the impact of individual foods on the microbial production of SCFAs and BCFAs, suggesting that the interaction between the dietary compounds composing a meal is the key factor influencing colonic microbes. Blackcurrant combined with other foods and breastmilk promoted the greatest increase in the production of acetate and total SCFAs, while pork combined with other foods and breastmilk decreased the production of total BCFAs.IMPORTANCELittle is known about the influence of complementary foods on the colonic microbiome of weaning infants. Traditional in vitro and in vivo microbiome methods are limited by their resource-consuming concerns. Modeling approaches represent a promising complementary tool to provide insights into the behavior of microbial communities. This study evaluated how foods combined with other foods and human milk affect the production of short-chain fatty acids and branched-chain fatty acids by colonic microbes of weaning infants using a rapid and inexpensive in silico approach. Foods and food combinations identified here are candidates for future experimental investigations, helping to fill a crucial knowledge gap in infant nutrition.

A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Exploring the metabolic profile of A. baumannii for antimicrobial development using genome-scale modeling

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modeling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model iACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii's cellular biomass and identified putative essential genes with no human counterparts, offering new candidates for future antimicrobial development. Finally, we assembled and curated the first collection of metabolic reconstructions for distinct A. baumannii strains and analyzed their growth characteristics. The presented models are in a standardized and well-curated format, enhancing their usability for multi-strain network reconstruction.

The impact of a child's inborn error of metabolism: the parents' perspectives on restrictions, discrimination, family planning, and emergency management

BACKGROUND

To investigate the impact of children's inborn error of metabolism (IEMs) on the children's and their parents' lives from the parents' perspective. We focused on disease-related restrictions in various issues of daily life, experienced discrimination, parental family planning, and management of metabolic emergencies.

METHODS

We conducted a questionnaire-based survey with 108 parents of 119 children with IEM who attended a metabolic outpatient clinic. The children were categorized into 4 cohorts, based on increasing disease severity (cohort 1: IEMs with lowest severity, cohort 4: IEMs with highest severity), and compared by using Tobit regressions.

RESULTS

The severity of the child's IEM was associated with an increase in the intensity of perceived restrictions from the parents' perspective for themselves and their children in all aspects of life: in general, in contact with friends, in the pursuit of hobbies, in childcare/school/occupation, and due to emotional stress. The highest intensity of restrictions in all cohorts was found for the parents themselves in contact with friends (compared to cohort 1: cohort 2: c. 3.556, p = 0.002; cohort 3: c. 4.159, p = 0.003; cohort 4: c. 7.224, p < 0.001). Parents of 8% of children reported that their children were discriminated against because of IEM, with the highest proportion of affected children (43%) in cohort 4. Parental family planning decisions were influenced in 34% of parents, with fear of recurrence being a predominant aspect. Of the parents of children diagnosed with IEMs associated with metabolic emergencies, 68% stated that they felt well or very well prepared for the occurrence of a metabolic emergency, and 100% of parents were able to name the necessary action steps from memory. Nevertheless, 58% stated that they experienced an occurring emergency as rather or very stressful.

CONCLUSIONS

From the parents' perspective, the intensity of restrictions increased with the severity of the child's IEM. The study shows the high impact of IEM on parents of children with IEM and the daily challenges they face. These findings emphasize the importance of comprehensive support for parents of children with IEM.

Modeling Microbial Communities: Perspective and Challenges

Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.

Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.

Hierarchical multi-task deep learning-assisted construction of human gut microbiota reactive oxygen species-scavenging enzymes database

In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O2-), hydrogen peroxide (H2O2), and hydroxyl radicals (OH-). ROS can be destructive, and an imbalance between oxidants and antioxidants in the body can lead to pathological inflammation. Inappropriate ROS production can cause oxidative damage, disrupting the balance in the body and potentially leading to DNA damage in intestinal epithelial cells and beneficial bacteria. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS. Accurately predicting the types of ROS-scavenging enzymes (ROSes) is crucial for understanding the oxidative stress mechanisms and formulating strategies to combat diseases related to the "gut-organ axis." Currently, there are no available ROSes databases (DBs). In this study, we propose a systematic workflow comprising three modules and employ a hierarchical multi-task deep learning approach to collect, expand, and explore ROSes-related entries. Based on this, we have developed the human gut microbiota ROSes DB (http://39.101.72.186/), which includes 7,689 entries. This DB provides user-friendly browsing and search features to support various applications. With the assistance of ROSes DB, various communication-based microbial interactions can be explored, further enabling the construction and analysis of the evolutionary and complex networks of ROSes DB in human gut microbiota species.IMPORTANCEReactive oxygen species (ROS) is generated during the process of oxygen reduction, including superoxide anion, hydrogen peroxide, and hydroxyl radicals. ROS can potentially cause damage to cells and DNA, leading to pathological inflammation within the body. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS, thereby maintaining a balance of microorganisms within the host. The study highlights the current absence of a ROSes DB, emphasizing the crucial importance of accurately predicting the types of ROSes for understanding oxidative stress mechanisms and developing strategies for diseases related to the "gut-organ axis." This research proposes a systematic workflow and employs a multi-task deep learning approach to establish the human gut microbiota ROSes DB. This DB comprises 7,689 entries and serves as a valuable tool for researchers to delve into the role of ROSes in the human gut microbiota.

Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

Emerging tools and best practices for studying gut microbial community metabolism

The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.

Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling

Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organism's metabolism using in silico genome-scale metabolic models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cell's maintenance.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes

Direct infusion-high-resolution mass spectrometry (DI-HRMS) allows for rapid profiling of complex mixtures of metabolites in blood, cerebrospinal fluid, tissue samples and cultured cells. Here, we present a DI-HRMS method suitable for the rapid determination of metabolic fluxes of isotopically labeled substrates in cultured cells and organoids. We adapted an automated annotation pipeline by selecting labeled adducts that best represent the majority of 13C and/or 15N-labeled glycolytic and tricarboxylic acid cycle intermediates as well as a number of their derivatives. Furthermore, valine, leucine and several of their degradation products were included. We show that DI-HRMS can determine anticipated and unanticipated alterations in metabolic fluxes along these pathways that result from the genetic alteration of single metabolic enzymes, including pyruvate dehydrogenase (PDHA1) and glutaminase (GLS). In addition, it can precisely pinpoint metabolic adaptations to the loss of methylmalonyl-CoA mutase in patient-derived liver organoids. Our results highlight the power of DI-HRMS in combination with stable isotopically labeled compounds as an efficient screening method for fluxomics.

Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Exposure to environmental toxicants is associated with gut microbiome dysbiosis, insulin resistance and obesity

Environmental toxicants (ETs) are associated with adverse health outcomes. Here we hypothesized that exposures to ETs are linked with obesity and insulin resistance partly through a dysbiotic gut microbiota and changes in the serum levels of secondary bile acids (BAs). Serum BAs, per- and polyfluoroalkyl substances (PFAS) and additional twenty-seven ETs were measured by mass spectrometry in 264 Danes (121 men and 143 women, aged 56.6 ± 7.3 years, BMI 29.7 ± 6.0 kg/m2) using a combination of targeted and suspect screening approaches. Bacterial species were identified based on whole-genome shotgun sequencing (WGS) of DNA extracted from stool samples. Personalized genome-scale metabolic models (GEMs) of gut microbial communities were developed to elucidate regulation of BA pathways. Subsequently, we compared findings from the human study with metabolic implications of exposure to perfluorooctanoic acid (PFOA) in PPARα-humanized mice. Serum levels of twelve ETs were associated with obesity and insulin resistance. High chemical exposure was associated with increased abundance of several bacterial species (spp.) of genus (Anaerotruncus, Alistipes, Bacteroides, Bifidobacterium, Clostridium, Dorea, Eubacterium, Escherichia, Prevotella, Ruminococcus, Roseburia, Subdoligranulum, and Veillonella), particularly in men. Conversely, females in the higher exposure group, showed a decrease abundance of Prevotella copri. High concentrations of ETs were correlated with increased levels of secondary BAs including lithocholic acid (LCA), and decreased levels of ursodeoxycholic acid (UDCA). In silico causal inference analyses suggested that microbiome-derived secondary BAs may act as mediators between ETs and obesity or insulin resistance. Furthermore, these findings were substantiated by the outcome of the murine exposure study. Our combined epidemiological and mechanistic studies suggest that multiple ETs may play a role in the etiology of obesity and insulin resistance. These effects may arise from disruptions in the microbial biosynthesis of secondary BAs.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett's Esophagus and High-Grade Dysplasia or Adenocarcinoma

BACKGROUND

Esophageal adenocarcinoma (EAC) is rising in incidence, and established risk factors do not explain this trend. Esophageal microbiome alterations have been associated with Barrett's esophagus (BE) and dysplasia and EAC. The oral microbiome is tightly linked to the esophageal microbiome; this study aimed to identify salivary microbiome-related factors associated with BE, dysplasia, and EAC.

METHODS

Clinical data and oral health history were collected from patients with and without BE. The salivary microbiome was characterized, assessing differential relative abundance of taxa by 16S rRNA gene sequencing and associations between microbiome composition and clinical features. Microbiome metabolic modeling was used to predict metabolite production.

RESULTS

A total of 244 patients (125 non-BE and 119 BE) were analyzed. Patients with high-grade dysplasia (HGD)/EAC had a significantly higher prevalence of tooth loss (P = 0.001). There were significant shifts with increased dysbiosis associated with HGD/EAC, independent of tooth loss, with the largest shifts within the genus Streptococcus. Modeling predicted significant shifts in the microbiome metabolic capacities, including increases in L-lactic acid and decreases in butyric acid and L-tryptophan production in HGD/EAC.

CONCLUSIONS

Marked dysbiosis in the salivary microbiome is associated with HGD and EAC, with notable increases within the genus Streptococcus and accompanying changes in predicted metabolite production. Further work is warranted to identify the biological significance of these alterations and to validate metabolic shifts.

IMPACT

There is an association between oral dysbiosis and HGD/EAC. Further work is needed to establish the diagnostic, predictive, and causal potential of this relationship.

Metabolic model-based ecological modeling for probiotic design

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

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis

Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

Model organism life extending therapeutics modulate diverse nodes in the drug-gene-microbe tripartite human longevity interactome

Advances in antiaging drug/lead discovery in animal models constitute a large body of literature on novel senotherapeutics and geroprotectives. However, with little direct evidence or mechanism of action in humans-these drugs are utilized as nutraceuticals or repurposed supplements without proper testing directions, appropriate biomarkers, or consistent in-vivo models. In this study, we take previously identified drug candidates that have significant evidence of prolonging lifespan and promoting healthy aging in model organisms, and simulate them in human metabolic interactome networks. Screening for drug-likeness, toxicity, and KEGG network correlation scores, we generated a library of 285 safe and bioavailable compounds. We interrogated this library to present computational modeling-derived estimations of a tripartite interaction map of animal geroprotective compounds in the human molecular interactome extracted from longevity, senescence, and dietary restriction-associated genes. Our findings reflect previous studies in aging-associated metabolic disorders, and predict 25 best-connected drug interactors including Resveratrol, EGCG, Metformin, Trichostatin A, Caffeic Acid and Quercetin as direct modulators of lifespan and healthspan-associated pathways. We further clustered these compounds and the functionally enriched subnetworks therewith to identify longevity-exclusive, senescence-exclusive, pseudo-omniregulators and omniregulators within the set of interactome hub genes. Additionally, serum markers for drug-interactions, and interactions with potentially geroprotective gut microbial species distinguish the current study and present a holistic depiction of optimum gut microbial alteration by candidate drugs. These findings provide a systems level model of animal life-extending therapeutics in human systems, and act as precursors for expediting the ongoing global effort to find effective antiaging pharmacological interventions.Communicated by Ramaswamy H. Sarma.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Omics data integration suggests a potential idiopathic Parkinson's disease signature

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.

Inferring microbial interactions with their environment from genomic and metagenomic data

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