Metabolomic analyses reveal new stage-specific features of COVID-19

Early Metabolomic and Immunologic Biomarkers as Prognostic Indicators for COVID-19

This prospective study in Hong Kong aimed at identifying prognostic metabolomic and immunologic biomarkers for Coronavirus Disease 2019 (COVID-19). We examined 327 patients, mean age 55 (19-89) years, in whom 33.6% were infected with Omicron and 66.4% were infected with earlier variants. The effect size of disease severity on metabolome outweighed others including age, gender, peak C-reactive protein (CRP), vitamin D and peak viral levels. Sixty-five metabolites demonstrated strong associations and the majority (54, 83.1%) were downregulated in severe disease (z score: -3.30 to -8.61). Ten cytokines/chemokines demonstrated strong associations (p < 0.001), and all were upregulated in severe disease. Multiple pairs of metabolomic/immunologic biomarkers showed significant correlations. Fourteen metabolites had the area under the receiver operating characteristic curve (AUC) > 0.8, suggesting a high predictive value. Three metabolites carried high sensitivity for severe disease: triglycerides in medium high-density lipoprotein (MHDL) (sensitivity: 0.94), free cholesterol-to-total lipids ratio in very small very-low-density lipoprotein (VLDL) (0.93), cholesteryl esters-to-total lipids ratio in chylomicrons and extremely large VLDL (0.92);whereas metabolites with the highest specificity were creatinine (specificity: 0.94), phospholipids in large VLDL (0.94) and triglycerides-to-total lipids ratio in large VLDL (0.93). Five cytokines/chemokines, namely, interleukin (IL)-6, IL-18, IL-10, macrophage inflammatory protein (MIP)-1b and tumour necrosis factor (TNF)-a, had AUC > 0.8. In conclusion, we demonstrated a tight interaction and prognostic potential of metabolomic and immunologic biomarkers enabling an outcome-based patient stratification.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases

Background

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures.

Aim

To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis.

Method

We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis.

Results

Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state.

Conclusion

This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Serum Metabolomics Uncovers the Mechanisms of Inulin in Preventing Non-Alcoholic Fatty Liver Disease

Inulin may be a promising therapeutic molecule for treating non-alcoholic fatty liver disease (NAFLD). However, the underlying mechanisms of its therapeutic activity remain unclear. To address this issue, a high-fat-diet-induced NAFLD mouse model was developed and treated with inulin. The NAFLD phenotype was evaluated via histopathological analysis and biochemical parameters, including serum levels of alanine aminotransferase, aspartate aminotransferase, liver triglycerides, etc. A serum metabolomics study was conducted using ultra-performance liquid chromatography coupled with tandem mass spectrometry. The results revealed that inulin mitigated NAFLD symptoms such as histopathological changes and liver cholesterol levels. Through the serum metabolomics study, 347 differential metabolites were identified between the model and control groups, and 139 differential metabolites were identified between the inulin and model groups. Additionally, 48 differential metabolites (such as phosphatidylserine, dihomo-γ-linolenic acid, L-carnitine, and 13-HODE) were identified as candidate targets of inulin and subjected to pathway enrichment analysis. The results revealed that these 48 differential metabolites were enriched in several metabolic pathways such as fatty acid biosynthesis and cardiolipin biosynthesis. Taken together, our results suggest that inulin might attenuate NAFLD partially by modulating 48 differential metabolites and their correlated metabolic pathways, constituting information that might help us find novel therapies for NAFLD.

Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19

The persistence of coronavirus disease 2019 (COVID-19)-related hospitalization severely threatens medical systems worldwide and has increased the need for reliable detection of acute status and prediction of mortality. We applied a systems biology approach to discover acute-stage biomarkers that could predict mortality. A total 247 plasma samples were collected from 103 COVID-19 (52 surviving COVID-19 patients and 51 COVID-19 patients with mortality), 51 patients with other infectious diseases (IDCs) and 41 healthy controls (HCs). Paired plasma samples were obtained from survival COVID-19 patients within 1 day after hospital admission and 1-3 days before discharge. There were clear differences between COVID-19 patients and controls, as well as substantial differences between the acute and recovery phases of COVID-19. Samples from patients in the acute phase showed suppressed immunity and decreased steroid hormone biosynthesis, as well as elevated inflammation and proteasome activation. These findings were validated by enzyme-linked immunosorbent assays and metabolomic analyses in a larger cohort. Moreover, excessive proteasome activity was a prominent signature in the acute phase among patients with mortality, indicating that it may be a key cause of poor prognosis. Based on these features, we constructed a machine learning panel, including four proteins [C-reactive protein (CRP), proteasome subunit alpha type (PSMA)1, PSMA7, and proteasome subunit beta type (PSMB)1)] and one metabolite (urocortisone), to predict mortality among COVID-19 patients (area under the receiver operating characteristic curve: 0.976) on the first day of hospitalization. Our systematic analysis provides a novel method for the early prediction of mortality in hospitalized COVID-19 patients.

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning

BACKGROUND

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.

METHODS

Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.

RESULTS

The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.

CONCLUSIONS

Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Immune rebalancing at the maternal-fetal interface of maternal SARS-CoV-2 infection during early pregnancy

The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a threat to pregnant women. However, the impact of early pregnancy SARS-CoV-2 infection on the maternal-fetal interface remains poorly understood. Here, we present a comprehensive analysis of single-cell transcriptomics and metabolomics in placental samples infected with SARS-CoV-2 during early pregnancy. Compared to control placentas, SARS-CoV-2 infection elicited immune responses at the maternal-fetal interface and induced metabolic alterations in amino acid and phospholipid profiles during the initial weeks post-infection. However, subsequent immune cell activation and heightened immune tolerance in trophoblast cells established a novel dynamic equilibrium that mitigated the impact on the maternal-fetal interface. Notably, the immune response and metabolic alterations at the maternal-fetal interface exhibited a gradual decline during the second trimester. Our study underscores the adaptive immune tolerance mechanisms and establishment of immunological balance during the first two trimesters following maternal SARS-CoV-2 infection.

Metabolomics Analysis Identifies Differential Metabolites as Biomarkers for Acute Myocardial Infarction

Myocardial infarction (MI), including ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI), is still a leading cause of death worldwide. Metabolomics technology was used to explore differential metabolites (DMs) as potential biomarkers for early diagnosis of STEMI and NSTEMI. In the study, 2531 metabolites, including 1925 DMs, were discovered. In the selected 27 DMs, 14 were successfully verified in a new cohort, and the AUC values were all above 0.8. There were 10 in STEMI group, namely L-aspartic acid, L-acetylcarnitine, acetylglycine, decanoylcarnitine, hydroxyphenyllactic acid, ferulic acid, itaconic acid, lauroylcarnitine, myristoylcarnitine, and cis-4-hydroxy-D-proline, and 5 in NSTEMI group, namely L-aspartic acid, arachidonic acid, palmitoleic acid, D-aspartic acid, and palmitelaidic acid. These 14 DMs may be developed as biomarkers for the early diagnosis of MI with high sensitivity and specificity. These findings have particularly important clinical significance for NSTEMI patients because these patients have no typical ECG changes.

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.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Omics-based profiles and biomarkers of respiratory infections: are we there yet?

From the influenza pandemic of 1918–1919 to the most recent COVID-19 pandemic, respiratory infections remain a leading cause of mortality worldwide [1, 2]. Concurrently, the development of high-throughput omics technologies has revolutionised research about host responses to known and emerging respiratory pathogens [3], accelerating our understanding of highly prevalent pulmonary diseases [4]. Notably, omics technology-based characterisation of pathogens and host pathophysiology have critically supported diagnostic and therapeutic global health efforts during both the influenza A H1N1 and SARS-CoV-2 pandemics [5–7]. Nonetheless, elucidation of key immune response mechanisms and development of host-targeted therapeutics remain important unrealised research and clinical priorities in the global fight against lower respiratory tract infections (LTRIs) [8, 9].

Descriptive omics-based clinical research provides valuable early steps in understanding host immune responses to respiratory pathogens in our global efforts to mitigate the impacts of severe respiratory infections with rapidly evolving technologies https://bit.ly/4bjJsvL

Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study

Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

[Acid-base balance and long COVID: comments on metabolic-respiratory alterations]

Long COVID is a multi-organ pathology with important sequelae that affect the health and welfare of survivors from the cellular bioenergetics, in this case, the importance of considering the acid-base balance within the processes of evaluation and treatment of long COVID by health and sports professionals was addressed, given that different investigations have found important modifications in mitochondrial function that result in ventilation failures, causing alterations in the compensation-decompensation of respiratory and renal pH control (metabolic-respiratory acidosis and alkalosis).

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.

COVID-19-associated liver injury: Adding fuel to the flame

COVID-19 is mainly characterized by respiratory disorders and progresses to multiple organ involvement in severe cases. With expansion of COVID-19 and SARS-CoV-2 research, correlative liver injury has been revealed. It is speculated that COVID-19 patients exhibited abnormal liver function, as previously observed in the SARS and MERS pandemics. Furthermore, patients with underlying diseases such as chronic liver disease are more susceptible to SARS-CoV-2 and indicate a poor prognosis accompanied by respiratory symptoms, systemic inflammation, or metabolic diseases. Therefore, COVID-19 has the potential to impair liver function, while individuals with preexisting liver disease suffer from much worse infected conditions. COVID-19 related liver injury may be owing to direct cytopathic effect, immune dysfunction, gut-liver axis interaction, and inappropriate medication use. However, discussions on these issues are infancy. Expanding research have revealed that angiotensin converting enzyme 2 (ACE2) expression mediated the combination of virus and target cells, iron metabolism participated in the virus life cycle and the fate of target cells, and amino acid metabolism regulated immune response in the host cells, which are all closely related to liver health. Further exploration holds great significance in elucidating the pathogenesis, facilitating drug development, and advancing clinical treatment of COVID-19-related liver injury. This article provides a review of the clinical and laboratory hepatic characteristics in COVID-19 patients, describes the etiology and impact of liver injury, and discusses potential pathophysiological mechanisms.

The effect of long-term moderate exercise on myocardial metabolome in rats

Regular moderate physical exercise is beneficial for the cardiovascular system. Our prior study has demonstrated a long-term moderate exercise (4-week of 60-min 74.0% V̇O2max treadmill running) is optimal in protecting from exhaustive exercise-induced cardiac ischemic injury. This study is aimed to investigate the effect of long-term moderate exercise on myocardial metabolome in rats. Thirteen male Sprague-Dawley rats were randomly assigned into the control group (C) and the long-term moderate exercise group (E). The targeted metabolomics of the myocardium was analyzed by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system. Results showed that the metabolites categories of bile acids (BAs), fatty acids (FAs), and phenylpropanoic acids were significantly decreased. The biosynthesis of unsaturated FAs pathway was significantly downregulated. The altered metabolites in the E Group included decreased FAs (pentadecanoic acid, 10Z-heptadecenoic acid, dihomo-gamma-linolenic acid, docosahexaenoic acid, docosapentaenoic acid, and 10Z-nonadecenoic acid), decreased BAs (chenodeoxycholic acid and beta-muricholic acid), decreased organic acids (glycolic acid and 2-hydroxyglutaric acid), decreased carbohydrate (N-acetylneuraminic acid, Neu5Ac), decreased amino acids (α-aminobutyric acid and norvaline), decreased phenylpropanoic acids (hydroxyphenyllactic acid), and benzoic acids (4-hydroxybenzoic acid and phthalic acid). The results indicated that long-term moderate exercise has promoted lipids utilization in myocardium while exerted little influence on carbohydrate metabolism and diminished many detrimental metabolites. Notably, decrease of myocardial carbohydrate Neu5Ac after long-term moderate exercise might predict a prospective metabolomics biomarker for cardioprotection. This research has displayed the effect of long-term moderate exercise on myocardial metabolomic profiling in rats and indicated some promising metabolites which can be applied for exercise benefits in future.

Mitochondrial dysfunction, lipids metabolism, and amino acid biosynthesis are key pathways for COVID-19 recovery

The metabolic alterations caused by SARS-CoV-2 infection reflect disease progression. To analyze molecules involved in these metabolic changes, a multiomics study was performed using plasma from 103 patients with different degrees of COVID-19 severity during the evolution of the infection. With the increased severity of COVID-19, changes in circulating proteomic, metabolomic, and lipidomic profiles increased. Notably, the group of severe and critical patients with high HRG and ChoE (20:3) and low alpha-ketoglutaric acid levels had a high chance of unfavorable disease evolution (AUC = 0.925). Consequently, patients with the worst prognosis presented alterations in the TCA cycle (mitochondrial dysfunction), lipid metabolism, amino acid biosynthesis, and coagulation. Our findings increase knowledge regarding how SARS-CoV-2 infection affects different metabolic pathways and help in understanding the future consequences of COVID-19 to identify potential therapeutic targets.

Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

INTRODUCTION

Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.

OBJECTIVES

In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.

METHODS

A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3',4'-didehydro-3'-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients' levels.

RESULTS & CONCLUSION

Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

Repressed Blautia-acetate immunological axis underlies breast cancer progression promoted by chronic stress

Chronic stress is a known risk factor for breast cancer, yet the underlying mechanisms are unclear. This study explores the potential involvement of microbial and metabolic signals in chronic stress-promoted breast cancer progression, revealing that reduced abundances of Blautia and its metabolite acetate may contribute to this process. Treatment with Blautia and acetate increases antitumor responses of CD8+ T cells and reverses stress-promoted breast cancer progression in female mice. Patients with depression exhibit lower abundances of Blautia and acetate, and breast cancer female patients with depression display lower abundances of acetate, decreased numbers of tumor-infiltrating CD8+ T cells, and an increased risk of metastasis. These results suggest that Blautia-derived acetate plays a crucial role in modulating the immune response to breast cancer, and its reduction may contribute to chronic stress-promoted cancer progression. Our findings advance the understanding of microbial and metabolic signals implicated in cancer in patients with depression and may provide therapeutic options for female patients with breast cancer and depression.

Metabolomics-directed nanotechnology in viral diseases management: COVID-19 a case study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently regarded as the twenty-first century's plague accounting for coronavirus disease 2019 (COVID-19). Besides its reported symptoms affecting the respiratory tract, it was found to alter several metabolic pathways inside the body. Nanoparticles proved to combat viral infections including COVID-19 to demonstrate great success in developing vaccines based on mRNA technology. However, various types of nanoparticles can affect the host metabolome. Considering the increasing proportion of nano-based vaccines, this review compiles and analyses how COVID-19 and nanoparticles affect lipids, amino acids, and carbohydrates metabolism. A search was conducted on PubMed, ScienceDirect, Web of Science for available information on the interrelationship between metabolomics and immunity in the context of SARS-CoV-2 infection and the effect of nanoparticles on metabolite levels. It was clear that SARS-CoV-2 disrupted several pathways to ensure a sufficient supply of its building blocks to facilitate its replication. Such information can help in developing treatment strategies against viral infections and COVID-19 based on interventions that overcome these metabolic changes. Furthermore, it showed that even drug-free nanoparticles can exert an influence on biological systems as evidenced by metabolomics.

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.

Comprehensive transcriptomics and metabolomics analyses reveal that hyperhomocysteinemia is a high risk factor for coronary artery disease in a chinese obese population aged 40-65: a prospective cross-sectional study

BACKGROUND

Clinical observations suggest a complex relationship between obesity and coronary artery disease (CAD). This study aimed to characterize the intermediate metabolism phenotypes among obese patients with CAD and without CAD.

METHODS

Sixty-two participants who consecutively underwent coronary angiography were enrolled in the discovery cohort. Transcriptional and untargeted metabolomics analyses were carried out to screen for key molecular changes between obese patients with CAD (CAD obese), without CAD (Non-CAD obese), and Non-CAD leans. A targeted GC-MS metabolomics approach was used to further identify differentially expressed metabolites in the validation cohorts. Regression and receiver operator curve analysis were performed to validate the risk model.

RESULTS

We found common aberrantly expressed pathways both at the transcriptional and metabolomics levels. These pathways included cysteine and methionine metabolism and arginine and proline metabolism. Untargeted metabolomics revealed that S-adenosylhomocysteine (SAH), 3-hydroxybenzoic acid, 2-hydroxyhippuric acid, nicotinuric acid, and 2-arachidonoyl glycerol were significantly elevated in the CAD obese group compared to the other two groups. In the validation study, targeted cysteine and methionine metabolomics analyses showed that homocysteine (Hcy), SAH, and choline were significantly increased in the CAD obese group compared with the Non-CAD obese group, while betaine, 5-methylpropanedioic acid, S-adenosylmethionine, 4-PA, and vitamin B2 (VB2) showed no significant differences. Multivariate analyses showed that Hcy was an independent predictor of obesity with CAD (hazard ratio 1.7; 95%CI 1.2-2.6). The area under the curve based on the Hcy metabolomic (HCY-Mtb) index was 0.819, and up to 0.877 for the HCY-Mtb.index plus clinical variables.

CONCLUSION

This is the first study to propose that obesity with hyperhomocysteinemia is a useful intermediate metabolism phenotype that could be used to identify obese patients at high risk for developing CAD.

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite-metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity.

An integrated deep learning framework for the interpretation of untargeted metabolomics data

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Targeting 2-Oxoglutarate-Dependent Dioxygenases Promotes Metabolic Reprogramming That Protects against Lethal SARS-CoV-2 Infection in the K18-hACE2 Transgenic Mouse Model

Dysregulation of host metabolism is a feature of lethal SARS-CoV-2 infection. Perturbations in α-ketoglutarate levels can elicit metabolic reprogramming through 2-oxoglutarate-dependent dioxygenases (2-ODDGs), leading to stabilization of the transcription factor HIF-1α. HIF1-α activation has been reported to promote antiviral mechanisms against SARS-CoV-2 through direct regulation of ACE2 expression (a receptor required for viral entry). However, given the numerous pathways HIF-1α serves to regulate it is possible that there are other undefined metabolic mechanisms contributing to the pathogenesis of SARS-CoV-2 independent of ACE2 downregulation. In this study, we used in vitro and in vivo models in which HIF-1α modulation of ACE2 expression was negated, allowing for isolated characterization of the host metabolic response within SARS-CoV-2 disease pathogenesis. We demonstrated that SARS-CoV-2 infection limited stabilization of HIF-1α and associated mitochondrial metabolic reprogramming by maintaining activity of the 2-ODDG prolyl hydroxylases. Inhibition of 2-ODDGs with dimethyloxalylglycine promoted HIF-1α stabilization following SARS-CoV-2 infection, and significantly increased survival among SARS-CoV-2-infected mice compared with vehicle controls. However, unlike previous reports, the mechanism by which activation of HIF-1α responses contributed to survival was not through impairment of viral replication. Rather, dimethyloxalylglycine treatment facilitated direct effects on host metabolism including increased glycolysis and resolution of dysregulated pools of metabolites, which correlated with reduced morbidity. Taken together, these data identify (to our knowledge) a novel function of α-ketoglutarate-sensing platforms, including those responsible for HIF-1α stabilization, in the resolution of SARS-CoV-2 infection and support targeting these metabolic nodes as a viable therapeutic strategy to limit disease severity during infection.

The landscape of abnormal pathway activation confers COVID-19 patients' molecular sequelae earlier than clinical phenotype

Rationale: The 2019 coronavirus disease (COVID-19) pandemic poses a significant threat to human health. After SARS-CoV-2 infection, major clinical concerns are organ damage and possible sequelae. Methods: In this study, we analyzed serum multi-omics data based on population-level, including healthy cohort, non-COVID-19 and COVID-19 covered different severity cohorts. We applied the pseudo-SpatioTemporal Consistency Alignment (pST-CA) strategy to correct for individualized disease course differences, and developed pseudo-deterioration timeline model and pseudo-recovery timeline model based on the "severe index" and "course index". Further, we comprehensively analyzed and discussed the dynamic damage signaling in COVID-19 deterioration and/or recovery, as well as the potential risk of sequelae. Results: The deterioration and course models based on the pST-CA strategy can effectively map the activation of blood molecular signals on cellular, pathway, functional and disease phenotypes in COVID-19 deterioration and throughout the disease course. The models revealed the neurological, cardiovascular, and hepatic toxicity present in SARS-CoV-2. The abundance of differentially expressed proteins and the activity of upstream regulators were comprehensively analyzed and evaluated to predict possible target drugs for SARS-CoV-2. On molecular docking simulation analysis, it was further demonstrated that blocking CEACAM1 is a potential therapeutic target for SARS-CoV-2. Conclusions: Clinically, the risk of organ failure and death in COVID-19 patients rises with increasing number of infections. Individualized sequelae prediction for patients and assessment of individualized intervenable targets and available drugs in combination with the upstream regulator analysis results are of great clinical value.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Background

Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods

This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results

Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion

The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Nanopore membrane chip-based isolation method for metabolomic analysis of plasma small extracellular vesicles from COVID-19 survivors

Multiple studies showed that metabolic disorders play a critical role in respiratory infectious diseases, including COVID-19. Metabolites contained in small extracellular vesicles (sEVs) are different from those in plasma at the acute stage, while the metabolic features of plasma sEVs of COVID-19 survivors remain unknown. Here, we used a nanopore membrane-based microfluidic chip for plasma sEVs separation, termed ExoSEC, and compared the sEVs obtained by UC, REG, and ExoSEC in terms the time, cost, purity, and metabolic features. The results indicated the ExoSEC was much less costly, provided higher purity by particles/proteins ratio, and achieved 205-fold and 2-fold higher sEVs yield, than UC and REG, respectively. Moreover, more metabolites were identified and several signaling pathways were significantly enriched in ExoSEC-sEVs compared to UC-sEVs and REG-sEVs. Furthermore, we detected 306 metabolites in plasma sEVs using ExoSEC from recovered asymptomatic (RA), moderate (RM), and severe/critical COVID-19 (RS) patients without underlying diseases 3 months after discharge. Our study demonstrated that COVID-19 survivors, especially RS, experienced significant metabolic alteration and the dysregulated pathways mainly involved fatty acid biosynthesis, phenylalanine metabolism, etc. Metabolites of the fatty acid biosynthesis pathway bore a significantly negative association with red blood cell counts and hemoglobin, which might be ascribed to hypoxia or respiratory failure in RM and RS but not in RA at the acute stage. Our study confirmed that ExoSEC could provide a practical and economical alternative for high throughput sEVs metabolomic study.

Plasma metabolomic characterization of SARS-CoV-2 Omicron infection

Omicron variants of SARS-CoV-2 have spread rapidly worldwide; however, most infected patients have mild or no symptoms. This study aimed to understand the host response to Omicron infections by performing metabolomic profiling of plasma. We observed that Omicron infections triggered an inflammatory response and innate immune, and adaptive immunity was suppressed, including reduced T-cell response and immunoglobulin antibody production. Similar to the original SARS-CoV-2 strain circulating in 2019, the host developed an anti-inflammatory response and accelerated energy metabolism in response to Omicron infection. However, differential regulation of macrophage polarization and reduced neutrophil function has been observed in Omicron infections. Interferon-induced antiviral immunity was not as strong in Omicron infections as in the original SARS-CoV-2 infections. The host response to Omicron infections increased antioxidant capacity and liver detoxification more than in the original strain. Hence, these findings suggest that Omicron infections cause weaker inflammatory alterations and immune responses than the original SARS-CoV-2 strain.

Three Klebsiella species as potential pathobionts generating endogenous ethanol in a clinical cohort of patients with auto-brewery syndrome: a case control study

BACKGROUND

Patients with auto-brewery syndrome (ABS) become inebriated after the ingestion of an alcohol-free, high-carbohydrate diet. Our previous work has shown that high-alcohol-producing (HiAlc) Klebsiella pneumoniae can generate excessive endogenous ethanol and cause non-alcoholic fatty liver disease (NAFLD). Therefore, it is reasonable to speculate that such bacteria might play an important role in the pathogenesis of ABS.

METHODS

The characteristics and metabolites of the intestinal flora from a clinical cohort of patients with ABS were analysed during different stages of disease and compared to a group of healthy controls. An in vitro culture system of relevant samples was used for screening drug sensitivity and ABS-inducing factors. Rabbit intestinal and murine models were established to verify if the isolated strains could induce ABS in vivo.

FINDINGS

We observed intestinal dysbiosis with decreased abundance of Firmicutes and increased of Proteobacteria in patients with ABS compared with healthy controls. The abundance of the genus Klebsiella in Enterobacteriaceae was strongly associated with fluctuations of patient's blood alcohol concentration. We isolated three species of HiAlc Klebsiella from ABS patients, which were able to induce ABS in mice. Monosaccharide content was identified as a potential food-related inducing factor for alcohol production. Treatments with antibiotics, a complex probiotic preparation and a low-carbohydrate diet not only alleviated ABS, but also erased ABS relapse during the follow-up observation of one of the patients.

INTERPRETATION

Excessive endogenous alcohol produced by HiAlc Klebsiella species was an underlying cause of bacterial ABS. Combined prescription of appropriate antibiotics, complex probiotic preparation and a controlled diet could be sufficient for treatment of bacteria-caused ABS.

FUNDING

The funders are listed in the acknowledgement.

A systematic review on impact of SARS-CoV-2 infection

Innumerable pathogens including RNA viruses have catastrophic pandemic propensity, in turn, SARS-CoV-2 infection is highly contagious. Emergence of SARS-CoV-2 variants with high mutation rate additionally codifies infectious ability of virus and arisen clinical imputations to human health. Although, our knowledge of mechanism of virus infection and its impact on host system has been substantially demystified, uncertainties about the emergence of virus are still not fully understood. To date, there are no potentially curative drugs are identified against the viral infection. Even though, drugs are repurposed in the initial period of infection, many are significantly negative in clinical trials. Moreover, the infection is dependent on organ status, co-morbid conditions, variant of virus and geographic region. This review article aims to comprehensively describe the SARS-CoV-2 infection and the impacts in the host cellular system. This review also briefly provides an overview of genome, proteome and metabolome associated risk to infection and the advancement of therapeutics in SARS-CoV-2 infection management.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Integrated Metabolic and Inflammatory Signatures Associated with Severity of, Fatality of, and Recovery from COVID-19

Severe manifestations of coronavirus disease 2019 (COVID-19) and mortality have been associated with physiological alterations that provide insights into the pathogenesis of the disease. Moreover, factors that drive recovery from COVID-19 can be explored to identify correlates of protection. The cellular metabolism represents a potential target to improve survival upon severe disease, but the associations between the metabolism and the inflammatory response during COVID-19 are not well defined. We analyzed blood laboratorial parameters, cytokines, and metabolomes of 150 individuals with mild to severe disease, of which 33 progressed to a fatal outcome. A subset of 20 individuals was followed up after hospital discharge and recovery from acute disease. We used hierarchical community networks to integrate metabolomics profiles with cytokines and markers of inflammation, coagulation, and tissue damage. Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) promotes significant alterations in the plasma metabolome, whose activity varies according to disease severity and correlates with oxygen saturation. Differential metabolism underlying death was marked by amino acids and related metabolites, such as glutamate, glutamyl-glutamate, and oxoproline, and lipids, including progesterone, phosphocholine, and lysophosphatidylcholines (lysoPCs). Individuals who recovered from severe disease displayed persistent alterations enriched for metabolism of purines and phosphatidylinositol phosphate and glycolysis. Recovery of mild disease was associated with vitamin E metabolism. Data integration shows that the metabolic response is a hub connecting other biological features during disease and recovery. Infection by SARS-CoV-2 induces concerted activity of metabolic and inflammatory responses that depend on disease severity and collectively predict clinical outcomes of COVID-19. IMPORTANCE COVID-19 is characterized by diverse clinical outcomes that include asymptomatic to mild manifestations or severe disease and death. Infection by SARS-CoV-2 activates inflammatory and metabolic responses that drive protection or pathology. How inflammation and metabolism communicate during COVID-19 is not well defined. We used high-resolution mass spectrometry to investigate small biochemical compounds (<1,500 Da) in plasma of individuals with COVID-19 and controls. Age, sex, and comorbidities have a profound effect on the plasma metabolites of individuals with COVID-19, but we identified significant activity of pathways and metabolites related to amino acids, lipids, nucleotides, and vitamins determined by disease severity, survival outcome, and recovery. Furthermore, we identified metabolites associated with acute-phase proteins and coagulation factors, which collectively identify individuals with severe disease or individuals who died of severe COVID-19. Our study suggests that manipulating specific metabolic pathways can be explored to prevent hyperinflammation, organ dysfunction, and death.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Microbiome-Metabolomics Analysis of the Impacts of Cryptosporidium muris Infection in BALB/C Mice

Cryptosporidium is a leading cause of diarrheal disease and mortality in young children worldwide. Cryptosporidium invades small intestinal epithelial cells and forms a unique intracellular niche, a process that may alter gut microbes and their production metabolites. However, the mechanism of interactions between gut microbes, metabolites, and parasites is poorly understood. Here, we first characterized the impacts of Cryptosporidium infection on gut microbiota using a microbiome-to-metabolome association study. BALB/c mice were gavaged with Cryptosporidium muris, and fecal samples were collected at 0, 7, 14, 21, and 28 days postinfection (dpi) to observe changes in the intestinal microbes of the body during parasite infection. The infected group had a significantly increased relative abundance of bacterial taxa, such as Lachnospiraceae and Prevotella (P < 0.05), associated with the biosynthesis of short-chain fatty acids (SCFAs). Metabolites related to the metabolic pathways, steroid hormone biosynthesis, and biosynthesis of unsaturated fatty acids pathway were upregulated at 7 dpi, indicating that related metabolites in the biosynthesis of unsaturated fatty acids may be essential for C. muris reproduction in vivo. The metabolites involved in metabolic pathways, bile secretion, and primary bile acid biosynthesis were upregulated at 14 dpi, and we speculate that these metabolites may be critical to the growth and development of Cryptosporidium oocysts in the host. Correlation analysis revealed that Firmicutes bacteria are significantly associated with α-linolenic acid metabolism pathways (P< 0.05). The gut microbiota changes dynamically, and the metabolites involved in fatty acid and bile acid biosynthesis may play important roles during cryptosporidiosis. Details of the gut microbiota and the metabolome after infection with Cryptosporidium may aid in the discovery of specific diagnostic markers and help us understand the changes in parasite metabolic pathways. IMPORTANCE Cryptosporidiosis is a gastrointestinal disease in humans and animals caused by the protozoan parasite Cryptosporidium. Control and treatment of the disease is challenging due to the lack of sensitive diagnostic tools and effective chemotherapy. The dynamics of gut microbiota and metabolites during Cryptosporidium infection may be the key to finding drugs and targets for parasite infection control. Our results indicate that C. muris infection can disrupt gut microbiota and metabolites, resulting in decreased bacterial abundance at the parasitic site. Unsaturated fatty acid pathway biosynthesis-related metabolites are significantly elevated at the patent period. Interestingly, the metabolite pathway that significantly elevated during peak parasite growth was bile acid, the metabolites of which may be important for the circulation of infection of Cryptosporidium oocysts in the host. The enhancing effects of short-chain fatty acid and bile acid metabolism on the growth and development of Cryptosporidium proposed in this study may provide a theoretical basis for future research on novel drugs and vaccines against this intestinal parasite.

Salivary Metabolomic Analysis Reveals Amino Acid Metabolism Shift in SARS-CoV-2 Virus Activity and Post-Infection Condition

The SARS-CoV-2 virus primarily infects salivary glands suggesting a change in the saliva metabolite profile; this shift may be used as a monitoring instrument during SARS-CoV-2 infection. The present study aims to determine the salivary metabolomic profile of patients with and post-SARS-CoV-19 infection. Patients were without (PCR-), with SARS-CoV-2 (PCR+), or post-SARS-CoV-2 infection. Unstimulated whole saliva was collected, and the ¹H spectra were acquired in a 500 MHz Bruker nuclear magnetic resonance spectrometer at 25 °C. They were subjected to multivariate analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), as well as univariate analysis through t-tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05. A distinction was found when comparing PCR- subjects to those with SARS-CoV-2 infection. When comparing the three groups, the PLS-DA cross-validation presented satisfactory accuracy (ACC = 0.69, R2 = 0.39, Q2 = 0.08). Seventeen metabolites were found in different proportions among the groups. The results suggested the downregulation of major amino acid levels, such as alanine, glutamine, histidine, leucine, lysine, phenylalanine, and proline in the PCR+ group compared to the PCR- ones. In addition, acetate, valerate, and capronic acid were higher in PCR- patients than in PCR+. Sucrose and butyrate were higher in post-SARS-CoV-2 infection compared to PCR-. In general, a reduction in amino acids was observed in subjects with and post-SARS-CoV-2 disease. The salivary metabolomic strategy NMR-based was able to differentiate between non-infected individuals and those with acute and post-SARS-CoV-19 infection.

Comparison of fecal and blood metabolome reveals inconsistent associations of the gut microbiota with cardiometabolic diseases

Blood metabolome is commonly used in human studies to explore the associations of gut microbiota-derived metabolites with cardiometabolic diseases. Here, in a cohort of 1007 middle-aged and elderly adults with matched fecal metagenomic (149 species and 214 pathways) and paired fecal and blood targeted metabolomics data (132 metabolites), we find disparate associations with taxonomic composition and microbial pathways when using fecal or blood metabolites. For example, we observe that fecal, but not blood butyric acid significantly associates with both gut microbiota and prevalent type 2 diabetes. These findings are replicated in an independent validation cohort involving 103 adults. Our results suggest that caution should be taken when inferring microbiome-cardiometabolic disease associations from either blood or fecal metabolome data.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Heritability of Protein and Metabolite Biomarkers Associated with COVID-19 Severity: A Metabolomics and Proteomics Analysis

OBJECTIVES

Prior studies have characterized protein and metabolite changes associated with SARS-CoV-2 infection; we hypothesized that these biomarkers may be part of heritable metabolic pathways in erythrocytes.

METHODS

Using a twin study of erythrocyte protein and metabolite levels, we describe the heritability of, and correlations among, previously identified biomarkers that correlate with COVID-19 severity. We used gene ontology and pathway enrichment analysis tools to identify pathways and biological processes enriched among these biomarkers.

RESULTS

Many COVID-19 biomarkers are highly heritable in erythrocytes. Among heritable metabolites downregulated in COVID-19, metabolites involved in amino acid metabolism and biosynthesis are enriched. Specific amino acid metabolism pathways (valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and arginine biosynthesis) are heritable in erythrocytes.

CONCLUSIONS

Metabolic pathways downregulated in COVID-19, particularly amino acid biosynthesis and metabolism pathways, are heritable in erythrocytes. This finding suggests that a component of the variation in COVID-19 severity may be the result of phenotypic variation in heritable metabolic pathways; future studies will be necessary to determine whether individual variation in amino acid metabolism pathways correlates with heritable outcomes of COVID-19.

In Silico Prediction of Hub Genes Involved in Diabetic Kidney and COVID-19 Related Disease by Differential Gene Expression and Interactome Analysis

Diabetic kidney disease (DKD) is a frequently chronic kidney pathology derived from diabetes comorbidity. This condition has irreversible damage and its risk factor increases with SARS-CoV-2 infection. The prognostic outcome for diabetic patients with COVID-19 is dismal, even with intensive medical treatment. However, there is still scarce information on critical genes involved in the pathophysiological impact of COVID-19 on DKD. Herein, we characterize differential expression gene (DEG) profiles and determine hub genes undergoing transcriptional reprogramming in both disease conditions. Out of 995 DEGs, we identified 42 shared with COVID-19 pathways. Enrichment analysis elucidated that they are significantly induced with implications for immune and inflammatory responses. By performing a protein-protein interaction (PPI) network and applying topological methods, we determine the following five hub genes: STAT1, IRF7, ISG15, MX1 and OAS1. Then, by network deconvolution, we determine their co-expressed gene modules. Moreover, we validate the conservancy of their upregulation using the Coronascape database (DB). Finally, tissue-specific regulation of the five predictive hub genes indicates that OAS1 and MX1 expression levels are lower in healthy kidney tissue. Altogether, our results suggest that these genes could play an essential role in developing severe outcomes of COVID-19 in DKD patients.

Metabolomic analysis of porcine intestinal epithelial cells during swine acute diarrhea syndrome coronavirus infection

Swine acute diarrhea syndrome coronavirus (SADS-CoV) is an enveloped, positive single-stranded RNA virus belonging to Coronaviridae family, Orthocoronavirinae subfamily, Alphacoronavirus genus. As one of the main causes of swine diarrhea, SADS-CoV has brought huge losses to the pig industry. Although we have a basic understanding of SADS-CoV, the research on the pathogenicity and interactions between host and virus are still limited, especially the metabolic changes induced by SADS-CoV infection. Here, we utilized a combination of untargeted metabolomics and lipomics to analyze the metabolic alteration in SADS-CoV infected cells. Significant changes were observed in 1257 of 2225 metabolites identified in untargeted metabolomics, while the number of lipomics was 435 out of 868. Metabolic pathway enrichment analysis showed that amino acid metabolism, tricarboxylic acid (TCA) cycle and ferroptosis were disrupted during viral infection, suggesting that these metabolic pathways may partake in pathological processes related to SADS-CoV pathogenesis. Collectively, our findings gain insights into the cellular metabolic disorder during SADS-CoV infection, offer a valuable resource for further exploration of the relationship between virus and host metabolic activities, and provide potential targets for the development of antiviral drugs.

Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry

BACKGROUND

The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce.

METHODS

Serum samples were obtained from aged mice (18-month-old) and young mice (3-month-old). LC-MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging-related biomarkers.

RESULTS

In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, q-value < 0.05, and Fold-Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D-glutamine and D-glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging-related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging-related indicators, including oleic acid, citric acid, D-glutamine, trypophol, and L-methionine.

CONCLUSIONS

Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.

Proinflammatory Innate Cytokines and Distinct Metabolomic Signatures Shape the T Cell Response in Active COVID-19

The underlying factors contributing to the evolution of SARS-CoV-2-specific T cell responses during COVID-19 infection remain unidentified. To address this, we characterized innate and adaptive immune responses with metabolomic profiling longitudinally at three different time points (0–3, 7–9, and 14–16 days post-COVID-19 positivity) from young, mildly symptomatic, active COVID-19 patients infected during the first wave in mid-2020. We observed that anti-RBD IgG and viral neutralization are significantly reduced against the delta variant, compared to the ancestral strain. In contrast, compared to the ancestral strain, T cell responses remain preserved against the delta and omicron variants. We determined innate immune responses during the early stage of active infection, in response to TLR 3/7/8-mediated activation in PBMCs and serum metabolomic profiling. Correlation analysis indicated PBMCs-derived proinflammatory cytokines, IL-18, IL-1β, and IL-23, and the abundance of plasma metabolites involved in arginine biosynthesis were predictive of a robust SARS-CoV-2-specific Th1 response at a later stage (two weeks after PCR positivity). These observations may contribute to designing effective vaccines and adjuvants that promote innate immune responses and metabolites to induce a long-lasting anti-SARS-CoV-2-specific T cell response.

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes

Background

Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness.

Methods

We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays.

Results

Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p&lt;0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p&lt;0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p&lt;0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p&lt;0.001) as an early criterion to accurately classify patients with critical outcomes.

Conclusion

Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.

Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study

Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls.

Update March 2022: management of hospitalised adults with coronavirus disease-19 (COVID-19): a European Respiratory Society living guideline

Since the identification of SARS-CoV-2 at the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic has affected more than 410 million people worldwide and killed almost 6 million [1, 2]. The predecessors of COVID-19, i.e. the SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) outbreaks, had been relatively self-limiting, preventing clinicians and researchers from establishing evidence-based specific therapeutic strategies [3]. Conversely, COVID-19 rapidly proved to be extremely fast spreading, which led stakeholders to encourage, guide, build or fund multidirectional therapeutic research strategies based on both repurposing and development of new agents [4–8]. In parallel, considerable efforts were directed at describing the disease and understanding the underlying mechanisms [9–13]. As a result, there has been a huge generation of evidence, as highlighted by the impressive number of COVID-19 publications (more than 200 000 since the end of 2019). As a consequence, it proved rapidly impossible for any clinician, researcher or decision-maker to gather and analyse all the corresponding literature to derive appropriate guidance [14]. The first step of such a process is to select the relevant high-quality research that can be used to answer the question(s) of interest [15]. Even if limiting the search to clinical trials, systematic reviews and meta-analyses, almost 4000 papers appear in the PubMed database, as of mid-February 2022. In June and July 2020, the European Respiratory Society (ERS) and the American Thoracic Society (ATS) released early guidance on several aspects of COVID-19 management (i.e. rehabilitation, palliative care and acute management); at that time, direct specific evidence was sparse or absent [16–18]. Simultaneously, the ERS launched a living guideline on the management of COVID-19. The format was that of a “short” guideline, as per ERS standards [19, 20], in that the purpose was to release the first iteration within 12 months. However, the number of PICO (Population, Intervention, Comparator, Outcomes) questions to be addressed (n=12) already exceeded markedly what the ERS considers as being feasible during such a short timeframe (i.e. n=1–2), which was a direct consequence of the high number of unanswered issues in the field of acute COVID-19 management, all corresponding to outstanding clinical needs. The first version of these guidelines was published in March 2021 and addressed the following potential therapeutic options: corticosteroids, interleukin (IL)-6 receptor antagonists, hydroxychloroquine, azithromycin and both combined, colchicine, lopinavir-ritonavir, remdesivir, interferon-β, anticoagulation and non-invasive ventilatory support [6, 21]. An update of the mortality meta-analyses for corticosteroids, hydroxychloroquine, azithromycin, remdesivir, anti-IL-6 monoclonal antibodies, colchicine, lopinavir/ritonavir and interferon-β was published in December 2021 [22].

The ERS COVID-19 guidelines make recommendations for corticosteroids, anti-IL-6 monoclonal antibodies, baricitinib, anticoagulation and non-invasive respiratory support for hospitalised patients with COVID-19 https://bit.ly/3QgHH7U

Qing-Wen-Jie-Re Mixture Ameliorates Poly (I:C)-Induced Viral Pneumonia Through Regulating the Inflammatory Response and Serum Metabolism

Qing-Wen-Jie-Re mixture (QWJR) has been used in the treatment of the coronavirus disease 2019 (COVID-19) in China. However, the protective mechanisms of QWJR on viral pneumonia remain unclear. In the present study, we first investigated the therapeutic effects of QWJR on a rat viral pneumonia model established by using polyinosinic-polycytidylic acid (poly (I:C)). The results indicated that QWJR could relieve the destruction of alveolar-capillary barrier in viral pneumonia rats, as represented by the decreased wet/dry weight (W/D) ratio in lung, total cell count and total protein concentration in bronchoalveolar lavage fluid (BALF). Besides, QWJR could also down-regulate the expression of inflammatory factors such as tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β and IL-6. More M1-type macrophage polarization was detected by calculating CD86⁺ cells and CD206⁺ cells and validated by the decline of inducible nitric oxide synthase (iNOS) and elevated arginase-1 (Arg-1) in lung. Finally, serum untargeted metabolomics analysis demonstrated that QWJR might take effect through regulating arginine metabolism, arachidonic acid (AA) metabolism, tricarboxylic acid (TCA) cycle, nicotinate and nicotinamide metabolism processes.

SARS-CoV-2 Causes Lung Inflammation through Metabolic Reprogramming and RAGE

Clinical studies indicate that patients infected with SARS-CoV-2 develop hyperinflammation, which correlates with increased mortality. The SARS-CoV-2/COVID-19-dependent inflammation is thought to occur via increased cytokine production and hyperactivity of RAGE in several cell types, a phenomenon observed for other disorders and diseases. Metabolic reprogramming has been shown to contribute to inflammation and is considered a hallmark of cancer, neurodegenerative diseases, and viral infections. Malfunctioning glycolysis, which normally aims to convert glucose into pyruvate, leads to the accumulation of advanced glycation end products (AGEs). Being aberrantly generated, AGEs then bind to their receptor, RAGE, and activate several pro-inflammatory genes, such as IL-1b and IL-6, thus, increasing hypoxia and inducing senescence. Using the lung epithelial cell (BEAS-2B) line, we demonstrated that SARS-CoV-2 proteins reprogram the cellular metabolism and increase pyruvate kinase muscle isoform 2 (PKM2). This deregulation promotes the accumulation of AGEs and senescence induction. We showed the ability of the PKM2 stabilizer, Tepp-46, to reverse the observed glycolysis changes/alterations and restore this essential metabolic process.

COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

Omics-based technologies have been largely adopted during this unprecedented global COVID-19 pandemic, allowing the scientific community to perform research on a large scale to understand the pathobiology of the SARS-CoV-2 infection and its replication into human cells. The application of omics techniques has been addressed to every level of application, from the detection of mutations, methods of diagnosis or monitoring, drug target discovery, and vaccine generation, to the basic definition of the pathophysiological processes and the biochemical mechanisms behind the infection and spread of SARS-CoV-2. Thus, the term COVIDomics wants to include those efforts provided by omics-scale investigations with application to the current COVID-19 research. This review summarizes the diverse pieces of knowledge acquired with the application of COVIDomics techniques, with the main focus on proteomics and metabolomics studies, in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease. Exploring the multiomics perspective and the concurrent data integration may provide new suitable therapeutic solutions to combat the COVID-19 pandemic.