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

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models

Novel psychoactive substances (NPS) represent a broad class of drugs new to the illicit market that often allow passing drug-screening tests. They are characterized by a variety of structures, rapid transience on the drug scene and mostly unknown metabolic profiles, thus creating an ever-changing scenario with evolving analytical targets. The present study aims at developing an indirect screening strategy for NPS monitoring, and specifically for new synthetic opioids (NSOs), based on assessing changes in endogenous urinary metabolite levels as a consequence of the systemic response following their intake. The experimental design involved in-vivo mice models: 16 animals of both sex received a single administration of morphine or fentanyl. Urine was collected before and after administration at different time points; the samples were then analysed with an untargeted metabolomics LC-HRMS workflow. According to our results, the intake of opioids resulted in an elevated energy demand, that was more pronounced on male animals, as evidenced by the increase in medium and long chain acylcarnitines levels. It was also shown that opioid administration disrupted the pathways related to catecholamines biosynthesis. The observed alterations were common to both morphine and fentanyl: this evidence indicate that they are not related to the chemical structure of the drug, but rather on the drug class. The proposed strategy may reinforce existing NPS screening approaches, by identifying indirect markers of drug assumption.

High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification

Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.

Evaluation of normalization strategies for GC-based metabolomics

INTRODUCTION

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

OBJECTIVES

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

METHODS

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

RESULTS

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

CONCLUSION

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

A benchmark study of data normalisation methods for PTR-TOF-MS exhaled breath metabolomics

Volatilomics is the branch of metabolomics dedicated to the analysis of volatile organic compounds in exhaled breath for medical diagnostic or therapeutic monitoring purposes. Real-time mass spectrometry (MS) technologies such as proton transfer reaction (PTR) MS are commonly used, and data normalisation is an important step to discard unwanted variation from non-biological sources, as batch effects and loss of sensitivity over time may be observed. As normalisation methods for real-time breath analysis have been poorly investigated, we aimed to benchmark known metabolomic data normalisation methods and apply them to PTR-MS data analysis. We compared seven normalisation methods, five statistically based and two using multiple standard metabolites, on two datasets from clinical trials for COVID-19 diagnosis in patients from the emergency department or intensive care unit. We evaluated different means of feature selection to select the standard metabolites, as well as the use of multiple repeat measurements of ambient air to train the normalisation methods. We show that the normalisation tools can correct for time-dependent drift. The methods that provided the best corrections for both cohorts were probabilistic quotient normalisation and normalisation using optimal selection of multiple internal standards. Normalisation also improved the diagnostic performance of the machine learning models, significantly increasing sensitivity, specificity and area under the receiver operating characteristic (ROC) curve for the diagnosis of COVID-19. Our results highlight the importance of adding an appropriate normalisation step during the processing of PTR-MS data, which allows significant improvements in the predictive performance of statistical models.Clinical trials: VOC-COVID-Diag (EudraCT 2020-A02682-37); RECORDS trial (EudraCT 2020-000296-21).

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Determining the Contribution of Micro/Nanoplastics to Antimicrobial Resistance: Challenges and Perspectives

Microorganisms colonizing the surfaces of microplastics form a plastisphere in the environment, which captures miscellaneous substances. The plastisphere, owning to its inherently complex nature, may serve as a "Petri dish" for the development and dissemination of antibiotic resistance genes (ARGs), adding a layer of complexity in tackling the global challenge of both microplastics and ARGs. Increasing studies have drawn insights into the extent to which the proliferation of ARGs occurred in the presence of micro/nanoplastics, thereby increasing antimicrobial resistance (AMR). However, a comprehensive review is still lacking in consideration of the current increasingly scattered research focus and results. This review focuses on the spread of ARGs mediated by microplastics, especially on the challenges and perspectives on determining the contribution of microplastics to AMR. The plastisphere accumulates biotic and abiotic materials on the persistent surfaces, which, in turn, offers a preferred environment for gene exchange within and across the boundary of the plastisphere. Microplastics breaking down to smaller sizes, such as nanoscale, can possibly promote the horizontal gene transfer of ARGs as environmental stressors by inducing the overgeneration of reactive oxygen species. Additionally, we also discussed methods, especially quantitatively comparing ARG profiles among different environmental samples in this emerging field and the challenges that multidimensional parameters are in great necessity to systematically determine the antimicrobial dissemination risk in the plastisphere. Finally, based on the biological sequencing data, we offered a framework to assess the AMR risks of micro/nanoplastics and biocolonizable microparticles that leverage multidimensional AMR-associated messages, including the ARGs' abundance, mobility, and potential acquisition by pathogens.

Fibroblast phenylalanine concentration as a surrogate biomarker of cellular number

Metabolomics studies in human dermal fibroblasts can elucidate the biological mechanisms associated with some diseases, but several methodological issues that increase variability have been identified. We aimed to quantify the amino acid levels in cultured fibroblasts and to apply different sample-based normalization approaches. Forty-four skin biopsies from control subjects were collected. Amino acids were measured in fibroblasts supernatants by UPLC-MS/MS. Statistical supervised and unsupervised studies were used. Spearman's test showed that phenylalanine displayed the second highest correlation with the remaining amino acids (mean r = 0.8), whereas the total protein concentration from the cell pellet showed a mean of r = 0.67. The lowest percentage of variation was obtained when amino acids were normalized by phenylalanine values, with a mean of 42% vs 57% when normalized by total protein values. When amino acid levels were normalized by phenylalanine, Principal Component Analysis and clustering analyses identified different fibroblasts groups. In conclusion, phenylalanine may be a suitable biomarker to estimate cellular content in cultured fibroblasts.

Serum metabolomic profiling identifies potential biomarkers in arthritis in older adults: an exploratory study

BACKGROUND

Seronegative elderly-onset rheumatoid arthritis (EORA)^neg and polymyalgia rheumatica (PMR) have similar clinical characteristics making them difficult to distinguish based on clinical features. We hypothesized that the study of serum metabolome could identify potential biomarkers of PMR vs. EORA^neg.

METHODS

Arthritis in older adults (ARTIEL) is an observational prospective cohort with patients older than 60 years of age with newly diagnosed arthritis. Patients' blood samples were compared at baseline with 18 controls. A thorough clinical examination was conducted. A Bruker Avance 600 MHz spectrometer was used to acquire Nuclear Magnetic Resonance (NMR) spectra of serum samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification.Student t-test, one-way ANOVA, binary linear regression and ROC curve, Pearson's correlation along with pathway analyses were conducted.

RESULTS

Twenty-eight patients were diagnosed with EORA^neg and 20 with PMR. EORA^neg patients had a mean disease activity score (DAS)-Erythrocyte Sedimentation Rate (ESR) of 6.21 ± 1.00. All PMR patients reported shoulder pain, and 90% reported pelvic pain. Fifty-eight polar metabolites were identified. Of these, 3-hydroxybutyrate, acetate, glucose, glycine, lactate, and o-acetylcholine (o-ACh), were significantly different between groups. Of interest, IL-6 correlated with different metabolites in PMR and EORA^neg suggesting different inflammatory activated pathways. Finally, lactate, o-ACh, taurine, and sex (female) were identified as distinguishable factors of PMR from EORA^neg with a sensitivity of 90%, specificity of 92.3%, and an AUC of 0.925 (p < 0.001).

CONCLUSION

These results suggest that EORA^neg and PMR have different serum metabolomic profiles that might be related to their pathobiology and can be used as biomarker to discriminate between both diseases.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide

Introduction

Immunometabolism examines the links between immune cell function and metabolism. Dysregulation of immune cell metabolism is now an established feature of innate immune cell activation. Advances in liquid chromatography mass spectrometry (LC-MS) technologies have allowed discovery of unique insights into cellular metabolomics. Here we have studied and compared different sample preparation techniques and data normalisation methods described in the literature when applied to metabolomic profiling of human monocytes.

Methods

Primary monocytes stimulated with lipopolysaccharide (LPS) for four hours was used as a study model. Monocytes (n=24) were freshly isolated from whole blood and stimulated for four hours with lipopolysaccharide (LPS). A methanol-based extraction protocol was developed and metabolomic profiling carried out using a Hydrophilic Interaction Liquid Chromatography (HILIC) LC-MS method. Data analysis pipelines used both targeted and untargeted approaches, and over 40 different data normalisation techniques to account for technical and biological variation were examined. Cytokine levels in supernatants were measured by ELISA.

Results

This method provided broad coverage of the monocyte metabolome. The most efficient and consistent normalisation method was measurement of residual protein in the metabolite fraction, which was further validated and optimised using a commercial kit. Alterations to the monocyte metabolome in response to LPS can be detected as early as four hours post stimulation. Broad and profound changes in monocyte metabolism were seen, in line with increased cytokine production. Elevated levels of amino acids and Krebs cycle metabolites were noted and decreases in aspartate and β-alanine are also reported for the first time. In the untargeted analysis, 154 metabolite entities were significantly altered compared to unstimulated cells. Pathway analysis revealed the most prominent changes occurred to (phospho-) inositol metabolism, glycolysis, and the pentose phosphate pathway.

Discussion

These data report the emergent changes to monocyte metabolism in response to LPS, in line with reports from later time points. A number of these metabolites are reported to alter inflammatory gene expression, which may facilitate the increases in cytokine production. Further validation is needed to confirm the link between metabolic activation and upregulation of inflammatory responses.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Development of a biomarker to monitor target engagement after treatment with dihydroorotate dehydrogenase inhibitors

Dihydroorotate dehydrogenase (DHODH) catalyzes a key step in pyrimidine biosynthesis and has recently been validated as a therapeutic target for malaria through clinical studies on the triazolopyrimidine-based Plasmodium DHODH inhibitor DSM265. Selective toxicity towards Plasmodium species could be achieved because malaria parasites lack pyrimidine salvage pathways, and DSM265 selectively inhibits Plasmodium DHODH over the human enzyme. However, while DSM265 does not inhibit human DHODH, it inhibits DHODH from several preclinical species, including mice, suggesting that toxicity could result from on-target DHODH inhibition in those species. We describe here the use of dihydroorotate (DHO) as a biomarker of DHODH inhibition. Treatment of mammalian cells with DSM265 or the mammalian DHODH inhibitor teriflunomide led to increases in DHO where the extent of biomarker buildup correlated with both dose and inhibitor potency on DHODH. Treatment of mice with leflunomide (teriflunomide prodrug) caused a large dose-dependent buildup of DHO in blood (up to 16-fold) and urine (up to 5,400-fold) that was not observed for mice treated with DSM265. Unbound plasma teriflunomide levels reached 20-85-fold above the mouse DHODH IC50, while free DSM265 levels were only 1.6-4.2-fold above, barely achieving ∼ IC90 concentrations, suggesting that unbound DSM265 plasma levels are not sufficient to block the pathway in vivo. Thus, any toxicity associated with DSM265 treatment in mice is likely caused by off-target mechanisms. The identification of a robust biomarker for mammalian DHODH inhibition represents an important advance to generally monitor for on-target effects in preclinical and clinical applications of DHODH inhibitors used to treat human disease.

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

MOTIVATION

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

RESULTS

MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.

AVAILABILITY AND IMPLEMENTATION

The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Trait Energy and Fatigue May Be Connected to Gut Bacteria among Young Physically Active Adults: An Exploratory Study

Recent scientific evidence suggests that traits energy and fatigue are two unique unipolar moods with distinct mental and physical components. This exploratory study investigated the correlation between mental energy (ME), mental fatigue (MF), physical energy (PE), physical fatigue (PF), and the gut microbiome. The four moods were assessed by survey, and the gut microbiome and metabolome were determined from 16 S rRNA analysis and untargeted metabolomics analysis, respectively. Twenty subjects who were 31 ± 5 y, physically active, and not obese (26.4 ± 4.4 kg/m2) participated. Bacteroidetes (45%), the most prominent phyla, was only negatively correlated with PF. The second most predominant and butyrate-producing phyla, Firmicutes (43%), had members that correlated with each trait. However, the bacteria Anaerostipes was positively correlated with ME (0.048, p = 0.032) and negatively with MF (−0.532, p = 0.016) and PF (−0.448, p = 0.048), respectively. Diet influences the gut microbiota composition, and only one food group, processed meat, was correlated with the four moods—positively with MF (0.538, p = 0.014) and PF (0.513, p = 0.021) and negatively with ME (−0.790, p < 0.001) and PE (−0.478, p = 0.021). Only the Firmicutes genus Holdemania was correlated with processed meat (r = 0.488, p = 0.029). Distinct metabolic profiles were observed, yet these profiles were not significantly correlated with the traits. Study findings suggest that energy and fatigue are unique traits that could be defined by distinct bacterial communities not driven by diet. Larger studies are needed to confirm these exploratory findings.

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.

Fish Skin and Gill Mucus: A Source of Metabolites for Non-Invasive Health Monitoring and Research

Mucous membranes such as the gill and skin mucosa in fish protect them against a multitude of environmental factors. At the same time, changes in the molecular composition of mucus may provide valuable information about the interaction of the fish with their environment, as well as their health and welfare. In this study, the metabolite profiles of the plasma, skin and gill mucus of freshwater Atlantic salmon (Salmo salar) were compared using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Several normalization procedures aimed to reduce unwanted variation in the untargeted data were tested. In addition, the basal metabolism of skin and gills, and the impact of the anesthetic benzocaine for euthanisation were studied. For targeted metabolomics, the commercial AbsoluteIDQ p400 HR kit was used to evaluate the potential differences in metabolic composition in epidermal mucus as compared to the plasma. The targeted metabolomics data showed a high level of correlation between different types of biological fluids from the same individual, indicating that mucus metabolite composition could be used for fish health monitoring and research.

Comparison of HPLC and NMR for quantification of the main volatile fatty acids in rumen digesta

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution.

Advances in Analyzing the Breast Cancer Lipidome and Its Relevance to Disease Progression and Treatment

Abnormal lipid metabolism is common in breast cancer with the three main subtypes, hormone receptor (HR) positive, human epidermal growth factor 2 (HER2) positive, and triple negative, showing common and distinct lipid dependencies. A growing body of studies identify altered lipid metabolism as impacting breast cancer cell growth and survival, plasticity, drug resistance, and metastasis. Lipids are a class of nonpolar or polar (amphipathic) biomolecules that can be produced in cells via de novo synthesis or acquired from the microenvironment. The three main functions of cellular lipids are as essential components of membranes, signaling molecules, and nutrient storage. The use of mass spectrometry-based lipidomics to analyze the global cellular lipidome has become more prevalent in breast cancer research. In this review, we discuss current lipidomic methodologies, highlight recent breast cancer lipidomic studies and how these findings connect to disease progression and therapeutic development, and the potential use of lipidomics as a diagnostic tool in breast cancer. A better understanding of the breast cancer lipidome and how it changes during drug resistance and tumor progression will allow informed development of diagnostics and novel targeted therapies.

MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

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.

A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.

Integration of Functional Genomic, Transcriptomic, and Metabolomic Data to Identify Key Features in Genomic Expression, Metabolites, and Metabolic Pathways of Babesia divergens

Upon invasion of red blood cells (RBCs), the Apicomplexa parasite Babesia divergens remains within the RBC for several hours and reproduces asexually, resulting in infective free merozoites that egress and destroy the host cell. Free merozoites rapidly seek and invade new uninfected RBCs. This repetitive cycle allows B. divergens to build a complex population of intraerythrocytic and extracellular stages in the bloodstream of humans and cattle, thus causing babesiosis. To compare biological aspects between B. divergens stages, including the different nature of their metabolism, could be key to our understanding of pathogenesis. Thus, we are currently assessing differences in the B. divergens metabolism of intra- and extracellular (free merozoites) life stages by the use of an integrative approach combining functional genomic, transcriptomic, differential expression, and metabolomic data acquired from sequencing and various analytical platforms. To our knowledge, this is the first effort to describe, in detail, the experimental procedures and integration of different omics to explore the regulation of the metabolism, invasion and proliferation mechanisms of B. divergens. This integrative approach can be used as a reference to study other Apicomplexa parasites.

[Urine metabolomics analysis based on ultra performance liquid chromatography-high resolution mass spectrometry combined with osmolality calibration sample concentration variability]

尿液是代谢组学研究中主要关注的体液样本之一。尿液样本中的代谢物浓度受饮食、疾病等因素影响变异较大,这极大阻碍了高质量组学数据的获取和可靠生物标志物的鉴定。研究为克服尿液样本的浓度变异性,在原始数据采集前,根据样本渗透压的大小,针对性地调整进样量或者稀释样本,从而确保代谢组学分析样本的渗透压与进样量的乘积相当,再经超高效液相色谱-高分辨质谱技术(UPLC-HRMS)分析,采用总离子丰度或总有用峰面积(MSTUS)对数据集进行归一化处理。研究利用临床样本及其梯度稀释的溶液,对该方法与现有研究普遍使用的方法进行了比较,随后通过先天性肾积水患者及健康志愿者的尿液样本做了进一步的方法学验证。数据集经校正后,峰面积RSD<30%的提取峰数量增加,主成分分析结果较校正前有更高的组内聚集和组间分群效应,正交偏最小二乘判别分析的统计模型更不易过拟合。与肌酐比较,渗透压值与质谱信号间呈现了更好的线性关系。以上结果表明,数据采集前通过样本渗透压进行校正,能有效消除因样本本身代谢物浓度变化引起的组内差异,提高方法的重复性和统计模型的可靠度。以渗透压为基准的校正策略,比肌酐校正法适用范围更广,结果也更准确。研究可对后续各类来源的尿液代谢组学研究提供数据归一化的指导和参考。

Urine NMR Metabolomics Profile of Preterm Infants With Necrotizing Enterocolitis Over the First Two Months of Life: A Pilot Longitudinal Case-Control Study

Objective: To investigate changes in the urine metabolome of very low birth weight preterm newborns with necrotizing enterocolitis (NEC) and feed intolerance, we conducted a longitudinal study over the first 2 months of life. The metabolome of NEC newborns was compared with two control groups that did not develop NEC: the first one included preterm babies with feed intolerance, while the second one preterm babies with good feed tolerance.

Methods: Newborns developing NEC within the 3 weeks of life were identified as early onset NEC, while the remaining as late onset NEC. Case-control matching was done according to the gestational age (±1 week), birth weight (± 200 g), and postnatal age. A total of 96 urine samples were collected and analyzed. In newborns with NEC, samples were collected before, during and after the diagnosis over the first 2 months of life, while in controls samples were collected as close as possible to the postnatal age of newborns with NEC. Proton nuclear magnetic resonance (¹H NMR) spectroscopy was used for metabolomic analysis. Data were analyzed by univariate and multivariate statistical analysis.

Results: In all the preterm newborns, urine levels of betaine, glycine, succinate, and citrate positively correlated with postnatal age. Suberate and lactate correlated with postnatal age in preterms with NEC and in controls with food intolerance, while N,N-dimethylglycine (N,N-DMG) correlated only in controls with good digestive tolerance. Preterm controls with feed intolerance showed a progressive significant decrease of N-methylnicotinamide and carnitine. Lactate, betaine, myo-inositol, urea, creatinine, and N,N-dimethylglycine discriminated late-onset NEC from controls with good feed tolerance.

Conclusion: Our findings are discussed in terms of contributions from nutritional and clinical managements of patients and gut microbiota.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Metabolomics, a Powerful Tool for Understanding Plant Abiotic Stress

Metabolomics is a technology that generates large amounts of data and contributes to obtaining wide and integral explanations of the biochemical state of a living organism. Plants are continuously affected by abiotic stresses such as water scarcity, high temperatures and high salinity, and metabolomics has the potential for elucidating the response-to-stress mechanisms and develop resistance strategies in affected cultivars. This review describes the characteristics of each of the stages of metabolomic studies in plants and the role of metabolomics in the characterization of the response of various plant species to abiotic stresses.

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.

A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.