METLIN: A Technology Platform for Identifying Knowns and Unknowns

Metabolomic profiling of intrauterine growth-restricted preterm infants: a matched case-control study

BACKGROUND

The biochemical variations occurring in intrauterine growth restriction (IUGR), when a fetus is unable to achieve its genetically determined potential, are not fully understood. The aim of this study is to compare the urinary metabolomic profile between IUGR and non-IUGR very preterm infants to investigate the biochemical adaptations of neonates affected by early-onset-restricted intrauterine growth.

METHODS

Neonates born <32 weeks of gestation admitted to neonatal intensive care unit (NICU) were enrolled in this prospective matched case-control study. IUGR was diagnosed by an obstetric ultra-sonographer and all relevant clinical data during NICU stay were captured. For each subject, a urine sample was collected within 48 h of life and underwent untargeted metabolomic analysis using mass spectrometry ultra-performance liquid chromatography. Data were analyzed using multivariate and univariate statistical analyses.

RESULTS

Among 83 enrolled infants, 15 IUGR neonates were matched with 19 non-IUGR controls. Untargeted metabolomic revealed evident clustering of IUGR neonates versus controls showing derangements of pathways related to tryptophan and histidine metabolism and aminoacyl-tRNA and steroid hormones biosynthesis.

CONCLUSIONS

Neonates with IUGR showed a distinctive urinary metabolic profile at birth. Although results are preliminary, metabolomics is proving to be a promising tool to explore biochemical pathways involved in this disease.

IMPACT

Very preterm infants with intrauterine growth restriction (IUGR) have a distinctive urinary metabolic profile at birth. Metabolism of glucocorticoids, sexual hormones biosynthesis, tryptophan-kynurenine, and methionine-cysteine pathways seem to operate differently in this sub-group of neonates. This is the first metabolomic study investigating adaptations exclusively in extremely and very preterm infants affected by early-onset IUGR. New knowledge on metabolic derangements in IUGR may pave the ways to further, more tailored research from a perspective of personalized medicine.

Indoor microbiome and allergic diseases: From theoretical advances to prevention strategies

The prevalence of allergic diseases, such as asthma, rhinitis, eczema, and sick building syndrome (SBS), has increased drastically in the past few decades. Current medications can only relieve the symptoms but not cure these diseases whose development is suggested to be greatly impacted by the indoor microbiome. However, no study comprehensively summarizes the progress and general rules in the field, impeding subsequent translational application. To close knowledge gaps between theoretical research and practical application, we conducted a comprehensive literature review to summarize the epidemiological, environmental, and molecular evidence of indoor microbiome studies. Epidemiological evidence shows that the potential protective indoor microorganisms for asthma are mainly from the phyla Actinobacteria and Proteobacteria, and the risk microorganisms are mainly from Bacilli, Clostridia, and Bacteroidia. Due to extremely high microbial diversity and geographic variation, different health-associated species/genera are detected in different regions. Compared with indoor microbial composition, indoor metabolites show more consistent associations with health, including microbial volatile organic compounds (MVOCs), lipopolysaccharides (LPS), indole derivatives, and flavonoids. Therefore, indoor metabolites could be a better indicator than indoor microbial taxa for environmental assessments and health outcome prediction. The interaction between the indoor microbiome and environmental characteristics (surrounding greenness, relative humidity, building confinement, and CO2 concentration) and immunology effects of indoor microorganisms (inflammatory cytokines and pattern recognition receptors) are briefly reviewed to provide new insights for disease prevention and treatment. Widely used tools in indoor microbiome studies are introduced to facilitate standard practice and the precise identification of health-related targets.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions

The mechanistic role of the airway microbiome in chronic obstructive pulmonary disease (COPD) remains largely unexplored. We present a landscape of airway microbe-host interactions in COPD through an in-depth profiling of the sputum metagenome, metabolome, host transcriptome and proteome from 99 patients with COPD and 36 healthy individuals in China. Multi-omics data were integrated using sequential mediation analysis, to assess in silico associations of the microbiome with two primary COPD inflammatory endotypes, neutrophilic or eosinophilic inflammation, mediated through microbial metabolic interaction with host gene expression. Hypotheses of microbiome-metabolite-host interaction were identified by leveraging microbial genetic information and established metabolite-human gene pairs. A prominent hypothesis for neutrophil-predominant COPD was altered tryptophan metabolism in airway lactobacilli associated with reduced indole-3-acetic acid (IAA), which was in turn linked to perturbed host interleukin-22 signalling and epithelial cell apoptosis pathways. In vivo and in vitro studies showed that airway microbiome-derived IAA mitigates neutrophilic inflammation, apoptosis, emphysema and lung function decline, via macrophage-epithelial cell cross-talk mediated by interleukin-22. Intranasal inoculation of two airway lactobacilli restored IAA and recapitulated its protective effects in mice. These findings provide the rationale for therapeutically targeting microbe-host interaction in COPD.

Metabolomic Profiling Reveals Common Metabolic Alterations in Plasma of Patients with Toxoplasma Infection and Schizophrenia

Toxoplasma gondii is an opportunistic protozoan parasite known to affect the human brain. The infection has been associated with an increased incidence of schizophrenia; however, the link between the two conditions remains unclear. This study aimed to compare the plasma metabolome of schizophrenia and non-schizophrenia subjects with or without Toxoplasma infection. Untargeted metabolomic profiling was carried out by liquid chromatography-mass spectrometry. Elevation of the α-hydroxyglutaric acid level and reduced adenosine monophosphate, inosine, hypoxanthine and xanthine were found in the subjects with either toxoplasmosis or schizophrenia alone. These results suggest that purine catabolism is a common metabolic alteration in Toxoplasma infection and schizophrenia. The roles of these metabolites on the pathogenesis of schizophrenia in relation to Toxoplasma infection warrant further studies.

Early Life to Adult Brain Lipidome Dynamic: A Temporospatial Study Investigating Dietary Polar Lipid Supplementation Efficacy

The lipid composition of the brain is well regulated during development, and the specific temporospatial distribution of various lipid species is essential for the development of optimal neural functions. Dietary lipids are the main source of brain lipids and thus contribute to the brain lipidome. Human milk is the only source of a dietary lipids for exclusively breastfed infant. Notably, it contains milk fat globule membrane (MFGM) enriched in polar lipids (PL). While early life is a key for early brain development, the interplay between dietary intake of polar lipids and spatial dynamics of lipid distribution during brain development is poorly understood. Here, we carried out an exploratory study to assess the early postnatal temporal profiling of brain lipidome between postnatal day (PND) 7 and PND 50 using matrix-assisted laser desorption ionization as a mass spectrometry imaging (MALDI-MSI) in an in vivo preclinical model. We also assessed the effect of chronic supplementation with PL extracted from alpha-lactalbumin-enriched whey protein concentrate (WPC) containing 10% lipids, including major lipid classes found in the brain (37% phospholipids and 15% sphingomyelin). MALDI-MSI of the spatial and temporal accretion of lipid species during brain development showed that the brain lipidome is changing heterogeneously along time during brain development. In addition, increases in 400+ PL supplement-dependent lipids were observed. PL supplementation had significant spatial and temporal effect on specific fatty esters, glycerophosphocholines, glycerophosphoethanolamines, and phosphosphingolipids. Interestingly, the average levels of these lipids per brain area tended to be constant in various brain structures across the age groups, paralleling the general brain growth. In contrast, other lipids, such as cytidine diphosphate diacylglycerol, diacylglycerophosphates, phosphocholines, specific ether-phosphoethanolamines, phosphosphingolipids, glycerophosphoinositols, and glycerophosphoserines showed clear age-dependent changes uncoupled from the general brain growth. These results suggest that the dietary PL supplementation may preferentially provide the building blocks for the general brain growth during development. Our findings add to the understanding of brain-nutrient relations, their temporospatial dynamics, and potential impact on neurodevelopment.

Drug-Initiated Activity Metabolomics Identifies Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation

Worldwide, obesity rates have doubled since the 1980s and in the USA alone, almost 40% of adults are obese, which is closely associated with a myriad of metabolic diseases such as type 2 diabetes and arteriosclerosis. Obesity is derived from an imbalance between energy intake and consumption, therefore balancing energy homeostasis is an attractive target for metabolic diseases. One therapeutic approach consists of increasing the number of brown-like adipocytes in the white adipose tissue (WAT). Whereas WAT stores excess energy, brown adipose tissue (BAT) can dissipate this energy overload in the form of heat, increasing energy expenditure and thus inhibiting metabolic diseases. To facilitate BAT production a high-throughput screening approach was developed on previously known drugs using human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes. The screening allowed us to discover that zafirlukast, an FDA-approved small molecule drug commonly used to treat asthma, was able to differentiate adipocyte precursors and white-biased adipocytes into functional brown adipocytes. However, zafirlukast is toxic to human cells at higher dosages. Drug-Initiated Activity Metabolomics (DIAM) was used to investigate zafirlukast as a BAT inducer, and the endogenous metabolite myristoylglycine was then discovered to mimic the browning properties of zafirlukast without impacting cell viability. Myristoylglycine was found to be bio-synthesized upon zafirlukast treatment and was unique in inducing brown adipocyte differentiation, raising the possibility of using endogenous metabolites and bypassing the exogenous drugs to potentially alleviate disease, in this case, obesity and other related metabolic diseases.

Quantification of Vitamins, Minerals, and Amino Acids in Black Walnut (Juglans nigra)

This paper aims to quantify the micronutrients in black walnut and address its human health benefits. The metabolic profiling of 11 black walnut cultivars was accomplished using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight high-resolution mass spectrometer. Results revealed that the highest concentration of vitamin B₉ was present in cultivar “Daniel” (avg. relative signal intensity 229.53 × 10⁴ mAU). “Surprise” and “Daniel” cultivars had the highest amount of vitamin B₅. However, vitamin A, D₃, E, and K showed no significant difference among the cultivars. The vitamin content levels among the cultivars were compared by applying one way ANOVA method with (P < 0.05) significance level. Mineral analysis for the black walnut kernel, Persian walnut, and black walnut protein powder was done using Inductively Coupled Plasma Optical Emission spectroscopy. The experimental data for black walnut kernel is 0.04 mg/g for Fe and 0.03 mg/g for Zn, and for black walnut, protein powder is 0.07 mg/g for Fe and 0.07 mg/g for Zn. The amino acid analysis and comparison with black walnut kernel show that black walnut flour and protein powder have a higher amount of essential and non-essential amino acids. Therefore, researchers, food process engineers, and food product developers should consider the health benefits of black walnuts and explore the commercial potential of this native agroforestry crop.

Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology

Molecular composition is intricately intertwined with cellular function, and elucidation of this relationship is essential for understanding life processes and developing next-generational therapeutics. Technological innovations in capillary electrophoresis (CE) and liquid chromatography (LC) mass spectrometry (MS) provide previously unavailable insights into cellular biochemistry by allowing for the unbiased detection and quantification of molecules with high specificity. This chapter presents our validated protocols integrating ultrasensitive MS with classical tools of cell, developmental, and neurobiology to assess the biological function of important biomolecules. We use CE and LC MS to measure hundreds of metabolites and thousands of proteins in single cells or limited populations of tissues in chordate embryos and mammalian neurons, revealing molecular heterogeneity between identified cells. By pairing microinjection and optical microscopy, we demonstrate cell lineage tracing and testing the roles the dysregulated molecules play in the formation and maintenance of cell heterogeneity and tissue specification in frog embryos (Xenopus laevis). Electrophysiology extends our workflows to characterizing neuronal activity in sections of mammalian brain tissues. The information obtained from these studies mutually strengthen chemistry and biology and highlight the importance of interdisciplinary research to advance basic knowledge and translational applications forward.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective. The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways. Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%). These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

Identification of Independent and Shared Metabolic Responses to High-Fiber and Antibiotic Treatments in Fecal Metabolome of Grow-Finish Pigs

Feeding high-fiber (HF) coproducts to grow–finish pigs as a cost-saving practice could compromise growth performance, while the inclusion of antibiotic growth promoters (AGPs) may improve it. The hindgut is a shared site of actions between fiber and AGPs. However, whether the metabolic interactions between them could occur in the digestive tract of pigs and then become detectable in feces have not been well-examined. In this study, wheat middling (WM), a HF coproduct, and bacitracin, a peptide antibiotic (AB), were fed to 128 grow–finish pigs for 98 days following a 2 × 2 factorial design, including antibiotic-free (AF) + low fiber (LF); AF + HF; AB + LF, and AB + HF, for growth and metabolic responses. The growth performance of the pigs was compromised by HF feedings but not by AB. A metabolomic analysis of fecal samples collected on day 28 of feeding showed that WM elicited comprehensive metabolic changes, especially in amino acids, fatty acids, and their microbial metabolites, while bacitracin caused selective metabolic changes, including in secondary bile acids. Limited metabolic interactions occurred between fiber and AB treatments. Moreover, the correlations between individual fecal metabolites and growth support the usage of fecal metabolome as a source of biomarkers for monitoring and predicting the metabolic performance of grow–finish pigs.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Microbial Metabolite 3-Indolepropionic Acid Mediates Immunosuppression

The microbial-derived metabolite, 3-indolepropionic acid (3-IPA), has been intensely studied since its origins were discovered in 2009; however, 3-IPA's role in immunosuppression has had limited attention. Untargeted metabolomic analyses of T-cell exhaustion and immunosuppression, represented by dysfunctional under-responsive CD8⁺ T cells, reveal a potential role of 3-IPA in these responses. T-cell exhaustion was examined via infection of two genetically related mouse strains, DBA/1J and DBA/2J, with lymphocytic choriomeningitis virus (LCMV) Clone 13 (Cl13). The different mouse strains produced disparate outcomes driven by their T-cell responses. Infected DBA/2J presented with exhausted T cells and persistent infection, and DBA/1J mice died one week after infection from cytotoxic T lymphocytes (CTLs)-mediated pulmonary failure. Metabolomics revealed over 70 metabolites were altered between the DBA/1J and DBA/2J models over the course of the infection, most of them in mice with a fatal outcome. Cognitive-driven prioritization combined with statistical significance and fold change were used to prioritize the metabolites. 3-IPA, a tryptophan-derived metabolite, was identified as a high-priority candidate for testing. To test its activity 3-IPA was added to the drinking water of the mouse models during LCMV Cl13 infection, with the results showing that 3-IPA allowed the mice to survive longer. This negative immune-modulation effect might be of interest for the modulation of CTL responses in events such as autoimmune diseases, type I diabetes or even COVID-19. Moreover, 3-IPA's bacterial origin raises the possibility of targeting the microbiome to enhance CTL responses in diseases such as cancer and chronic infection.

Plasma metabolomic analysis indicates flavonoids and sorbic acid are associated with incident diabetes: A nested case-control study among Women's Interagency HIV Study participants

INTRODUCTION

Lifestyle improvements are key modifiable risk factors for Type 2 diabetes mellitus (DM) however specific influences of biologically active dietary metabolites remain unclear. Our objective was to compare non-targeted plasma metabolomic profiles of women with versus without confirmed incident DM. We focused on three lipid classes (fatty acyls, prenol lipids, polyketides).

MATERIALS AND METHODS

Fifty DM cases and 100 individually matched control participants (80% with human immunodeficiency virus [HIV]) were enrolled in a case-control study nested within the Women's Interagency HIV Study. Stored blood samples (1-2 years prior to DM diagnosis among cases; at the corresponding timepoint among matched controls) were assayed in triplicate for metabolomics. Time-of-flight liquid chromatography mass spectrometry with dual electrospray ionization modes was utilized. We considered 743 metabolomic features in a two-stage feature selection approach with conditional logistic regression models that accounted for matching strata.

RESULTS

Seven features differed by DM case status (all false discovery rate-adjusted q<0.05). Three flavonoids (two flavanones, one isoflavone) were respectively associated with lower odds of DM (all q<0.05), and sorbic acid was associated with greater odds of DM (all q<0.05).

CONCLUSION

Flavonoids were associated with lower odds of incident DM while sorbic acid was associated with greater odds of incident DM.

Bias caused by incomplete metabolite extraction and matrix effect: Evaluation of critical factors for plasma sample preparation prior to metabolomics

Metabolomics is an omics strategy to study the metabolite alteration in the biological system. Unbiased observation of the metabolite level is essential for targeted metabolite quantification and untargeted metabolic profiling. State-of-the-art instruments and versatile tools have been developed for accurate observation of metabolic alterations in various studies. Several analytical pitfalls, such as sample overloading and signal-saturation-induced bias, have been revealed and addressed. In this study, we proposed incomplete-metabolite-extraction-caused bias is also an important issue that results in biased observation when performing metabolomics. In the demonstration example, numerous metabolites exhibited no significant difference between extracted plasma samples with different plasma contents, which is attributed to incomplete-metabolite-extraction-caused bias and matrix effect. Matrix effect is a well-known factor that result in biased observation, it can be reduced by sample dilution and compensated by using stable isotope labelled internal standards. The detection of metabolite signals in the following consecutive extractions provided further evidence of incomplete metabolite extraction. The completeness of metabolite extraction is crucial for unbiased observation of metabolic profile changes. To address this issue, we optimized the extraction time and methanol volume to reduce the incomplete-metabolite-extraction-caused bias and evaluated the metabolite signals in consecutive extractions. Methanol extraction performed with a plasma-to-methanol ratio of 1:14 resulted in metabolite responses of less than 18.1 % in the second extractions observed by metabolomic profiling. Finally, the optimized sample preparation procedure and untargeted profiling platform were applied to detect metabolite alterations associated with patients with cerebrovascular diseases and several features with significant difference were successfully identified. This study revealed and evaluated the bias caused by incomplete metabolite extraction and matrix effect in the commonly used methanol extraction method for human plasma sample preparation for metabolomics. We anticipate the proposed metabolite extraction evaluation method could benefit more clinical and biological metabolomics studies.

Metabolic Pathways Associated With Psychoneurological Symptoms in Children With Cancer Receiving Chemotherapy

CONTEXT

Children with cancer undergoing chemotherapy experience a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, and depressive symptoms. Metabolomics is promising to differentiate metabolic pathways associated with the PNS cluster.

OBJECTIVES

Identify metabolic pathways associated with the PNS cluster in children with cancer before and after chemotherapy.

METHODS

Pain, fatigue, anxiety, and depressive symptoms were assessed using the Pediatric PROMIS scales. T-scores were computed and divided dichotomously by a cutoff point of 50; the PNS cluster was a sum of the four symptoms ranging from 0 (all T-scores <50) to 4 (all T-scores ≥50). Serum metabolites were processed using liquid chromatography mass-spectrometry untargeted metabolomics approach. Linear regression models examined metabolites associated with the PNS cluster. Metabolic pathway enrichment analysis was performed.

RESULTS

Participant demographics (n = 40) were 55% female, 60% white, 62.5% aged 13-19 years, and 62.5% diagnoses of Hodgkin's lymphoma and B-cell acute lymphocytic leukemia. Among 9276 unique metabolic features, 454 were associated with pain, 281 with fatigue, 596 with anxiety, 551 with depressive symptoms, and 300 with the PNS cluster across one chemotherapy cycle. Fatty acids pathways were associated with pain: de novo fatty acid biosynthesis (p < .001), fatty acid metabolism (p = .001), fatty acid activation (p = .004), and omega-3 fatty acid metabolism (p = .009). Tryptophan amino acid pathway was associated with fatigue (p < .001), anxiety (p = .015), and the PNS cluster (p = .037). Carnitine shuttle was associated with the PNS cluster (p = .015).

CONCLUSION

Fatty acids and amino acids pathways were associated with PNS in children undergoing chemotherapy. These findings require further investigation in a larger sample.

High Intensity Concentric-Eccentric Exercise Under Hypoxia Changes the Blood Metabolome of Trained Athletes

The aim of this study was to determine alterations of the metabolome in blood plasma in response to concentric-eccentric leg exercise performed at a simulated altitude of 3,500 m. To do so, we recruited 11 well-trained subjects and performed an untargeted metabolomics analysis of plasma samples obtained before, 20 min after as well as on day 8 after five sets of maximal, concentric-eccentric leg exercises that lasted 90 s each. We identified and annotated 115 metabolites through untargeted liquid chromatography-mass spectrometry metabolomics and used them to further calculate 20 sum/ratio of metabolites. A principal component analysis (PCA) revealed differences in-between the overall metabolome at rest and immediately after exercise. Interestingly, some systematic changes of relative metabolite concentrations still persisted on day 8 after exercise. The first two components of the PCA explained 34% of the relative concentrations of all identified metabolites analyzed together. A volcano plot indicates that 35 metabolites and two metabolite ratios were significantly changed directly after exercise, such as metabolites related to carbohydrate and TCA metabolism. Moreover, we observed alterations in the relative concentrations of amino acids (e.g., decreases of valine, leucine and increases in alanine) and purines (e.g., increases in hypoxanthine, xanthine and uric acid). In summary, high intensity concentric-eccentric exercise performed at simulated altitude systematically changed the blood metabolome in trained athletes directly after exercise and some relative metabolite concentrations were still changed on day 8. The importance of that persisting metabolic alterations on exercise performance should be studied further.

A Global Regulatory Network for Dysregulated Gene Expression and Abnormal Metabolic Signaling in Immune Cells in the Microenvironment of Graves' Disease and Hashimoto's Thyroiditis

Background

Although the pathogenetic mechanisms of Hashimoto’s thyroiditis (HT) and Graves’ disease (GD) have been elucidated, the molecular mechanisms by which the abnormal immune function of cellular subpopulations trigger an autoimmune attack on thyroid tissue largely remains unexplained.

Methods

The study included 2 HT patients, 2 GD patients, and 1 control donor. The thyroid samples were extracted for single-cell RNA sequencing, whole transcriptome, full-length transcriptome (Oxford Nanopore Technologies), and metabolome sequencing. Identification of immune cells with dysregulated gene expression and abnormal metabolic signaling was performed in the microenvironment, both at the bulk and single-cell levels. Based on functional enrichment analysis, the biological processes and pathways involved in abnormal immune cells were further explored. Finally, according to cell communication analysis, the global regulatory network of immune cells was constructed.

Results

CD4⁺ T cells, CD8⁺ T cells, and macrophages were abnormally increased in patients with HT and GD. The differentially expressed genes of these cells were significantly involved in signaling pathways, including Th1 and Th2 cell differentiation, Th17 cell differentiation, cytokine–cytokine receptor interaction, and NF-kappa B signaling pathway. Moreover, in HT, CD4⁺ T cells interact with macrophages via the IL16-CCR5/FGF10-FGFR1/CXCL13-CXCR3 axis, and macrophages interact with CD8⁺ T cells via the CD70-CD27 axis, thereby activating the T-cell receptor signaling pathway and NF-kappa B signaling pathway. In GD, CD4⁺ T cells interact with macrophages via the CXCR3-CXCL10/PKM-CD44/MHCII-NFKBIE axis, and macrophages interact with CD8⁺ T cells via the IFNG-IFNGR1/CCR7-CCL21 axis, thereby activating T-cell receptor signaling pathway, Th1 and Th2 cell differentiation, and chemokine signaling pathway.

Conclusion

In HT and GD, immune dysregulated cells interact and activate relevant immune pathways and further aggravate the immune response. This may trigger the immune cells to target the thyroid tissue and influence the development of the disease.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (t_R), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The t_R values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

Plant Host Traits Mediated by Foliar Fungal Symbionts and Secondary Metabolites

Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, Panicum virgatum L. This created a limited range of plant growth and physiology (2-370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, Lasso regression identified 12 metabolites that explained from 14 to 43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.

Host-genotype-dependent cecal microbes are linked to breast muscle metabolites in Chinese chickens

In chickens, the effect of host genetics on the gut microbiota is not fully understood, and the extent to which the heritable gut microbes affect chicken metabolism and physiology is still an open question. Here, we explored the interactions among chicken genetics, the cecal microbiota and metabolites in breast muscle from ten chicken breeds in China. We found that different chicken breeds displayed distinct cecal microbial community structures and functions, and 15 amplicon sequence variants (ASVs) were significantly associated with host genetics through different genetic loci, such as those related to the intestinal barrier function. We identified five heritable ASVs significantly associated with 53 chicken muscle metabolites, among which the Megamonas probably affected lipid metabolism through the production of propionate. Our study revealed that the chicken genetically associated cecal microbes may have the potential to affect the bird’s physiology and metabolism.

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The cecal microbiota are different among ten chicken breeds

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The chicken genetics influences the cecal microbiota structures and functions

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The chicken heritable cecal microbes are associated with muscle metabolites

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Megamonas may affect lipid metabolism by the production of propionate

Association of nanoparticle exposure with serum metabolic disorders of healthy adults in printing centers

Printers are everyday devices in both our homes and workplaces. We have previously found high occupational exposure levels to toner-based printer emitted nanoparticles (PEPs) at printing centers. To elucidate the potential health effects from exposure to PEPs, a total of 124 human serum samples were collected from 32 workers in the printing centers during the repeated follow-up measurements, and global serum metabolomics were analyzed in three ways: correlation between metabolic response and personal exposure (dose response exposure); metabolite response changes between Monday and Friday of a work week (short-term exposure), and metabolite response in relation to length of service in a center (long-term exposure). A total of 52 key metabolites changed significantly in relation to nanoparticle exposure levels. The primary dysregulated pathways included inflammation and immunity related arginine and tryptophan metabolism. Besides, some distinct metabolite expression patterns were found to occur during the transition from short-term to long-term exposures, suggesting cumulative effect of PEPs exposure. These findings, for the first time, highlight the inhalation exposure responses to printer emitted nanoparticles at the metabolite level, potentially serving as pre-requisites for whole organism and population responses, and are inline with emerging findings on potential health effects.

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 .

Biofluid Metabolomics and Lipidomics of Mice Exposed to External Very High-Dose Rate Radiation

High-throughput biodosimetry methods to determine exposure to ionizing radiation (IR) that can also be easily scaled to multiple testing sites in emergency situations are needed in the event of malicious attacks or nuclear accidents that may involve a substantial number of civilians. In the event of an improvised nuclear device (IND), a complex IR exposure will have a very high-dose rate (VHDR) component from an initial blast. We have previously addressed low-dose rate (LDR, ≤1 Gy/day) exposures from internal emitters on biofluid small molecule signatures, but further research on the VHDR component of the initial blast is required. Here, we exposed 8- to 10-week-old male C57BL/6 mice to an acute dose of 3 Gy using a reference dose rate of 0.7 Gy/min or a VHDR of 7 Gy/s, collected urine and serum at 1 and 7 d, then compared the metabolite signatures using either untargeted (urine) or targeted (serum) approaches with liquid chromatography mass spectrometry platforms. A Random Forest classification approach showed strikingly similar changes in urinary signatures at 1 d post-irradiation with VHDR samples grouping closer to control samples at 7 d. Identical metabolite panels (carnitine, trigonelline, xanthurenic acid, N6,N6,N6-trimethyllysine, spermine, and hexosamine-valine-isoleucine-OH) could differentiate IR exposed individuals with high sensitivity and specificity (area under the receiver operating characteristic (AUROC) curves 0.89-1.00) irrespective of dose rate at both days. For serum, the top 25 significant lipids affected by IR exposure showed slightly higher perturbations at 0.7 Gy/min vs. 7 Gy/s; however, identical panels showed excellent sensitivity and specificity at 1 d (three hexosylceramides (16:0), (18:0), (24:0), sphingomyelin [26:1], lysophosphatidylethanolamine [22:1]). Mice could not be differentiated from control samples at 7 d for a 3 Gy exposure based on serum lipid signatures. As with LDR exposures, we found that identical biofluid small molecule signatures can identify IR exposed individuals irrespective of dose rate, which shows promise for more universal applications of metabolomics for biodosimetry.

A hybrid inorganic-biological artificial photosynthesis system for energy-efficient food production

Artificial photosynthesis systems are proposed as an efficient alternative route to capture CO₂ to produce additional food for growing global demand. Here a two-step CO₂ electrolyser system was developed to produce a highly concentrated acetate stream with a 57% carbon selectivity (CO₂ to acetate), allowing its direct use for the heterotrophic cultivation of yeast, mushroom-producing fungus and a photosynthetic green alga, in the dark without inputs from biological photosynthesis. An evaluation of nine crop plants found that carbon from exogenously supplied acetate incorporates into biomass through major metabolic pathways. Coupling this approach to existing photovoltaic systems could increase solar-to-food energy conversion efficiency by about fourfold over biological photosynthesis, reducing the solar footprint required. This technology allows for a reimagination of how food can be produced in controlled environments.

Metabolomics and lipidomics to identify biomarkers of effect related to exposure to non-dioxin-like polychlorinated biphenyls in pigs

Recent epidemiological studies show that current levels of exposure to polychlorinated biphenyls (PCBs) remain of great concern, as there is still a link between such exposures and the development of chronic environmental diseases. In this sense, most studies have focused on the health effects caused by exposure to dioxin-like PCBs (DL-PCBs), although chemical exposure to non-dioxin-like PCB (NDL-PCB) congeners is more significant. In addition, adverse effects of PCBs have been documented in humans after accidental and massive exposure, but little is known about the effect of chronic exposure to low-dose PCB mixtures. In this work, exposure to Aroclor 1260 (i.e. a commercially available mixture of PCBs consisting primarily of NDL-PCB congeners) in pigs is investigated as new evidence in the risk assessment of NDL-PCBs. This animal model has been selected due to the similarities with human metabolism and to support previous toxicological studies carried out with more frequently used animal models. Dietary exposure doses in the order of few ng/kg body weight (b.w.) per day were applied. As expected, exposure to Aroclor 1260 led to the bioaccumulation of NDL-PCBs in perirenal fat of pigs. Metabolomics and lipidomics have been applied to reveal biomarkers of effect related to Aroclor 1260 exposure, and by extension to NDL-PCB exposure, for 21 days. In the metabolomics analysis, 33 metabolites have been identified (level 1 and 2) as significantly altered by the Aroclor 1260 administration, while in the lipidomics analysis, 39 metabolites were putatively annotated (level 3) and associated with NDL-PCB exposure. These biomarkers are mainly related to the alteration of fatty acid metabolism, glycerophospholipid metabolism and tryptophan-kynurenine pathway.

Non-targeted metabolomics revealing the effects of bisphenol analogues on human liver cancer cells

Bisphenol analogues (BPs) are widely used in plastics, food packaging and other commercial products as non safer alternative of BPA. As emerging environmental contaminants, BPs have received considerable attention for their adverse effects on human health. However, their effects on liver metabolisms are only marginally understood. In this study, high-resolution mass spectrometry-based global metabolomics and extracellular flux (XF) analysis were applied to characterize the cellular metabolome alterations and reveal the possible mechanisms of the metabolic disorders associated with BPs-induced toxicity in HepG2 cells. BPE, BPB and BPAP with similar chemical structures were selected to compare their interference with different metabolic pathways. A total of 61 key metabolite profiles were significantly altered after exposure to the three BPs. Overall, BPs altered metabolites which are associated with energy metabolism, oxidative stress, cell proliferation and nucleotides synthesis. The primary dysregulated pathways included energy and nucleotides synthesis related Purine and Glycolysis/Gluconeogenesis metabolism. In addition, attenuated mitochondrial function and enhanced glycolysis were found under BPB and BPAP treatment. While attenuated glycolysis was observed under BPE treatment. These findings may provide potential biomarkers indicating the cytotoxicity of BPs and prompt a deeper understanding of the intramolecular metabolic processes induced by BPs exposure.

Characterization of the Halochromic Gloeocapsin Pigment, a Cyanobacterial Biosignature for Paleobiology and Astrobiology

Ultraviolet (UV)-screening compounds represent a substantial asset for the survival of cyanobacteria in extreme environments exposed to high doses of UV radiations on modern and early Earth. Among these molecules, the halochromic pigment gloeocapsin remains poorly characterized and studied. In this study, we identified a gloeocapsin-producing cultivable cyanobacteria: the strain Phormidesmis nigrescens ULC007. We succeeded to extract, to partially purify, and to compare the dark blue pigment from both the ULC007 culture and an environmental Gloeocapsa alpina dominated sample. FT-IR and Raman spectra of G. alpina and P. nigrescens ULC007 pigment extracts strongly suggested a common backbone structure. The high-pressure liquid chromatography-UV-MS/MS analysis of the ULC007 pigment extract allowed to narrow down the molecular formula of gloeocapsin to potentially five candidates within three classes of halochromic molecules: anthraquinone derivatives, coumarin derivatives, and flavonoids. With the discovery of gloeocapsin in P. nigrescens, the production of this pigment is now established for three lineages of cyanobacteria (including G. alpina, P. nigrescens, and Solentia paulocellulare) that belong to three distinct orders (Chroococcales, Pleurocapsales, Synechoccocales), inhabiting very diverse environments. This suggests that gloeocapsin production was a trait of their common ancestor or was acquired by lateral gene transfer. This work represents an important step toward the elucidation of the structure of this enigmatic pigment and its biosynthesis, and it potentially provides a new biosignature for ancient cyanobacteria. It also gives a glimpse on the evolution of UV protection strategies, which are relevant for early phototrophic life on Earth and possibly beyond.

A Comprehensive Database for DNA Adductomics

The exposure of human DNA to genotoxic compounds induces the formation of covalent DNA adducts, which may contribute to the initiation of carcinogenesis. Liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) is a powerful tool for DNA adductomics, a new research field aiming at screening known and unknown DNA adducts in biological samples. The lack of databases and bioinformatics tool in this field limits the applicability of DNA adductomics. Establishing a comprehensive database will make the identification process faster and more efficient and will provide new insight into the occurrence of DNA modification from a wide range of genotoxicants. In this paper, we present a four-step approach used to compile and curate a database for the annotation of DNA adducts in biological samples. The first step included a literature search, selecting only DNA adducts that were unequivocally identified by either comparison with reference standards or with nuclear magnetic resonance (NMR), and tentatively identified by tandem HRMS/MS. The second step consisted in harmonizing structures, molecular formulas, and names, for building a systematic database of 279 DNA adducts. The source, the study design and the technique used for DNA adduct identification were reported. The third step consisted in implementing the database with 303 new potential DNA adducts coming from different combinations of genotoxicants with nucleobases, and reporting monoisotopic masses, chemical formulas, .cdxml files, .mol files, SMILES, InChI, InChIKey and IUPAC nomenclature. In the fourth step, a preliminary spectral library was built by acquiring experimental MS/MS spectra of 15 reference standards, generating in silico MS/MS fragments for all the adducts, and reporting both experimental and predicted fragments into interactive web datatables. The database, including 582 entries, is publicly available (https://gitlab.com/nexs-metabolomics/projects/dna_adductomics_database). This database is a powerful tool for the annotation of DNA adducts measured in (HR)MS. The inclusion of metadata indicating the source of DNA adducts, the study design and technique used, allows for prioritization of the DNA adducts of interests and/or to enhance the annotation confidence. DNA adducts identification can be further improved by integrating the present database with the generation of authentic MS/MS spectra, and with user-friendly bioinformatics tools.

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Monitoring of Environmental Contaminants in Mixed-Use Watersheds Combining Targeted and Nontargeted Analysis with Passive Sampling

Understanding the environmental fate, transport, and occurrence of pesticides and pharmaceuticals in aquatic environments is of utmost concern to regulators. Traditionally, monitoring of environmental contaminants in surface water has consisted of liquid chromatography-tandem mass spectrometry analyses for a set of targeted compounds in discrete samples. These targeted approaches are limited by the fact that they only provide information on compounds within a target list present at the time and location of sampling. To address these limitations, there has been considerable interest in suspect screening and nontargeted analysis (NTA), which allow for the detection of all ionizable compounds in the sample with the added benefit of data archiving for retrospective mining. Even though NTA can detect a large number of contaminants, discrete samples only provide a snapshot perspective of the chemical disposition of an aquatic environment at the time of sampling, potentially missing episodic events. We evaluated two types of passive chemical samplers for nontargeted analysis in mixed-use watersheds. Nontargeted data were processed using MS-DIAL to screen against our in-house library and public databases of more than 1300 compounds. The data showed that polar organic chemicals integrative samplers (POCIS) were able to capture the largest number of analytes with better reproducibility than organic compound-diffusive gradients in thin film (o-DGT), resulting from the greater amount of binding sorbent. We also showed that NTA combined with passive sampling gives a more representative picture of the contaminants present at a given site and enhances the ability to identify the nature of point and nonpoint pollution sources and ecotoxicological impacts. Environ Toxicol Chem 2022;41:1131-1143. © 2021 Her Majesty the Queen in Right of Canada Environmental Toxicology and Chemistry © 2021 SETAC. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.

Artifactual FA dimers mimic FAHFA signals in untargeted metabolomics pipelines

FA esters of hydroxy FAs (FAHFAs) are lipokines with extensive structural and regional isomeric diversity that impact multiple physiological functions, including insulin sensitivity and glucose homeostasis. Because of their low molar abundance, FAHFAs are typically quantified using highly sensitive LC-MS/MS methods. Numerous relevant MS databases house in silico-spectra that allow identification and speciation of FAHFAs. These provisional chemical feature assignments provide a useful starting point but could lead to misidentification. To address this possibility, we analyzed human serum with a commonly applied high-resolution LC-MS untargeted metabolomics platform. We found that many chemical features are putatively assigned to the FAHFA lipid class based on exact mass and fragmentation patterns matching spectral databases. Careful validation using authentic standards revealed that many investigated signals provisionally assigned as FAHFAs are in fact FA dimers formed in the LC-MS pipeline. These isobaric FA dimers differ structurally only by the presence of an olefinic bond. Furthermore, stable isotope-labeled oleic acid spiked into human serum at subphysiological concentrations showed concentration-dependent formation of a diverse repertoire of FA dimers that analytically mimicked FAHFAs. Conversely, validated FAHFA species did not form spontaneously in the LC-MS pipeline. Together, these findings underscore that FAHFAs are endogenous lipid species.  However, nonbiological FA dimers forming in the setting of high concentrations of FFAs can be misidentified as FAHFAs. Based on these results, we assembled a FA dimer database to identify nonbiological FA dimers in untargeted metabolomics datasets.

Space- and Time-Resolved Metabolomics of a High-Grade Serous Ovarian Cancer Mouse Model

The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

BACKGROUND

There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.

METHODS

Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.

FINDINGS

3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance.

CONCLUSIONS

The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.

FUNDING

NIHR Imperial BRC; UKRI.

Small Molecule Signatures of Mice Lacking T-cell p38 Alternate Activation, a Model for Immunosuppression Conditions, after Total-Body Irradiation

Several diagnostic biodosimetry tools have been in development that may aid in radiological/nuclear emergency responses. Of these, correlating changes in non-invasive biofluid small-molecule signatures to tissue damage from ionizing radiation exposure show promise for inclusion in predictive biodosimetry models. Integral to dose reconstruction has been determining how genotypic variation in the general population will affect model performance. Here, we used a mouse model that lacks the T-cell receptor specific alternative p38 pathway [p38αβY323F, double knock-in (DKI) mice] to determine how attenuated autoimmune and inflammatory responses may affect dose reconstruction. We exposed adult male DKI mice (8-10 weeks old) to 2 and 7 Gy in parallel with wild-type mice and assessed perturbations in urine (days 1, 3, 7) and serum (day 1) using a global metabolomics approach. A multidimensional scaling plot showed excellent separation of radiation-exposed groups in wild-type mice with slightly dampened responses in DKI mice. Validated metabolite panels were developed for urine [N6,N6,N6-trimethyllysine (TML), N1-acetylspermidine, spermidine, carnitine, acylcarnitine C21H35NO5, 4-aminohippuric acid] and serum [phenylalanine, glutamine, propionylcarnitine, lysophosphatidylcholine (LysoPC 14:0), LysoPC (22:5)] to determine the area under the receiver operating characteristic curve (AUROC). For both urine and serum, excellent sensitivity and specificity (AUROC > 0.90) was observed for 0 Gy vs. 7 Gy groups irrespective of genotype using identical metabolite panels. Similarly, excellent to fair classification (AUROC > 0.75) was observed for ≤2 Gy vs. 7 Gy mice for both genotypes, however, model performance declined (AUROC < 0.75) between genotypes after irradiation. Overall, these results suggest immunosuppression should not compromise small molecule multiplex panels used in dose reconstruction for biodosimetry.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN

Neutral loss (NL) spectral data presents a mirror of MS² data and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS²) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS² data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS² fragment ion database for characterizing structurally related molecules, we have created a MS² to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN's MS² data into a neutral loss database (METLIN-NL) on over 860 000 individual molecular standards. The platform includes both the MS² to NL converter and a graphical user interface enabling comparative analyses between MS² and NL data. Examples of NL spectral data are shown with oxylipin analogues and two structurally related statin molecules to demonstrate NL spectra and their ability to help characterize structural similarity. Mirroring MS² data to generate NL spectral data offers a unique dimension for chemical and metabolite structure characterization.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Untargeted plasma metabolomics identifies broad metabolic perturbations in glycogen storage disease type I

BACKGROUND

The metabolic defect in glycogen storage disease type I (GSDI) results in fasting hypoglycemia and typical secondary metabolic abnormalities (eg, hypertriglyceridemia, hyperlactatemia, hyperuricemia). The aim of this study was to assess further perturbations of the metabolic network in GSDI patients under ongoing treatment.

METHODS

In this prospective observational study, plasma samples of 14 adult patients (11 GSDIa, 3 GSDIb. Mean age 26.4 years, range 16-46 years) on standard treatment were compared to a cohort of 31 healthy controls utilizing ultra-high performance liquid chromatography (UHPLC) in combination with high resolution tandem mass spectrometry (HR-MS/MS) and subsequent statistical multivariate analysis. In addition, plasma fatty acid profiling was performed by GC/EI-MS.

RESULTS

The metabolomic profile showed alterations of metabolites in different areas of the metabolic network in both GSD subtypes, including pathways of fuel metabolism and energy generation, lipids and fatty acids, amino acid and methyl-group metabolism, the urea cycle, and purine/pyrimidine metabolism. These alterations were present despite adequate dietary treatment, did not correlate with plasma triglycerides or lactate, both parameters typically used to assess the quality of metabolic control in clinical practice, and were not related to the presence or absence of complications (ie, nephropathy or liver adenomas).

CONCLUSION

The metabolic defect of GSDI has profound effects on a variety of metabolic pathways in addition to the known typical abnormalities. These alterations are present despite optimized dietary treatment, which may contribute to the risk of developing long-term complications, an inherent problem of GSDI which appears to be only partly modified by current therapy.

In vivo mechanotransduction: Effect of acute exercise on the metabolomic profiles of mouse synovial fluid

Objective

Exercise is known to induce beneficial effects in synovial joints. However, the mechanisms underlying these are unclear. Synovial joints experience repeated mechanical loading during exercise. These mechanical stimuli are transduced into biological responses through cellular mechanotransduction. Mechanotransduction in synovial joints is typically studied in tissues. However, synovial fluid directly contacts all components of the joint, and thus may produce a whole-joint picture of the mechanotransduction response to loading. The objective of this study was to determine if metabolic phenotypes are present in the synovial fluid after acute exercise as a first step to understanding the beneficial effects of exercise on the joint.

Material and methods

Mice underwent a single night of voluntary wheel running or standard housing and synovial fluid was harvested for global metabolomic profiling by LC-MS. Hierarchical unsupervised clustering, partial least squares discriminant, and pathway analysis provided insight into exercise-induced mechanotransduction.

Results

Acute exercise produced a distinct metabolic phenotype in synovial fluid. Mechanosensitive metabolites included coenzyme A derivatives, prostaglandin derivatives, phospholipid species, tryptophan, methionine, vitamin D3, fatty acids, and thiocholesterol. Enrichment analysis identified several pathways previously linked to exercise including amino acid metabolism, inflammatory pathways, citrulline-nitric oxide cycle, catecholamine biosynthesis, ubiquinol biosynthesis, and phospholipid metabolism.

Conclusion

To our knowledge, this is the first study to investigate metabolomic profiles of synovial fluid during in vivo mechanotransduction. These profiles indicate that exercise induced stress-response processes including both pro- and anti-inflammatory pathways. Further research will expand these results and define the relationship between the synovial fluid and the serum.

Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Investigation and dynamic profiling of oligopeptides, free amino acids and derivatives during Pu-erh tea fermentation by ultra-high performance liquid chromatography tandem mass spectrometry

Microbial fermentation is the critical step of Pu-erh tea manufacture, which will induce dramatic changes in the chemical composition and content of tea. In this research, we applied multi-methods based on UHPLC-Q-TOF/MS to profile the dynamic changes of oligopeptides, free amino acids, and derivatives (OPADs) during Pu-erh fermentation and predicted the potential bioactivities in silico. A total of 60 oligopeptides, 18 free amino acids, and 42 amino acid derivatives were identified, and the contents of most of them decreased after fermentation. But several N-acetyl amino acids increased 7-36 times after fermentation, and they might be the potential inhibitors of neurokinin-1 receptor. Moreover, the results of metamicrobiology showed Aspergillus niger and Aspergillus luchuensis were more prominent to metabolize protein, oligopeptides, and amino acids. Overall, these findings provide valuable insights about dynamic variations of OPADs during Pu-erh tea fermentation and are beneficial for guiding practical fermentation and quality control of Pu-erh tea.