PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

Enantiomer-specific effects of metamifop on serum metabolism in rats

Metamifop (MET) is a widely used pesticides in paddy field and it has good weed control effect. As a chiral pesticide that may be hazardous to human health through food chain transmission, there could be selective differences in the metabolism and toxicity of its enantiomers, so the study of chiral MET may offer an assessment of MET toxicity and stereoselectivity at the enantiomeric level. A total of 39, 43 and 31 differential metabolites were screened from the data sets of Rac-, R-(-)- and S-(+)-MET, respectively. Metabolic pathway analysis revealed that MET and its enantiomers primarily affected sphingolipid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, α-linolenic acid metabolism and arachidonic acid metabolism. Rac- and S-(+)-MET affected the synthesis of glycosylphosphatidylinositol (GPI)-anchored biomolecules. R-(-)- and S-(+)-MET affected glutathione metabolism. R-(-)-MET affected vitamin B6 metabolism, selenium compound metabolism, and steroid biosynthesis. Pyrimidine metabolism was only affected by Rac-MET. The experimental results indicated that MET and its enantiomers may affect the nervous and immune systems in rats. Further inter-group difference analysis also demonstrated stereoselectivity of MET and its enantiomers on rat serum metabolism. These findings may provide more detailed information on the toxicity of Rac-, S-(+)- and R-(-)-MET in rat, as well as some context for assessing the environmental risk of the three agents to organisms.

FragHub: A Mass Spectral Library Data Integration Workflow

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Multi-omics approach to understand the impact of sun exposure on an in vitro skin ecosystem and evaluate a new broad-spectrum sunscreen

A reconstructed human epidermal model (RHE) colonized with human microbiota and sebum was developed to reproduce the complexity of the skin ecosystem in vitro. The RHE model was exposed to simulated solar radiation (SSR) with or without SPF50+ sunscreen (with UVB, UVA, long-UVA, and visible light protection). Structural identification of discriminant metabolites was acquired by nuclear magnetic resonance and metabolomic fingerprints were identified using reverse phase-ultra high-performance liquid chromatography-high resolution mass spectrometry, followed by pathway enrichment analysis. Over 50 metabolites were significantly altered by SSR (p < 0.05, log2 values), showing high skin oxidative stress (glutathione and purine pathways, urea cycle) and altered skin microbiota (branched-chain amino acid cycle and tryptophan pathway). 16S and internal transcribed spacer rRNA sequencing showed the relative abundance of various bacteria and fungi altered by SSR. This study identified highly accurate metabolomic fingerprints and metagenomic modifications of sun-exposed skin to help elucidate the interactions between the skin and its microbiota. Application of SPF50+ sunscreen protected the skin ecosystem model from the deleterious effects of SSR and preserved the physiological interactions within the skin ecosystem. These innovative technologies could thus be used to evaluate the effectiveness of sunscreen.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Bridging data management platforms and visualization tools to enable ad-hoc and smart analytics in life sciences

Core facilities have to offer technologies that best serve the needs of their users and provide them a competitive advantage in research. They have to set up and maintain instruments in the range of ten to a hundred, which produce large amounts of data and serve thousands of active projects and customers. Particular emphasis has to be given to the reproducibility of the results. More and more, the entire process from building the research hypothesis, conducting the experiments, doing the measurements, through the data explorations and analysis is solely driven by very few experts in various scientific fields. Still, the ability to perform the entire data exploration in real-time on a personal computer is often hampered by the heterogeneity of software, the data structure formats of the output, and the enormous data sizes. These impact the design and architecture of the implemented software stack. At the Functional Genomics Center Zurich (FGCZ), a joint state-of-the-art research and training facility of ETH Zurich and the University of Zurich, we have developed the B-Fabric system, which has served for more than a decade, an entire life sciences community with fundamental data science support. In this paper, we sketch how such a system can be used to glue together data (including metadata), computing infrastructures (clusters and clouds), and visualization software to support instant data exploration and visual analysis. We illustrate our in-daily life implemented approach using visualization applications of mass spectrometry data.

ChemFOnt: the chemical functional ontology resource

The Chemical Functional Ontology (ChemFOnt), located at https://www.chemfont.ca, is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals. These include primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals. ChemFOnt is a FAIR-compliant resource intended to bring the same rigor, standardization and formal structure to the terms and terminology used in biochemistry, food chemistry and environmental chemistry as the gene ontology (GO) has brought to molecular biology. ChemFOnt is available as both a freely accessible, web-enabled database and a downloadable Web Ontology Language (OWL) file. Users may download and deploy ChemFOnt within their own chemical databases or integrate ChemFOnt into their own analytical software to generate machine readable relationships that can be used to make new inferences, enrich their omics data sets or make new, non-obvious connections between chemicals and their direct or indirect effects. The web version of the ChemFOnt database has been designed to be easy to search, browse and navigate. Currently ChemFOnt contains data on 341 627 chemicals, including 515 332 terms or definitions. The functional hierarchy for ChemFOnt consists of four functional 'aspects', 12 functional super-categories and a total of 173 705 functional terms. In addition, each of the chemicals are classified into 4825 structure-based chemical classes. ChemFOnt currently contains 3.9 million protein-chemical relationships and ∼10.3 million chemical-functional relationships. The long-term goal for ChemFOnt is for it to be adopted by databases and software tools used by the general chemistry community as well as the metabolomics, exposomics, metagenomics, genomics and proteomics communities.