Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation.

Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study.

INTRODUCTION: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication. METHODS: Baseline urine was collected prospectively from healthy participants (n=100), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods. RESULTS: A total of 90 metabolites were identified, with 40 significantly altered (p<0.05, false discovery rate <0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10-20), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings. CONCLUSIONS: This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

Does machine learning have a role in the prediction of asthma in children?

Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.

Improvement in the Prediction of Neonatal Hypoxic-Ischemic Encephalopathy with the Integration of Umbilical Cord Metabolites and Current Clinical Makers.

OBJECTIVE: To validate our previously identified candidate metabolites, and to assess the ability of these metabolites to predict hypoxic-ischemic encephalopathy (HIE) both individually and combined with clinical data. STUDY DESIGN: Term neonates with signs of perinatal asphyxia, with and without HIE, and matched controls were recruited prospectively at birth from 2 large maternity units. Umbilical cord blood was collected for later batch metabolomic analysis by mass spectroscopy along with clinical details. The optimum selection of clinical and metabolites features with the ability to predict the development of HIE was determined using logistic regression modelling and machine learning techniques. Outcome of HIE was determined by clinical Sarnat grading and confirmed by electroencephalogram grade at 24 hours. RESULTS: Fifteen of 27 candidate metabolites showed significant alteration in infants with perinatal asphyxia or HIE when compared with matched controls. Metabolomic data predicted the development of HIE with an area under the curve of 0.67 (95% CI, 0.62-0.71). Lactic acid and alanine were the primary metabolite predictors for the development of HIE, and when combined with clinical data, gave an area under the curve of 0.96 (95% CI, 0.92-0.95). CONCLUSIONS: By combining clinical and metabolic data, accurate identification of infants who will develop HIE is possible shortly after birth, allowing early initiation of therapeutic hypothermia.

A strategy for advancing for population-based scientific discovery using the metabolome: the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group.

Metabolomic Epidemiology is a growing area of research within the metabolomics research community. In response to this, we describe the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group. The overall mission of this group is to promote the growth and understanding of metabolomic epidemiology as an independent research discipline and to drive collaborative efforts that can shape the field. In this article we define metabolomic epidemiology and identify the key challenges that need to be addressed in order to advance population-based scientific discovery in metabolomics.

Sensitive and quantitative determination of short-chain fatty acids in human serum using liquid chromatography mass spectrometry.

Short-chain fatty acids (SCFAs) are increasingly being monitored to elucidate the link between gut health and disease. These metabolites are routinely measured in faeces, but their determination in serum is more challenging due to their low concentrations. A method for the determination of eight SCFAs in serum is described here. High-resolution mass spectrometry and gas chromatography were used to identify the presence of isomeric interferences, which were then overcome through a combination of chromatographic separation and judicious choice of MS fragment ion. The SCFAs were derivatised to form 3-nitrophenylhydrazones before being separated on a reversed-phase column and then detected using liquid chromatography tandem mass spectrometry (LC-QQQ-MS). The LODs and LOQs of SCFAs using this method were in the range 1 to 7 ng mL-1 and 3 to 19 ng mL-1, respectively. The recovery of the SCFAs in serum ranged from 94 to 114% over the three concentration ranges tested.

Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks.

INTRODUCTION: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. OBJECTIVES: We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. METHODS: We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. RESULTS: The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. CONCLUSION: We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners.

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. OBJECTIVES: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics. METHODS: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. RESULTS: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). CONCLUSIONS: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.

INTRODUCTION: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. OBJECTIVES: We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. METHODS: We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. RESULTS: There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. CONCLUSION: The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing.

BACKGROUND: A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. AIM OF REVIEW: To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. KEY SCIENTIFIC CONCEPTS OF REVIEW: This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

The application of artificial neural networks in metabolomics: a historical perspective.

BACKGROUND: Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods. AIM OF REVIEW: We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics. KEY SCIENTIFIC CONCEPT OF REVIEW: Is metabolomics ready for the return of artificial neural networks?

Towards quality assurance and quality control in untargeted metabolomics studies.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day 'Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies' held at the National Cancer Institute's Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

Characterizing the plasma metabolome during 14 days of live-high, train-low simulated altitude: A metabolomic approach.

NEW FINDINGS: What is the central question of this study? Does 14 days of live-high, train-low simulated altitude alter an individual's metabolomic/metabolic profile? What is the main finding and its importance? This study demonstrated that ∼200 h of moderate simulated altitude exposure resulted in greater variance in measured metabolites between subject than within subject, which indicates individual variability during the adaptive phase to altitude exposure. In addition, metabolomics results indicate that altitude alters multiple metabolic pathways, and the time course of these pathways is different over 14 days of altitude exposure. These findings support previous literature and provide new information on the acute adaptation response to altitude. ABSTRACT: The purpose of this study was to determine the influence of 14 days of normobaric hypoxic simulated altitude exposure at 3000 m on the human plasma metabolomic profile. For 14 days, 10 well-trained endurance runners (six men and four women; 29 ± 7 years of age) lived at 3000 m simulated altitude, accumulating 196.4 ± 25.6 h of hypoxic exposure, and trained at ∼600 m. Resting plasma samples were collected at baseline and on days 3 and 14 of altitude exposure and stored at -80°C. Plasma samples were analysed using liquid chromatography-high-resolution mass spectrometry to construct a metabolite profile of altitude exposure. Mass spectrometry of plasma identified 36 metabolites, of which eight were statistically significant (false discovery rate probability 0.1) from baseline to either day 3 or day 14. Specifically, changes in plasma metabolites relating to amino acid metabolism (tyrosine and proline), glycolysis (adenosine) and purine metabolism (adenosine) were observed during altitude exposure. Principal component canonical variate analysis showed significant discrimination between group means (P < 0.05), with canonical variate 1 describing a non-linear recovery trajectory from baseline to day 3 and then back to baseline by day 14. Conversely, canonical variate 2 described a weaker non-recovery trajectory and increase from baseline to day 3, with a further increase from day 3 to 14. The present study demonstrates that metabolomics can be a useful tool to monitor metabolic changes associated with altitude exposure. Furthermore, it is apparent that altitude exposure alters multiple metabolic pathways, and the time course of these changes is different over 14 days of altitude exposure.

OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma.

Integration of multiomics data remains a key challenge in fulfilling the potential of comprehensive systems biology. Multiple-block orthogonal projections to latent structures (OnPLS) is a projection method that simultaneously models multiple data matrices, reducing feature space without relying on a priori biological knowledge. In order to improve the interpretability of OnPLS models, the associated multi-block variable influence on orthogonal projections (MB-VIOP) method is used to identify variables with the highest contribution to the model. This study combined OnPLS and MB-VIOP with interactive visualization methods to interrogate an exemplar multiomics study, using a subset of 22 individuals from an asthma cohort. Joint data structure in six data blocks was assessed: transcriptomics; metabolomics; targeted assays for sphingolipids, oxylipins, and fatty acids; and a clinical block including lung function, immune cell differentials, and cytokines. The model identified seven components, two of which had contributions from all blocks (globally joint structure) and five that had contributions from two to five blocks (locally joint structure). Components 1 and 2 were the most informative, identifying differences between healthy controls and asthmatics and a disease-sex interaction, respectively. The interactions between features selected by MB-VIOP were visualized using chord plots, yielding putative novel insights into asthma disease pathogenesis, the effects of asthma treatment, and biological roles of uncharacterized genes. For example, the gene ATP6 V1G1, which has been implicated in osteoporosis, correlated with metabolites that are dysregulated by inhaled corticoid steroids (ICS), providing insight into the mechanisms underlying bone density loss in asthma patients taking ICS. These results show the potential for OnPLS, combined with MB-VIOP variable selection and interaction visualization techniques, to generate hypotheses from multiomics studies and inform biology.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative).

BACKGROUND: The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data. CONTENT: This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices. SUMMARY: Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

(1)H-NMR urinary metabolomic profiling for diagnosis of gastric cancer.

BACKGROUND: Metabolomics has shown promise in gastric cancer (GC) detection. This research sought to identify whether GC has a unique urinary metabolomic profile compared with benign gastric disease (BN) and healthy (HE) patients. METHODS: Urine from 43 GC, 40 BN, and 40 matched HE patients was analysed using (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy, generating 77 reproducible metabolites (QC-RSD <25%). Univariate and multivariate (MVA) statistics were employed. A parsimonious biomarker profile of GC vs HE was investigated using LASSO regularised logistic regression (LASSO-LR). Model performance was assessed using Receiver Operating Characteristic (ROC) curves. RESULTS: GC displayed a clear discriminatory biomarker profile; the BN profile overlapped with GC and HE. LASSO-LR identified three discriminatory metabolites: 2-hydroxyisobutyrate, 3-indoxylsulfate, and alanine, which produced a discriminatory model with an area under the ROC of 0.95. CONCLUSIONS: GC patients have a distinct urinary metabolite profile. This study shows clinical potential for metabolic profiling for early GC diagnosis.

Reductions in circulating levels of IL-16, IL-7 and VEGF-A in myalgic encephalomyelitis/chronic fatigue syndrome.

Recently, differences in the levels of various chemokines and cytokines were reported in patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) as compared with controls. Moreover, the analyte profile differed between chronic ME/CFS patients of long duration versus patients with disease of less than 3years. In the current study, we measured the plasma levels of 34 cytokines, chemokines and growth factors in 100 chronic ME/CFS patients of long duration and in 79 gender and age-matched controls. We observed highly significant reductions in the concentration of circulating interleukin (IL)-16, IL-7, and Vascular Endothelial Growth Factor A (VEGF-A) in ME/CFS patients. All three biomarkers were significantly correlated in a multivariate cluster analysis. In addition, we identified significant reductions in the concentrations of fractalkine (CX3CL1) and monokine-induced-by-IFN-γ (MIG; CXCL9) along with increases in the concentrations of eotaxin 2 (CCL24) in ME/CFS patients. Our data recapitulates previous data from another USA ME/CFS cohort in which circulating levels of IL-7 were reduced. Also, a reduced level of VEGF-A was reported previously in sera of patients with Gulf War Illness as well as in cerebral spinal fluid samples from a different cohort of USA ME/CFS patients. To our knowledge, we are the first to test for levels of IL-16 in ME/CFS patients. In combination with previous data, our work suggests that the clustered reduction of IL-7, IL-16 and VEGF-A may have physiological relevance to ME/CFS disease. This profile is ME/CFS-specific since measurement of the same analytes present in chronic infectious and autoimmune liver diseases, where persistent fatigue is also a major symptom, failed to demonstrate the same changes. Further studies of other ME/CFS and overlapping disease cohorts are warranted in future.

Rapid inflammasome activation in microglia contributes to brain disease in HIV/AIDS.

BACKGROUND: Human immunodeficiency virus type 1(HIV-1) infects and activates innate immune cells in the brain resulting in inflammation and neuronal death with accompanying neurological deficits. Induction of inflammasomes causes cleavage and release of IL-1ß and IL-18, representing pathogenic processes that underlie inflammatory diseases although their contribution HIV-associated brain disease is unknown. RESULTS: Investigation of inflammasome-associated genes revealed that IL-1ß, IL-18 and caspase-1 were induced in brains of HIV-infected persons and detected in brain microglial cells. HIV-1 infection induced pro-IL-1ß in human microglia at 4 hr post-infection with peak IL-1ß release at 24 hr, which was accompanied by intracellular ASC translocation and caspase-1 activation. HIV-dependent release of IL-1ß from a human macrophage cell line, THP-1, was inhibited by NLRP3 deficiency and high extracellular [K+]. Exposure of microglia to HIV-1 gp120 caused IL-1ß production and similarly, HIV-1 envelope pseudotyped viral particles induced IL-1ß release, unlike VSV-G pseudotyped particles. Infection of cultured feline macrophages by the related lentivirus, feline immunodeficiency virus (FIV), also resulted in the prompt induction of IL-1ß. In vivo FIV infection activated multiple inflammasome-associated genes in microglia, which was accompanied by neuronal loss in cerebral cortex and neurological deficits. Multivariate analyses of data from FIV-infected and uninfected animals disclosed that IL-1ß, NLRP3 and caspase-1 expression in cerebral cortex represented key molecular determinants of neurological deficits. CONCLUSIONS: NLRP3 inflammasome activation was an early and integral aspect of lentivirus infection of microglia, which was associated with lentivirus-induced brain disease. Inflammasome activation in the brain might represent a potential target for therapeutic interventions in HIV/AIDS.

MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis.

First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis. With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to the original server was necessary. MetaboAnalyst 2.0, which is the successor to MetaboAnalyst, represents just such an upgrade. MetaboAnalyst 2.0 now contains dozens of new features and functions including new procedures for data filtering, data editing and data normalization. It also supports multi-group data analysis, two-factor analysis as well as time-series data analysis. These new functions have also been supplemented with: (i) a quality-control module that allows users to evaluate their data quality before conducting any analysis, (ii) a functional enrichment analysis module that allows users to identify biologically meaningful patterns using metabolite set enrichment analysis and (iii) a metabolic pathway analysis module that allows users to perform pathway analysis and visualization for 15 different model organisms. In developing MetaboAnalyst 2.0 we have also substantially improved its graphical presentation tools. All images are now generated using anti-aliasing and are available over a range of resolutions, sizes and formats (PNG, TIFF, PDF, PostScript, or SVG). To improve its performance, MetaboAnalyst 2.0 is now hosted on a much more powerful server with substantially modified code to take advantage the server's multi-core CPUs for computationally intensive tasks. MetaboAnalyst 2.0 also maintains a collection of 50 or more FAQs and more than a dozen tutorials compiled from user queries and requests. A downloadable version of MetaboAnalyst 2.0, along detailed instructions for local installation is now available as well.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma.

BACKGROUND: Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants. METHODS: We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value. FINDINGS: The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5). INTERPRETATION: This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health. FUNDING: Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).