Leveraging metabolic modeling to identify functional metabolic alterations associated with COVID-19 disease severity

Lopes T, de Oliveira JM, Raimundo JRS, Furtado DZS, Fonseca FLA, Oliveira RV, Cardoso DR, Carrilho E, Assunção NA. Multiplatform Metabolomics: Enhancing the Severity Risk Prognosis of SARS-CoV-2 Infection

Concerns about the SARS-CoV-2 outbreak (COVID-19) continue to persist even years later, with the emergence of new variants and the risk of disease severity. Common clinical symptoms, like cough, fever, and respiratory symptoms, characterize the noncritical patients, classifying them from mild to moderate. In a more severe and complex scenario, the virus infection can affect vital organs, resulting, for instance, in pneumonia and impaired kidney and heart function. However, it is well-known that subclinical symptoms at a metabolic level can be observed previously but require a proper diagnosis because viral replication on the host leaves a track with a different profile depending on the severity of the illness. Metabolomic profiles of mild, moderate, and severe COVID-19 patients were obtained by multiple platforms (LC-HRMS and MALDI-MS), increasing the chance to elucidate a prognosis for severity risk. A strong link was discovered between phenylalanine metabolism and increased COVID-19 severity symptoms, a pathway linked to cardiac and neurological consequences. Glycerophospholipids and sphingolipid metabolisms were also dysregulated linearly with the increasing symptom severity, which can be related to virus proliferation, immune system avoidance, and apoptosis escaping. Our data, endorsed by other literature, strengthens the notion that these pathways might play a vital role in a patient's prognosis.

Obesity-dependent molecular alterations in fatal COVID-19: A retrospective postmortem study of metabolomic profile of adipose tissue

We investigated the effects of obesity on metabolic, inflammatory, and oxidative stress parameters in the adipose tissue of patients with fatal COVID-19. Postmortem biopsies of subcutaneous adipose tissue were obtained from 25 unvaccinated inpatients who passed from COVID-19, stratified as nonobese (N-OB; body mass index [BMI], 26.5 ± 2.3 kg m-2) or obese (OB BMI 34.2 ± 5.1 kg m-2). Univariate and multivariate analyses revealed that body composition was responsible for most of the variations detected in the metabolome, with greater dispersion observed in the OB group. Fifteen metabolites were major segregation factors. Results from the OB group showed higher levels of creatinine, myo-inositol, O-acetylcholine, and succinate, and lower levels of sarcosine. The N-OB group showed lower levels of glutathione peroxidase activity, as well as higher content of IL-6 and adiponectin. We revealed significant changes in the metabolomic profile of the adipose tissue in fatal COVID-19 cases, with high adiposity playing a key role in these observed variations. These findings highlight the potential involvement of metabolic and inflammatory pathways, possibly dependent on hypoxia, shedding light on the impact of obesity on disease pathogenesis and suggesting avenues for further research and possible therapeutic targets.

Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes

Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.

3-Methylglutarylcarnitine: A biomarker of mitochondrial dysfunction

The acylcarnitines comprise a wide range of acyl groups linked via an ester bond to the hydroxyl group of L-carnitine. Mass spectrometry methods are capable of measuring the relative abundance of hundreds of acylcarnitines in a single drop of blood. As such, acylcarnitines can serve as sensitive biomarkers of disease. For certain acylcarnitines, however, their biochemical origin, and biomedical significance, remain unclear. One such example is 3-methylglutaryl (3MG) carnitine (C5-3M-DC). Whereas 3MG carnitine levels are normally very low, elevated levels are detected in discrete inborn errors of metabolism (IEM) as well as different forms of heart disease. Moreover, acute injury, including γ radiation exposure, paraquat poisoning, and traumatic brain injury manifest elevated levels of 3MG carnitine in blood and/or urine. Recent evidence indicates that two distinct biosynthetic routes to 3MG carnitine exist. The first, caused by an inherited deficiency in the leucine catabolism pathway enzyme, 3-hydroxy-3-methylglutaryl (HMG) CoA lyase, leads to a buildup of trans-3-methylglutaconyl (3MGC) CoA. Reduction of the double bond in trans-3MGC CoA generates 3MG CoA, which is then converted to 3MG carnitine by carnitine acyltransferase. This route, however, cannot explain why 3MG carnitine levels increase in IEMs that do not affect leucine metabolism or various chronic and acute disease states. In these cases, disease-related defects in aerobic energy metabolism result in diversion of acetyl CoA to trans-3MGC CoA. Once formed, trans-3MGC CoA is reduced to 3MG CoA and esterified to form 3MG carnitine. Thus, 3MG carnitine, represents a potential biomarker of disease processes associated with compromised mitochondrial energy metabolism.

COVID-19: A complex disease with a unique metabolic signature

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart

In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco's modified eagle medium (DMEM) and Ham's medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham's medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

Molecular and cellular mechanisms involved in tissue-specific metabolic modulation by SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is triggered by the SARS-CoV-2, which is able to infect and cause dysfunction not only in lungs, but also in multiple organs, including central nervous system, skeletal muscle, kidneys, heart, liver, and intestine. Several metabolic disturbances are associated with cell damage or tissue injury, but the mechanisms involved are not yet fully elucidated. Some potential mechanisms involved in the COVID-19-induced tissue dysfunction are proposed, such as: (a) High expression and levels of proinflammatory cytokines, including TNF-α IL-6, IL-1β, INF-α and INF-β, increasing the systemic and tissue inflammatory state; (b) Induction of oxidative stress due to redox imbalance, resulting in cell injury or death induced by elevated production of reactive oxygen species; and (c) Deregulation of the renin-angiotensin-aldosterone system, exacerbating the inflammatory and oxidative stress responses. In this review, we discuss the main metabolic disturbances observed in different target tissues of SARS-CoV-2 and the potential mechanisms involved in these changes associated with the tissue dysfunction.