A decade after the metabolomics standards initiative it's time for a revision

Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19

BACKGROUND

The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-date metabolomics data integrated with clinical data to estimate early mortality risk in critically ill COVID-19 patients. Our study addresses the critical gap in current research by utilizing current patient samples, providing fresh insights into the pathophysiology of the disease in a partially immunized global population.

METHODS

One hundred elderly patients with severe COVID-19 infection, including 46 survivors and 54 non-survivors, were recruited in January-February 2023 at the Second Hospital affiliated with Harbin Medical University. A predictive model within 24 h of admission was developed using blood metabolomics and clinical data. Differential metabolite analysis and other techniques were used to identify relevant characteristics. Model performance was assessed by comparing the area under the receiver operating characteristic curve (AUROC). The final prediction model was externally validated in a cohort of 50 COVID-19 elderly critically ill patients at the First Hospital affiliated with Harbin Medical University during the same period.

RESULTS

Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. One metabolite indicator, Itaconic acid, and four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the five variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.952 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.963 and a specificity of 0.957.

CONCLUSIONS

The prognostic outcome of COVID-19 elderly patients is significantly influenced by the levels of Itaconic acid, LYM, IL-6, PCT, and CRP upon admission. These five indicators can be utilized to assess the mortality risk in affected individuals.

From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

Metabolomic Hallmarks of Obesity and Metabolic Dysfunction-Associated Steatotic Liver Disease

From a detailed review of 90 experimental and clinical metabolomic investigations of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD), we have developed metabolomic hallmarks for both obesity and MASLD. Obesity studies were conducted in mice, rats, and humans, with consensus biomarker groups in plasma/serum being essential and nonessential amino acids, energy metabolites, gut microbiota metabolites, acylcarnitines and lysophosphatidylcholines (LPC), which formed the basis of the six metabolomic hallmarks of obesity. Additionally, mice and rats shared elevated cholesterol, humans and rats shared elevated fatty acids, and humans and mice shared elevated VLDL/LDL, bile acids and phosphatidylcholines (PC). MASLD metabolomic studies had been performed in mice, rats, hamsters, cows, geese, blunt snout breams, zebrafish, and humans, with the biomarker groups in agreement between experimental and clinical investigations being energy metabolites, essential and nonessential amino acids, fatty acids, and bile acids, which lay the foundation of the five metabolomic hallmarks of MASLD. Furthermore, the experimental group had higher LPC/PC and cholesteryl esters, and the clinical group had elevated acylcarnitines, lysophosphatidylethanolamines/phosphatidylethanolamines (LPE/PE), triglycerides/diglycerides, and gut microbiota metabolites. These metabolomic hallmarks aid in the understanding of the metabolic role played by obesity in MASLD development, inform mechanistic studies into underlying disease pathogenesis, and are critical for new metabolite-inspired therapies.

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis

Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based diagnostic approaches. This study utilizes ultraperformance liquid chromatography coupled to an electrospray ionization source and quadrupole time-of-flight untargeted metabolomics combined with biochemometrics to identify novel serum biomarkers for PD. Analyzing a Brazilian cohort of serum samples from 39 PD patients and 15 healthy controls, we identified 15 metabolites significantly associated with PD, with 11 reported as potential biomarkers for the first time. Key disrupted metabolic pathways include caffeine metabolism, arachidonic acid metabolism, and primary bile acid biosynthesis. Our machine learning model demonstrated high accuracy, with the Rotation Forest boosting model achieving 94.1% accuracy in distinguishing PD patients from controls. It is based on three new PD biomarkers (downregulated: 1-lyso-2-arachidonoyl-phosphatidate and hypoxanthine and upregulated: ferulic acid) and surpasses the general 80% diagnostic accuracy obtained from initial clinical evaluations conducted by specialists. Besides, this machine learning model based on a decision tree allowed for visual and easy interpretability of affected metabolites in PD patients. These findings could improve the detection and monitoring of PD, paving the way for more precise diagnostics and therapeutic interventions. Our research emphasizes the critical role of metabolomics and machine learning in advancing our understanding of the chemical profile of neurodegenerative diseases.

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

Bioprospecting-based untargeted metabolomics identifies alkaloids as potential anti-inflammatory bioactive markers of Ocotea species (Lauraceae)

BACKGROUND

Species within the Ocotea genus (Lauraceae), have demonstrated an interesting profile of bioactivities. Renowned for their diverse morphology and intricate specialized metabolite composition, Ocotea species have re-emerged as compelling candidates for bioprospecting in drug discovery research. However, it is a genus insufficiently studied, particularly regarding anti-inflammatory activity.

PURPOSE

To investigate the anti-inflammatory activity of Ocotea spp. extracts and determine the major markers in this genus.

METHODS

Extracts of 60 different Ocotea spp. were analysed by an ex vivo anti-inflammatory assay in human whole blood. The experiment estimates the prostaglandin E2 levels, which is one of the main mediators of the inflammatory cascade, responsible for the classical symptoms of fever, pain, and other common effects of the inflammatory process. Untargeted metabolomics analysis through liquid chromatography coupled with high-resolution mass spectrometry was performed, along with statistical analysis, to investigate which Ocotea metabolites are correlated with their anti-inflammatory activity.

RESULTS

The anti-inflammatory screening indicated that 49 out of 60 Ocotea spp. extracts exhibited significant inhibition of PGE2 release compared to the vehicle (p < 0.05). Furthermore, 10 of these extracts showed statistical similarity to the reference drugs. The bioactive markers were accurately identified using multivariate statistics combined with a fold change (> 1.5) and adjusted false discovery rate analysis as unknown compounds and alkaloids, with a majority of aporphine and benzylisoquinolines. These alkaloids were annotated with an increased level of confidence since MSE spectra were compared with comprehensive databases.

CONCLUSION

This study represents the first bioprospecting report revealing the anti-inflammatory potential of several Ocotea spp. The determination of their anti-inflammatory markers could contribute to drug discovery and the chemical knowledge of the Ocotea genus.

A Systematic Review on Metabolomics Analysis in Hearing Impairment: Is It a Possible Tool in Understanding Auditory Pathologies?

With more than 466 million people affected, hearing loss represents the most common sensory pathology worldwide. Despite its widespread occurrence, much remains to be explored, particularly concerning the intricate pathogenic mechanisms underlying its diverse phenotypes. In this context, metabolomics emerges as a promising approach. Indeed, lying downstream from molecular biology's central dogma, the metabolome reflects both genetic traits and environmental influences. Furthermore, its dynamic nature facilitates well-defined changes during disease states, making metabolomic analysis a unique lens into the mechanisms underpinning various hearing impairment forms. Hence, these investigations may pave the way for improved diagnostic strategies, personalized interventions and targeted treatments, ultimately enhancing the clinical management of affected individuals. In this comprehensive review, we discuss findings from 20 original articles, including human and animal studies. Existing literature highlights specific metabolic changes associated with hearing loss and ototoxicity of certain compounds. Nevertheless, numerous critical issues have emerged from the study of the current state of the art, with the lack of standardization of methods, significant heterogeneity in the studies and often small sample sizes being the main limiting factors for the reliability of these findings. Therefore, these results should serve as a stepping stone for future research aimed at addressing the aforementioned challenges.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

SMetaS: A Sample Metadata Standardizer for Metabolomics

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.

Mass Spectrometry-Based Multi-omics Integration with a Single Set of C. elegans Samples

Mass spectrometry-based large-scale multi-omics research has proven to be powerful in answering biological questions; nonetheless, it faces many challenges from sample preparation to downstream data integration. To efficiently extract biomolecules of different physicochemical properties, preparation of various sample type needs specific tailoring, especially of difficult ones, such as Caenorhabditis elegans. In this study, we sought to develop a multi-omics sample preparation method starting with a single set ofC. elegans samples to save time, minimize variability, expand biomolecule coverage, and promote multi-omics integration. We investigated tissue disruption methods to effectively release biomolecules and optimized extraction strategies to achieve broader and more reproducible biomolecule coverage in proteomics, lipidomics, and metabolomics workflows. In our assessment, we also considered speediness and usability of the approaches. The developed method was validated through a study of 16C. elegans samples designed to shine light on mitochondrial unfolded protein response (UPRmt), induced by three unique stressors─knocking down electron transfer chain element cco-1, mitochondrial ribosome protein S5 mrps-5, and antibiotic treatment Doxycycline. Our findings suggested that the method achieved great coverage of proteome, lipidome, and metabolome with high reproducibility and validated that all stressors triggered UPRmt in C. elegans, although generating unique molecular signatures. Innate immune response was activated, and triglycerides were decreased under all three stressor conditions. Additionally, Doxycycline treatment elicited more distinct proteomic, lipidomic, and metabolomic response than the other two treatments. This method has been successfully used to process Saccharomyces cerevisiae (data not shown) and can likely be applied to other organisms for multi-omics research.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Lipid Biogeochemistry and Modern Lipidomic Techniques

Lipids are structurally diverse biomolecules that serve multiple roles in cells. As such, they are used as biomarkers in the modern ocean and as paleoproxies to explore the geological past. Here, I review lipid geochemistry, biosynthesis, and compartmentalization; the varied uses of lipids as biomarkers; and the evolution of analytical techniques used to measure and characterize lipids. Advancements in high-resolution accurate-mass mass spectrometry have revolutionized the lipidomic and metabolomic fields, both of which are quickly being integrated into marine meta-omic studies. Lipidomics allows us to analyze tens of thousands of features, providing an open analytical window and the ability to quantify unknown compounds that can be structurally elucidated later. However, lipidome annotation is not a trivial matter and represents one of the biggest challenges for oceanographers, owing in part to the lack of marine lipids in current in silico databases and data repositories. A case study reveals the gaps in our knowledge and open opportunities to answer fundamental questions about molecular-level control of chemical reactions and global-scale patterns in the lipidscape.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for &gt;40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management

BACKGROUND

Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work.

OBJECTIVE

The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources.

KEY REPORTING PRACTICES

Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.

Ecosystem-specific microbiota and microbiome databases in the era of big data

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

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

INTRODUCTION

Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories.

OBJECTIVES

To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management.

METHODS

We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles.

RESULTS

PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases.

CONCLUSION

PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Bacille Calmette-Guérin vaccine reprograms human neonatal lipid metabolism in vivo and in vitro

Vaccines have generally been developed with limited insight into their molecular impact. While systems vaccinology enables characterization of mechanisms of action, these tools have yet to be applied to infants, who are at high risk of infection and receive the most vaccines. Bacille Calmette-Guérin (BCG) protects infants against disseminated tuberculosis (TB) and TB-unrelated infections via incompletely understood mechanisms. We employ mass-spectrometry-based metabolomics of blood plasma to profile BCG-induced infant responses in Guinea-Bissau in vivo and the US in vitro. BCG-induced lysophosphatidylcholines (LPCs) correlate with both TLR-agonist- and purified protein derivative (PPD, mycobacterial antigen)-induced blood cytokine production in vitro, raising the possibility that LPCs contribute to BCG immunogenicity. Analysis of an independent newborn cohort from The Gambia demonstrates shared vaccine-induced metabolites, such as phospholipids and sphingolipids. BCG-induced changes to the plasma lipidome and LPCs may contribute to its immunogenicity and inform the development of early life vaccines.

Anti-Inflammatory Markers of Hops Cultivars (Humulus lupulus L.) Evaluated by Untargeted Metabolomics Strategy

Hops (Humulus lupulus L.) are edible flowers commonly used to add flavour and aroma to beer, besides they have rich chemical diversity and medicinal potential. In this work, an ex vivo anti-inflammatory assay via the LPS-induced signalling pathway and metabolomics approaches were performed to evaluate the ability of hops to inhibit the production of prostaglandin E2 (PGE2) inflammatory mediator and analyze which metabolites produced by the nine different hop cultivars are potential anti-inflammatory markers. Columbus, Chinook and Hallertau Mittelfrüh hop cultivars yielded extracts with PGE2 release inhibition rates of 86.7, 92.5 and 73.5 %, respectively. According to the multivariate statistical analysis, the majority of the metabolites correlated with the activity were prenylated phloroglucinol and phenolic homologs. These results suggest promissory anti-inflammatory hop metabolites.

The minimum information required for a glycomics experiment (MIRAGE): reporting guidelines for capillary electrophoresis

The Minimum Information Required for a Glycomics Experiment (MIRAGE) is an initiative to standardize the reporting of glycoanalytical methods and to assess their reproducibility. To date, the MIRAGE Commission has published several reporting guidelines that describe what information should be provided for sample preparation methods, mass spectrometry methods, liquid chromatography (LC) analysis, exoglycosidase digestions, glycan microarray methods and nuclear magnetic resonance methods. Here we present the first version of reporting guidelines for glyco(proteo)mics analysis by capillary electrophoresis (CE) for standardized and high-quality reporting of experimental conditions in the scientific literature. The guidelines cover all aspects of a glyco(proteo)mics CE experiment including sample preparation, CE operation mode (CZE, CGE, CEC, MEKC, cIEF, cITP), instrument configuration, capillary separation conditions, detection, data analysis, and experimental descriptors. These guidelines are linked to other MIRAGE guidelines and are freely available through the project website https://www.beilstein-institut.de/en/projects/mirage/guidelines/#ce_analysis (doi:10.3762/mirage.7).

Development of RIKEN Plant Metabolome MetaDatabase

The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

Characterization of Cabernet Sauvignon Wines by Untargeted HS-SPME GC-QTOF-MS

Untargeted metabolomics approaches are emerging as powerful tools for the quality evaluation and authenticity of food and beverages and have been applied to wine science. However, most fail to report the method validation, quality assurance and/or quality control applied, as well as the assessment through the metabolomics-methodology pipeline. Knowledge of Mexican viticulture, enology and wine science remains scarce, thus untargeted metabolomics approaches arise as a suitable tool. The aim of this study is to validate an untargeted HS-SPME-GC-qTOF/MS method, with attention to data processing to characterize Cabernet Sauvignon wines from two vineyards and two vintages. Validation parameters for targeted methods are applied in conjunction with the development of a recursive analysis of data. The combination of some parameters for targeted studies (repeatability and reproducibility < 20% RSD; linearity > 0.99; retention-time reproducibility < 0.5% RSD; match-identification factor < 2.0% RSD) with recursive analysis of data (101 entities detected) warrants that both chromatographic and spectrometry-processing data were under control and provided high-quality results, which in turn differentiate wine samples according to site and vintage. It also shows potential biomarkers that can be identified. This is a step forward in the pursuit of Mexican wine characterization that could be used as an authentication tool.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Metabolomic Evaluation of Ralstonia solanacearum Cold Shock Protein Peptide (csp22)-Induced Responses in Solanum lycopersicum

Ralstonia solanacearum, the causal agent of bacterial wilt, is one of the most destructive bacterial plant pathogens. This is linked to its evolutionary adaptation to evade host surveillance during the infection process since many of the pathogen's associated molecular patterns escape recognition. However, a 22-amino acid sequence of R. solanacearum-derived cold shock protein (csp22) was discovered to elicit an immune response in the Solanaceae. Using untargeted metabolomics, the effects of csp22-elicitation on the metabolome of Solanum lycopersicum leaves were investigated. Additionally, the study set out to discover trends that may suggest that csp22 inoculation bestows enhanced resistance on tomato against bacterial wilt. Results revealed the redirection of metabolism toward the phenylpropanoid pathway and sub-branches thereof. Compared to the host response with live bacteria, csp22 induced a subset of the discriminant metabolites, but also metabolites not induced in response to R. solanacearum. Here, a spectrum of hydroxycinnamic acids (especially ferulic acid), their conjugates and derivatives predominated as signatory biomarkers. From a metabolomics perspective, the results support claims that csp22 pre-treatment of tomato plants elicits increased resistance to R. solanacearum infection and contribute to knowledge on plant immune systems operation at an integrative level. The functional significance of these specialized compounds may thus support a heightened state of defense that can be applied to ward off attacking pathogens or toward priming of defense against future infections.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Metabolomics Data Treatment: Basic Directions of the Full Process

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

Metabolomics has achieved great progress over the last 20 years, and it is currently considered a mature research field. As a result, the number of applications in toxicology, biomarker, and drug discovery has also increased. Toxicometabolomics has emerged as a powerful strategy to provide complementary information to study molecular-level toxic effects, which can be combined with a wide range of toxicological assessments and models. The zebrafish model has gained importance in recent decades as a bridging tool between in vitro assays and mammalian in vivo studies in the field of toxicology. Furthermore, as this vertebrate model is a low-cost system and features highly conserved metabolic pathways found in humans and mammalian models, it is a promising tool for toxicometabolomics. This short review aims to introduce zebrafish researchers interested in understanding the effects of chemical exposure using metabolomics to the challenges and possibilities of the field, with a special focus on toxicometabolomics-based mass spectrometry. The overall goal is to provide insights into analytical strategies to generate and identify high-quality metabolomic experiments focusing on quality management systems (QMS) and the importance of data reporting and sharing.

Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

INTRODUCTION

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

OBJECTIVE

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

METHODS

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.

RESULTS

The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.

CONCLUSION

The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.

Nuclear magnetic resonance spectroscopy of biofluids for osteoarthritis

BACKGROUND

Osteoarthritis is a common degenerative musculoskeletal disease of synovial joints. It is characterized by a metabolic imbalance resulting in articular cartilage degradation, reduced elastoviscosity of synovial fluid and an altered chondrocyte phenotype. This is often associated with reduced mobility, pain and poor quality of life. Subsequently, with an ageing world population, osteoarthritis is of increasing concern to public health. Nuclear magnetic resonance (NMR) spectroscopy can be applied to characterize the metabolomes of biofluids, determining changes associated with osteoarthritis pathology, identifying potential biomarkers of disease and alterations to metabolic pathways.

SOURCES OF DATA

A comprehensive search of PubMed and Web of Science databases using combinations of the following keywords: 'NMR Spectroscopy', 'Blood', 'Plasma', 'Serum', 'Urine', 'Synovial Fluid' and 'Osteoarthritis' for articles published from 2000 to 2020.

AREAS OF AGREEMENT

The number of urine metabolomics studies using NMR spectroscopy to investigate osteoarthritis is low, whereas the use of synovial fluid is significantly higher. Several differential metabolites have previously been identified and mapped to metabolic pathways involved in osteoarthritis pathophysiology.

AREAS OF CONTROVERSY

Conclusions are sometimes conservative or overinflated, which may reflect the variation in reporting standards. NMR metabolic experimental design may require further consideration, as do the animal models used for such studies.

GROWING POINTS

There are various aspects which require improvement within the field. These include stricter adherence to the Metabolomics Standards Initiative, inclusive of the standardization of metabolite identifications; increased utilization of integrating NMR metabolomics with other 'omic' disciplines; and increased deposition of raw experimental files into open access online repositories, allowing greater transparency and enabling additional future analyses.

AREAS TIMELY FOR DEVELOPING RESEARCH

Overall, this research area could be improved by the inclusion of more heterogeneous cohorts, reflecting varying osteoarthritis phenotypes, and larger group sizes ensuring studies are not underpowered. To correlate local and systemic environments, the use of blood for diagnostic purposes, over the collection of synovial fluid, requires increased attention. This will ultimately enable biomarkers of disease to be determined that may provide an earlier diagnosis, or provide potential therapeutic targets for osteoarthritis, ultimately improving patient prognosis.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its ‘mwTab’ text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the ‘mwtab’ Python library and package have been continuously updated to mirror the changes in the ‘mwTab’ and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The ‘mwtab’ Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs.

Metabolic consequences for mice lacking Endosialin: LC-MS/MS-based metabolic phenotyping of serum from C56Bl/6J Control and CD248 knock-out mice

INTRODUCTION

The Endosialin/CD248/TEM1 protein is expressed in adipose tissue and its expression increases with obesity. Recently, genetic deletion of CD248 has been shown to protect mice against atherosclerosis on a high fat diet.

OBJECTIVES

We investigated the effect of high fat diet feeding on visceral fat pads and circulating lipid profiles in CD248 knockout mice compared to controls.

METHODS

From 10 weeks old, CD248^-/- and ^+/+ mice were fed either chow (normal) diet or a high fat diet for 13 weeks. After 13 weeks the metabolic profiles and relative quantities of circulating lipid species were assessed using ultra high performance liquid chromatography-quadrupole time-of flight mass spectrometry (UHPLC-MS) with high resolution accurate mass (HRAM) capability.

RESULTS

We demonstrate a specific reduction in the size of the perirenal fat pad in CD248^-/- mice compared to CD248^+/+, despite similar food intake. More strikingly, we identify significant, diet-dependent differences in the serum metabolic phenotypes of CD248 null compared to age and sex-matched wildtype control mice. Generalised protection from HFD-induced lipid accumulation was observed in CD248 null mice compared to wildtype, with particular reduction noted in the lysophosphatidylcholines, phosphatidylcholines, cholesterol and carnitine.

CONCLUSIONS

Overall these results show a clear and protective metabolic consequence of CD248 deletion in mice, implicating CD248 in lipid metabolism or trafficking and opening new avenues for further investigation using anti-CD248 targeting agents.

The peppermint breath test: a benchmarking protocol for breath sampling and analysis using GC-MS

Exhaled breath contains hundreds of volatile organic compounds (VOCs) which offers the potential for diagnosing and monitoring a wide range of diseases. As the breath research field has grown, sampling and analytical practices have become highly varied between groups. Standardisation would allow meta-analyses of data from multiple studies and greater confidence in published results. The Peppermint Consortium has been formed to address this task of standardisation. In the current study we aimed to generate initial benchmark values for thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis of breath samples containing peppermint-derived VOCs. Headspace analysis of peppermint oil capsules was performed to determine compounds of interest. Ten healthy participants were recruited by three groups. Each participant provided a baseline breath sample prior to taking a peppermint capsule, with further samples collected at 60, 90, 165, 285 and 360 min following ingestion. Sampling and analytical protocols were different for each institution, in line with their usual practice. Samples were analysed by TD-GC-MS and benchmarking values determined for the time taken for detected peppermint VOCs to return to baseline values. Sixteen compounds were identified in the capsule headspace. Additionally, 2,3-dehydro-1,8-cineole was uniquely found in the breath samples, with a washout profile that suggested it was a product of peppermint metabolism. Five compounds (α-pinene, β-pinene, eucalyptol, menthol and menthone) were quantified by all three groups. Differences in recovery were observed between the groups, particularly for menthone and menthol. The average time taken for VOCs to return to baseline was selected as the benchmark and were 441, 648, 1736, 643 and 375 min for α-pinene, β-pinene, eucalyptol, menthone and menthol respectively. An initial set of easy-to-measure benchmarking values for assessing the performance of TD-GC-MS systems for the analysis of VOCs in breath is presented. These values will be updated when more groups provide additional data.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.

Metabolic Phenotyping Study of Mouse Brains Following Acute or Chronic Exposures to Ethanol

The chronic and acute effect of ethanol administration on the metabolic phenotype of mouse brain was studied in a C57BL/6 mouse model of ethanol abuse using both untargeted and targeted ultra performance liquid chromatography-tandem mass spectrometry. Two experiments based on either chronic (8 week) exposure to ethanol of both male and female mice or acute exposure of male mice for 11 days, plus 2 oral gavage doses of 25% ethanol, were undertaken. Marked differences were found in amino acids, nucleotides, nucleosides, and related metabolites as well as a number of different lipids. Using untargeted metabolite profiling, acute ethanol exposure found significant decreases in several metabolites including nucleosides, fatty acids, glycerophosphocholine, and a number of phospholipids, while chronic exposure resulted in increases in several amino acids with notable decreases in adenosine, acetylcarnitine, and galactosylceramides. Similarly, targeted metabolite analysis, focusing on the hydrophilic fraction of the brain tissue extract, identified significant decreases in the metabolism of amino acids and derivatives, as well as purine degradation especially after chronic exposure to ethanol.

Toward Comprehensive Per- and Polyfluoroalkyl Substances Annotation Using FluoroMatch Software and Intelligent High-Resolution Tandem Mass Spectrometry Acquisition

Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment and pose a potential health hazard. Suspect and nontarget screening with liquid chromatography (LC)-high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. To date, no automated open source PFAS data analysis software exists to mine these extensive data sets. We introduce FluoroMatch, which automates file conversion, chromatographic peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ∼7 000 PFAS fragmentation patterns based on rules derived from standards and literature, and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (iterative exclusion) nearly doubled the number of annotations. The software application is demonstrated by characterizing PFAS in landfill leachate as well as in leachate foam generated to concentrate the compounds for remediation purposes. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 71% of the all-ion fragmentation (CF2)n related fragments. By improving the throughput and coverage of PFAS annotation, FluoroMatch will accelerate the discovery of PFAS posing significant human risk.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.