Multi-country metabolic signature discovery for chicken health classification.

INTRODUCTION: To decrease antibiotic resistance, their use as growth promoters in the agricultural sector has been largely abandoned. This may lead to decreased health due to infectious disease or microbiome changes leading to gut inflammation. OBJECTIVES: We aimed to generate a m/z signature classifying chicken health in blood, and obtain biological insights from the resulting m/z signature. METHODS: We used direct infusion mass-spectrometry to determine a machine-learned metabolomics signature that classifies chicken health from a blood sample. We then challenged the resulting models by investigating the classification capability of the signature on novel data obtained at poultry houses in previously unseen countries using a Leave-One-Country-Out (LOCO) cross-validation strategy. Additionally, we optimised the number of mass/charge (m/z) values required to maximise the classification capability of Random Forest models, by developing a novel ranking system based on combined univariate t-test and fold-change analyses and building models based on this ranking through forward and reverse feature selection. RESULTS: The multi-country and LOCO models could classify chicken health. Both resulting 25-m/z and 3784-m/z signatures reliably classified chicken health in multiple countries. Through mummichog enrichment analysis on the large m/z signature, we found changes in amino acid metabolism, including branched chain amino acids and polyamines. CONCLUSION: We reliably classified chicken health from blood, independent of genetic-, farm-, feed- and country-specific confounding factors. The 25-m/z signature can be used to aid development of a per-metabolite panel. The extended 3784-m/z version can be used to gain a deeper understanding of the metabolic causes and consequences of low chicken health. Together, they may facilitate future treatment, prevention and intervention.

The media composition as a crucial element in high-throughput metabolic network reconstruction.

In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.

Metagenome-assembled genomes indicate that antimicrobial resistance genes are highly prevalent among urban bacteria and multidrug and glycopeptide resistances are ubiquitous in most taxa.

Introduction: Every year, millions of deaths are associated with the increased spread of antimicrobial resistance genes (ARGs) in bacteria. With the increasing urbanization of the global population, the spread of ARGs in urban bacteria has become a more severe threat to human health. Methods: In this study, we used metagenome-assembled genomes (MAGs) recovered from 1,153 urban metagenomes in multiple urban locations to investigate the fate and occurrence of ARGs in urban bacteria. Additionally, we analyzed the occurrence of these ARGs on plasmids and estimated the virulence of the bacterial species. Results: Our results showed that multidrug and glycopeptide ARGs are ubiquitous among urban bacteria. Additionally, we analyzed the deterministic effects of phylogeny on the spread of these ARGs and found ARG classes that have a non-random distribution within the phylogeny of our recovered MAGs. However, few ARGs were found on plasmids and most of the recovered MAGs contained few virulence factors. Discussion: Our results suggest that the observed non-random spreads of ARGs are not due to the transfer of plasmids and that most of the bacteria observed in the study are unlikely to be virulent. Additional research is needed to evaluate whether the ubiquitous and widespread ARG classes will become entirely prevalent among urban bacteria and how they spread among phylogenetically distinct species.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

Ablation of liver Fxr results in an increased colonic mucus barrier in mice.

BACKGROUND & AIMS: The interorgan crosstalk between the liver and the intestine has been the focus of intense research. Key in this crosstalk are bile acids, which are secreted from the liver into the intestine, interact with the microbiome, and upon absorption reach back to the liver. The bile acid-activated farnesoid X receptor (Fxr) is involved in the gut-to-liver axis. However, liver-to-gut communication and the roles of bile acids and Fxr remain elusive. Herein, we aim to get a better understanding of Fxr-mediated liver-to-gut communication, particularly in colon functioning. METHODS: Fxr floxed/floxed mice were crossed with cre-expressing mice to yield Fxr ablation in the intestine (Fxr-intKO), liver (Fxr-livKO), or total body (Fxr-totKO). The effects on colonic gene expression (RNA sequencing), the microbiome (16S sequencing), and mucus barrier function by ex vivo imaging were analysed. RESULTS: Despite relatively small changes in biliary bile acid concentration and composition, more genes were differentially expressed in the colons of Fxr-livKO mice than in those of Fxr-intKO and Fxr-totKO mice (3272, 731, and 1824, respectively). The colons of Fxr-livKO showed increased expression of antimicrobial genes, Toll-like receptors, inflammasome-related genes and genes belonging to the 'Mucin-type O-glycan biosynthesis' pathway. Fxr-livKO mice have a microbiome profile favourable for the protective capacity of the mucus barrier. The thickness of the inner sterile mucus layer was increased and colitis symptoms reduced in Fxr-livKO mice. CONCLUSIONS: Targeting of FXR is at the forefront in the battle against metabolic diseases. We show that ablation of Fxr in the liver greatly impacts colonic gene expression and increased the colonic mucus barrier. Increasing the mucus barrier is of utmost importance to battle intestinal diseases such as inflammatory bowel disease, and we show that this might be done by antagonising FXR in the liver. LAY SUMMARY: This study shows that the communication of the liver to the intestine is crucial for intestinal health. Bile acids are key players in this liver-to-gut communication, and when Fxr, the master regulator of bile acid homoeostasis, is ablated in the liver, colonic gene expression is largely affected, and the protective capacity of the mucus barrier is increased.

DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations.

MOTIVATION: Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools do not typically take species-specific experimental and curated genomic data into account. RESULTS: We developed Data-drivEn METabolic nEtwork Refinement (DEMETER), a Constraint-Based Reconstruction and Analysis (COBRA) Toolbox extension, which enables the efficient, simultaneous refinement of thousands of draft genome-scale reconstructions, while ensuring adherence to the quality standards in the field, agreement with available experimental data and refinement of pathways based on manually refined genome annotations. AVAILABILITY AND IMPLEMENTATION: DEMETER and tutorials are freely available at https://github.com/opencobra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data.

Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities.

MOTIVATION: The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. RESULTS: To address this gap, we created a comprehensive toolbox to model (i) microbe-microbe and host-microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. AVAILABILITY AND IMPLEMENTATION: The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

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

A multitude of factors contribute to complex diseases and can be measured with 'omics' methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources 'Human metabolism', 'Gut microbiome', 'Disease', 'Nutrition', and 'ReconMaps'. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH's unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

Modeling metabolism of the human gut microbiome.

The human gut microbiome plays an important part in human health. The complexity of the microbiome makes it difficult to determine the detailed metabolic functions and cross-talk occurs between the individual species. In silico systems biology studies of the microbiome can help to identify metabolite exchanges among gut microbes. Constraint-based reconstruction and analysis methods use biochemically accurate genome-scale metabolic networks of microorganisms to simulate metabolism between species in a given microbiome and help generate novel hypotheses on microbial interactions. Here, we review metabolic modeling studies that have investigated metabolic functions of the gut microbiome.

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota.

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host-microbiome interactions.

Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires.

BACKGROUND: The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context. RESULTS: We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides. CONCLUSIONS: We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes.

The human gut microbiota supplies its host with essential nutrients, including B-vitamins. Using the PubSEED platform, we systematically assessed the genomes of 256 common human gut bacteria for the presence of biosynthesis pathways for eight B-vitamins: biotin, cobalamin, folate, niacin, pantothenate, pyridoxine, riboflavin, and thiamin. On the basis of the presence and absence of genome annotations, we predicted that each of the eight vitamins was produced by 40-65% of the 256 human gut microbes. The distribution of synthesis pathways was diverse; some genomes had all eight biosynthesis pathways, whereas others contained no de novo synthesis pathways. We compared our predictions to experimental data from 16 organisms and found 88% of our predictions to be in agreement with published data. In addition, we identified several pairs of organisms whose vitamin synthesis pathway pattern complemented those of other organisms. This analysis suggests that human gut bacteria actively exchange B-vitamins among each other, thereby enabling the survival of organisms that do not synthesize any of these essential cofactors. This result indicates the co-evolution of the gut microbes in the human gut environment. Our work presents the first comprehensive assessment of the B-vitamin synthesis capabilities of the human gut microbiota. We propose that in addition to diet, the gut microbiota is an important source of B-vitamins, and that changes in the gut microbiota composition can severely affect our dietary B-vitamin requirements.

Binding proteins enhance specific uptake rate by increasing the substrate-transporter encounter rate.

Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. Several advantages of using binding proteins in such uptake systems have been proposed. However, a systematic, rigorous and quantitative analysis of the function of binding proteins is lacking. By combining knowledge of selection pressure and physiochemical constraints, we derive kinetic, thermodynamic, and stoichiometric properties of binding-protein dependent transport systems that enable a maximal import activity per amount of transporter. Under the hypothesis that this maximal specific activity of the transport complex is the selection objective, binding protein concentrations should exceed the concentration of both the scarce nutrient and the transporter. This increases the encounter rate of transporter with loaded binding protein at low substrate concentrations, thereby enhancing the affinity and specific uptake rate. These predictions are experimentally testable, and a number of observations confirm them.