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).

Sphingolipid classes and the interrelationship with pediatric asthma and asthma risk factors.

BACKGROUND: While dysregulated sphingolipid metabolism has been associated with risk of childhood asthma, the specific sphingolipid classes and/or mechanisms driving this relationship remain unclear. We aimed to understand the multifaceted role between sphingolipids and other established asthma risk factors that complicate this relationship. METHODS: We performed targeted LC-MS/MS-based quantification of 77 sphingolipids in plasma from 997 children aged 6 years from two independent cohorts (VDAART and COPSAC2010 ). We examined associations of circulatory sphingolipids with childhood asthma, lung function, and three asthma risk factors: functional SNPs in ORMDL3, low vitamin D levels, and reduced gut microbial maturity. Given racial differences between these cohorts, association analyses were performed separately and then meta-analyzed together. RESULTS: We observed elevations in circulatory sphingolipids with asthma phenotypes and risk factors; however, there were differential associations of sphingolipid classes with clinical outcomes and/or risk factors. While elevations from metabolites involved in ceramide recycling and catabolic pathways were associated with asthma and worse lung function [meta p-value range: 1.863E-04 to 2.24E-3], increased ceramide levels were associated with asthma risk factors [meta p-value range: 7.75E-5 to .013], but not asthma. Further investigation identified that some ceramides acted as mediators while some interacted with risk factors in the associations with asthma outcomes. CONCLUSION: This study demonstrates the differential role that sphingolipid subclasses may play in asthma and its risk factors. While overall elevations in sphingolipids appeared to be deleterious overall; elevations in ceramides were uniquely associated with increases in asthma risk factors only; while elevations in asthma phenotypes were associated with recycling sphingolipids. Modification of asthma risk factors may play an important role in regulating sphingolipid homeostasis via ceramides to affect asthma. Further function work may validate the observed associations.

An epidemiological introduction to human metabolomic investigations.

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Elevated third trimester corticosteroid levels are associated with fewer offspring infections.

Respiratory infections are a leading cause of morbidity and mortality in early life, and recurrent infections increase the risk of developing chronic diseases. The maternal environment during pregnancy can impact offspring health, but the factors leading to increased infection proneness have not been well characterized during this period. Steroids have been implicated in respiratory health outcomes and may similarly influence infection susceptibility. Our objective was to describe relationships between maternal steroid levels and offspring infection proneness. Using adjusted Poisson regression models, we evaluated associations between sixteen androgenic and corticosteroid metabolites during pregnancy and offspring respiratory infection incidence across two pre-birth cohorts (N = 774 in VDAART and N = 729 in COPSAC). Steroid metabolites were measured in plasma samples from pregnant mothers across all trimesters of pregnancy by ultrahigh-performance-liquid-chromatography/mass-spectrometry. We conducted further inquiry into associations of steroids with related respiratory outcomes: asthma and lung function spirometry. Higher plasma corticosteroid levels in the third trimester of pregnancy were associated with lower incidence of offspring respiratory infections (P = 4.45 × 10-7 to 0.002) and improved lung function metrics (P = 0.020-0.036). Elevated maternal androgens were generally associated with increased offspring respiratory infections and worse lung function, with some associations demonstrating nominal significance at P < 0.05, but these trends were inconsistent across individual androgens. Increased maternal plasma corticosteroid levels in the late second and third trimesters were associated with lower infections and better lung function in offspring, which may represent a potential avenue for intervention through corticosteroid supplementation in late pregnancy to reduce offspring respiratory infection susceptibility in early life.Clinical Trial Registry information: VDAART and COPSAC were originally conducted as clinical trials; VDAART: ClinicalTrials.gov identifier NCT00920621; COPSAC: ClinicalTrials.gov identifier NCT00798226.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles.

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration.

We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography-Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

Area under the dark adaptation curve as a reliable alternate measure of dark adaptation response.

PURPOSE: Quantification of dark adaptation (DA) response using the conventional rod intercept time (RIT) requires very long testing time and may not be measurable in the presence of impairments due to diseases such as age-related macular degeneration (AMD). The goal of this study was to investigate the advantages of using area under the DA curve (AUDAC) as an alternative to the conventional parameters to quantify DA response. METHODS: Data on 136 eyes (AMD: 98, normal controls: 38) from an ongoing longitudinal study on AMD were used. DA was measured using the AdaptDx 20 min protocol. AUDAC was computed from the raw DA characteristic curve at different time points, including 6.5 min and 20 min (default). The presence of AMD in the given eye was predicted using a logistic regression model within the leave-one-out cross-validation framework, with DA response as the predictor while adjusting for age and gender. The DA response variable was either the AUDAC values computed at 6.5 min (AUDAC6.5) or at 20 min (AUDAC20) cut-off, or the conventional RIT. RESULTS: AUDAC6.5 was strongly correlated with AUDAC20 (ß=86, p<0.001, R2=0.87). The accuracy of predicting the presence of AMD using AUDAC20 was 76%, compared with 79% when using RIT, the current gold standard. In addition, when limiting AUDAC calculation to 6.5 min cut-off, the predictive accuracy of AUDAC6.5 was 80%. CONCLUSIONS: AUDAC can be a valuable measure to quantify the overall DA response and can potentially facilitate shorter testing duration while maintaining diagnostic accuracy.

Metabo-Endotypes of Asthma Reveal Differences in Lung Function: Discovery and Validation in Two TOPMed Cohorts.

Rationale: Current guidelines do not sufficiently capture the heterogeneous nature of asthma; a more detailed molecular classification is needed. Metabolomics represents a novel and compelling approach to derive asthma endotypes (i.e., subtypes defined by functional and/or pathobiological mechanisms). Objectives: To validate metabolomic-driven endotypes of asthma and explore their underlying biology. Methods: In the Genetics of Asthma in Costa Rica Study (GACRS), untargeted metabolomic profiling, similarity network fusion, and spectral clustering was used to identify metabo-endotypes of asthma, and differences in asthma-relevant phenotypes across these metabo-endotypes were explored. The metabo-endotypes were recapitulated in the Childhood Asthma Management Program (CAMP), and clinical differences were determined. Metabolomic drivers of metabo-endotype membership were investigated by meta-analyzing findings from GACRS and CAMP. Measurements and Main Results: Five metabo-endotypes were identified in GACRS with significant differences in asthma-relevant phenotypes, including prebronchodilator (p-ANOVA = 8.3 × 10-5) and postbronchodilator (p-ANOVA = 1.8 × 10-5) FEV1/FVC. These differences were validated in the recapitulated metabo-endotypes in CAMP. Cholesterol esters, trigylcerides, and fatty acids were among the most important drivers of metabo-endotype membership. The findings suggest dysregulation of pulmonary surfactant homeostasis may play a role in asthma severity. Conclusions: Clinically meaningful endotypes may be derived and validated using metabolomic data. Interrogating the drivers of these metabo-endotypes has the potential to help understand their pathophysiology.

Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration.

The purpose of this study was to analyze the association between plasma metabolite levels and dark adaptation (DA) in age-related macular degeneration (AMD). This was a cross-sectional study including patients with AMD (early, intermediate, and late) and control subjects older than 50 years without any vitreoretinal disease. Fasting blood samples were collected and used for metabolomic profiling with ultra-performance liquid chromatography-mass spectrometry (LC-MS). Patients were also tested with the AdaptDx (MacuLogix, Middletown, PA, USA) DA extended protocol (20 min). Two measures of dark adaptation were calculated and used: rod-intercept time (RIT) and area under the dark adaptation curve (AUDAC). Associations between dark adaption and metabolite levels were tested using multilevel mixed-effects linear modelling, adjusting for age, gender, body mass index (BMI), smoking, race, AMD stage, and Age-Related Eye Disease Study (AREDS) formulation supplementation. We included a total of 71 subjects: 53 with AMD (13 early AMD, 31 intermediate AMD, and 9 late AMD) and 18 controls. Our results revealed that fatty acid-related lipids and amino acids related to glutamate and leucine, isoleucine and valine metabolism were associated with RIT (p < 0.01). Similar results were found when AUDAC was used as the outcome. Fatty acid-related lipids and amino acids are associated with DA, thus suggesting that oxidative stress and mitochondrial dysfunction likely play a role in AMD and visual impairment in this condition.

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.

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.

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?

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.