Serum Metabolomics Analysis of Asthma in Different Inflammatory Phenotypes: A Cross-Sectional Study in Northeast China

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.

Metabolomics in Animal Models of Bronchial Asthma and Its Translational Importance for Clinics

Bronchial asthma is an extremely heterogenous chronic respiratory disorder with several distinct endotypes and phenotypes. These subtypes differ not only in the pathophysiological changes and/or clinical features but also in their response to the treatment. Therefore, precise diagnostics represent a fundamental condition for effective therapy. In the diagnostic process, metabolomic approaches have been increasingly used, providing detailed information on the metabolic alterations associated with human asthma. Further information is brought by metabolomic analysis of samples obtained from animal models. This article summarizes the current knowledge on metabolomic changes in human and animal studies of asthma and reveals that alterations in lipid metabolism, amino acid metabolism, purine metabolism, glycolysis and the tricarboxylic acid cycle found in the animal studies resemble, to a large extent, the changes found in human patients with asthma. The findings indicate that, despite the limitations of animal modeling in asthma, pre-clinical testing and metabolomic analysis of animal samples may, together with metabolomic analysis of human samples, contribute to a novel way of personalized treatment of asthma patients.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Global metabolomics study on the pathogenesis of pediatric medulloblastoma via UPLC- Q/E-MS/MS

Medulloblastoma is one of the most frequent malignant brain tumors in infancy and childhood. Early diagnosis and treatment are quite crucial for the prognosis. However, the pathogenesis of medulloblastoma is still not completely clarified. High-resolution mass spectrometry has enabled a comprehensive investigation on the mechanism of disease from the perspective of metabolism. Herein, we compared the difference of metabolic profiles of serum between medulloblastoma (n = 33) and healthy control (HC, n = 16) by using UPLC-Q/E-MS/MS. Principal component analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA) intuitively revealed the significantly distinct metabolic profiles between medulloblastoma and HC (p < 0.01 for permutation test on OPLS-DA model). Total of 25 significantly changed metabolites were identified. ROC analysis reported that six of them (Phosphatidic acid (8:0/15:0), 3'-Sialyllactose, Isocoproporphyrin, Acetylspermidine, Fructoseglycine and 3-Hydroxydodecanedioate) showed high specificity and precision to be potential diagnosis biomarkers (AUC > 0.98). Functional analysis discovered that there are four pathways notably perturbed for medulloblastoma. These pathways are related with the dysfunction of arachidonic acid metabolism, steroid hormone biosynthesis, and folate-related metabolism. The target intervention on these pathways may reduce the mortality of medulloblastoma.

Integration of transcriptomics and metabolomics to reveal the effect of ginsenoside Rg3 on allergic rhinitis in mice

Increasing studies have demonstrated that ginsenoside Rg3 (Rg3) plays an important role in the prevention and treatment of various diseases, including allergic lower airway inflammation such as asthma. To investigate the role of Rg3 in allergic upper airway disease, the effect and therapeutic mechanism of Rg3 in allergic rhinitis (AR) were studied. Ovalbumin-induced AR model mice were intragastrically administered with Rg3. Nasal symptoms, levels of IgE, IL-4, IL-5, IL-13, SOD and MDA in serum, and histopathological analysis of nasal mucosa were used to evaluate the effect of Rg3 on ameliorating AR in mice. Moreover, nasal mucosa samples from the normal control group, AR model group and high dosage of Rg3 were collected to perform omics analysis. The differentially expressed genes and significantly changed metabolites were screened based on transcriptomics and metabolomics analyses, respectively. Integrative analysis was further performed to confirm the hub genes, metabolites and pathways. After Rg3 intervention, the nasal symptoms and inflammatory infiltration were effectively improved, the levels of IgE, IL-4, IL-5, IL-13 and MDA were significantly reduced, and the level of SOD was obviously increased. The results of the qRT-PCR assay complemented the transcriptomic findings. Integrated analysis showed that Rg3 played an anti-AR role mainly by regulating the interaction network, which was constructed by 12 genes, 8 metabolites and 4 pathways. Our findings suggested that Rg3 had a therapeutic effect on ovalbumin-induced AR in mice by inhibiting inflammation development and reducing oxidative stress. The present study could provide a potential natural agent for the treatment of AR.

Blood-Based Biomarkers for Eosinophilic Esophagitis and Concomitant Atopic Diseases: A Look into the Potential of Extracellular Vesicles

Eosinophilic esophagitis (EoE) is a chronic, Th2-inflammatory disease of the esophagus that can severely affect food intake. Currently, diagnosis and assessing response to treatment of EoE is highly invasive and requires endoscopy with esophageal biopsies. Finding non-invasive and accurate biomarkers is important for improving patient well-being. Unfortunately, EoE is usually accompanied by other atopies, which make it difficult to identify specific biomarkers. Providing an update of circulating EoE biomarkers and concomitant atopies is therefore timely. This review summarizes the current knowledge in EoE blood biomarkers and two of its most common comorbidities, bronchial asthma (BA) and atopic dermatitis (AD), focusing on dysregulated proteins, metabolites, and RNAs. It also revises the current knowledge on extracellular vesicles (EVs) as non-invasive biomarkers for BA and AD, and concludes with the potential use of EVs as biomarkers in EoE.

Appraisal of treatment outcomes in integrative medicine using metabonomics: Taking non-alcoholic fatty liver disease with spleen deficiency syndrome as an example

OBJECTIVE

Appraisal of treatment outcomes in integrative medicine is a challenge due to a gap between the concepts of Western medicine (WM) disease and traditional Chinese medicine (TCM) syndrome. This study presents an approach for the appraisal of integrative medicine that is based on targeted metabolomics. We use non-alcoholic fatty liver disease with spleen deficiency syndrome as a test case.

METHODS

A patient-reported outcome (PRO) scale was developed based on literature review, Delphi consensus survey, and reliability and validity test, to quantitatively evaluate spleen deficiency syndrome. Then, a metabonomic foundation for the treatment of non-alcoholic fatty liver disease with spleen deficiency syndrome was identified via a longitudinal interventional trial and targeted metabolomics. Finally, an integrated appraisal model was established by identifying metabolites that responded in the treatment of WM disease and TCM syndrome as positive outcomes and using other aspects of the metabonomic foundation as independent variables.

RESULTS

Ten symptoms and signs were included in the spleen deficiency PRO scale. The internal reliability, content validity, discriminative validity and structural validity of the scale were all qualified. Based on treatment responses to treatments for WM disease (homeostasis model assessment of insulin resistance) or TCM syndrome (spleen deficiency PRO scale score) from a previous randomized controlled trial, two cohorts comprised of 30 participants each were established for targeted metabolomics detection. Twenty-five metabolites were found to be involved in successful treatment outcomes to both WM and TCM, following quantitative comparison and multivariate analysis. Finally, the model of the integrated appraisal system was exploratively established using binary logistic regression; it included 9 core metabolites and had the prediction probability of 83.3%.

CONCLUSION

This study presented a new and comprehensive research route for integrative appraisal of treatment outcomes for WM disease and TCM syndrome. Critical research techniques used in this research included the development of a TCM syndrome assessment tool, a longitudinal interventional trial with verified TCM treatment, identification of homogeneous metabolites, and statistical modeling.

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

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

Sputum Metabolomic Profiling Reveals Metabolic Pathways and Signatures Associated With Inflammatory Phenotypes in Patients With Asthma

Purpose

The molecular links between metabolism and inflammation that drive different inflammatory phenotypes in asthma are poorly understood. We aimed to identify the metabolic signatures and underlying molecular pathways of different inflammatory asthma phenotypes.

Methods

In the discovery set (n = 119), untargeted ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was applied to characterize the induced sputum metabolic profiles of asthmatic patients with different inflammatory phenotypes using orthogonal partial least-squares discriminant analysis (OPLS-DA), and pathway topology enrichment analysis. In the validation set (n = 114), differential metabolites were selected to perform targeted quantification. Correlations between targeted metabolites and clinical indices in asthmatic patients were analyzed. Logistic and negative binomial regression models were established to assess the association between metabolites and severe asthma exacerbations.

Results

Seventy-seven differential metabolites were identified in the discovery set. Pathway topology analysis uncovered that histidine metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism as well as phenylalanine, tyrosine and tryptophan biosynthesis were involved in the pathogenesis of different asthma phenotypes. In the validation set, 24 targeted quantification metabolites were significantly expressed between asthma inflammatory phenotypes. Finally, adenosine 5′-monophosphate (adjusted relative risk [adj RR] = 1.000; 95% confidence interval [CI] = 1.000–1.000; P = 0.050), allantoin (adj RR = 1.000; 95% CI = 1.000–1.000; P = 0.043) and nicotinamide (adj RR = 1.001; 95% CI = 1.000–1.002; P = 0.021) were demonstrated to predict severe asthma exacerbation rates.

Conclusions

Different inflammatory asthma phenotypes have specific metabolic profiles in induced sputum. The potential metabolic signatures may identify therapeutic targets in different inflammatory asthma phenotypes.

Understanding uncontrolled severe allergic asthma by integration of omic and clinical data

BACKGROUND

Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features.

METHODS

Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms.

RESULTS

Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy.

CONCLUSIONS

UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.

Metabolomic Associations of Asthma in the Hispanic Community Health Study/Study of Latinos

Asthma disproportionally affects Hispanic and/or Latino backgrounds; however, the relation between circulating metabolites and asthma remains unclear. We conducted a cross-sectional study associating 640 individual serum metabolites, as well as twelve metabolite modules, with asthma in 3347 Hispanic/Latino background participants (514 asthmatics, 15.36%) from the Hispanic/Latino Community Health Study/Study of Latinos. Using survey logistic regression, per standard deviation (SD) increase in 1-arachidonoyl-GPA (20:4) was significantly associated with 32% high odds of asthma after accounting for clinical risk factors (p = 6.27 × 10−5), and per SD of the green module, constructed using weighted gene co-expression network, was suggestively associated with 25% high odds of asthma (p = 0.006). In the stratified analyses by sex and Hispanic and/or Latino backgrounds, the effect of 1-arachidonoyl-GPA (20:4) and the green module was predominantly observed in women (OR = 1.24 and 1.37, p < 0.001) and people of Cuban and Puerto-Rican backgrounds (OR = 1.25 and 1.27, p < 0.01). Mutations in Fatty Acid Desaturase 2 (FADS2) affected the levels of 1-arachidonoyl-GPA (20:4), and Mendelian Randomization analyses revealed that high genetically regulated 1-arachidonoyl-GPA (20:4) levels were associated with increased odds of asthma (p < 0.001). The findings reinforce a molecular basis for asthma etiology, and the potential causal effect of 1-arachidonoyl-GPA (20:4) on asthma provides an opportunity for future intervention.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Research Progress of Metabolomics in Asthma

Asthma is a highly heterogeneous disease, but the pathogenesis of asthma is still unclear. It is well known that the airway inflammatory immune response is the pathological basis of asthma. Metabolomics is a systems biology method to analyze the difference of low molecular weight metabolites (<1.5 kDa) and explore the relationship between metabolic small molecules and pathophysiological changes of the organisms. The functional interdependence between immune response and metabolic regulation is one of the cores of the body's steady-state regulation, and its dysfunction will lead to a series of metabolic disorders. The signal transduction effect of specific metabolites may affect the occurrence of the airway inflammatory immune response, which may be closely related to the pathogenesis of asthma. Emerging metabolomic analysis may provide insights into the pathogenesis and diagnosis of asthma. The review aims to analyze the changes of metabolites in blood/serum/plasma, urine, lung tissue, and exhaled breath condensate (EBC) samples, and further reveals the potential pathogenesis of asthma according to the disordered metabolic pathways.

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.

The effect of strength-endurance training on serum and urine metabolic profiles of female adolescent volleyball athletes

Aim

Limited investigations on metabolic responses to exercise training in female adolescent volleyball athletes exist. The aim of this study was to obtain serum and urine metabolite markers in female adolescent volleyball athletes within 2-week strength-endurance training using a metabolomics approach coupled with biochemical analysis, which would be potential biomarkers for evaluating the physiological state of athletes.

Methods

Twelve female adolescent volleyball athletes were recruited for 2-week strength-endurance training. Differential serum and urine metabolic profiles between the pre- and post-training group were obtained on gas chromatography coupled to mass spectrometry (GC-MS) and data subsequently underwent orthogonal partial least-squares analysis (OPLS).

Results

Strength-endurance training exerted a significant influence on the athletes' serum and urine metabolic profiles. The changed metabolites were primarily involved in energy metabolism, lipid metabolism and amino acids metabolism. Results support the hypothesis that female athletes displayed an increased propensity to oxidize lipids as the major energy source. Exposure to strength-endurance training also led to a significant increase in cortisol, but a decrease in testosterone, indicating disordered hormone adjustment. Exercise-induced oxidative stress occurred, as was evidenced by the decrease in reduced glutathione, and increases in blood malondialdehyde and oxidized glutathione. Since the muscle damage markers creatine kinase and lactate dehydrogenase did not show significant changes, the training might not cause cell membrane damage and the athletes did not cross the adaptive injury level.

Conclusion

By measurement of endogenous metabolites, the metabolomics study has the potential to reveal the global physiological changes in response to exercise training.

Cerebrospinal Fluid Inflammatory Markers in Alzheimer's Disease: Influence of Comorbidities

BACKGROUND

Alzheimer's disease (AD) develops into dementia after several years, and subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are used as intermediary diagnoses of increasing severity. Inflammation is an important part of AD pathology and provides potential novel biomarkers and treatment targets.

OBJECTIVE

To identify novel potential biomarkers of AD in cerebrospinal fluid (CSF) and create a molecular pattern of inflammatory factors providing differentiation between AD and SCI.

METHODS

We analyzed 43 inflammatory-related mediators in CSF samples from a cohort of SCI and AD cases vetted for confounding factors (Training cohort). Using multivariate analysis (MVA), a model for discrimination between SCI and AD was produced, which we then applied to a larger nonvetted cohort (named Test cohort). The data were analyzed for factors showing differences between diagnostic groups and factors that differed between the vetted and non-vetted cohorts. The relationship of the factors to the agreement between model and clinical diagnosis was investigated.

RESULTS

A good MVA model able to discriminate AD from SCI without including tangle and plaque biomarkers was produced from the Training cohort. The model showed 50% agreement with clinical diagnosis in the Test cohort. Comparison of the cohorts indicated different patterns of factors distinguishing SCI from AD. As an example, soluble interleukin (IL)-6Rα showed lower levels in AD cases in the Training cohort, whereas placental growth factor (PlGF) and serum amyloid A (SAA) levels were higher in AD cases of the Test cohort. The levels of p-tau were also higher in the Training cohort.

CONCLUSION

This study provides new knowledge regarding the involvement of inflammation in AD by indicating different patterns of factors in CSF depending on whether potential confounding comorbidities are present or not, and presents sIL-6Rα as a potential new biomarker for improved diagnosis of AD.

Asthma phenotypes and endotypes: a systematic review

INTRODUCTION

Asthma is a complex disorder characterized by expiratory airflow limitation, wheeze, shortness of breath, chest tightness and cough, which can vary over time and in intensity. Being highly heterogeneous, asthma was characterized and classified in several asthma phenotypes and endotypes from 1947 until today. The present systematic review aims to summarize and describe evidence that was published in the last ten years in the field of asthma phenotyping and endotyping.

EVIDENCE ACQUISITION

The systematic review resumed high-quality evidence (clinical trials and randomized control trials) retrieved on MEDLINE and EMBASE databanks and involving adult asthmatic populations. Analyses of literature were conducted according to PRISMA and CASP guidelines.

EVIDENCE SYNTHESIS

Querying MEDLINE and EMBASE databanks, 5019 and 12261 entries were retrieved, respectively. Applying limitations for year of publication, age of participants, and type of publication, the search results were reduced to 98 and 132 articles, respectively. After data abstraction and resolution of duplications, only 50 articles were further evaluated. The research products were then classified first in macro-areas of interest (phenotypes or endotypes) and then in detailed micro-areas.

CONCLUSIONS

This systematic review overviews the principal findings available from high-quality literature in the last decade concerning asthma phenotypes and endotypes. Asthma has been described from different points of view, characterizing symptoms, microbiota composition, comorbidities, viral infections, and airway and/or systemic inflammatory status. The comprehension of precise mechanisms underlying asthma pathogenesis is thereby the basis for the development of novel therapeutic strategies, likely essential to the development of precision medicine.

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

Urinary Metabolomic Profiling Reveals Biological Pathways and Predictive Signatures Associated with Childhood Asthma

Background

Despite considerable efforts, the pathogenic mechanisms of asthma are still incompletely understood, due to its heterogeneous nature. However, metabolomics can offer a global view of a biological system, making it a valuable tool for further elucidation of mechanisms and biomarker discovery in asthma.

Methods

GC-MS-based metabolomic analysis was conducted for comparison of urine metabolic profiles between asthmatic children (n=30) and healthy controls (n=30).

Results

An orthogonal projections to latent structures discriminant-analysis model revealed a clear separation of the asthma and control groups (R ² _x =0.137, R ² _y =0.947, Q ²=0.82). A total of 20 differential metabolites were identified as discriminant factors, of which eleven were significantly increased and nine decreased in the asthma group compared to the control group. Pathway-enrichment analysis based on these differential metabolites indicated that sphingolipid metabolism, protein biosynthesis, and citric acid cycle were strongly associated with asthma. Among the identified metabolites, 2-hydroxybutanoic acid showed excellent discriminatory performance for distinguishing asthma from healthy controls, with an AUC of 0.969.

Conclusion

Our study revealed significant changes in the urine metabolome of asthma patients. Several perturbed pathways (eg, sphingolipid metabolism and citric acid cycle) may be related to asthma pathogenesis, and 2-hydroxybutanoic acid could serve as a potential biomarker for asthma diagnosis.

Metabolic profiling of organic and fatty acids in chronic and autoimmune diseases

Metabolomics is a powerful tool of omics that permits the simultaneous identification of metabolic perturbations in several autoimmune and chronic diseases. Several parameters can affect a metabolic profile, from the population characteristics to the selection of the analytical method. In the current chapter, we summarize the main analytical methods and results of the metabolic profiling of fatty and organic acids performed in human metabolomic studies for asthma, COPD, psoriasis and Hashimoto's thyroiditis. We discuss the most significant metabolic alterations associated with these diseases, after comparison of either a single patient's group with healthy controls or several patient's subgroups of different disease severity and phenotype with healthy controls or of a patient's group before and after treatment. Finally, we present critical metabolic patterns that are associated with each disease and their potency for the unraveling of disease pathogenesis, prediction, diagnosis, patient stratification and treatment selection.

Therapeutic Effect and Mechanism Study of Rhodiola wallichiana var. cholaensis Injection to Acute Blood Stasis Using Metabolomics Based on UPLC-Q/TOF-MS

In traditional Chinese medicine theory, blood stasis syndrome (BSS), characterized by blood flow retardation and blood stagnation, is one of the main pathologic mechanisms and clinical syndromes of cardiovascular diseases (CVDs). Rhodiola wallichiana var. cholaensis injection (RWCI) is made from dry roots and stems of RWC via the processes of decoction, alcohol precipitation, filtration, and dilution. Studies indicated the extracts of RWC could alleviate CVDs; however, the mechanism had not been illustrated. In the present study, the acute blood stasis rat model was established to investigate the pathogenesis of BSS and the therapeutic mechanism of RWCI against BSS. Hemorheological parameters (whole blood viscosity and plasma viscosity) and inflammatory factors (TNF-α and IL-6) were used to evaluate the success of the BSS rat model and RWCI efficacy. 14 and 33 differential metabolites were identified from plasma and urine samples using the metabolomics approach based on ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The results of multivariate analysis displayed that there were significant separations among model, control, and treatment groups, but the high-dose RWCI treatment group was closer to the control group. 9 perturbed metabolic pathways were related to BSS's development and RWCI intervention. 5 metabolic pathways (arachidonic acid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, retinol metabolism, and steroid hormone biosynthesis) showed apparent correlations. These differential metabolites and perturbed metabolic pathways might provide a novel view to understand the pathogenesis of BSS and the pharmacological mechanism of RWCI.

Phenotype-Specific Therapeutic Effect of Rhodiola wallichiana var. cholaensis Combined with Dexamethasone on Experimental Murine Asthma and Its Comprehensive Pharmacological Mechanism

The heterogeneity of asthma involves complex pathogenesis leading to confusion regarding the choice of therapeutic strategy. In the clinic, asthma is commonly classified as having either eosinophilic asthma (EA) or non-eosinophilic asthma (NEA) phenotypes. Microbiota colonizing in airways has been demonstrated to induce distinct phenotypes of asthma and the resistance to steroids. Rhodiola wallichiana var. cholaensis (RWC) has the potential to alleviate asthmatic inflammation according to recent studies, but its pharmacological mechanisms remain unclarified. In our study, murine asthmatic phenotypes were established and treated with RWC and/or dexamethasone (DEX). Combined treatment with RWC and DEX could improve spirometry and airway hyperresponsiveness (AHR) in asthmatic phenotypes, alleviate steroid resistance in NEA, and reduce the inflammatory infiltration of the both phenotypes. The combined treatment increased Th1, regulated the imbalance of Th2/Th1, and decreased the related cytokines in EA. As for NEA, the combined treatment reduced Th17 and promoted the accumulation of regulatory T cells (Tregs) in lung. A microbiome study based on 16S rDNA sequencing technique revealed the significantly changed structure of the lower airway microbiota after combined treatment in NEA, with 4 distinct genera and 2 species identified. OPLS-DA models of metabolomics analysis based on UPLC-Q/TOF-MS technique identified 34 differentiated metabolites and 8 perturbed metabolic pathways. A joint multiomics study predicted that the colonized microbiota in airways might be associated with susceptibility of asthma and steroid resistance, which involved systematic and pulmonary metabolic perturbation. In summary, the pharmacological network of RWC included the complicated interaction mechanisms of immune regulation, microbiota change, and metabolic perturbation.