Maternal prebiotic supplementation during pregnancy and lactation modifies the microbiome and short chain fatty acid profile of both mother and infant.

BACKGROUND & AIMS: Improving maternal gut health in pregnancy and lactation is a potential strategy to improve immune and metabolic health in offspring and curtail the rising rates of inflammatory diseases linked to alterations in gut microbiota. Here, we investigate the effects of a maternal prebiotic supplement (galacto-oligosaccharides and fructo-oligosaccharides), ingested daily from <21 weeks' gestation to six months' post-partum, in a double-blinded, randomised placebo-controlled trial. METHODS: Stool samples were collected at multiple timepoints from 74 mother-infant pairs as part of a larger, double-blinded, randomised controlled allergy intervention trial. The participants were randomised to one of two groups; with one group receiving 14.2 g per day of prebiotic powder (galacto-oligosaccharides GOS and fructo-oligosaccharides FOS in ratio 9:1), and the other receiving a placebo powder consisting of 8.7 g per day of maltodextrin. The faecal microbiota of both mother and infants were assessed based on the analysis of bacterial 16S rRNA gene (V4 region) sequences, and short chain fatty acid (SCFA) concentrations in stool. RESULTS: Significant differences in the maternal microbiota profiles between baseline and either 28-weeks' or 36-weeks' gestation were found in the prebiotic supplemented women. Infant microbial beta-diversity also significantly differed between prebiotic and placebo groups at 12-months of age. Supplementation was associated with increased abundance of commensal Bifidobacteria in the maternal microbiota, and a reduction in the abundance of Negativicutes in both maternal and infant microbiota. There were also changes in SCFA concentrations with maternal prebiotics supplementation, including significant differences in acetic acid concentration between intervention and control groups from 20 to 28-weeks' gestation. CONCLUSION: Maternal prebiotic supplementation of 14.2 g per day GOS/FOS was found to favourably modify both the maternal and the developing infant gut microbiome. These results build on our understanding of the importance of maternal diet during pregnancy, and indicate that it is possible to intervene and modify the development of the infant microbiome by dietary modulation of the maternal gut microbiome.

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).

Providing metabolomics education and training: pedagogy and considerations.

BACKGROUND: Metabolomics is a highly multidisciplinary and non-standardised research field. Metabolomics researchers must possess and apply extensive cross-disciplinary content knowledge, subjective experience-based judgement, and the associated diverse skill sets. Accordingly, appropriate educational and training initiatives are important in developing this knowledge and skills base in the metabolomics community. For these initiatives to be successful, they must consider both pedagogical best practice and metabolomics-specific contextual challenges. AIM OF REVIEW: The aim of this review is to provide consolidated pedagogical guidance for educators and trainers in metabolomics educational and training programmes. KEY SCIENTIFIC CONCEPTS OF REVIEW: In this review, we discuss the principles of pedagogical best practice as they relate to metabolomics. We then discuss the challenges and considerations in developing and delivering education and training in metabolomics. Finally, we present examples from our own teaching practice to illustrate how pedagogical best practice can be integrated into metabolomics education and training programmes.

Metabolomics reveals mouse plasma metabolite responses to acute exercise and effects of disrupting AMPK-glycogen interactions.

Introduction: The AMP-activated protein kinase (AMPK) is a master regulator of energy homeostasis that becomes activated by exercise and binds glycogen, an important energy store required to meet exercise-induced energy demands. Disruption of AMPK-glycogen interactions in mice reduces exercise capacity and impairs whole-body metabolism. However, the mechanisms underlying these phenotypic effects at rest and following exercise are unknown. Furthermore, the plasma metabolite responses to an acute exercise challenge in mice remain largely uncharacterized. Methods: Plasma samples were collected from wild type (WT) and AMPK double knock-in (DKI) mice with disrupted AMPK-glycogen binding at rest and following 30-min submaximal treadmill running. An untargeted metabolomics approach was utilized to determine the breadth of plasma metabolite changes occurring in response to acute exercise and the effects of disrupting AMPK-glycogen binding. Results: Relative to WT mice, DKI mice had reduced maximal running speed (p < 0.0001) concomitant with increased body mass (p < 0.01) and adiposity (p < 0.001). A total of 83 plasma metabolites were identified/annotated, with 17 metabolites significantly different (p < 0.05; FDR<0.1) in exercised (↑6; ↓11) versus rested mice, including amino acids, acylcarnitines and steroid hormones. Pantothenic acid was reduced in DKI mice versus WT. Distinct plasma metabolite profiles were observed between the rest and exercise conditions and between WT and DKI mice at rest, while metabolite profiles of both genotypes converged following exercise. These differences in metabolite profiles were primarily explained by exercise-associated increases in acylcarnitines and steroid hormones as well as decreases in amino acids and derivatives following exercise. DKI plasma showed greater decreases in amino acids following exercise versus WT. Conclusion: This is the first study to map mouse plasma metabolomic changes following a bout of acute exercise in WT mice and the effects of disrupting AMPK-glycogen interactions in DKI mice. Untargeted metabolomics revealed alterations in metabolite profiles between rested and exercised mice in both genotypes, and between genotypes at rest. This study has uncovered known and previously unreported plasma metabolite responses to acute exercise in WT mice, as well as greater decreases in amino acids following exercise in DKI plasma. Reduced pantothenic acid levels may contribute to differences in fuel utilization in DKI mice.

Changes to the Gut Microbiome in Young Children Showing Early Behavioral Signs of Autism.

The human gut microbiome has increasingly been associated with autism spectrum disorder (ASD), which is a neurological developmental disorder, characterized by impairments to social interaction. The ability of the gut microbiota to signal across the gut-brain-microbiota axis with metabolites, including short-chain fatty acids, impacts brain health and has been identified to play a role in the gastrointestinal and developmental symptoms affecting autistic children. The fecal microbiome of older children with ASD has repeatedly shown particular shifts in the bacterial and fungal microbial community, which are significantly different from age-matched neurotypical controls, but it is still unclear whether these characteristic shifts are detectable before diagnosis. Early microbial colonization patterns can have long-lasting effects on human health, and pre-emptive intervention may be an important mediator to more severe autism. In this study, we characterized both the microbiome and short-chain fatty acid concentrations of fecal samples from young children between 21 and 40 months who were showing early behavioral signs of ASD. The fungal richness and acetic acid concentrations were observed to be higher with increasing autism severity, and the abundance of several bacterial taxa also changed due to the severity of ASD. Bacterial diversity and SCFA concentrations were also associated with stool form, and some bacterial families were found with differential abundance according to stool firmness. An exploratory analysis of the microbiome associated with pre-emptive treatment also showed significant differences at multiple taxonomic levels. These differences may impact the microbial signaling across the gut-brain-microbiota axis and the neurological development of the children.

Lipopolysaccharide-induced interferon response networks at birth are predictive of severe viral lower respiratory infections in the first year of life.

Appropriate innate immune function is essential to limit pathogenesis and severity of severe lower respiratory infections (sLRI) during infancy, a leading cause of hospitalization and risk factor for subsequent asthma in this age group. Employing a systems biology approach to analysis of multi-omic profiles generated from a high-risk cohort (n=50), we found that the intensity of activation of an LPS-induced interferon gene network at birth was predictive of sLRI risk in infancy (AUC=0.724). Connectivity patterns within this network were stronger among susceptible individuals, and a systems biology approach identified IRF1 as a putative master regulator of this response. These findings were specific to the LPS-induced interferon response and were not observed following activation of viral nucleic acid sensing pathways. Comparison of responses at birth versus age 5 demonstrated that LPS-induced interferon responses but not responses triggered by viral nucleic acid sensing pathways may be subject to strong developmental regulation. These data suggest that the risk of sLRI in early life is in part already determined at birth, and additionally that the developmental status of LPS-induced interferon responses may be a key determinant of susceptibility. Our findings provide a rationale for the identification of at-risk infants for early intervention aimed at sLRI prevention and identifies targets which may be relevant for drug development.

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.

Fecal sample collection methods and time of day impact microbiome composition and short chain fatty acid concentrations.

Associations between the human gut microbiome and health outcomes continues to be of great interest, although fecal sample collection methods which impact microbiome studies are sometimes neglected. Here, we expand on previous work in sample optimization, to promote high quality microbiome data. To compare fecal sample collection methods, amplicons from the bacterial 16S rRNA gene (V4) and fungal (ITS2) region, as well as short chain fatty acid (SCFA) concentrations were determined in fecal material over three timepoints. We demonstrated that spot sampling of stool results in variable detection of some microbial members, and inconsistent levels of SCFA; therefore, sample homogenization prior to subsequent analysis or subsampling is recommended. We also identify a trend in microbial and metabolite composition that shifts over two consecutive stool collections less than 25 h apart. Lastly, we show significant differences in bacterial composition that result from collecting stool samples in OMNIgene·Gut tube (DNA Genotec) or Stool Nucleic Acid Collection and Preservation Tube (NORGEN) compared to immediate freezing. To assist with planning fecal sample collection and storage procedures for microbiome investigations with multiple analyses, we recommend participants to collect the first full bowel movement of the day and freeze the sample immediately after collection.

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.

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.

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.

BACKGROUND: Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. AIM OF REVIEW: This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. KEY SCIENTIFIC CONCEPTS OF REVIEW: System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition.

High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.

Metabolomics analysis identifies sex-associated metabotypes of oxidative stress and the autotaxin-lysoPA axis in COPD.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and a leading cause of mortality and morbidity worldwide. The aim of this study was to investigate the sex dependency of circulating metabolic profiles in COPD.Serum from healthy never-smokers (healthy), smokers with normal lung function (smokers), and smokers with COPD (COPD; Global Initiative for Chronic Obstructive Lung Disease stages I-II/A-B) from the Karolinska COSMIC cohort (n=116) was analysed using our nontargeted liquid chromatography-high resolution mass spectrometry metabolomics platform.Pathway analyses revealed that several altered metabolites are involved in oxidative stress. Supervised multivariate modelling showed significant classification of smokers from COPD (p=2.8×10-7). Sex stratification indicated that the separation was driven by females (p=2.4×10-7) relative to males (p=4.0×10-4). Significantly altered metabolites were confirmed quantitatively using targeted metabolomics. Multivariate modelling of targeted metabolomics data confirmed enhanced metabolic dysregulation in females with COPD (p=3.0×10-3) relative to males (p=0.10). The autotaxin products lysoPA (16:0) and lysoPA (18:2) correlated with lung function (forced expiratory volume in 1 s) in males with COPD (r=0.86; p<0.0001), but not females (r=0.44; p=0.15), potentially related to observed dysregulation of the miR-29 family in the lung.These findings highlight the role of oxidative stress in COPD, and suggest that sex-enhanced dysregulation in oxidative stress, and potentially the autotaxin-lysoPA axis, are associated with disease mechanisms and/or prevalence.

Metabolomics analysis identifies different metabotypes of asthma severity.

In this study, we sought to determine whether asthma has a metabolic profile and whether this profile is related to disease severity.We characterised the serum from 22 healthy individuals and 54 asthmatics (12 mild, 20 moderate, 22 severe) using liquid chromatography-high-resolution mass spectrometry-based metabolomics. Selected metabolites were confirmed by targeted mass spectrometry assays of eicosanoids, sphingolipids and free fatty acids.We conclusively identified 66 metabolites; 15 were significantly altered with asthma (p≤0.05). Levels of dehydroepiandrosterone sulfate, cortisone, cortisol, prolylhydroxyproline, pipecolate and N-palmitoyltaurine correlated significantly (p<0.05) with inhaled corticosteroid dose, and were further shifted in individuals treated with oral corticosteroids. Oleoylethanolamide increased with asthma severity independently of steroid treatment (p<0.001). Multivariate analysis revealed two patterns: 1) a mean difference between controls and patients with mild asthma (p=0.025), and 2) a mean difference between patients with severe asthma and all other groups (p=1.7×10-4). Metabolic shifts in mild asthma, relative to controls, were associated with exogenous metabolites (e.g. dietary lipids), while those in moderate and severe asthma (e.g. oleoylethanolamide, sphingosine-1-phosphate, N-palmitoyltaurine) were postulated to be involved in activating the transient receptor potential vanilloid type 1 (TRPV1) receptor, driving TRPV1-dependent pathogenesis in asthma.Our findings suggest that asthma is characterised by a modest systemic metabolic shift in a disease severity-dependent manner, and that steroid treatment significantly affects metabolism.

Lipid mediator profile in vernix caseosa reflects skin barrier development.

Vernix caseosa (VC) is a protective layer that covers the skin of most human newborns. This study characterized the VC lipid mediator profile, and examined its relationship to gestational period, gender of the newborn and maternal lifestyle. VC collected at birth from 156 newborns within the ALADDIN birth cohort was analyzed and 3 different groups of lipid mediators (eicosanoids and related oxylipin analogs, endocannabinoids and sphingolipids) were screened using LC-MS/MS. A total of 54 compounds were detected in VC. A number of associations between lipid mediators and the gestational period were observed, including increases in the ceramide to sphingomyelin ratio as well as the endocannabinoids anandamide and 2-arachidonoylglycerol. Gender-specific differences in lipid mediator levels were observed for all 3 lipid classes. In addition, levels of the linoleic acid oxidation products 9(10)-epoxy-12Z-octadecenoic and 12(13)-epoxy-9Z-octadecenoic acid (EpOMEs) as well as 12,13-dihydroxy-9Z-octadecenoic acid (DiHOME) were increased in VC of children from mothers with an anthroposophic lifestyle. Accordingly, VC was found to be rich in multiple classes of bioactive lipid mediators, which evidence lifestyle, gender and gestational week dependencies. Levels of lipid mediators in VC may therefore be useful as early stage non-invasive markers of the development of the skin as a protective barrier.

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.