The serum amino acid profile in COVID-19

Integrative Metabolomic and Lipidomic Signatures of SARS-CoV-2 VOCs: Correlations with Hematological and Biochemical Markers

In the present study, we investigated biochemical, hematological, lipidomic, and metabolomic alterations associated with different SAR-CoV-2 variants of concern (VOCs), such as WT, α, β, γ, and δ, as well as their impact on COVID-19 severity. Across the first and second waves in India, a machine learning approach was used in 3134 COVID-19 patients, and nine critical biochemical and hematological parameters, namely, C-reactive protein (CRP), D-dimer, ferritin, neutrophil, WBC count, lymphocyte, urea, creatine, and lactate dehydrogenase (LDH), were identified. Furthermore, through metabolic and lipidomic profiles of lung and colon cells transfected with spike VOCs, notable dysregulation was exhibited by the delta variant correlated with characteristic pathways such as catecholamine and thyroid hormone synthesis. A corroborating meta-analysis also highlighted the involvement of urea and amino acid metabolism pathways. Overall, our study provides crucial insights into metabolic and biochemical disruptions caused by VOCs, contributing to a better understanding of COVID-19 pathogenesis and the development of targeted interventions.

Plasma metabolomics signatures predict COVID-19 patient outcome at ICU admission comparable to clinical scores

SARS-CoV-2 significantly impacts the human metabolome. This study aims to evaluate the predictive capability of a comprehensive module clustering approach in plasma metabolomics for identifying the risk of critical complications in COVID-19 patients admitted to intensive care units (ICUs). We conducted a prospective monocenter study, gathering blood samples within 24 h of ICU admission, alongside clinical, biological, and demographic patient characteristics. Subsequently, we quantified patients' plasma metabolome using a comprehensive untargeted metabolomics approach. First, we stratified patients based on a composite outcome score indicating critical status. Analysis of potential predictors revealed that older patients with higher severity scores and pronounced alterations in key biological parameters are more likely to experience critical complications. Next, we identified 6,667 metabolic features clustered into 57 annotated metabolic modules across all patients by employing an integrative metabolomics approach. Furthermore, we identified the most differentially expressed metabolic modules related to patients' outcomes. Moreover, we defined the top five most predictive metabolites of critical status: homoserine, urobilinogen, methionine, xanthine and pipecolic acid. These five predictors alone demonstrated similar or superior performance compared to clinical and demographic variables in predicting patients' outcomes. This innovative metabolic module inference approach offers a valuable framework for identifying patients prone to complications upon ICU admission for COVID-19. Its potential applications extend to enhancing patient management across diverse clinical settings.

Metabolic Profiling Reveals Potential Prognostic Biomarkers for SFTS: Insights into Disease Severity and Clinical Outcomes

Background/Objectives: Severe fever with thrombocytopenia syndrome (SFTS) is a viral infection primarily found in Asia, with a case fatality rate of about 10%. Despite its increasing prevalence, the underlying pathogenic mechanisms remain poorly understood, limiting the development of effective therapeutic interventions. Methods: We employed an untargeted metabolomics approach using liquid chromatography-mass spectrometry (LC-MS) to analyze serum samples from 78 SFTS patients during the acute phase of their illness. Differential metabolic features between survival and fatal cases were identified through multivariate statistical analysis. Furthermore, we constructed a metabolic prognostic model based on these biomarkers to predict disease severity. Results: Significant alterations were observed in four key metabolic pathways: sphingolipid metabolism, biosynthesis of phenylalanine, tyrosine, and tryptophan, primary bile acid biosynthesis, and phenylalanine metabolism. Elevated levels of phenyllactic acid and isocitric acid were strongly associated with adverse outcomes and demonstrated high discriminatory power in distinguishing fatal cases from survivors. The metabolic prognostic model incorporating these biomarkers achieved a sensitivity of 75% and a specificity of 90% in predicting disease severity. Conclusions: Our findings highlight the pivotal role of metabolic dysregulation in the pathogenesis of SFTS and suggest that targeting specific metabolic pathways could open new avenues for therapeutic development. The identification of prognostic biomarkers provides a valuable tool for early risk stratification and timely clinical intervention, potentially improving patient outcomes.

A combined proteomic and metabolomic analysis of the early aborted embryonic tissues with maternal COVID-19 infection

COVID-19 still spreads worldwide, and repeated infections are hard to avoid. Maternal infection during pregnancy is associated with adverse maternal and neonatal outcomes. Our study used a multi-omics profiling method to explore the proteome and metabolome alteration in early embryonic development after COVID-19 infection. A total of 30 chorionic tissues after artificial abortion (15 infection and 15 no-infection samples) were collected, and the UHPLC-MS/MS and LC-MS/MS were applied in the present study. As a result, 311 significantly differentially expressed proteins were identified. The function annotations revealed that the thermogenesis pathway is the most significantly enriched signaling pathway; PRKAG2, IGF1R, and RPS6KB2 were identified as the hub proteins. There were 359 metabolites significantly altered after infection. The functional annotations revealed that amino acid metabolism was significantly affected, especially beta-alanine metabolism, glutamate metabolism, and histidine metabolism pathways. The metabolites in ovarian steroidogenesis showed a down-regulating trend in the infection group. Finally, we combined the results of proteins and metabolomics analysis. The biosynthesis of the cofactors pathway was identified as significantly enriched in both proteomics and metabolomics datasets. Our findings provide a network of protein regulation and metabolite perturbation during early embryonic development with COVID-19 infection. Our findings can provide valuable insights for further exploration of the complex mechanism of COVID-19-associated pregnancy complications and outcomes. SIGNIFICANCE: COVID-19 has developed into the most prominent and deadliest pandemic respiratory disease in the world, and repeated infections are complicated to avoid. COVID-19 infection during pregnancy increases the risk of adverse maternal and neonatal outcomes, such as preterm birth and stillbirth. However, previous studies mainly focused on its effect on pregnant women, such as the clinical characteristics and gestation outcomes. There is no relevant report about the effects of virus infection on embryos in early pregnancy. The effects of COVID-19 infection changes of the proteins and metabolites during early embryonic development are undefined. Our findings provide an association between protein regulation, metabolite perturbation, and COVID-19 infection, which can provide valuable insights for further exploration of the complex mechanism COVID-19 COVID-19-associated pregnancy complications and adverse pregnancy outcomes.

Metabolic Pattern of Brain Death-NMR-Based Metabolomics of Cerebrospinal Fluid

The aim of this study was to gain insight into the biochemical status of cerebrospinal fluid in the presence of brain death in life-supported patients. The biochemical status was determined via in vitro NMR spectroscopy of cerebrospinal fluid (CSF) obtained by lumbar puncture from 22 patients with confirmed brain death and compared with that of 34 control patients (without neurological diseases). Forty-one NMR signals from raw CSF samples and 20 signals from lipid extracts were analyzed using univariate and multivariate statistical methods. ANOVA revealed significant differences in all analyzed signals. No single biochemical marker was found to predict brain death. The CSF metabolic profiles of patients who died differed significantly from those of patients in the control group. There were many statistically significantly different compounds, including amino acids, ketone bodies, lactate, pyruvate, citrate, guanidinoacetate, choline, and glycerophosphocholine. Analysis of lipids revealed significant differences in cholesterol, estriol, and phosphoethanolamine. Discriminant analysis allows the analysis of metabolic profiles instead of single biomarkers of cerebrospinal fluid compounds. The results of our analysis allowed us to split the groups-the control group, which consisted of patients with a normal biochemical CSF composition, and the brain death group-with confirmed brain death.

Morphological, anatomical, and bioactive properties of Hypericum scabrum L.: effects on diabetes mellitus, Alzheimer's disease, and HDFa fibroblasts and U87-MG cancer cells

Diabetes mellitus (DM) and cancer are multifactorial diseases with significant health consequences, and their relationship with aging makes them particularly challenging. Epidemiological data suggests that individuals with DM are more susceptible to certain cancers. This study examined the bioactive properties of Hypericum scabrum extracts, including methanol, hexane, and others, focusing on their inhibitory effects on key enzymes associated with DM and neurodegenerative diseases, such as acetylcholinesterase, butyrylcholinesterase, α-amylase, and α-glucosidase. Additionally, the impact of these extracts on human fibroblast (HDFa) and glioblastoma (U87MG) cancer cells was evaluated. The methanol extract was analyzed for elemental composition using ICP-MS, secondary metabolites, and amino acids via LC-MS/MS and underwent morphological and anatomical characterization. The methanol extract demonstrated notable inhibitory activity, with an IC50 value of < 1 µg/mL against α-glucosidase, surpassing acarbose in efficacy. The flower essential oil exhibited the highest inhibition (79.95%) of butyrylcholinesterase and the strongest acetylcholinesterase inhibition (21.62%). Elemental analysis revealed high concentrations of Na and K, while quinic acid and proline were identified as major metabolites, with proline concentrations reaching 494.0482 nmol/mL in the aerial part extract. The anticancer assays revealed higher cytotoxicity in U87MG glioblastoma cells compared to HDFa fibroblasts, suggesting potential applications for cancer therapy. The plant grows 20-50 cm tall, with yellow flowers and ovoid-ribbed capsules containing brown, reniform seeds. Its leaves are amphistomatic and ornamented, while stems feature striate cuticles and paracytic stomata. The pollen grains are microreticulate with syncolporate apertures. These results underscore the promising therapeutic potential of H. scabrum in managing DM, cancer, and neurodegenerative diseases, with its ability to inhibit key enzymes and show selective cytotoxicity against cancer cells.

Analyzing Quinoa Seeds: LC-MS/MS and GC-MS/MS Approaches for Identifying a Nutrient-Rich Functional Food in Alzheimer's and Diabetes Care

Quinoa is a versatile, nutrient-dense, gluten-free pseudocereal, increasingly recognized for its potential health benefits, particularly in managing diabetes mellitus and Alzheimer's disease-two conditions with growing links. Recent research on 14 quinoa genotypes explored fatty oil composition and bioactive potential of quinoa extracts. Linoleic acid was predominant fatty acid (46.60%-56.33%), whereas alanine was the most concentrated amino acid across all active extracts (192.4887-1578.0355 nmol/mL). French Vanilla hexane extract exhibited the strongest α-glucosidase inhibition (75.71%), with Santa Ana hexane extract showing the highest α-amylase inhibition (28.58%). Additionally, Titicaca hexane extract displayed notable acetylcholinesterase inhibition (22.22%), and Moqu Arochilla methanol extract had the most potent butyrylcholinesterase inhibition (47.20%). Antioxidant assays revealed that Salcedo and French Vanilla extracts had exceptional 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity, with positive correlations found between total phenolic content and antioxidant activity (DPPH, β-carotene, FRAP). These findings emphasize quinoa's potential as a functional food, offering significant benefits for disease management.

Longitudinal 1H NMR-Based Metabolomics in Saliva Unveils Signatures of Transition from Acute to Post-Acute Phase of SARS-CoV-2 Infection

COVID-19 can range from a mild to severe acute respiratory syndrome and also could result in multisystemic damage. Additionally, many people develop post-acute symptoms associated with immune and metabolic disturbances in response to viral infection, requiring longitudinal and multisystem studies to understand the complexity of COVID-19 pathophysiology. Here, we conducted a 1H Nuclear Magnetic Resonance metabolomics in saliva of symptomatic subjects presenting mild and moderate respiratory symptoms to investigate prospective changes in the metabolism induced after acute-phase SARS-CoV-2 infection. Saliva from 119 donors presenting non-COVID and COVID-19 respiratory symptoms were evaluated in the acute phase (T1) and the post-acute phase (T2). We found two clusters of metabolite fluctuation in the COVID-19 group. Cluster 1, metabolites such as glucose, (CH3)3 choline-related metabolites, 2-hydroxybutyrate, BCAA, and taurine increased in T2 relative to T1, and in cluster 2, acetate, creatine/creatinine, phenylalanine, histidine, and lysine decreased in T2 relative to T1. Metabolic fluctuations in the COVID-19 group were associated with overweight/obesity, vaccination status, higher viral load, and viral clearance of the respiratory tract. Our data unveil metabolic signatures associated with the transition to the post-acute phase of SARS-CoV-2 infection that may reflect tissue damage, inflammatory process, and activation of tissue repair cascade. Thus, they contribute to describing alterations in host metabolism that may be associated with prolonged symptoms of COVID-19.

Comparison of Amino Acid Profile, ACE Inhibitory Activity, and Organic Acid Profile of Cow and Goat Yogurts Produced with Lactobacillus acidophilus LA-5, Bifidobacterium animalis subsp. lactis BB-12, and Classical Yogurt Culture

In this study, we aimed to produce a standard, more functional, and nutritious yogurt by using 5 different combinations of cow milk and goat milk and 2 types of starter cultures (classical yogurt culture and commercial probiotic culture). It was determined that the use of different milk types and different starter cultures in yogurt production had a statistically very significant effect (P < 0.01) on all physicochemical, microbiological, and biochemical properties. In addition, the storage period was effective on all parameters examined at varying rates. In the context, the use of goat milk in the experimental yogurt samples caused an increase in the ACE inhibitory activity values and the count of S. thermophilus, while the use of cow milk caused an increase in serum separation and pH values. On the other hand, serum separation, pH values, and ACE inhibitory activity and phenylalanine and leucine levels were found to be higher in the yogurts produced by using ABT-2 probiotic culture. It was observed that an increase in the levels of asparagine, aspartic acid, proline, and serine, as well as lactic acid, orotic acid, and citric acid, is higher in the yogurts produced by using classical yogurt culture. It has been concluded that the combination of goat milk and cow milk at different proportions and the use of probiotic culture together in yogurt production can produce yogurt that is more functional and richer in terms of organic compounds and essential amino acids.

Peripheral biomarkers as a predictor of poor prognosis in severe cases of COVID-19

We evaluated glycemia and triglyceride, hepatic, muscular, and renal damage markers, redox profile, and leptin and ghrelin hormone levels in COVID-19 patients. We also conducted statistical analysis to verify the potential of biomarkers to predict poor prognosis and the correlation between them in severe cases. We assessed glycemia and the levels of triglycerides, hepatic, muscular, and renal markers in automatized biochemical analyzer. The leptin and ghrelin hormones were assessed by the ELISA assay. Severe cases presented high glycemia and triglyceride levels. Hepatic, muscular, and renal biomarkers were altered in severe patients. Oxidative stress status was found in severe COVID-19 patients. Severe cases also had increased levels of leptin. The ROC curves indicated many biomarkers as poor prognosis predictors in severe cases. The Spearman analysis showed that biomarkers correlate between themselves. Patients with COVID-19 showed significant dysregulation in the levels of several peripheral biomarkers. We bring to light that a robust panel of peripheral biomarkers and hormones predict poor prognosis in severe cases of COVID-19 and biomarkers correlate with each other. Early monitoring of these biomarkers may lead to appropriate clinical interventions in patients infected by SARS-CoV2.

Investigation of the Elemental Contents, Functional and Nutraceutical Properties of Kefirs Enriched with Spirulina platensis, an Eco-friendly and Alternative Protein Source

In this study, the effect of the use of S. platensis, which is presented as an eco-friendly and alternative protein source, in the production of kefir, a probiotic dairy product, on various physicochemical properties as well as FAA, ACE inhibitory activity, proteolysis, TPC, DPPH, ABTS+, and mineral values was investigated. It was observed that the addition of S. platensis at different ratios to the kefir samples had a statistically very significant (p < 0.01) effect on all physicochemical analyses; L. mesenteroides count; all amino acids except isoleucine, aspartic acid, and glutamic acid; ACE inhibitory activity, TN, TCAN, TCAN/TN, mM Gly, TPC, DPPH, ABTS+, Na, Mg, K, and Fe. In plain kefir samples, mineral contents were determined by order of K > P > Na > Ca > Mg > Zn >> Fe > Cr > Cr > Mn. Furthermore, a general increase was observed in FAA, ACE inhibitory activity, TPC, DPPH, ABTS+, and mineral values, as well as in the counts of Lactococcus spp. and L. mesenteroides in the samples, depending on the proportion of S. platensis added, compared to plain kefir samples. In this context, it was concluded that the addition of S. platensis to kefir at different rates could meet various components required by the body on a daily basis and result in a nutraceutical product.

Metabolic predictors of COVID-19 mortality and severity: a survival analysis

Introduction

The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.

Methods

In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes.

Results

A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.

Conclusions

Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.

Exploring free amino acid profiles in Crimean-Congo hemorrhagic fever patients: Implications for disease progression

This study investigated the intricate interplay between Crimean-Congo hemorrhagic fever virus infection and alterations in amino acid metabolism. The primary aim is to elucidate the impact of Crimean-Congo hemorrhagic fever (CCHF) on specific amino acid concentrations and identify potential metabolic markers associated with viral infection. One hundred ninety individuals participated in this study, comprising 115 CCHF patients, 30 CCHF negative patients, and 45 healthy controls. Liquid chromatography-tandem mass spectrometry techniques were employed to quantify amino acid concentrations. The amino acid metabolic profiles in CCHF patients exhibit substantial distinctions from those in the control group. Patients highlight distinct metabolic reprogramming, notably characterized by arginine, histidine, taurine, glutamic acid, and glutamine metabolism shifts. These changes have been associated with the underlying molecular mechanisms of the disease. Exploring novel therapeutic and diagnostic strategies addressing specific amino acids may offer potential means to mitigate the severity of the disease.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Association between Gut Microbiota Composition and Long-Term Vaccine Immunogenicity following Three Doses of CoronaVac

BACKGROUND

Neutralizing antibody level wanes with time after COVID-19 vaccination. We aimed to study the relationship between baseline gut microbiota and immunogenicity after three doses of CoronaVac.

METHODS

This was a prospective cohort study recruiting three-dose CoronaVac recipients from two centers in Hong Kong. Blood samples were collected at baseline and one year post-first dose for virus microneutralization (vMN) assays to determine neutralization titers. The primary outcome was high immune response (defined as with vMN titer ≥ 40). Shotgun DNA metagenomic sequencing of baseline fecal samples identified potential bacterial species and metabolic pathways using Linear Discriminant Analysis Effect Size (LEfSe) analysis. Univariate and multivariable logistic regression models were used to identify high response predictors.

RESULTS

In total, 36 subjects were recruited (median age: 52.7 years [IQR: 47.9-56.4]; male: 14 [38.9%]), and 18 had low immune response at one year post-first dose vaccination. Eubacterium rectale (log10LDA score = 4.15, p = 0.001; relative abundance of 1.4% vs. 0, p = 0.002), Collinsella aerofaciens (log10LDA score = 3.31, p = 0.037; 0.39% vs. 0.18%, p = 0.038), and Streptococcus salivarius (log10LDA score = 2.79, p = 0.021; 0.05% vs. 0.02%, p = 0.022) were enriched in low responders. The aOR of high immune response with E. rectale, C. aerofaciens, and S. salivarius was 0.03 (95% CI: 9.56 × 10-4-0.32), 0.03 (95% CI: 4.47 × 10-4-0.59), and 10.19 (95% CI: 0.81-323.88), respectively. S. salivarius had a positive correlation with pathways enriched in high responders like incomplete reductive TCA cycle (log10LDA score = 2.23). C. aerofaciens similarly correlated with amino acid biosynthesis-related pathways. These pathways all showed anti-inflammation functions.

CONCLUSION

E. rectale,C. aerofaciens, and S. salivarius correlated with poorer long-term immunogenicity following three doses of CoronaVac.

Interindividual- and blood-correlated sweat phenylalanine multimodal analytical biochips for tracking exercise metabolism

In situ monitoring of endogenous amino acid loss through sweat can provide physiological insights into health and metabolism. However, existing amino acid biosensors are unable to quantitatively assess metabolic status during exercise and are rarely used to establish blood-sweat correlations because they only detect a single concentration indicator and disregard sweat rate. Here, we present a wearable multimodal biochip integrated with advanced electrochemical electrodes and multipurpose microfluidic channels that enables simultaneous quantification of multiple sweat indicators, including phenylalanine and chloride, as well as sweat rate. This combined measurement approach reveals a negative correlation between sweat phenylalanine levels and sweat rates among individuals, which further enables identification of individuals at high metabolic risk. By tracking phenylalanine fluctuations induced by protein intake during exercise and normalizing the concentration indicator by sweat rates to reduce interindividual variability, we demonstrate a reliable method to correlate and analyze sweat-blood phenylalanine levels for personal health monitoring.

Maternal serum amino acid levels as predictors of premature rupture of membranes: A comprehensive analysis

INTRODUCTION

The aim of this study is to investigate the association between altered maternal serum amino acids (AAs) levels and premature rupture of membranes (pPROM) in pregnant women.

METHODS

We conducted a case-control study involving 60 pregnant women diagnosed with pPROM and 60 healthy pregnant women as controls. Amino acid levels were quantified using high-performance liquid chromatography. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive capability of specific AAs for pPROM.

RESULTS

Our findings revealed that lysine, glycine, and glutamic acid levels were significantly elevated in the pPROM group compared with the control group. Lysine, with a threshold value exceeding 137.90 μmol/L, exhibited the highest predictive accuracy, with an area under the curve (AUC) of 0.796 (p < 0.001), sensitivity of 66.7 %, and specificity of 80.0 %. Glycine, with a cut-off value of >242.48 μmol/L, had an AUC of 0.789 (p < 0.001), sensitivity of 83.3 %, and specificity of 65.0 %. Glutamic acid, at a threshold of 111.40 μmol/L, demonstrated an AUC of 0.787 (p < 0.001), sensitivity of 88.3 %, and specificity of 65.0 %. These AAs could effectively predict the occurrence of pPROM.

CONCLUSION

Elevated blood levels of lysine, glycine, and glutamic acid were found to be associated with pPROM. These AAs serve as potential predictive biomarkers for pPROM, with lysine showing the highest AUC and sensitivity. Identifying such biomarkers may contribute to the development of non-invasive diagnostic tools for pPROM risk assessment, enabling timely interventions and improved maternal and fetal outcomes.

Phytochemical Analysis and Evaluation of Genotoxic, Antigenotoxic Effects in Bacterial Models, and Hypoglycemic Activity of Epilobium angustifolium L. with Bioguided Isolation of Active Compounds

This study examined the effects of methanol extract and its sub-extracts from Epilobium angustifolium on α-glucosidase and α-amylase activity. Secondary metabolites and amino acids were quantified using LC-MS/MS. Dichloromethane sub-extract displayed the highest activity and was chosen for further investigation. Despite the widespread use of E. angustifolium, genotoxicity studies were conducted to assess its safety. Dichloromethane significantly inhibited α-glucosidase (IC50 =17.340 μg/mL), making it approximately 293 times more effective than acarbose. Six known compounds, including gallic acid (1), a mixture of quercetin-3-O-α-galactoside (2a) and quercetin-3-O-α-glucoside (2b), quercetin-3-O-α-glucuronic acid (3), quercetin-3-O-α-rhamnoside (4), and kaempferol-3-O-α-rhamnoside (5) were identified. Quercetin-3-O-α-rhamnoside exhibited the highest inhibition of α-glucosidase (IC50 =1735±85 μM), making it 3.70 times more effective than acarbose. Dichloromethane also showed significant antigenotoxic activity against mutagenesis induced by NaN3, 9-AA, 4-NPD, and MNNG. Gallic acid was found in the highest abundance (13253.6931 ng/mL) in the methanolic extract. Furthermore, L-Aspartic acid was the most concentrated amino acid (363.5620 nmol/mL) in the methanolic extract.

A comparative NMR-based metabolomics study of lung parenchyma of severe COVID-19 patients

COVID-19 was the most significant infectious-agent-related cause of death in the 2020-2021 period. On average, over 60% of those admitted to ICU facilities with this disease died across the globe. In severe cases, COVID-19 leads to respiratory and systemic compromise, including pneumonia-like symptoms, acute respiratory distress syndrome, and multiorgan failure. While the upper respiratory tract and lungs are the principal sites of infection and injury, most studies on the metabolic signatures in COVID-19 patients have been carried out on serum and plasma samples. In this report we attempt to characterize the metabolome of lung parenchyma extracts from fatal COVID-19 cases and compare them with that from other respiratory diseases. Our findings indicate that the metabolomic profiles from fatal COVID-19 and non-COVID-19 cases are markedly different, with the former being the result of increased lactate and amino acid metabolism, altered energy pathways, oxidative stress, and inflammatory response. Overall, these findings provide additional insights into the pathophysiology of COVID-19 that could lead to the development of targeted therapies for the treatment of severe cases of the disease, and further highlight the potential of metabolomic approaches in COVID-19 research.

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Amino acids, post-translational modifications, nitric oxide, and oxidative stress in serum and urine of long COVID and ex COVID human subjects

In this study, we investigated the status of amino acids, their post-translational modifications (PTM), major nitric oxide (NO) metabolites and of malondialdehyde (MDA) as a biomarker of oxidative stress in serum and urine samples of long COVID (LoCo, n = 124) and ex COVID (ExCo, n = 24) human subjects collected in 2022. Amino acids and metabolites were measured by gas chromatography-mass spectrometry (GC-MS) methods using stable-isotope labelled analogs as internal standards. There were no differences with respect to circulating and excretory arginine and asymmetric dimethylarginine (ADMA). LoCo participants excreted higher amounts of guanidino acetate than ExCo participants (17.8 ± 10.4 µM/mM vs. 12.6 ± 8.86 µM/mM, P = 0.005). By contrast, LoCo participants excreted lower amounts of the advanced glycation end-product (AGE) NG-carboxyethylarginine (CEA) than ExCo participants did (0.675 ± 0.781 µM/mM vs. 1.16 ± 2.04 µM/mM, P = 0.0326). The serum concentrations of MDA did not differ between the groups, indicating no elevated oxidative stress in LoCo or ExCo. The serum concentration of nitrite was lower in LoCo compared to ExCo (1.96 ± 0.92 µM vs. 2.56 ± 1.08 µM; AUC, 0.718), suggesting altered NO synthesis in the endothelium. The serum concentration of nitrite correlated inversely with the symptom anxiety (r = - 0.293, P = 0.0003). The creatinine-corrected urinary excretion of Lys and its metabolite L-5-hydroxy-Lys correlated positively with COVID toes (r = 0.306, P = 0.00027) and sore throat (r = 0.302, P = 0.0003). Our results suggest that amino acid metabolism, PTM and oxidative stress are not severely affected in long COVID. LoCo participants may have a lower circulating NO reservoir than ExCo.

A paper-based chemical tongue based on the charge transfer complex of ninhydrin with an array of metal-doped carbon dots discriminates natural amino acids and several of their enantiomers

Simultaneous detection of multiple amino acids (AAs) instead of individual AAs is inherently worthwhile for improving diagnostic accuracy in clinical applications. Here, a facile and reliable colorimetric microfluidic paper-based analytical device (μPAD) using carbon dots doped with transition metals (Cr3+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, and Zn2+) has been provided to detect and discriminate 20 natural amino acids. To make the colourless metal-doped carbon dots suitable for colorimetric assays, they were mixed with ninhydrin to form a charge transfer complex. This optical tongue system, which was constructed by dropping mixtures of ninhydrin with a series of metal-doped carbon dots on a paper substrate in an array format, represented obvious but different colorimetric signatures for every examined amino acid. Since bovine serum albumin was used as a chiral selector reagent for synthesizing the CDs, the sensor device represented excellent selectivity to identify enantiomeric species of AAs. This is the first optical array device that can simultaneously discriminate AAs and several of their enantiomers. We employed various statistical and chemometric methods to analyze the digital data library collected by Image J software, including principal component analysis (PCA), linear discriminant analysis (LDA), and hierarchical cluster analysis (HCA). Twenty AAs could be well distinguished at various concentrations (10.00, 5.00, 2.50, and 1.25 mM). The colorimetric patterns were highly repeatable and were characteristic of individual AAs. Besides qualitative analysis, the designed μPAD-based optical tongue represented quantitative analysis ability, e.g., for lysine in the concentration ranges of 0.005-20.0 mM with a detection limit of 1.0 × 10-6 M and for arginine in the concentration range of 0.12-20.00 mM with a detection limit of 80.0 × 10-6 M. In addition, the binary, ternary, and quaternary mixtures of AAs could also be well recognized with this sensor.

Metabolomics Study Revealing Purines as Potential Diagnostic Biomarkers of Acute Respiratory Distress Syndrome in Patients with Community─Acquired Pneumonia

Community-acquired pneumonia (CAP) is a significant threat to human health and the leading cause of acute respiratory distress syndrome (ARDS). We aimed to reveal the metabolic profiling whether can be used for assessing CAP with or without ARDS (nARDS) and therapeutic effects on CAP patients after treatment. Urine samples were collected at the onset and recovery periods, and metabolomics was employed to identify robust biomarkers. 19 metabolites were significantly changed in the ARDS relative to nARDS, mainly involving purines and fatty acids. After treatment, 7 metabolites in the nARDS and 14 in the ARDS were found to be significantly dysregulated, including fatty acids and amino acids. In the validation cohort, we observed that the biomarker panel consisted of N2,N2-dimethylguanosine, 1-methyladenosine, 3-methylguanine, 1-methyladenosine, and uric acid exhibited better AUCs of 0.900 than pneumonia severity index and acute physiology and chronic health evaluation II (APACHE II) scores between the ARDS and nARDS. Combining L-phenylalanine, phytosphingosine, and N-acetylaspartylglutamate as biomarkers for discriminating the nARDS and ARDS patients after treatment exhibited good AUCs of 0.811 and 0.821, respectively. The metabolic pathway and defined biomarkers may serve as crucial indicators for predicting the development of ARDS in CAP patients and for assessing therapeutic effects.

The plasma metabolome of long COVID patients two years after infection

One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as "long COVID") which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Amino acid profiling as a screening and prognostic biomarker in active tuberculosis patients

BACKGROUND

Tuberculosis (TB) is one of the world's most deadly chronic infectious diseases; early diagnosis contributes to reducing disease transmission among populations. However, discovering novel diagnostic and prognostic biomarkers is still an important topic in the field of TB. Amino acid is the basic unit of protein composition, and its structure and physicochemical characteristics are more stable. Therefore, it is a potential target for TB diagnosis and the prediction of TB development.

METHODS

In this study, the blood of healthy people (HC), TB patients (TB), cured TB (RxTB), and other non-TB pneumonia patients (PN) were collected to detect the levels of amino acids in whole blood and plasma using ultra-high performance liquid chromatography coupled with mass spectrometry.

RESULTS

We detected that the amino acid levels correlated with participants status (TB, HC, RxTB, or PN) and the degree of lung damage. The results showed that phenylalanine had a good effect on the screening of TB (AUC = 0.924). We then built a TB prediction model. The model, which was based on the ratio of plasma amino acid content to whole blood amino acid content, showed good performance for the screening of TB, with 84% (95% CI = 60-97) sensitivity and 97% (95% CI = 83-100) specificity. The result of correlation between the HRCT score and amino acid level indicated that the glutamine content of plasma was significantly inversely associated with disease severity. Additionally, ornithine levels in the plasma of RxTB group reduced and four amino acids of which the ratio in plasma to whole blood showed significantly changed.

CONCLUSIONS

Taken together, amino acid profiling can be used for TB screening, and a multiparameter profiling model is better. The profiling can also reflect the severity of lung damage. Moreover, the amino acid profile is useful for reflecting the efficacy of TB treatment.

Ketogenic Diet and Ketone Bodies as Clinical Support for the Treatment of SARS-CoV-2-Review of the Evidence

One of the proposed nutritional therapies to support drug therapy in COVID-19 is the use of a ketogenic diet (KD) or ketone bodies. In this review, we summarized the evidence from tissue, animal, and human models and looked at the mechanisms of action of KD/ketone bodies against COVID-19. KD/ketone bodies were shown to be effective at the stage of virus entry into the host cell. The use of β-hydroxybutyrate (BHB), by preventing the metabolic reprogramming associated with COVID-19 infection and improving mitochondrial function, reduced glycolysis in CD4+ lymphocytes and improved respiratory chain function, and could provide an alternative carbon source for oxidative phosphorylation (OXPHOS). Through multiple mechanisms, the use of KD/ketone bodies supported the host immune response. In animal models, KD resulted in protection against weight loss and hypoxemia, faster recovery, reduced lung injury, and resulted in better survival of young mice. In humans, KD increased survival, reduced the need for hospitalization for COVID-19, and showed a protective role against metabolic abnormalities after COVID-19. It appears that the use of KD and ketone bodies may be considered as a clinical nutritional intervention to assist in the treatment of COVID-19, despite the fact that numerous studies indicate that SARS-CoV-2 infection alone may induce ketoacidosis. However, the use of such an intervention requires strong scientific validation.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group^-, n = 23) and with previous COVID-19 infection (group⁺, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.

Glutamine Deficiency Promotes Immune and Endothelial Cell Dysfunction in COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has caused the death of almost 7 million people worldwide. While vaccinations and new antiviral drugs have greatly reduced the number of COVID-19 cases, there remains a need for additional therapeutic strategies to combat this deadly disease. Accumulating clinical data have discovered a deficiency of circulating glutamine in patients with COVID-19 that associates with disease severity. Glutamine is a semi-essential amino acid that is metabolized to a plethora of metabolites that serve as central modulators of immune and endothelial cell function. A majority of glutamine is metabolized to glutamate and ammonia by the mitochondrial enzyme glutaminase (GLS). Notably, GLS activity is upregulated in COVID-19, favoring the catabolism of glutamine. This disturbance in glutamine metabolism may provoke immune and endothelial cell dysfunction that contributes to the development of severe infection, inflammation, oxidative stress, vasospasm, and coagulopathy, which leads to vascular occlusion, multi-organ failure, and death. Strategies that restore the plasma concentration of glutamine, its metabolites, and/or its downstream effectors, in conjunction with antiviral drugs, represent a promising therapeutic approach that may restore immune and endothelial cell function and prevent the development of occlusive vascular disease in patients stricken with COVID-19.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Phenylalanine metabolism and tetrahydrobiopterin bio-availability in COVID-19 and HIV

Various metabolomics studies have reported increased phenylalanine serum concentrations in SARS-CoV-2 positive cases and have correlated increased phenylalanine with COVID-19 severity. In this study, we report similar results based upon metabolomics analysis of serum collected from a South African cohort of adults with confirmed COVID-19. The novelty of this study is the inclusion of HIV positive cases in the African context. We found that pre-existing HIV co-infection exacerbates the disruption of phenylalanine metabolism in COVID-19. What is lacking in literature is biological context and deeper understanding of perturbed phenylalanine metabolism in COVID-19. We delve deep into the metabolism of phenylalanine in COVID-19 and posit new insights for COVID-19 cases co-infected with HIV; namely, HIV-COVID-19 co-infected individuals do not have sufficient bioavailability of tetrahydrobiopterin (BH₄). Hence, we identify BH₄ as a potential supplement to alleviate/lessen COVID-19 symptoms.

An overview on role of nutrition on COVID-19 immunity: Accumulative review from available studies

The novel coronavirus infection (COVID-19) conveys a serious global threat to health and economy. A common predisposing factor for development to serious progressive disease is presence of a low-grade inflammation, e.g., as seen in diabetes, metabolic syndrome, and heart failure. Micronutrient deficiencies may also contribute to the development of this state. Therefore, the aim of the present study is to explore the role of the nutrition to relieve progression of COVID-19. According PRISMA protocol, we conducted an online databases search including Scopus, PubMed, Google Scholar and web of science for published literatures in the era of COVID-19 Outbreak regarding to the status of nutrition and COVID-19 until December 2021. There were available studies (80 studies) providing direct evidence regarding the associations between the status of nutrition and COVID-19 infection. Adequate nutritional supply is essential for resistance against other viral infections and also for improvement of immune function and reduction of inflammation. Hence, it is suggested that nutritional intervention which secures an adequate status might protect against the novel coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome - coronavirus-2) and mitigate its course. We also recommend initiation of adequate nutritional supplementation in high-risk areas and/or soon after the time of suspected infection with SARS-CoV-2. Subjects in high-risk groups should have high priority for applying this nutritive adjuvant therapy that should be started prior to administration of specific and supportive medical measures.

Association between Circulating Amino Acids and COVID-19 Severity

The severity of the symptoms associated with COVID-19 is highly variable, and has been associated with circulating amino acids as a group of analytes in metabolomic studies. However, for each individual amino acid, there are discordant results among studies. The aims of the present study were: (i) to investigate the association between COVID-19-symptom severity and circulating amino-acid concentrations; and (ii) to assess the ability of circulating amino-acid levels to predict adverse outcomes (intensive-care-unit admission or hospital death). We studied a sample of 736 participants from the Biobanque Québécoise COVID-19. All participants tested positive for COVID-19, and the severity of symptoms was determined using the World-Health-Organization criteria. Circulating amino acids were measured by HPLC-MS/MS. We used logistic models to assess the association between circulating amino acids concentrations and the odds of presenting mild vs. severe or mild vs. moderate symptoms, as well as their accuracy in predicting adverse outcomes. Patients with severe COVID-19 symptoms were older on average, and they had a higher prevalence of obesity and type 2 diabetes. Out of 20 amino acids tested, 16 were significantly associated with disease severity, with phenylalanine (positively) and cysteine (inversely) showing the strongest associations. These associations remained significant after adjustment for age, sex and body mass index. Phenylalanine had a fair ability to predict the occurrence of adverse outcomes, similar to traditionally measured laboratory variables. A multivariate model including both circulating amino acids and clinical variables had a 90% accuracy at predicting adverse outcomes in this sample. In conclusion, patients presenting severe COVID-19 symptoms have an altered amino-acid profile, compared to those with mild or moderate symptoms.

Combining Phenylalanine and Leucine Levels Predicts 30-Day Mortality in Critically Ill Patients Better than Traditional Risk Factors with Multicenter Validation

In critically ill patients, risk scores are used; however, they do not provide information for nutritional intervention. This study combined the levels of phenylalanine and leucine amino acids (PLA) to improve 30-day mortality prediction in intensive care unit (ICU) patients and to see whether PLA could help interpret the nutritional phases of critical illness. We recruited 676 patients with APACHE II scores ≥ 15 or intubated due to respiratory failure in ICUs, including 537 and 139 patients in the initiation and validation (multicenter) cohorts, respectively. In the initiation cohort, phenylalanine ≥ 88.5 μM (indicating metabolic disturbance) and leucine < 68.9 μM (indicating malnutrition) were associated with higher mortality rate. Based on different levels of phenylalanine and leucine, we developed PLA scores. In different models of multivariable analyses, PLA scores predicted 30-day mortality independent of traditional risk scores (p < 0.001). PLA scores were then classified into low, intermediate, high, and very-high risk categories with observed mortality rates of 9.0%, 23.8%, 45.6%, and 81.8%, respectively. These findings were validated in the multicenter cohort. PLA scores predicted 30-day mortality better than APACHE II and NUTRIC scores and provide a basis for future studies to determine whether PLA-guided nutritional intervention improves the outcomes of patients in ICUs.

SARS-CoV-2 Infection and Candidate Biomarkers

Severe acute respiratory syndrome coronavirus 2 is a virus that can still infect individuals and whose deadly effects continue despite the current vaccines and drugs. Since 2019, many studies on the pathogenesis of the disease have been completed and continue to be done. In addition to the diagnosis and treatment of the disease, many molecules that can be markers of the disease have been investigated. In the early stages of the pandemic, many nonspecific and infection-related laboratory findings and chest computed tomography were used to obtain information about the diagnosis of the disease. The more individual molecules became associated with the disease yet. The purpose of this review is to summarize the impact and role of many molecules associated with coronavirus disease-2019 infection that have been previously used and newly revealed. Numerous studies are summarized in this review. The obtained data show that previously used laboratory findings and new potential biomarkers are not specific to the disease. New potential biomarkers have been associated with the severity of the disease itself, as can be seen with lung imaging and even with routine laboratory findings. One of the important points that are seen frequently in studies is that the effectiveness of these molecules has been shown not only in coronavirus disease-2019 infection but also in many other diseases. This removes the pathogenesis of the disease from being a unique mechanism created by the Severe acute respiratory syndrome coronavirus 2 and provides a general perspective formed by viral or bacterial infections. However, there are still many molecular changes that need to be investigated. Future studies will continue to update themselves with the mutations of the virus.

Urine Metabolites Enable Fast Detection of COVID-19 Using Mass Spectrometry

The COVID-19 pandemic boosted the development of diagnostic tests to meet patient needs and provide accurate, sensitive, and fast disease detection. Despite rapid advancements, limitations related to turnaround time, varying performance metrics due to different sampling sites, illness duration, co-infections, and the need for particular reagents still exist. As an alternative diagnostic test, we present urine analysis through flow-injection-tandem mass spectrometry (FIA-MS/MS) as a powerful approach for COVID-19 diagnosis, targeting the detection of amino acids and acylcarnitines. We adapted a method that is widely used for newborn screening tests on dried blood for urine samples in order to detect metabolites related to COVID-19 infection. We analyzed samples from 246 volunteers with diagnostic confirmation via PCR. Urine samples were self-collected, diluted, and analyzed with a run time of 4 min. A Lasso statistical classifier was built using 75/25% data for training/validation sets and achieved high diagnostic performances: 97/90% sensitivity, 95/100% specificity, and 95/97.2% accuracy. Additionally, we predicted on two withheld sets composed of suspected hospitalized/symptomatic COVID-19-PCR negative patients and patients out of the optimal time-frame collection for PCR diagnosis, with promising results. Altogether, we show that the benchmarked FIA-MS/MS method is promising for COVID-19 screening and diagnosis, and is also potentially useful after the peak viral load has passed.

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia

Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.

Dysregulation of glutamine/glutamate metabolism in COVID-19 patients: A metabolism study in African population and mini meta-analysis

Coronavirus disease 2019 (COVID-19) remains a serious global threat. The metabolic analysis had been successfully applied in the efforts to uncover the pathological mechanisms and biomarkers of disease severity. Here we performed a quasi-targeted metabolomic analysis on 56 COVID-19 patients from Sierra Leone in western Africa, revealing the metabolomic profiles and the association with disease severity, which was confirmed by the targeted metabolomic analysis of 19 pairs of COVID-19 patients. A meta-analysis was performed on published metabolic data of COVID-19 to verify our findings. Of the 596 identified metabolites, 58 showed significant differences between severe and nonsevere groups. The pathway enrichment of these differential metabolites revealed glutamine and glutamate metabolism as the most significant metabolic pathway (Impact = 0.5; -log10P = 1.959). Further targeted metabolic analysis revealed six metabolites with significant intergroup differences, with glutamine/glutamate ratio significantly associated with severe disease, negatively correlated with 10 clinical parameters and positively correlated with SPO₂ (r_s = 0.442, p = 0.005). Mini meta-analysis indicated elevated glutamate was related to increased risk of COVID-19 infection (pooled odd ratio [OR] = 2.02; 95% confidence interval [CI]: 1.17-3.50) and severe COVID-19 (pooled OR = 2.28; 95% CI: 1.14-4.56). In contrast, elevated glutamine related to decreased risk of infection and severe COVID-19, the pooled OR were 0.30 (95% CI: 0.20-0.44), and 0.44 (95% CI: 0.19-0.98), respectively. Glutamine and glutamate metabolism are associated with COVID-19 severity in multiple populations, which might confer potential therapeutic target of COVID-19, especially for severe patients.

Analysis of Chitinase-3-Like Protein 1, IL-1-Alpha, and IL-6 as Novel Inflammatory Biomarkers for COVID-19

The aim of our study was to investigate the potential role of IL-1-alpha, IL-6, and chitinase 3-like protein-1 (CHI3L1) as potential biomarkers for COVID-19. Sixty adult SARS Cov-2 PCR-positive patients (22 mild, 25 moderate, and 13 severe) and 50 healthy controls were included in this study. The serum levels of CHI3L1, IL-1-alpha, and IL-6 for all study participants were measured by protein-specific ELISAs. Mean serum CHI3L1 levels in patients with severe disease (7,185.5 ± 1,109.4) were significantly higher than in the moderate (3,977.4 ± 1,260.3), mild (1,379.5 ± 598.8), and control (329.5 ± 128.4) groups (P = 0.001). There was no difference in IL-1-alpha levels between the patient and control groups (P = 0.083). IL-6 levels differed significantly, being lowest in the control group (35.9 ± 13.7), 89.1 ± 23.4 in the mild group, 156.2 ± 29.6 in the moderate group, and the highest in the severe group (214.9 ± 28.1) (P = 0.001). A strong significant correlation was found between disease severity and serum IL-6 and CHI3L1 values (r = 0.894 and r = 0.905, respectively, and P < 0.001 for both). Serum CHI3L1 and IL-6 levels exhibited a linear correlation with the clinical course of COVID-19 infection. These results indicate that inhibitors of IL-6 and/or CHI3L1 may provide useful treatments for COVID-19.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Early taurine administration as a means for halting the cytokine storm progression in COVID-19 patients

Around the world, more than 6.2 million individuals have died as a result of coronavirus disease 2019 (COVID-19). According to a recent survey conducted among immunologists, epidemiologists, and virologists, this disease is expected to become endemic. This implies that the disease could have a continuous presence and/or normal frequency in the population. Pharmacological interventions to prevent infection, as well as to treat the patients at an early phase of illness to avoid hospitalization are essential additions to the vaccines. Taurine is known to inhibit the generation of all inflammatory mediators linked to the cytokine storm. It can also protect against lung injury by suppressing increased oxidants production and promoting the resolution of the inflammatory process. Neutrophil lactoferrin degranulation stimulated by taurine may have antiviral effects against SARS-CoV-2, limiting viral replication. It is hypothesized that if taurine is administered early in the onset of COVID-19 disease, it may stop the cytokine storm from progressing, lowering morbidity and mortality.

Plasma Levels and Renal Handling of Amino Acids Contribute to Determination of Risk of Mortality or Feed of Ventilation in Patients with COVID-19

COVID-19 infection may lead to serious complications, e.g., need for mechanical ventilation or death in some cases. A retrospective analysis of patients referred to our COVID Emergency Department, indiscriminately, was performed. A routine lab analysis measured amino acids in plasma and urine of patients. Data of surviving and deceased patients and those requiring or not requiring mechanical ventilation were compared, and logistic regression analyses have been performed. Deceased patients were older, had higher blood glucose, potassium, AST, LDH, troponin, d-dimer, hsCRP, procalcitonin, interleukin-6 levels (p < 0.05 for all). They had lower plasma serine, glycine, threonine, tryptophan levels (p < 0.01), higher tyrosine and phenylalanine levels (p < 0.05), and higher fractional excretion of arginine, methionine, and proline (p < 0.05) than survivors. In a regression model, age, severity score of COVID-pneumonia, plasma levels of threonine and phenylalanine were predictors of in-hospital mortality. There was a difference in ventilated vs. non-ventilated patients in CT-scores, glucose, and renal function (p < 0.001). Using logistic regression, CT-score, troponin, plasma level, and fractional excretion of glycine were predictors of ventilation. Plasma levels and renal excretion of certain amino acids are associated with the outcome of COVID-19 infection beside other parameters such as the CT-score or age.

Plasma Metabolomic Alterations Induced by COVID-19 Vaccination Reveal Putative Biomarkers Reflecting the Immune Response

Vaccination is currently the most effective strategy for the mitigation of the COVID-19 pandemic. mRNA vaccines trigger the immune system to produce neutralizing antibodies (NAbs) against SARS-CoV-2 spike proteins. However, the underlying molecular processes affecting immune response after vaccination remain poorly understood, while there is significant heterogeneity in the immune response among individuals. Metabolomics have often been used to provide a deeper understanding of immune cell responses, but in the context of COVID-19 vaccination such data are scarce. Mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR)-based metabolomics were used to provide insights based on the baseline metabolic profile and metabolic alterations induced after mRNA vaccination in paired blood plasma samples collected and analysed before the first and second vaccination and at 3 months post first dose. Based on the level of NAbs just before the second dose, two groups, "low" and "high" responders, were defined. Distinct plasma metabolic profiles were observed in relation to the level of immune response, highlighting the role of amino acid metabolism and the lipid profile as predictive markers of response to vaccination. Furthermore, levels of plasma ceramides along with certain amino acids could emerge as predictive biomarkers of response and severity of inflammation.

Human Cytochrome P450 2C9 and Its Polymorphic Modifications: Electroanalysis, Catalytic Properties, and Approaches to the Regulation of Enzymatic Activity

The electrochemical properties of cytochrome P450 2C9 (CYP2C9) and polymorphic modifications P450 2C9*2 (CYP2C9*2) and P450 2C9*3 (CYP2C9*3) were studied. To analyze the comparative electrochemical and electrocatalytic activity, the enzymes were immobilized on electrodes modified with a membrane-like synthetic surfactant (didodecyldimethylammonium bromide (DDAB)). An adequate choice of the type of modified electrode was confirmed by cyclic voltammetry of cytochromes P450 under anaerobic conditions, demonstrating well-defined peaks of reduction and oxidation of the heme iron. The midpoint potential, Emid, of cytochrome P450 2C9 is −0.318 ± 0.01 V, and Emid = −0.324 ± 0.01 V, and Emid = −0.318 ± 0.03 V for allelic variant 2C9*2 and allelic variant 2C9*3, respectively. In the presence of substrate diclofenac under aerobic conditions, cytochrome P450 2C9 and its polymorphic modifications P450 2C9*2 and P450 2C9*3 exhibit catalytic properties. Stimulation of the metabolism of diclofenac by cytochrome P450 2C9 in the presence of antioxidant medications mexidol and taurine was shown.