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

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD+ pathway and SIRT1 activation

OBJECTIVES

The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata.

METHODS

The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data.

RESULTS

Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway.

CONCLUSIONS

Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.

Methylprednisolone therapy induces differential metabolic trajectories in severe COVID-19 patients

IMPORTANCE

The SARS-CoV-2 virus infection in humans induces significant inflammatory and systemic reactions and complications of which corticosteroids like methylprednisolone have been recommended as treatment. Our understanding of the metabolic and metabolomic pathway dysregulations while using intravenous corticosteroids in COVID-19 is limited. This study will help enlighten the metabolic and metabolomic pathway dysregulations underlying high daily doses of intravenous methylprednisolone in COVID-19 patients compared to those receiving placebo. The information on key metabolites and pathways identified in this study together with the crosstalk with the inflammation and biochemistry components may be used, in the future, to leverage the use of methylprednisolone in any future pandemics from the coronavirus family.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

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.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression. We collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected ('non-susceptible') and subjects who became infected during the follow-up ('susceptible'), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography. We show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection. This study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.

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

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

Urine Parameters in Patients with COVID-19 Infection

A urine test permits the measure of several urinary markers. This is a non-invasive method for early monitoring of potential kidney damage. In COVID-19 patients, alterations of urinary markers were observed. This review aims to evaluate the utility of urinalysis in predicting the severity of COVID-19. A total of 68 articles obtained from PubMed studies reported that (i) the severity of disease was related to haematuria and proteinuria and that (ii) typical alterations of the urinary sediment were noticed in COVID-19-associated AKI patients. This review emphasizes that urinalysis and microscopic examination support clinicians in diagnosing and predicting COVID-19 severity.

Laboratory Findings and Biomarkers in Long COVID: What Do We Know So Far? Insights into Epidemiology, Pathogenesis, Therapeutic Perspectives and Challenges

Long COVID (LC) encompasses a constellation of long-term symptoms experienced by at least 10% of people after the initial SARS-CoV-2 infection, and so far it has affected about 65 million people. The etiology of LC remains unclear; however, many pathophysiological pathways may be involved, including viral persistence; a chronic, low-grade inflammatory response; immune dysregulation and a defective immune response; the reactivation of latent viruses; autoimmunity; persistent endothelial dysfunction and coagulopathy; gut dysbiosis; hormonal and metabolic dysregulation; mitochondrial dysfunction; and autonomic nervous system dysfunction. There are no specific tests for the diagnosis of LC, and clinical features including laboratory findings and biomarkers may not specifically relate to LC. Therefore, it is of paramount importance to develop and validate biomarkers that can be employed for the prediction, diagnosis and prognosis of LC and its therapeutic response, although this effort may be hampered by challenges pertaining to the non-specific nature of the majority of clinical manifestations in the LC spectrum, small sample sizes of relevant studies and other methodological issues. Promising candidate biomarkers that are found in some patients are markers of systemic inflammation, including acute phase proteins, cytokines and chemokines; biomarkers reflecting SARS-CoV-2 persistence, the reactivation of herpesviruses and immune dysregulation; biomarkers of endotheliopathy, coagulation and fibrinolysis; microbiota alterations; diverse proteins and metabolites; hormonal and metabolic biomarkers; and cerebrospinal fluid biomarkers. At present, there are only two reviews summarizing relevant biomarkers; however, they do not cover the entire umbrella of current biomarkers, their link to etiopathogenetic mechanisms or the diagnostic work-up in a comprehensive manner. Herein, we aim to appraise and synopsize the available evidence on the typical laboratory manifestations and candidate biomarkers of LC, their classification based on pathogenetic mechanisms and the main LC symptomatology in the frame of the epidemiological and clinical aspects of the syndrome and furthermore assess limitations and challenges as well as potential implications in candidate therapeutic interventions.