Small molecule metabolites: discovery of biomarkers and therapeutic targets.

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Effect of short-term use of FFP2 (N95) masks on the salivary metabolome of young healthy volunteers: a pilot study.

The use of face masks has become an integral part of public life in the post-pandemic era. However, the understanding of the effect of wearing masks on physiology remains incomplete and is required for informing public health policies. For the first time, we report the effects of wearing FFP2 masks on the metabolic composition of saliva, a proximal matrix to breath, along with cardiopulmonary parameters. Un-induced saliva was collected from young (31.2 ± 6.3 years) healthy volunteers (n = 10) before and after wearing FFP2 (N95) masks for 30 minutes and analyzed using GCMS. The results showed that such short-term mask use did not cause any significant change in heart rate, pulse rate or SpO2. Three independent data normalization approaches were used to analyze the changes in metabolomic signature. The individuality of the overall salivary metabotype was found to be unaffected by mask use. However, a trend of an increase in the salivary abundance of L-fucose, 5-aminovaleric acid, putrescine and phloretic acid was indicated irrespective of the method of data normalization. Quantitative analysis confirmed increases in concentrations of these metabolites in saliva of paired samples amid high inter-individual variability. The results showed that while there was no significant change in measured physiological parameters and individual salivary metabotypes, mask use was associated with correlated changes in these metabolites plausibly originating from altered microbial metabolic activity. These results might also explain the change in odour perception reported to be associated with mask use. Potential implications of these changes on mucosal health and immunity warrants further investigation to evolve more prudent mask use policies.

Neutrophil metabolomics in severe COVID-19 reveal GAPDH as a suppressor of neutrophil extracellular trap formation.

Severe COVID-19 is characterized by an increase in the number and changes in the function of innate immune cells including neutrophils. However, it is not known how the metabolome of immune cells changes in patients with COVID-19. To address these questions, we analyzed the metabolome of neutrophils from patients with severe or mild COVID-19 and healthy controls. We identified widespread dysregulation of neutrophil metabolism with disease progression including in amino acid, redox, and central carbon metabolism. Metabolic changes in neutrophils from patients with severe COVID-19 were consistent with reduced activity of the glycolytic enzyme GAPDH. Inhibition of GAPDH blocked glycolysis and promoted pentose phosphate pathway activity but blunted the neutrophil respiratory burst. Inhibition of GAPDH was sufficient to cause neutrophil extracellular trap (NET) formation which required neutrophil elastase activity. GAPDH inhibition increased neutrophil pH, and blocking this increase prevented cell death and NET formation. These findings indicate that neutrophils in severe COVID-19 have an aberrant metabolism which can contribute to their dysfunction. Our work also shows that NET formation, a pathogenic feature of many inflammatory diseases, is actively suppressed in neutrophils by a cell-intrinsic mechanism controlled by GAPDH.

Alterations in immunophenotype and metabolic profile of mononuclear cells during follow up in children with multisystem inflammatory syndrome (MIS-C).

Introduction: Although children seem to be less susceptible to COVID-19, some of them develop a rare but serious hyperinflammatory condition called multisystem inflammatory syndrome in children (MIS-C). While several studies describe the clinical conditions of acute MIS-C, the status of convalescent patients in the months after acute MIS-C is still unclear, especially the question of persistence of changes in the specific subpopulations of immune cells in the convalescent phase of the disease. Methods: We therefore analyzed peripheral blood of 14 children with MIS-C at the onset of the disease (acute phase) and 2 to 6 months after disease onset (post-acute convalescent phase) for lymphocyte subsets and antigen-presenting cell (APC) phenotype. The results were compared with six healthy age-matched controls. Results: All major lymphocyte populations (B cells, CD4 + and CD8+ T cells, and NK cells) were decreased in the acute phase and normalized in the convalescent phase. T cell activation was increased in the acute phase, followed by an increased proportion of γ/δ-double-negative T cells (γ/δ DN Ts) in the convalescent phase. B cell differentiation was impaired in the acute phase with a decreased proportion of CD21 expressing, activated/memory, and class-switched memory B cells, which normalized in the convalescent phase. The proportion of plasmacytoid dendritic cells, conventional type 2 dendritic cells, and classical monocytes were decreased, while the proportion of conventional type 1 dendritic cells was increased in the acute phase. Importantly the population of plasmacytoid dendritic cells remained decreased in the convalescent phase, while other APC populations normalized. Immunometabolic analysis of peripheral blood mononuclear cells (PBMCs) in the convalescent MIS-C showed comparable mitochondrial respiration and glycolysis rates to healthy controls. Conclusions: While both immunophenotyping and immunometabolic analyzes showed that immune cells in the convalescent MIS-C phase normalized in many parameters, we found lower percentage of plasmablasts, lower expression of T cell co-receptors (CD3, CD4, and CD8), an increased percentage of γ/δ DN Ts and increased metabolic activity of CD3/CD28-stimulated T cells. Overall, the results suggest that inflammation persists for months after the onset of MIS-C, with significant alterations in some immune system parameters, which may also impair immune defense against viral infections.

Alterations of Fecal Metabolome Associated with BBIBP-CorV Vaccines against the SARS-CoV-2 Virus.

BACKGROUND: The SARS-CoV-2 vaccine has been implemented in response to the 2019 Coronavirus Disease (COVID-19) pandemic worldwide. Dysregulation of gut metabolite is associated with COVID-19 patients. However, the effect of vaccination on the gut metabolite remains unknown, and it is critical to investigate the shifts in metabolic profiles following vaccine treatment. METHODS: In the present study, we conducted a case-control study to assess the fecal metabolic profiles between individuals who received two doses of intramuscular injection of an inactivated SARS-CoV-2 vaccine candidate (BBIBP-CorV) (n = 20), and matched unvaccinated controls (n = 20) using untargeted gas chromatography and time-of-flight mass spectrometry (GC-TOF/MS). RESULTS: Significant different metabolic profiles were observed between subjects receiving SARS-CoV-2 virus vaccines and the unvaccinated. Among a total of 243 metabolites from 27 ontology classes identified in the study cohort, 64 metabolic markers and 15 ontology classes were dramatically distinct between vaccinated and unvaccinated individuals. There were 52 enhanced (such as Desaminotyrosine, Phenylalanine) and 12 deficient metabolites (such as Octadecanol, 1-Hexadecanol) in vaccinated individuals. Along with altered metabolic compositions, multiple functional pathways in Small MoleculePathway Database (SMPDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) varied between groups. Our results indicated that urea cycle; alanine, aspartate, and glutamate metabolism; arginine and proline metabolism; phenylalanine metabolism and tryptophan metabolism were abundant after vaccination. Additionally, correlation analysis showed that intestinal microbiome was related to alteration in metabolite composition and functions. CONCLUSIONS: The present study indicated the alterations in the gut metabolome after COVID-19 vaccination and the findings provide a valuable resource for in-depth exploration of mechanisms between gut metabolite and SARS-CoV-2 virus vaccines.

Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults.

COVID-19 represents a multi-system infectious disease with broad-spectrum manifestations, including changes in host metabolic processes connected to the disease pathogenesis. Understanding biochemical dysregulation patterns as a consequence of COVID-19 illness promises to be crucial for tracking disease course and clinical outcomes. Surface-enhanced Raman scattering (SERS) has attracted considerable interest in biomedical diagnostics for the sensitive detection of intrinsic profiles of unique fingerprints of serum biomolecules indicative of SARS-CoV-2 infection in a label-free format. Here, we applied label-free SERS and chemometrics for rapid interrogation of temporal metabolic dynamics in longitudinal sera of mildly infected non-hospitalized patients (n = 22), at 4 and 16 weeks post PCR-positive diagnosis, and compared them with negative controls (n = 8). SERS spectral markers revealed distinct metabolic profiles in patient sera that significantly deviated from the healthy metabolic state at the two sampling time intervals. Multivariate and univariate analyses of the spectral data identified abundance dynamics in amino acids, lipids, and protein vibrations as the key spectral features underlying the metabolic differences detected in convalescent samples and perhaps associated with patient recovery progression. A validation study performed using spontaneous Raman spectroscopy yielded spectral data results that corroborated SERS spectral findings and confirmed the detected disease-specific molecular phenotypes in clinical samples. Label-free SERS promises to be a valuable analytical technique for rapid screening of the metabolic phenotype induced by SARS-CoV-2 infection to allow appropriate healthcare intervention.

Metabolic Profile of Patients with Long COVID: A Cross-Sectional Study.

A significant proportion of patients experience a wide range of symptoms following acute coronavirus disease 2019 (COVID-19). Laboratory analyses of long COVID have demonstrated imbalances in metabolic parameters, suggesting that it is one of the many outcomes induced by long COVID. Therefore, this study aimed to illustrate the clinical and laboratory markers related to the course of the disease in patients with long COVID. Participants were selected using a clinical care programme for long COVID in the Amazon region. Clinical and sociodemographic data and glycaemic, lipid, and inflammatory screening markers were collected, and cross-sectionally analysed between the long COVID-19 outcome groups. Of the 215 participants, most were female and not elderly, and 78 were hospitalised during the acute COVID-19 phase. The main long COVID symptoms reported were fatigue, dyspnoea, and muscle weakness. Our main findings show that abnormal metabolic profiles (such as high body mass index measurement and high triglyceride, glycated haemoglobin A1c, and ferritin levels) are more prevalent in worse long COVID presentations (such as previous hospitalisation and more long-term symptoms). This prevalence may suggest a propensity for patients with long COVID to present abnormalities in the markers involved in cardiometabolic health.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Platelets' morphology, metabolic profile, exocytosis, and heterotypic aggregation with leukocytes in relation to severity and mortality of COVID-19-patients.

Roles of platelets during infections surpass the classical thrombus function and are now known to modulate innate immune cells. Leukocyte-platelet aggregations and activation-induced secretome are among factors recently gaining interest but little is known about their interplay with severity and mortality during the course of SARS-Cov-2 infection. The aim of the present work is to follow platelets' bioenergetics, redox balance, and calcium homeostasis as regulators of leukocyte-platelet interactions in a cohort of COVID-19 patients with variable clinical severity and mortality outcomes. We investigated COVID-19 infection-related changes in platelet counts, activation, morphology (by flow cytometry and electron microscopy), bioenergetics (by Seahorse analyzer), mitochondria function (by high resolution respirometry), intracellular calcium (by flow cytometry), reactive oxygen species (ROS, by flow cytometry), and leukocyte-platelet aggregates (by flow cytometry) in non-intensive care unit (ICU) hospitalized COVID-19 patients (Non-ICU, n=15), ICU-survivors of severe COVID-19 (ICU-S, n=35), non-survivors of severe COVID-19 (ICU-NS, n=60) relative to control subjects (n=31). Additionally, molecular studies were carried out to follow gene and protein expressions of mitochondrial electron transport chain complexes (ETC) in representative samples of isolated platelets from the studied groups. Our results revealed that COVID-19 infection leads to global metabolic depression especially in severe patients despite the lack of significant impacts on levels of mitochondrial ETC genes and proteins. We also report that severe patients' platelets exhibit hyperpolarized mitochondria and significantly lowered intracellular calcium, concomitantly with increased aggregations with neutrophil. These changes were associated with increased populations of giant platelets and morphological transformations usually correlated with platelets activation and inflammatory signatures, but with impaired exocytosis. Our data suggest that hyperactive platelets with impaired exocytosis may be integral parts in the pathophysiology dictating severity and mortality in COVID-19 patients.

A metabolic readout of the urine metabolome of COVID-19 patients.

Analysis of urine samples from COVID-19 patients by 1H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used 1H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.

The gut microbiota and metabolome are associated with diminished COVID-19 vaccine-induced antibody responses in immunosuppressed inflammatory bowel disease patients.

BACKGROUND: Patients with inflammatory bowel disease (IBD) treated with anti-TNF therapy exhibit attenuated humoral immune responses to vaccination against SARS-CoV-2. The gut microbiota and its functional metabolic output, which are perturbed in IBD, play an important role in shaping host immune responses. We explored whether the gut microbiota and metabolome could explain variation in anti-SARS-CoV-2 vaccination responses in immunosuppressed IBD patients. METHODS: Faecal and serum samples were prospectively collected from infliximab-treated patients with IBD in the CLARITY-IBD study undergoing vaccination against SARS-CoV-2. Antibody responses were measured following two doses of either ChAdOx1 nCoV-19 or BNT162b2 vaccine. Patients were classified as having responses above or below the geometric mean of the wider CLARITY-IBD cohort. 16S rRNA gene amplicon sequencing, nuclear magnetic resonance (NMR) spectroscopy and bile acid profiling with ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) were performed on faecal samples. Univariate, multivariable and correlation analyses were performed to determine gut microbial and metabolomic predictors of response to vaccination. FINDINGS: Forty-three infliximab-treated patients with IBD were recruited (30 Crohn's disease, 12 ulcerative colitis, 1 IBD-unclassified; 26 with concomitant thiopurine therapy). Eight patients had evidence of prior SARS-CoV-2 infection. Seventeen patients (39.5%) had a serological response below the geometric mean. Gut microbiota diversity was lower in below average responders (p = 0.037). Bilophila abundance was associated with better serological response, while Streptococcus was associated with poorer response. The faecal metabolome was distinct between above and below average responders (OPLS-DA R2X 0.25, R2Y 0.26, Q2 0.15; CV-ANOVA p = 0.038). Trimethylamine, isobutyrate and omega-muricholic acid were associated with better response, while succinate, phenylalanine, taurolithocholate and taurodeoxycholate were associated with poorer response. INTERPRETATION: Our data suggest that there is an association between the gut microbiota and variable serological response to vaccination against SARS-CoV-2 in immunocompromised patients. Microbial metabolites including trimethylamine may be important in mitigating anti-TNF-induced attenuation of the immune response. FUNDING: JLA is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-502), funded by Imperial College London and The Joyce and Norman Freed Charitable Trust. BHM is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases at Imperial College London receives financial and infrastructure support from the NIHR Imperial Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. Metabolomics studies were performed at the MRC-NIHR National Phenome Centre at Imperial College London; this work was supported by the Medical Research Council (MRC), the National Institute of Health Research (NIHR) (grant number MC_PC_12025) and infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC). The NIHR Exeter Clinical Research Facility is a partnership between the University of Exeter Medical School College of Medicine and Health, and Royal Devon and Exeter NHS Foundation Trust. This project is supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.

Impact of high prebiotic and probiotic dietary education in the SARS-CoV-2 era: improved cardio-metabolic profile in schizophrenia spectrum disorders.

BACKGROUND: The development of new aetiological premises, such as the microbiota-gut-brain axis theory, evidences the influence of dietary and nutritional patterns on mental health, affecting the patient's quality of life in terms of physical and cardiovascular health. The aim was to determine the impact of a nutritional programme focused on increasing the intake of prebiotic and probiotic food on cardio-metabolic status in individuals with schizophrenia spectrum disorders in the contextual setting of the SARS-CoV-2 era. METHODS: A randomised clinical trial (two-arm, double-blind, balanced-block, six-month intervention) was conducted in a group of 50 individuals diagnosed with schizophrenia spectrum disorder during the SARS-CoV-2 confinement period. The control group received conventional dietary counselling on an individual basis. In the intervention group, an individual nutritional education programme with a high content of prebiotics and probiotics (dairy and fermented foods, green leafy vegetables, high-fibre fruit, whole grains, etc.) was established. Data on cardiovascular status were collected at baseline, three and six months. In addition, anthropometric parameters were analysed monthly. RESULTS: Forty-four subjects completed follow-up and were analysed. Statistical differences (p < 0.05) were found in all anthropometric variables at baseline and six months of intervention. A 27.4% reduction in the prevalence of metabolic syndrome risk factors in all its components was evidenced, leading to a clinically significant improvement (decrease in cardiovascular risk) in the intervention group at six months. CONCLUSIONS: The development of a nutritional programme focused on increasing the dietary content of prebiotics and probiotics effectively improves the cardio-metabolic profile in schizophrenia spectrum disorders. Therefore, nursing assumes an essential role in the effectiveness of dietary interventions through nutritional education and the promotion of healthy lifestyles. Likewise, nursing acquires a relevant role in interdisciplinary coordination in confinement contexts. TRIAL REGISTRATION: The study protocol complied with the Declaration of Helsinki for medical studies; the study received ethical approval from referral Research Ethics Committee in November 2019 (reg. no. 468) and retrospectively registered in clinicaltrials.gov (NCT04366401. First Submitted: 28th April 2020; First Registration: 25th June 2020).

The impact of a telehealth intervention on the metabolic profile of diabetes mellitus patients during the COVID-19 pandemic - A randomized clinical trial.

OBJECTIVES: The objective of this study was to evaluate the impact of a telehealth intervention on metabolic outcomes and self-perceptions of the patients regarding their management of diabetes during the COVID-19 pandemic. METHODS: This is a non-blind randomized controlled clinical trial to assess a telehealth intervention. We included adults with diabetes mellitus. The outcomes assessed were the level of HbA1c, lipid profile, blood pressure levels, weight, body mass index and self-perceptions about diabetes management. RESULTS: A total of 150 individuals with diabetes participated in the study and at the end of telehealth intervention there were no changes in the patient's HbA1c levels between intervention and control groups for neither type 1 (8.1% vs. 8.6%; p = 0.11) nor type 2 diabetes (8.6% vs. 9.0%; p = 0.09), respectively. From the rest of the metabolic profile, triglyceride levels from type 1 diabetes group was the only variable that demonstrated improvement with telehealth intervention (66.5% intervention group vs. 86.5% control group; p = 0.05). CONCLUSIONS: After 4 months of telehealth intervention, no statistically significant results were observed in HbA1c nor in secondary outcomes (with the exception of triglycerides for the type 1 diabetes group).

[Impact of COVID-19 related lockdown on the metabolic profile of people with HIV infection in Argentina]. / Impacto de la cuarentena por COVID-19 en el perfil metabólico de personas con infección por VIH en Argentina.

BACKGROUND: The spread of SARS-CoV-2 required widespread lockdown to mitigate the pandemic. Argentine authorities imposed preventive social isolation for 234 days (March 20th to November 9th 2020). This measure led to major changes in the population's lifestyle. AIM: To examine the influence of COVID-19 lockdown measures on the metabolic profile of HIV-infected patients in Argentina. METHODS: Retrospective cohort study of 10,239 HIV-infected patients under follow up in a private clinic for HIV care. Adult patients with ongoing antiretroviral therapy (ART) and a baseline determination of blood glucose, total cholesterol, HDL-cholesterol and triglycerides done before lockdown (BL: second semester of 2019) and a second determination during lockdown (DL: May 2020) were included. Patients with recent changes in ART that may have metabolic impact, those starting lipid/glucose lowering agents and pregnant women were excluded. Categorical variables were compared using the χ2 test or Fisher's exact test, and continuous variables using the t-test or the Mann-Whitney test. A two-tailed value of p < 0.05 was considered significant. RESULTS: 540 individuals were included, median of age was 47 years and 74.6% were male. Median body mass index was 26.1 and 94.6% had low cardiovascular risk. There was a significant increase in the percentage of patients that met criteria for hyperglycemia (BL 4.8% and DL 8.5%, p < 0.001). We also observed significant (p < 0.001) increase in median (IQR) BL vs DL values in LDL-cholesterol [109 (90-128) vs 118 (97-139) mg/dL]; and triglycerides [120 (87-172) vs. 132 mg/dL (96-184)]. The proportion of patients with hyper-LDL cholesterolemia according to individual cardiovascular risk increased from 12.6 to 17.2% (p = 0.04). CONCLUSION: Our results suggest that quarantine, at least in its initial phases, may have a negative impact on the metabolic profile of this population.

Integrated microbiome and metabolome analysis reveals the potential therapeutic mechanism of Qing-Fei-Pai-Du decoction in mice with coronavirus-induced pneumonia.

Current studies have shown that gut microbiota may be closely related to the severity of coronavirus disease 2019 (COVID-19) by regulating the host immune response. Qing-Fei-Pai-Du decoction (QFPDD) is the recommended drug for clinical treatment of patients with COVID-19 in China, but whether it exerts a therapeutic effect by modulating the immune response through gut microbiota remains unclear. In this study, we evaluated the therapeutic effects of QFPDD in pneumonia model mice and performed 16S rRNA sequencing and serum and lung tissue metabolomic analysis to explore the underlying mechanisms during the treatment. Then, Spearman correlation analysis was performed on gut microbiome, serum metabolome, and immune-inflammation-related indicators. Our results suggest that QFPDD can restore the richness and diversity of gut microbiota, and multiple gut microbiota (including Alistipes, Odoribacter, Staphylococcus, Lachnospiraceae_NK4A136_group Enterorhabdus, and unclassified_f_Lachnospiraceae) are significantly associated with immune-inflammation-related indicators. In addition, various types of lipid metabolism changes were observed in serum and lung tissue metabolome, especially glycerophospholipids and fatty acids. A total of 27 differential metabolites (DMs) were significantly correlated with immune-inflammation-related indicators, including 9 glycerophospholipids, 7 fatty acids, 3 linoleic acid, 2 eicosanoids, 2 amino acids, 2 bile acids, and 2 others. Interestingly, these DMs showed a good correlation with the gut microbiota affected by QFPDD. The above results suggest that QFPDD can improve the immune function and reduce inflammation in pneumonia model mice by remodeling gut microbiota and host metabolism.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Plasma metabolome and cytokine profile reveal glycylproline modulating antibody fading in convalescent COVID-19 patients.

The COVID-19 pandemic has incurred tremendous costs worldwide and is still threatening public health in the "new normal." The association between neutralizing antibody levels and metabolic alterations in convalescent patients with COVID-19 is still poorly understood. In the present work, we conducted absolutely quantitative profiling to compare the plasma cytokines and metabolome of ordinary convalescent patients with antibodies (CA), convalescents with rapidly faded antibodies (CO), and healthy subjects. As a result, we identified that cytokines such as M-CSF and IL-12p40 and plasma metabolites such as glycylproline (gly-pro) and long-chain acylcarnitines could be associated with antibody fading in COVID-19 convalescent patients. Following feature selection, we built machine-learning-based classification models using 17 features (six cytokines and 11 metabolites). Overall accuracies of more than 90% were attained in at least six machine-learning models. Of note, the dipeptide gly-pro, a product of enzymatic peptide cleavage catalyzed by dipeptidyl peptidase 4 (DPP4), strongly accumulated in CO individuals compared with the CA group. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination experiments in healthy mice demonstrated that supplementation of gly-pro down-regulates SARS-CoV-2-specific receptor-binding domain antibody levels and suppresses immune responses, whereas the DPP4 inhibitor sitagliptin can counteract the inhibitory effects of gly-pro upon SARS-CoV-2 vaccination. Our findings not only reveal the important role of gly-pro in the immune responses to SARS-CoV-2 infection but also indicate a possible mechanism underlying the beneficial outcomes of treatment with DPP4 inhibitors in convalescent COVID-19 patients, shedding light on therapeutic and vaccination strategies against COVID-19.

Effect of a Functional Phospholipid Metabolome-Protein Association Pathway on the Mechanism of COVID-19 Disease Progression.

This study aimed to explore the clinical practice of phospholipid metabolic pathways in COVID-19. In this study, 48 COVID-19 patients and 17 healthy controls were included. Patients were divided into mild (n=40) and severe (n=8) according to their severity. Phospholipid metabolites, TCA circulating metabolites, eicosanoid metabolites, and closely associated enzymes and transfer proteins were detected in the plasma of all individuals using metabolomics and proteomics assays, respectively. 30 of the 33 metabolites found differed significantly (P<0.05) between patients and healthy controls (P<0.05), with D-dimmer significantly correlated with all of the lysophospholipid metabolites (LysoPE, LysoPC, LysoPI and LPA). In particular, we found that phosphatidylinositol (PI) and phosphatidylcholine (PC) could identify patients from healthy controls (AUC 0.771 and 0.745, respectively) and that the severity of the patients could be determined (AUC 0.663 and 0.809, respectively). The last measurement before discharge also revealed significant changes in both PI and PC. For the first time, our study explores the significance of the phospholipid metabolic system in COVID-19 patients. Based on molecular pathway mechanisms, three important phospholipid pathways related to Ceramide-Malate acid (Cer-SM), Lysophospholipid (LPs), and membrane function were established. Clinical values discovered included the role of Cer in maintaining the inflammatory internal environment, the modulation of procoagulant LPA by upstream fibrinolytic metabolites, and the role of PI and PC in predicting disease aggravation.

Small-molecule metabolome identifies potential therapeutic targets against COVID-19.

Respiratory viruses are transmitted and acquired via the nasal mucosa, and thereby may influence the nasal metabolome composed of biochemical products produced by both host cells and microbes. Studies of the nasal metabolome demonstrate virus-specific changes that sometimes correlate with viral load and disease severity. Here, we evaluate the nasopharyngeal metabolome of COVID-19 infected individuals and report several small molecules that may be used as potential therapeutic targets. Specimens were tested by qRT-PCR with target primers for three viruses: Influenza A (INFA), respiratory syncytial virus (RSV), and SARS-CoV-2, along with unaffected controls. The nasopharyngeal metabolome was characterized using an LC-MS/MS-based screening kit capable of quantifying 141 analytes. A machine learning model identified 28 discriminating analytes and correctly categorized patients with a viral infection with an accuracy of 96% (R2 = 0.771, Q2 = 0.72). A second model identified 5 analytes to differentiate COVID19-infected patients from those with INFA or RSV with an accuracy of 85% (R2 = 0.442, Q2 = 0.301). Specifically, Lysophosphatidylcholines-a-C18:2 (LysoPCaC18:2) concentration was significantly increased in COVID19 patients (P < 0.0001), whereas beta-hydroxybutyric acid, Methionine sulfoxide, succinic acid, and carnosine concentrations were significantly decreased (P < 0.0001). This study demonstrates that COVID19 infection results in a unique nasopharyngeal metabolomic signature with carnosine and LysoPCaC18:2 as potential therapeutic targets.

1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization.

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.