Discriminant biomarkers of acute respiratory distress syndrome associated to H1N1 influenza identified by metabolomics HPLC-QTOF-MS/MS platform

Mechanism of Jinzhen Oral Liquid against influenza-induced lung injury based on metabonomics and gut microbiome

ETHNOPHARMACOLOGICAL RELEVANCE

Jinzhen Oral Liquid (JZOL) is a traditional Chinese patent medicine and widely used clinically, which consists of eight herbs including Bovis Calculus Atifactus, Fritillariae Ussuriensis Bulbus (Fritillaria ussuriensis Maxim.), Caprae Hircus Cornu, Rhei Radix et Rhizoma (Rheum palmatum L.), Scutellariae Radix (Scutellaria baicalensis Georgi), Glycyrrhizae Radix et Rhizoma (Glycyrrhiza uralensis Fisch. ex DC.), Chloriti Lapis, and Gypsum Fibrosum (Their ratio is 9.45 : 47.25: 94.5 : 31.5: 15.75 : 31.5: 15.75 : 23.62). A large number of clinical studies have proved that JZOL has a good antiviral effect and can treat lung injury, pneumonia, and bronchitis caused by a variety of viral infections.

AIM OF THE STUDY

Influenza infection frequently exhibit dysregulation of gut microbiota and host metabolomes, but the mechanism of JZOL is still unclear and needs to be further explored. Here, after influenza virus infection induced lung injury, the regulation roles of JZOL in metabolic and gut microbiota balances are investigated to comprehensively elucidate its therapeutic mechanism.

MATERIALS AND METHODS

A mouse model of lung injury was replicated via intranasal instillation of influenza A (H1N1). The efficacy of JZOL was evaluated by pathological sections, lung index, the levels of TNF-α and IFN-γ, and viral load in lung tissue. Its modulation of endogenous metabolites and gut microbiota was assessed using plasma metabolomic technique and 16S rRNA high-throughput sequencing technique.

RESULTS

JZOL not only significantly relieved lung inflammation and edema in influenza mice, but also alleviated the disturbance of endogenous metabolites and the imbalance of gut microbiota mainly by regulating glycerophospholipid and fatty acid metabolism and Lactobacillus. The anti-influenza effects of JZOL were gut microbiota dependent, as demonstrated by antibiotic treatment. The altered metabolites were significantly correlated with Lactobacillus and pharmacodynamic indicators, further confirming the reliability of these results.

CONCLUSIONS

JZOL attenuates H1N1 influenza infection induced lung injury by regulating lipid metabolism via the modulation of Lactobacillus. The results support the clinical application of JZOL, and are useful to further understand the mechanism of TCM in the treatment of influenza.

Metabolomics profile in acute respiratory distress syndrome by nuclear magnetic resonance spectroscopy in patients with community-acquired pneumonia

Background

Acute respiratory distress syndrome (ARDS) is a challenging clinical problem. Discovering the potential metabolic alterations underlying the ARDS is important to identify novel therapeutic target and improve the prognosis. Serum and urine metabolites can reflect systemic and local changes and could help understanding metabolic characterization of community-acquired pneumonia (CAP) with ARDS.

Methods

Clinical data of patients with suspected CAP at the First Affiliated Hospital of Wenzhou Medical University were collected from May 2020 to February 2021. Consecutive patients with CAP were enrolled and divided into two groups: CAP with and without ARDS groups. ¹H nuclear magnetic resonance-based metabolomics analyses of serum and urine samples were performed before and after treatment in CAP with ARDS (n = 43) and CAP without ARDS (n = 45) groups. Differences metabolites were identifed in CAP with ARDS. Furthermore, the receiver operating characteristic (ROC) curve was utilized to identify panels of significant metabolites for evaluating therapeutic effects on CAP with ARDS. The correlation heatmap was analyzed to further display the relationship between metabolites and clinical characteristics.

Results

A total of 20 and 42 metabolites were identified in the serum and urine samples, respectively. Serum metabolic changes were mainly involved in energy, lipid, and amino acid metabolisms, while urine metabolic changes were mainly involved in energy metabolism. Elevated levels of serum 3-hydroxybutyrate, lactate, acetone, acetoacetate, and decreased levels of serum leucine, choline, and urine creatine and creatinine were detected in CAP with ARDS relative to CAP without ARDS. Serum metabolites 3-hydroxybutyrate, acetone, acetoacetate, citrate, choline and urine metabolite 1-methylnicotinamide were identified as a potential biomarkers for assessing therapeutic effects on CAP with ARDS, and with AUCs of 0.866 and 0.795, respectively. Moreover, the ROC curve analysis revealed that combined characteristic serum and urine metabolites exhibited a better classification system for assessing therapeutic effects on CAP with ARDS, with a AUC value of 0.952. In addition, differential metabolites strongly correlated with clinical parameters in patients with CAP with ARDS.

Conclusions

Serum- and urine-based metabolomics analyses identified characteristic metabolic alterations in CAP with ARDS and might provide promising circulatory markers for evaluating therapeutic effects on CAP with ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02075-w.

Infection Biomarkers Based on Metabolomics

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Background

Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection.

Methods

We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery.

Findings

A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations.

Interpretation

This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing.

Insights into the molecular basis of tick-borne encephalitis from multiplatform metabolomics

Background

Tick-borne encephalitis virus (TBEV) is the most prevalent arbovirus, with a tentative estimate of 10,000 to 10,500 infections occurring in Europe and Asia every year. Endemic in Northeast China, tick-borne encephalitis (TBE) is emerging as a major threat to public health, local economies and tourism. The complicated array of host physiological changes has hampered elucidation of the molecular mechanisms underlying the pathogenesis of this disease.

Methodology/Principle findings

System-level characterization of the serum metabolome and lipidome of adult TBEV patients and a healthy control group was performed using liquid chromatography tandem mass spectrometry. By tracking metabolic and lipid changes during disease progression, crucial physiological changes that coincided with disease stages could be identified. Twenty-eight metabolites were significantly altered in the sera of TBE patients in our metabolomic analysis, and 14 lipids were significantly altered in our lipidomics study. Among these metabolites, alpha-linolenic acid, azelaic acid, D-glutamine, glucose-1-phosphate, L-glutamic acid, and mannose-6-phosphate were altered compared to the control group, and PC(38:7), PC(28:3;1), TAG(52:6), etc. were altered based on lipidomics. Major perturbed metabolic pathways included amino acid metabolism, lipid and oxidative stress metabolism (lipoprotein biosynthesis, arachidonic acid biosynthesis, leukotriene biosynthesis and sphingolipid metabolism), phospholipid metabolism and triglyceride metabolism. These metabolites were significantly perturbed during disease progression, implying their latent utility as prognostic markers.

Conclusions/Significance

TBEV infection causes distinct temporal changes in the serum metabolome and lipidome, and many metabolites are potentially involved in the acute inflammatory response and immune regulation. Our global analysis revealed anti- and pro-inflammatory processes in the host and changes to the entire metabolic profile. Relationships between metabolites and pathologies were established. This study provides important insight into the pathology of TBE, including its pathology, and lays the foundation for further research into putative markers of TBE disease.

Tick-borne encephalitis virus (TBEV) with extreme contagiousness is a key danger to public health systems in Europe and Asia. To date, little information is obtained about the molecular mechanism underlying infection, and although commercial vaccines against TBEV exist, there is no specific treatment for the disease. Metabolomics and lipidomics offer multiple-visions of metabolome and lipidome sights and help elucidating metabolic to disease phenotype. Serum metabolism and lipidome analysis were performed based on mass spectrometer (MS) platform on a cohort of TBEV patients. About 400 metabolites performed crucial shifts in TBEV patients compared with healthy subjects. This study revealed that in the stage of infection, the host metabolome is tightly regulated, with anti-inflammatory processes modulating pro-inflammatory processes implying the self-limiting phenotype of TBEV and the inherent regulation in humans. The crucial perturbed metabolic pathways contained amino acid metabolism, fatty acid metabolism and phospholipid metabolism. This study provides a powerful and new approach to decipher the interactions between host and virus. These potential metabolites provide high sensitivity and specificity and have the capacity to function as biomarkers for disease surveillance and estimation of therapeutic interventions.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

Metabolites of microbiota response to tryptophan and intestinal mucosal immunity: A therapeutic target to control intestinal inflammation

In a complex, diverse intestinal environment, commensal microbiota metabolizes excessive dietary tryptophan to produce more bioactive metabolites connecting with kinds of diverse process, such as host physiological defense, homeostasis, excessive immune activation and the progression and outcome of different diseases, such as inflammatory bowel disease, irritable bowel syndrome and others. Although commensal microbiota includes bacteria, fungi, and protozoa and all that, they often have the similar metabolites in tryptophan metabolism process via same or different pathways. These metabolites can work as signal to activate the innate immunity of intestinal mucosa and induce the rapid inflammation response. They are critical in reconstruction of lumen homeostasis as well. This review aims to seek the potential function and mechanism of microbiota-derived tryptophan metabolites as targets to regulate and shape intestinal immune function, which mainly focused on two aspects. First, analyze the character of tryptophan metabolism in bacteria, fungi, and protozoa, and assess the functions of their metabolites (including indole and eight other derivatives, serotonin (5-HT) and d-tryptophan) on regulating the integrity of intestinal epithelium and the immunity of the intestinal mucosa. Second, focus on the mediator and pathway for their recognition, transfer and crosstalk between microbiota-derived tryptophan metabolites and intestinal mucosal immunity. Disruption of intestinal homeostasis has been described in different intestinal inflammatory diseases, available data suggest the remarkable potential of tryptophan-derived aryl hydrocarbon receptor agonists, indole derivatives on lumen equilibrium. These metabolites as preventive and therapeutic interventions have potential to promote proinflammatory or anti-inflammatory responses of the gut.

Auxiliary role of mesenchymal stem cells as regenerative medicine soldiers to attenuate inflammatory processes of severe acute respiratory infections caused by COVID-19

Acute respiratory infections as one of the most common problems of healthcare systems also can be considered as an important reason for worldwide morbidity and mortality from infectious diseases. Coronaviruses are a group of well-known respiratory viruses that can cause acute respiratory infections. At the current state, the 2019 novel coronavirus is cited as the most worldwide problematic agent for the respiratory system. According to investigations, people with old age and underlying diseases are at higher risk of 2019 novel coronavirus infection. Indeed, they may show a severe form of the disease (with severe acute respiratory infections). Based on the promising role of cell therapy and regenerative medicine approaches in the treatment of several life-threatening diseases, it seems that applying cell-based approaches can also be a hopeful strategy for improving subjects with severe acute respiratory infections caused by the 2019 novel coronavirus. Herein, due to the amazing effects of mesenchymal stem cells in the treatment of various diseases, this review focuses on the auxiliary role of mesenchymal stem cells to reduce inflammatory processes of acute respiratory infections caused by the 2019 novel coronavirus.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Non-Targeted UHPLC-Q-TOF/MS-Based Metabolomics Reveals a Metabolic Shift from Glucose to Glutamine in CPB Cells during ISKNV Infection Cycle

Infectious spleen and kidney necrosis virus (ISKNV) has caused serious economic losses in the cultured mandarin fish (Siniperca chuatsi) industry in China. Host metabolism alteration induced by disease infection may be the core problem of pathogenesis. However, to date, little is known about the disease-induced fish metabolism changes. In this study, we first reported ISKNV, the fish virus, induced metabolism alteration. The metabolomics profiles of Chinese perch brain cells (CPB) post-ISKNV infection at progressive time points were analyzed using the UHPLC-Q-TOF/MS technique. A total of 98 differential metabolites were identified. In the samples harvested at 24 hours post-infection (hpi; the early stage of ISKNV infection), 49 differential metabolites were identified comparing with control cells, including 31 up-regulated and 18 down-regulated metabolites. And in the samples harvested at 72 hpi (the late stage of ISKNV infection), 49 differential metabolites were identified comparing with control cells, including 27 up-regulated and 22 down-regulated metabolites. These differential metabolites were involved in many pathways related with viral pathogenesis. Further analysis on the major differential metabolites related to glucose metabolism and amino acid metabolism revealed that both glucose metabolism and glutamine metabolism were altered and a metabolic shift was determined from glucose to glutamine during ISKNV infection cycle. In ISKNV-infected cells, CPB cells prefer to utilize glucose for ISKNV replication at the early stage of infection, while they prefer to utilize glutamine to synthetize lipid for ISKNV maturation at the late stage of infection. These findings may improve the understanding of the interaction between ISKNV and host, as well as provide a new insight for elucidating the ISKNV pathogenic mechanism.

Fatty Acid Metabolism is Associated With Disease Severity After H7N9 Infection

BACKGROUND

Human infections with the H7N9 virus could lead to lung damage and even multiple organ failure, which is closely associated with a high mortality rate. However, the metabolic basis of such systemic alterations remains unknown.

METHODS

This study included hospitalized patients (n = 4) with laboratory-confirmed H7N9 infection, healthy controls (n = 9), and two disease control groups comprising patients with pneumonia (n = 9) and patients with pneumonia who received steroid treatment (n = 10). One H7N9-infected patient underwent lung biopsy for histopathological analysis and expression analysis of genes associated with lung homeostasis. H7N9-induced systemic alterations were investigated using metabolomic analysis of sera collected from the four patients by using ultra-performance liquid chromatography-mass spectrometry. Chest digital radiography and laboratory tests were also conducted.

FINDINGS

Two of the four patients did not survive the clinical treatments with antiviral medication, steroids, and oxygen therapy. Biopsy revealed disrupted expression of genes associated with lung epithelial integrity. Histopathological analysis demonstrated severe lung inflammation after H7N9 infection. Metabolomic analysis indicated that fatty acid metabolism may be inhibited during H7N9 infection. Serum levels of palmitic acid, erucic acid, and phytal may negatively correlate with the extent of lung inflammation after H7N9 infection. The changes in fatty acid levels may not be due to steroid treatment or pneumonia.

INTERPRETATION

Altered structural and secretory properties of the lung epithelium may be associated with the severity of H7N9-infection-induced lung disease. Moreover, fatty acid metabolism level may predict a fatal outcome after H7N9 virus infection.