Explore potential plasma biomarkers of acute respiratory distress syndrome (ARDS) using GC-MS metabolomics analysis

Repeated exposure to eucalyptus wood smoke alters pulmonary gene and metabolic profiles in male Long-Evans rats

Exposure to wildfire smoke is associated with both acute and chronic cardiopulmonary illnesses, which are of special concern for wildland firefighters who experience repeated exposure to wood smoke. It is necessary to better understand the underlying pathophysiology by which wood smoke exposure increases pulmonary disease burdens in this population. We hypothesize that wood smoke exposure produces pulmonary dysfunction, lung inflammation, and gene expression profiles associated with future pulmonary complications. Male Long-Evans rats were intermittently exposed to smoldering eucalyptus wood smoke at 2 concentrations, low (11.0 ± 1.89 mg/m3) and high (23.7 ± 0.077 mg/m3), over a 2-week period. Whole-body plethysmography was measured intermittently throughout. Lung tissue and lavage fluid were collected 24 h after the final exposure for transcriptomics and metabolomics. Increasing smoke exposure upregulated neutrophils and select cytokines in the bronchoalveolar lavage fluid. In total, 3446 genes were differentially expressed in the lungs of rats in the high smoke exposure and only 1 gene in the low smoke exposure (Cd151). Genes altered in the high smoke group reflected changes to the Eukaryotic Initiation Factor 2 stress and oxidative stress responses, which mirrored metabolomics analyses. xMWAS-integrated analysis revealed that smoke exposure significantly altered pathways associated with oxidative stress, lung morphogenesis, and tumor proliferation pathways. These results indicate that intermittent, 2-week exposure to eucalyptus wood smoke leads to transcriptomic and metabolic changes in the lung that may predict future lung disease development. Collectively, these findings provide insight into cellular signaling pathways that may contribute to the chronic pulmonary conditions observed in wildland firefighters.

Metabolic signatures of acute respiratory distress syndrome: COVID versus non-COVID

Acute respiratory distress syndrome (ARDS) is a fatal pulmonary disorder characterized by severe hypoxia and inflammation. ARDS is commonly triggered by systemic and pulmonary infections, with bacteria and viruses. Notable pathogens include Pseudomonas aeruginosa, Streptococcus aureus, Enterobacter species, coronaviruses, influenza viruses, and herpesviruses. COVID-19 ARDS represents the latest etiological phenotype of the disease. The pathogenesis of ARDS caused by bacteria and viruses exhibits variations in host immune responses and lung mesenchymal injury. We postulate that the systemic and pulmonary metabolomics profiles of ARDS induced by COVID-19 pathogens may exhibit distinctions compared with those induced by other infectious agents. This review aims to compare metabolic signatures in blood and lung specimens specifically within the context of ARDS. Both prevalent and phenotype-specific metabolomic signatures, including but not limited to glycolysis, ketone body production, lipid oxidation, and dysregulation of the kynurenine pathways, were thoroughly examined in this review. The distinctions in metabolic signatures between COVID-19 and non-COVID ARDS have the potential to reveal new biomarkers, elucidate pathogenic mechanisms, identify druggable targets, and facilitate differential diagnosis in the future.

A Clinical Breathomics Dataset

This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.

GC-MS Based Metabolomics Analysis to Evaluate Short-Term Effect of Tumor Removal on Patients with Early-Stage Breast Cancer

In this study, it was aimed to demonstrate the short-term effect of breast cancer surgery and tumor removal on the metabolomic profiles of patients with early-stage breast cancer. This cohort consisted of 18 early-stage breast carcinoma patients who had breast cancer surgery to remove tumor and surrounding tissues. The blood samples obtained preoperatively and 24 h after surgery were used in this investigation. Gas chromatography-mass spectrometry (GC-MS) based metabolomic analysis was performed to determine the metabolites. The GC-MS-based metabolomics profile enabled the identification of 162 metabolites in the plasma samples. Postoperatively, glyceric acid, phosphoric acid, O-phosphocolamine, 2-hydroxyethyliminodiacetic acid, N-acetyl-D-mannosamine, N-acetyl-5-hydroxytryptamine, methyl stearate, methyl oleate, iminodiacetic acid, glycerol 1-phosphate, β-glycerol phosphate and aspartic acid were found to be significantly increased (P < 0.05 for all), whereas saccharic acid, leucrose, gluconic acid, citramalic acid and acetol were significantly decreased (P < 0.05 for all). Breast cancer surgery and tumor removal has an impact on the metabolomic profiles of patients with early-stage breast cancer. These findings can be used for understanding the pathogenesis of breast cancer biology and screening the success of the surgery.

Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes

In the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.

Metabolic Profiling at COVID-19 Onset Shows Disease Severity and Sex-Specific Dysregulation

Background

metabolic changes through SARS-CoV-2 infection has been reported but not fully comprehended. This metabolic dysregulation affects multiple organs during COVID-19 and its early detection can be used as a prognosis marker of severity. Therefore, we aimed to characterize metabolic and cytokine profile at COVID-19 onset and its relationship with disease severity to identify metabolic profiles predicting disease progression.

Material and Methods

we performed a retrospective cross-sectional study in 123 COVID-19 patients which were stratified as asymptomatic/mild, moderate and severe according to the highest COVID-19 severity status, and a group of healthy controls. We performed an untargeted plasma metabolic profiling (gas chromatography and capillary electrophoresis-mass spectrometry (GC and CE-MS)) and cytokine evaluation.

Results

After data filtering and identification we observed 105 metabolites dysregulated (66 GC-MS and 40 CE-MS) which shown different expression patterns for each COVID-19 severity status. These metabolites belonged to different metabolic pathways including amino acid, energy, and nitrogen metabolism among others. Severity-specific metabolic dysregulation was observed, as an increased transformation of L-tryptophan into L-kynurenine. Thus, metabolic profiling at hospital admission differentiate between severe and moderate patients in the later phase of worse evolution. Several plasma pro-inflammatory biomarkers showed significant correlation with deregulated metabolites, specially with L-kynurenine and L-tryptophan. Finally, we describe a strong sex-related dysregulation of metabolites, cytokines and chemokines between severe and moderate patients. In conclusion, metabolic profiling of COVID-19 patients at disease onset is a powerful tool to unravel the SARS-CoV-2 molecular pathogenesis.

Conclusions

This technique makes it possible to identify metabolic phenoconversion that predicts disease progression and explains the pronounced pathogenesis differences between sexes.

Cluster analysis and profiling of airway fluid metabolites in pediatric acute hypoxemic respiratory failure

Hierarchal clustering of amino acid metabolites may identify a metabolic signature in children with pediatric acute hypoxemic respiratory failure. Seventy-four immunocompetent children, 41 (55.4%) with pediatric acute respiratory distress syndrome (PARDS), who were between 2 days to 18 years of age and within 72 h of intubation for acute hypoxemic respiratory failure, were enrolled. We used hierarchal clustering and partial least squares-discriminant analysis to profile the tracheal aspirate airway fluid using quantitative LC–MS/MS to explore clusters of metabolites that correlated with acute hypoxemia severity and ventilator-free days. Three clusters of children that differed by severity of hypoxemia and ventilator-free days were identified. Quantitative pathway enrichment analysis showed that cysteine and methionine metabolism, selenocompound metabolism, glycine, serine and threonine metabolism, arginine biosynthesis, and valine, leucine, and isoleucine biosynthesis were the top five enriched, impactful pathways. We identified three clusters of amino acid metabolites found in the airway fluid of intubated children important to acute hypoxemia severity that correlated with ventilator-free days < 21 days. Further studies are needed to validate our findings and to test our models.

The serum metabolome of COVID-19 patients is distinctive and predictive

BACKGROUND

Metabolism is critical for sustaining life, immunity and infection, but its role in COVID-19 is not fully understood.

METHODS

Seventy-nine COVID-19 patients, 78 healthy controls (HCs) and 30 COVID-19-like patients were recruited in a prospective cohort study. Samples were collected from COVID-19 patients with mild or severe symptoms on admission, patients who progressed from mild to severe symptoms, and patients who were followed from hospital admission to discharge. The metabolome was assayed using gas chromatography-mass spectrometry.

RESULTS

Serum butyric acid, 2-hydroxybutyric acid, l-glutamic acid, l-phenylalanine, l-serine, l-lactic acid, and cholesterol were enriched in COVID-19 and COVID-19-like patients versus HCs. Notably, d-fructose and succinic acid were enriched, and citric acid and 2-palmitoyl-glycerol were depleted in COVID-19 patients compared to COVID-19-like patients and HCs, and these four metabolites were not differentially distributed in non-COVID-19 groups. COVID-19 patients had enriched 4-deoxythreonic acid and depleted 1,5-anhydroglucitol compared to HCs and enriched oxalic acid and depleted phosphoric acid compared to COVID-19-like patients. A combination of d-fructose, citric acid and 2-palmitoyl-glycerol distinguished COVID-19 patients from HCs and COVID-19-like patients, with an area under the curve (AUC) > 0.92 after validation. The combination of 2-hydroxy-3-methylbutyric acid, 3-hydroxybutyric acid, cholesterol, succinic acid, L-ornithine, oleic acid and palmitelaidic acid predicted patients who progressed from mild to severe COVID-19, with an AUC of 0.969. After discharge, nearly one-third of metabolites were recovered in COVID-19 patients.

CONCLUSIONS

The serum metabolome of COVID-19 patients is distinctive and has important value in investigating pathogenesis, determining a diagnosis, predicting severe cases, and improving treatment.

Exploring the metabolic phenotypes associated with different host inflammation of acute respiratory distress syndrome (ARDS) from lung metabolomics in mice

RATIONALE

The aim of this study was to analyze the metabolomics of lung with different host inflammation of acute respiratory distress syndrome (ARDS) for the identification of biomarkers for predicting severity under different inflammatory conditions.

METHODS

Cecal ligation and puncture (CLP) and lipopolysaccharide (LPS)-intratracheal injection induced acute lung injury (ALI) were used. A mouse model was used to explore lung metabolomic biomarkers in ALI/ARDS. The splenectomy model was used as an auxiliary method to distinguish between hyper- and hypo-inflammatory subtypes. Plasma, lung tissue and bronchoalveolar lavage fluid (BALF) samples were collected from mice after CLP/LPS. The severity of lung injury was evaluated. Expression of tumor necrosis factor-α (TNF-α) in mice serum and lung was tested by enzyme-linked immunosorbent assay (ELISA) and polymer chain reaction (PCR). Polymorphonuclear cells in BALF were counted. The lung metabolites were detected by gas chromatography/mass spectrometry (GC/MS), and the metabolic pathways predicted using the KEGG database.

RESULTS

The LPS/CLP-Splen group had more severe lung injury than the corresponding ALI group; that in the CLP-Splen group was more serious than in the LPS-Splen group. TNF-α expression was significantly elevated in the serum and lung tissue after LPS or CLP, and higher in the LPS/CLP-Splen group than in the corresponding ALI group. The level of TNF-α in the CLP-Splen group was elevated significantly over that in the LPS-Splen group. Both these groups also showed significant neutrophil exudation within the lungs. During differential inflammation, more differential metabolites were detected in the lungs of the CLP group ALI mice than in the LPS group. A total of 41 compounds were detected in the lungs of the CLP and CLP-Splen groups. Contrastingly, eight compounds were detected in the lungs of the LPS and LPS-Splen groups. The LPS-Splen and CLP-Splen groups had significant neutrophil exudation in the lung. Random forest analysis of lung-targeted metabolomics data indicated 4-hydroxyphenylacetic acid, 1-aminocyclopentanecarboxylic acid (ACPC), cis-aconitic acid, and hydroxybenzoic acid as strong predictors of the hyper-inflammatory subgroup in the CLP group. Furthermore, with splenectomy, 13 differential metabolic pathways between the CLP and LPS groups were revealed.

CONCLUSIONS

Hyper-inflammatory subgroups of ARDS have a greater inflammatory response and a more active lung metabolism. Combined with the host inflammation background, biomarkers from metabolomics could help evaluate the response severity of ARDS.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Systems Biology ARDS Research with a Focus on Metabolomics

Acute respiratory distress syndrome (ARDS) is a clinical syndrome that inflicts a considerably heavy toll in terms of morbidity and mortality. While there are multitudes of conditions that can lead to ARDS, the vast majority of ARDS cases are caused by a relatively small number of diseases, especially sepsis and pneumonia. Currently, there is no clinically agreed upon reliable diagnostic test for ARDS, and the detection or diagnosis of ARDS is based on a constellation of laboratory and radiological tests in the absence of evidence of left ventricular dysfunction, as specified by the Berlin definition of ARDS. Virtually all the ARDS biomarkers to date have been proven to be of very limited clinical utility. Given the heterogeneity of ARDS due to the wide variation in etiology, clinical and molecular manifestations, there is a current scientific consensus agreement that ARDS is not just a single entity but rather a spectrum of conditions that need further study for proper classification, the identification of reliable biomarkers and the adequate institution of therapeutic targets. This scoping review aims to elucidate ARDS omics research, focusing on metabolomics and how metabolomics can boost the study of ARDS biomarkers and help to facilitate the identification of ARDS subpopulations.

Increased mortality of acute respiratory distress syndrome was associated with high levels of plasma phenylalanine

Background

There is a dearth of drug therapies available for the treatment of acute respiratory distress syndrome (ARDS). Certain metabolites play a key role in ARDS and could serve as potential targets for developing therapies against this respiratory disorder. The present study was designed to determine such “functional metabolites” in ARDS using metabolomics and in vivo experiments in a mouse model.

Methods

Metabolomic profiles of blood plasma from 42 ARDS patients and 28 healthy controls were captured using Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) assay. Univariate and multivariate statistical analysis were performed on metabolomic profiles from blood plasma of ARDS patients and healthy controls to screen for “functional metabolites”, which were determined by variable importance in projection (VIP) scores and P value. Pathway analysis of all the metabolites was performed. The mouse model of ARDS was established to investigate the role of “functional metabolites” in the lung injury and mortality caused by the respiratory disorder.

Results

The metabolomic profiles of patients with ARDS were significantly different from healthy controls, difference was also observed between metabolomic profiles of the non-survivors and the survivors among the ARDS patient pool. Levels of Phenylalanine, D-Phenylalanine and Phenylacetylglutamine were significantly increased in non-survivors compared to the survivors of ARDS. Phenylalanine metabolism was the most notably altered pathway between the non-survivors and survivors of ARDS patients. In vivo animal experiments demonstrated that high levels of Phenylalanine might be associated with the severer lung injury and increased mortality of ARDS.

Conclusion

Increased mortality of acute respiratory distress syndrome was associated with high levels of plasma Phenylalanine.

Trial registration

Chinese Clinical Trial Registry, ChiCTR1800015930. Registered 29 April 2018, http://www.chictr.org.cn/edit.aspx?pid=25609&htm=4

Metabolomics biotechnology, applications, and future trends: a systematic review

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed.