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The skin microbiota plays an essential role in helping the host adapt to different environments and maintain health. By examining the characteristics of amphibian skin flora alongside ontogenetic traits, we can gain insights into the adaptation mechanisms of amphibian skin flora to environmental changes during development. In this study, we analyzed the skin microbiota of Quasipaa spinosa during metamorphosis using Illumina sequencing. Venn diagrams and UpSet analysis revealed that the LTS (hindlimb tadpoles’ skin, aquatic habitat) and FTS (forelimb tadpoles’ skin, shift from aquatic to amphibious habitats) groups exhibited a higher number of unique amplicon sequence variants (ASVs), while the TS (tadpoles’ skin, aquatic habitat) and LFS (land frogs’ skin, amphibious habitats) groups displayed a lower abundance of ASVs. Diversity analysis indicated similarities in the microorganisms between the LTS and the FTS groups, with higher microbial diversity compared to the TS and the LFS groups. Additionally, microbial co-occurrence network analysis indicated a more stable microecology in the LTS group and FTS group. Proteobacteria, Firmicutes, and Bacteroidota were identified as the dominant phyla, although their relative abundances varied widely among groups. LEfSe (Linear discriminant analysis effect size) showed significant enrichment of beneficial bacteria at various developmental stages, including Bacteroides, Bacillus, and Lactobacillus. Functional prediction analysis shows significant differences in skin microorganism functions across various developmental stages, with a primary focus on metabolic functions. This study provides valuable insights into the compositional dynamics of skin microbiota in Q. spinosa at various developmental stages.

Background: Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. Methods: To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017–2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, asari and centWave, were applied. Results: In comparison to centWave, asari showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. Conclusions: This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers.

The model organism Caenorhabditis elegans and its relationship with the gut microbiome are gaining traction, especially for the study of neurodegenerative diseases such as Parkinson’s Disease (PD). Gut microbes are known to be able to alter kynurenine metabolites in the host, directly influencing innate immunity in C. elegans. While the mitochondrial unfolded protein response (UPRmt) was first characterized in C. elegans in 2007, its relevance in host–microbiome interactions has only become apparent in recent years. In this review, we provide novel insights into the current understanding of the microbiome–gut–brain axis with a focus on tripartite interactions between the UPRmt, kynurenine pathway, and microbiome in C. elegans, and explore their relationships for PD remediations.

Abstract Background Staphylococcus aureus is one of the prevalent etiological agents of contagious bovine mastitis, causing a significant economic burden on the global dairy industry. Given the emergence of antibiotic resistance (ABR) and possible zoonotic spillovers, S aureus from mastitic cattle pose threat to both veterinary and public health. Therefore, assessment of their ABR status and pathogenic translation in human infection models is crucial. Results In this study, 43 S. aureus isolates associated with bovine mastitis obtained from four different Canadian provinces (Alberta, Ontario, Quebec, and Atlantic provinces) were tested for ABR and virulence through phenotypic and genotypic profiling. All 43 isolates exhibited crucial virulence characteristics such as hemolysis, and biofilm formation, and six isolates from ST151, ST352, and ST8 categories showed ABR. Genes associated with ABR (tetK, tetM, aac6’, norA, norB, lmrS, blaR, blaZ, etc.), toxin production (hla, hlab, lukD, etc.), adherence (fmbA, fnbB, clfA, clfB, icaABCD, etc.), and host immune invasion (spa, sbi, cap, adsA, etc.) were identified by analyzing whole-genome sequences. Although none of the isolates possessed human adaptation genes, both groups of ABR and antibiotic-susceptible isolates demonstrated intracellular invasion, colonization, infection, and death of human intestinal epithelial cells (Caco-2), and Caenorhabditis elegans. Notably, the susceptibilities of S. aureus towards antibiotics such as streptomycin, kanamycin, and ampicillin were altered when the bacteria were internalized in Caco-2 cells and C. elegans. Meanwhile, tetracycline, chloramphenicol, and ceftiofur were comparatively more effective with ≤ 2.5 log10 reductions of intracellular S. aureus. Conclusions This study demonstrated the potential of S. aureus isolated from mastitis cows to possess virulence characteristics enabling invasion of intestinal cells thus calling for developing therapeutics capable of targeting drug-resistant intracellular pathogens for effective disease management.

Aging is the system-wide loss of homeostasis, eventually leading to death. There is growing evidence that the microbiome not only evolves with its aging host, but also directly affects aging via the modulation of metabolites involved in important cellular functions. The widely used model organism C. elegans exhibits high selectivity towards its native microbiome members which confer a range of differential phenotypes and possess varying functional capacities. The ability of one such native microbiome species, Chryseobacterium sp. CHNTR56 MYb120, to improve the lifespan of C. elegans and to promote the production of Vitamin B6 in the co-colonizing member Comamonas sp. 12022 MYb131 are some of its beneficial effects on the worm host. We hypothesize that studying its metabolic influence on the different life stages of the worm could provide further insights into mutualistic interactions. The present work applied LC-MS untargeted metabolomics and isotope labeling to study the impact of the native microbiome member Chryseobacterium sp. CHNTR56 MYb120 on the metabolism of C. elegans. In addition to the upregulation of biosynthesis and detoxification pathway intermediates, we found that Chryseobacterium sp. CHNTR56 MYb120 upregulates the glyoxylate shunt in mid-adult worms which is linked to the upregulation of trehalose, an important metabolite for desiccation tolerance in older worms.

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn’s disease, as well as between glycine and coronary heart disease.

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

The roles of the healthy microbiome on the host and the relationships between members of the microbiome remain to be fully characterized. Due to the complexity of the interactions between the mammalian microbiome and its host, the use of model organisms such as the nematode worm Caenorhabditis elegans is a promising strategy to study host-microbiome interactions in vivo, as well as bacterial crosstalk within the host. Previously it was found that native bacterial isolates of the worm, Chryseobacterium sp. CHNTR56 MYb120 and Comamonas sp. 12022 MYb131, possess genomic diversity in the biosynthesis of the active form of vitamin B6, pyridoxal 5′-phosphate (PLP), and contribute to host fitness and lifespan extension. However, the relative contribution of PLP from each isolate, as well as the existence of interbacterial relationships within the worm gut remain to be characterized. In the present work, we investigated the presence and measured the abundance of PLP in the isolates and in the worms grown with the isolates using ultraperformance liquid chromatography tandem-mass spectrometry (UPLC-MS/MS). Our analyses confirmed the presence of PLP in vitro and in vivo. The elevated abundance of PLP in the isolates (which reached statistically significant levels when the two isolates were combined), and within worms grown with the combination of bacterial isolates, compared to control, indicated synergism between the isolates in the production of PLP. Isotope labeling revealed that Comamonas sp. 12022 MYb131 was the main provider of PLP in worms grown with the combination of bacterial isolates. The dominance of this isolate inside the worm was further confirmed by a colonization assay. An untargeted metabolomics analysis of the bacteria showed that the pathways related to cell growth, protein synthesis and lipid synthesis/energy production were regulated in the combination group in comparison with Comamonas sp. 12022 MYb131 alone. Furthermore, glutamine, involved in the de novo synthesis of purine and pyrimidines, was specifically abundant in this group, indicating the potential role of this metabolite in initiating and sustaining bacterial growth. This bacterial crosstalk is suggested to promote the growth of Comamonas sp. 12022 MYb131 in vivo, and synthesis of bacterial metabolites such as PLP in the worm gut.

This proof-of-concept study characterizes the Japanese quail (Coturnix japonica) hepatic metabolome following exposure to benzo[a]pyrene, chlorpyrifos, ethinylestradiol, fluoxetine hydrochloride, hexabromocyclododecane, lead(II)nitrate, seleno-L-methionine, and trenbolone in embryos and adults. The analysis revealed effects on lipid metabolism following exposure to several chemicals at both life stages. The most pronounced effects were observed in embryos exposed to 41.1 μg/g chlorpyrifos. This work highlighted challenges and the need for further avian metabolomics studies.

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus–host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.