Metabolomics to Predict Antiviral Drug Efficacy in COVID-19

Dysregulated nicotinamide adenine dinucleotide metabolome in patients hospitalized with COVID-19

Nicotinamide adenine dinucleotide (NAD+) depletion has been postulated as a contributor to the severity of COVID-19; however, no study has prospectively characterized NAD+ and its metabolites in relation to disease severity in patients with COVID-19. We measured NAD+ and its metabolites in 56 hospitalized patients with COVID-19 and in two control groups without COVID-19: (1) 31 age- and sex-matched adults with comorbidities, and (2) 30 adults without comorbidities. Blood NAD+ concentrations in COVID-19 group were only slightly lower than in the control groups (p < 0.05); however, plasma 1-methylnicotinamide concentrations were significantly higher in patients with COVID-19 (439.7 ng/mL, 95% CI: 234.0, 645.4 ng/mL) than in age- and sex-matched controls (44.5 ng/mL, 95% CI: 15.6, 73.4) and in healthy controls (18.1 ng/mL, 95% CI 15.4, 20.8; p < 0.001 for each comparison). Plasma nicotinamide concentrations were also higher in COVID-19 group and in controls with comorbidities than in healthy control group. Plasma concentrations of 2-methyl-2-pyridone-5-carboxamide (2-PY), but not NAD+, were significantly associated with increased risk of death (HR = 3.65; 95% CI 1.09, 12.2; p = 0.036) and escalation in level of care (HR = 2.90, 95% CI 1.01, 8.38, p = 0.049). RNAseq and RTqPCR analyses of PBMC mRNA found upregulation of multiple genes involved in NAD+ synthesis as well as degradation, and dysregulation of NAD+-dependent processes including immune response, DNA repair, metabolism, apoptosis/autophagy, redox reactions, and mitochondrial function. Blood NAD+ concentrations are modestly reduced in COVID-19; however, NAD+ turnover is substantially increased with upregulation of genes involved in both NAD+ biosynthesis and degradation, supporting the rationale for NAD+ augmentation to attenuate disease severity.

Development of favipiravir dry powders for intranasal delivery: An integrated cocrystal and particle engineering approach via spray freeze drying

The therapeutic potential of pharmaceutical cocrystals in intranasal applications remains largely unexplored despite progressive advancements in cocrystal research. We present the application of spray freeze drying (SFD) in successful fabrication of a favipiravir-pyridinecarboxamide cocrystal nasal powder formulation for potential treatment of broad-spectrum antiviral infections. Preliminary screening via mechanochemistry revealed that favipiravir (FAV) can cocrystallize with isonicotinamide (INA), but not nicotinamide (NCT) and picolinamide (PIC) notwithstanding their structural similarity. The cocrystal formation was characterized by differential scanning calorimetry, Fourier-transform infrared spectroscopy, and unit cell determination through Rietveld refinement of powder X-ray analysis. FAV-INA crystalized in a monoclinic space group P21/c with a unit cell volume of 1223.54(3) Å3, accommodating one FAV molecule and one INA molecule in the asymmetric unit. The cocrystal was further reproduced as intranasal dry powders by SFD, of which the morphology, particle size, in vitro drug release, and nasal deposition were assessed. The non-porous flake shaped FAV-INA powders exhibited a mean particle size of 19.79 ± 2.61 μm, rendering its suitability for intranasal delivery. Compared with raw FAV, FAV-INA displayed a 3-fold higher cumulative fraction of drug permeated in Franz diffusion cells at 45 min (p = 0.001). Dose fraction of FAV-INA deposited in the nasal fraction of a customized 3D-printed nasal cast reached over 80 %, whereas the fine particle fraction remained below 6 % at a flow rate of 15 L/min, suggesting high nasal deposition whilst minimal lung deposition. FAV-INA was safe in RPMI 2650 nasal and SH-SY5Y neuroblastoma cells without any in vitro cytotoxicity observed. This study demonstrated that combining the merits of cocrystallization and particle engineering via SFD can propel the development of advanced dry powder formulations for intranasal drug delivery.

Emerging Landscape of Preclinical Models for Studying COVID-19 Neurologic Diseases

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and has globally infected 768 million people and caused over 6 million deaths. COVID-19 primarily affects the respiratory system but increasing reports of neurologic symptoms associated with COVID-19 have been reported in the literature. The exact mechanism behind COVID-19 neurologic pathophysiology remains poorly understood due to difficulty quantifying clinical neurologic symptoms in humans and correlating them to findings in human post-mortem samples and animal models. Thus, robust preclinical experimental models for COVID-19 neurologic manifestations are urgently needed. Here, we review recent advances in in vitro, in vivo, and other models and technologies for studying COVID-19 including primary cell cultures, pluripotent stem cell-derived neurons and organoids, rodents, nonhuman primates, 3D bioprinting, artificial intelligence, and multiomics. We specifically focus our discussion on the contribution, recent advancements, and limitations these preclinical models have on furthering our understanding of COVID-19's neuropathic physiology. We also discuss these models' roles in the screening and development of therapeutics, vaccines, antiviral drugs, and herbal medicine, and on future opportunities for COVID-19 neurologic research and clinical management.

A global analysis of COVID-19 infection fatality rate and its associated factors during the Delta and Omicron variant periods: an ecological study

Background

The Omicron variant of SARS-CoV-2 is more highly infectious and transmissible than prior variants of concern. It was unclear which factors might have contributed to the alteration of COVID-19 cases and deaths during the Delta and Omicron variant periods. This study aimed to compare the COVID-19 average weekly infection fatality rate (AWIFR), investigate factors associated with COVID-19 AWIFR, and explore the factors linked to the increase in COVID-19 AWIFR between two periods of Delta and Omicron variants.

Materials and methods

An ecological study has been conducted among 110 countries over the first 12 weeks during two periods of Delta and Omicron variant dominance using open publicly available datasets. Our analysis included 102 countries in the Delta period and 107 countries in the Omicron period. Linear mixed-effects models and linear regression models were used to explore factors associated with the variation of AWIFR over Delta and Omicron periods.

Findings

During the Delta period, the lower AWIFR was witnessed in countries with better government effectiveness index [β = -0.762, 95% CI (-1.238)-(-0.287)] and higher proportion of the people fully vaccinated [β = -0.385, 95% CI (-0.629)-(-0.141)]. In contrast, a higher burden of cardiovascular diseases was positively associated with AWIFR (β = 0.517, 95% CI 0.102-0.932). Over the Omicron period, while years lived with disability (YLD) caused by metabolism disorders (β = 0.843, 95% CI 0.486-1.2), the proportion of the population aged older than 65 years (β = 0.737, 95% CI 0.237-1.238) was positively associated with poorer AWIFR, and the high proportion of the population vaccinated with a booster dose [β = -0.321, 95% CI (-0.624)-(-0.018)] was linked with the better outcome. Over two periods of Delta and Omicron, the increase in government effectiveness index was associated with a decrease in AWIFR [β = -0.438, 95% CI (-0.750)-(-0.126)]; whereas, higher death rates caused by diabetes and kidney (β = 0.472, 95% CI 0.089-0.855) and percentage of population aged older than 65 years (β = 0.407, 95% CI 0.013-0.802) were associated with a significant increase in AWIFR.

Conclusion

The COVID-19 infection fatality rates were strongly linked with the coverage of vaccination rate, effectiveness of government, and health burden related to chronic diseases. Therefore, proper policies for the improvement of vaccination coverage and support of vulnerable groups could substantially mitigate the burden of COVID-19.

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.

Spectroscopic methods for COVID-19 detection and early diagnosis

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.

A review of mass spectrometry-based analyses to understand COVID-19 convalescent plasma mechanisms of action

The spread of coronavirus disease 2019 (COVID‐19) viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has become a worldwide pandemic claiming several thousands of lives worldwide. During this pandemic, several studies reported the use of COVID‐19 convalescent plasma (CCP) from recovered patients to treat severely or critically ill patients. Although this historical and empirical treatment holds immense potential as a first line of response against eventual future unforeseen viral epidemics, there are several concerns regarding the efficacy and safety of this approach. This critical review aims to pinpoint the possible role of mass spectrometry‐based analysis in the identification of unique molecular component proteins, peptides, and metabolites of CCP that explains the therapeutic mechanism of action against COVID‐19. Additionally, the text critically reviews the potential application of mass spectrometry approaches in the search for novel plasma biomarkers that may enable a rapid and accurate assessment of the safety and efficacy of CCP. Considering the relative low‐cost value involved in the CCP therapy, this proposed line of research represents a tangible scientific challenge that will be translated into clinical practice and help save several thousand lives around the world, specifically in low‐ and middle‐income countries.

Sera Metabolomics Characterization of Patients at Different Stages in Wuhan Identifies Critical Biomarkers of COVID-19

We have witnessed the 2-year-long global rampage of COVID-19 caused by the wide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, knowledge about biomarkers of the entire COVID-19 process is limited. Identification of the systemic features of COVID-19 will lead to critical biomarkers and therapeutic targets for early intervention and clinical disease course prediction. Here, we performed a comprehensive analysis of clinical measurements and serum metabolomics in 199 patients with different stages of COVID-19. In particular, our study is the first serum metabolomic analysis of critical rehabilitation patients and critical death patients. We found many differential metabolites in the comparison of metabolomic results between ordinary, severe, and critical patients and uninfected patients. Through the metabolomic results of COVID-19 patients in various stages, and critical rehabilitation patients and critical death patients, we identified a series of differential metabolites as biomarkers, a separate queue and precise distinction, and predicted COVID-19 verification. These differentially expressed metabolites, included 1,2-di-(9Z,12Z-octadecadienoyl)-sn-glycero-3-phosphate, propylparaben, 20-hydroxyeicosatetraenoic acid, triethanolamine, chavicol, disialosyl galactosyl globoside, 1-arachidonoylglycerophosphoinositol, and alpha-methylstyrene, all of which have been identified for the first time as biomarkers in COVID-19 progression. These biomarkers are involved in many pathological and physiological pathways of COVID-19, for example, immune responses, platelet degranulation, and metabolism which might result in pathogenesis. Our results showed valuable information about metabolites obviously altered in COVID-19 patients with different stages, which could shed light on the pathogenesis as well as serve as potential therapeutic agents of COVID-19.

Treatment of SARS-CoV-2-induced pneumonia with NAD+ and NMN in two mouse models

The global COVID-19 epidemic has spread rapidly around the world and caused the death of more than 5 million people. It is urgent to develop effective strategies to treat COVID-19 patients. Here, we revealed that SARS-CoV-2 infection resulted in the dysregulation of genes associated with NAD⁺ metabolism, immune response, and cell death in mice, similar to that in COVID-19 patients. We therefore investigated the effect of treatment with NAD⁺ and its intermediate (NMN) and found that the pneumonia phenotypes, including excessive inflammatory cell infiltration, hemolysis, and embolization in SARS-CoV-2-infected lungs were significantly rescued. Cell death was suppressed substantially by NAD⁺ and NMN supplementation. More strikingly, NMN supplementation can protect 30% of aged mice infected with the lethal mouse-adapted SARS-CoV-2 from death. Mechanically, we found that NAD⁺ or NMN supplementation partially rescued the disturbed gene expression and metabolism caused by SARS-CoV-2 infection. Thus, our in vivo mouse study supports trials for treating COVID-19 patients by targeting the NAD⁺ pathway.

COVID-19: Are We Facing Secondary Pellagra Which Cannot Simply Be Cured by Vitamin B3?

Immune response to SARS-CoV-2 and ensuing inflammation pose a huge challenge to the host’s nicotinamide adenine dinucleotide (NAD⁺) metabolism. Humans depend on vitamin B3 for biosynthesis of NAD⁺, indispensable for many metabolic and NAD⁺-consuming signaling reactions. The balance between its utilization and resynthesis is vitally important. Many extra-pulmonary symptoms of COVID-19 strikingly resemble those of pellagra, vitamin B3 deficiency (e.g., diarrhoea, dermatitis, oral cavity and tongue manifestations, loss of smell and taste, mental confusion). In most developed countries, pellagra is successfully eradicated by vitamin B3 fortification programs. Thus, conceivably, it has not been suspected as a cause of COVID-19 symptoms. Here, the deregulation of the NAD⁺ metabolism in response to the SARS-CoV-2 infection is reviewed, with special emphasis on the differences in the NAD⁺ biosynthetic pathway’s efficiency in conditions predisposing for the development of serious COVID-19. SARS-CoV-2 infection-induced NAD⁺ depletion and the elevated levels of its metabolites contribute to the development of a systemic disease. Acute liberation of nicotinamide (NAM) in antiviral NAD⁺-consuming reactions potentiates “NAM drain”, cooperatively mediated by nicotinamide N-methyltransferase and aldehyde oxidase. “NAM drain” compromises the NAD⁺ salvage pathway’s fail-safe function. The robustness of the host’s NAD⁺ salvage pathway, prior to the SARS-CoV-2 infection, is an important determinant of COVID-19 severity and persistence of certain symptoms upon resolution of infection.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

INTRODUCTION

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

OBJECTIVES

Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).

METHODS

High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created.

RESULTS

The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86).

CONCLUSION

Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Does acute and persistent metabolic dysregulation in COVID19 point to novel biomarkers and future therapeutic strategies?

When the coronavirus disease 2019 (COVID-19) pandemic first appeared in December of 2019, the pathophysiological underpinnings of the disease were largely unknown. Scientists, physicians and government institutions from around the globe took an “all-hands on deck” approach with the hope of identifying potential therapies to treat as well as understand the pathophysiology of the disease [1]. Currently, more than 4800 clinical trials listed on clinicaltrials.gov have been performed or proposed around the world, many with subjects from vastly different ethnic and racial backgrounds, as well as different standard-of-care strategies [2]. Despite this effort, apart from monoclonal antibodies, few therapies have emerged as effective treatments of COVID-19; vaccines remain the best approach to control and mitigate the pandemic [3].

Metabolomics changes in COVID-19 predict acute patient outcomes and suggest a role for a bioenergetic crisis. Thus, metabolomics changes in COVID-19 may serve as a biomarker and provide insight into pathogenic mechanisms and pharmacologic targets.https://bit.ly/2XkJeU8

SARS-CoV2 Infection Alters Tryptophan Catabolism and Phospholipid Metabolism

Coronavirus disease 2019 (COVID-19) has so far infected hundreds of million individuals, with several million deaths worldwide. The lack of understanding of the disease pathophysiology and the host’s immune response has resulted in this rapid spread of the disease on a global scale. In this respect, we employed UPLC-MS to compare the metabolites in the serum from COVID-19-positive patients and COVID-19-recovered subjects to determine the metabolic changes responsible for an infection. Our investigations revealed significant increase in the levels of serum phospholipids including sphingomyelins, phosphatidylcholines and arachidonic acid in the serum of COVID-19-positive patients as compared to COVID-19-recovered individuals. We further show increased levels of tryptophan and its metabolites in the serum of COVID-19-positive patients thus emphasizing the role of tryptophan metabolism in the disease pathogenesis of COVID-19. Future studies are required to determine the changes in the lipid and tryptophan metabolism at various stages of COVID-19 disease development, progression and recovery to better understand the host–pathogen interaction and the long-term effects of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection in humans.

NAD+ Degrading Enzymes, Evidence for Roles During Infection

Declines in cellular nicotinamide adenine dinucleotide (NAD) contribute to metabolic dysfunction, increase susceptibility to disease, and occur as a result of pathogenic infection. The enzymatic cleavage of NAD⁺ transfers ADP-ribose (ADPr) to substrate proteins generating mono-ADP-ribose (MAR), poly-ADP-ribose (PAR) or O-acetyl-ADP-ribose (OAADPr). These important post-translational modifications have roles in both immune response activation and the advancement of infection. In particular, emergent data show viral infection stimulates activation of poly (ADP-ribose) polymerase (PARP) mediated NAD⁺ depletion and stimulates hydrolysis of existing ADP-ribosylation modifications. These studies are important for us to better understand the value of NAD⁺ maintenance upon the biology of infection. This review focuses specifically upon the NAD⁺ utilising enzymes, discusses existing knowledge surrounding their roles in infection, their NAD⁺ depletion capability and their influence within pathogenic infection.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

Mass spectrometry based metabolomics approach on the elucidation of volatile metabolites formation in fermented foods: A mini review

Metabolomics can be applied for comparative and quantitative analyses of the metabolic changes induced by microorganisms during fermentation. In particular, mass spectrometry (MS) is a powerful tool for metabolomics that is widely used for elucidating biomarkers and patterns of metabolic changes. Fermentation involves the production of volatile metabolites via diverse and complex metabolic pathways by the activities of microbial enzymes. These metabolites can greatly affect the organoleptic properties of fermented foods. This review provides an overview of the MS-based metabolomics techniques applied in studies of fermented foods, and the major metabolic pathways and metabolites (e.g., sugars, amino acids, and fatty acids) derived from their metabolism. In addition, we suggest an efficient tool for understanding the metabolic patterns and for identifying novel markers in fermented foods.

Mass Spectrometry-Based Human Breath Analysis: Towards COVID-19 Diagnosis and Research

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath. Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers. Mass spectrometry (MS)-based approaches offer a promising analytical platform for human breath analysis due to their high speed, specificity, sensitivity, reproducibility, and broad coverage, as well as its versatile coupling methods with different chromatographic separation, and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19. Herein, we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples, including metabolites, proteins, microorganisms, and elements. New features of breath sampling and analysis are highlighted. Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.

Endothelial metabolism in pulmonary vascular homeostasis and acute respiratory distress syndrome

Capillary endothelial cells possess a specialized metabolism necessary to adapt to the unique alveolar-capillary environment. Here, we highlight how endothelial metabolism preserves the integrity of the pulmonary circulation by controlling vascular permeability, defending against oxidative stress, facilitating rapid migration and angiogenesis in response to injury, and regulating the epigenetic landscape of endothelial cells. Recent reports on single-cell RNA-sequencing reveal subpopulations of pulmonary capillary endothelial cells with distinctive reparative capacities, which potentially offer new insight into their metabolic signature. Lastly, we discuss broad implications of pulmonary vascular metabolism on acute respiratory distress syndrome, touching on emerging findings of endotheliitis in coronavirus disease 2019 (COVID-19) lungs.

Could metabolomics drive the fate of COVID-19 pandemic? A narrative review on lights and shadows

Human Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection activates a complex interaction host/virus, leading to the reprogramming of the host metabolism aimed at the energy supply for viral replication. Alterations of the host metabolic homeostasis strongly influence the immune response to SARS-CoV-2, forming the basis of a wide range of outcomes, from the asymptomatic infection to the onset of COVID-19 and up to life-threatening acute respiratory distress syndrome, vascular dysfunction, multiple organ failure, and death. Deciphering the molecular mechanisms associated with the individual susceptibility to SARS-CoV-2 infection calls for a system biology approach; this strategy can address multiple goals, including which patients will respond effectively to the therapeutic treatment. The power of metabolomics lies in the ability to recognize endogenous and exogenous metabolites within a biological sample, measuring their concentration, and identifying perturbations of biochemical pathways associated with qualitative and quantitative metabolic changes. Over the last year, a limited number of metabolomics- and lipidomics-based clinical studies in COVID-19 patients have been published and are discussed in this review. Remarkable alterations in the lipid and amino acid metabolism depict the molecular phenotype of subjects infected by SARS-CoV-2; notably, structural and functional data on the lipids-virus interaction may open new perspectives on targeted therapeutic interventions. Several limitations affect most metabolomics-based studies, slowing the routine application of metabolomics. However, moving metabolomics from bench to bedside cannot imply the mere determination of a given metabolite panel; rather, slotting metabolomics into clinical practice requires the conversion of metabolic patient-specific data into actionable clinical applications.

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

A metabolomic endotype of bioenergetic dysfunction predicts mortality in critically ill patients with acute respiratory failure

Acute respiratory failure (ARF) requiring mechanical ventilation, a complicating factor in sepsis and other disorders, is associated with high morbidity and mortality. Despite its severity and prevalence, treatment options are limited. In light of accumulating evidence that mitochondrial abnormalities are common in ARF, here we applied broad spectrum quantitative and semiquantitative metabolomic analyses of serum from ARF patients to detect bioenergetic dysfunction and determine its association with survival. Plasma samples from surviving and non-surviving patients (N = 15/group) were taken at day 1 and day 3 after admission to the medical intensive care unit and, in survivors, at hospital discharge. Significant differences between survivors and non-survivors (ANOVA, 5% FDR) include bioenergetically relevant intermediates of redox cofactors nicotinamide adenine dinucleotide (NAD) and NAD phosphate (NADP), increased acyl-carnitines, bile acids, and decreased acyl-glycerophosphocholines. Many metabolites associated with poor outcomes are substrates of NAD(P)-dependent enzymatic processes, while alterations in NAD cofactors rely on bioavailability of dietary B-vitamins thiamine, riboflavin and pyridoxine. Changes in the efficiency of the nicotinamide-derived cofactors' biosynthetic pathways also associate with alterations in glutathione-dependent drug metabolism characterized by substantial differences observed in the acetaminophen metabolome. Based on these findings, a four-feature model developed with semi-quantitative and quantitative metabolomic results predicted patient outcomes with high accuracy (AUROC = 0.91). Collectively, this metabolomic endotype points to a close association between mitochondrial and bioenergetic dysfunction and mortality in human ARF, thus pointing to new pharmacologic targets to reduce mortality in this condition.

The Ability to Normalise Energy Metabolism in Advanced COVID-19 Disease Seems to Be One of the Key Factors Determining the Disease Progression—A Metabolomic NMR Study on Blood Plasma

Background: COVID-19 represents a severe inflammatory condition. Our work was designed to monitor the longitudinal dynamics of the metabolomic response of blood plasma and to reveal presumable discrimination in patients with positive and negative outcomes of COVID-19 respiratory symptoms. Methods: Blood plasma from patients, divided into subgroups with positive (survivors) and negative (worsening condition, non-survivors) outcomes, on Days 1, 3, and 7 after admission to hospital, was measured by NMR spectroscopy. Results: We observed changes in energy metabolism in both groups of COVID-19 patients; initial hyperglycaemia, indicating lowered glucose utilisation, was balanced with increased production of 3-hydroxybutyrate as an alternative energy source and accompanied by accelerated protein catabolism manifested by an increase in BCAA levels. These changes were normalised in patients with positive outcome by the seventh day, but still persisted one week after hospitalisation in patients with negative outcome. The initially decreased glutamine plasma level normalised faster in patients with positive outcome. Patients with negative outcome showed a more pronounced Phe/Tyr ratio, which is related to exacerbated and generalised inflammatory processes. Almost ideal discrimination from controls was proved. Conclusions: Distinct metabolomic responses to severe inflammation initiated by SARS-CoV-2 infection may serve towards complementary personalised pharmacological and nutritional support to improve patient outcomes.

The complex combination of COVID-19 and diabetes: pleiotropic changes in glucose metabolism

PURPOSE

Angiotensin converting enzyme 2 (ACE2) is the door for SARS-CoV-2, expressed in critical metabolic tissues. So, it is rational that the new virus causes pleiotropic alterations in glucose metabolism, resulting in the complication of pre-existing diabetes's pathophysiology or creating new disease mechanisms. However, it seems that less attention has been paid to this issue. This review aimed to highlight the importance of long-term consequences and pleiotropic alterations in glucose metabolism following COVID-19 and emphasize the need for basic and clinical research in metabolism and endocrinology.

RESULTS

SARS-CoV-2 shifts cellular metabolism from oxidative phosphorylation to glycolysis, which leads to a decrease in ATP generation. Together with metabolic imbalance, the impaired immune system elevates the susceptibility of patients with diabetes to this deadly virus. SARS-CoV-2-induced metabolic alterations in immune cells can result in hyper inflammation and a cytokine storm. Metabolic dysfunction may affect therapies against SARS-CoV-2 infection. The effective control of metabolic complications could prove useful therapeutic targets for combating COVID-19. It is also necessary to understand the long-term consequences that will affect patients with diabetes who survived COVID-19.

CONCLUSIONS

Since the pathophysiology of COVID-19 is still mostly unknown, identifying the metabolic mechanisms contributing to its progression is essential to provide specific ways to prevent and improve this dangerous virus's detrimental effects. The findings show that the new virus may induce new-onset diabetes with uncertain metabolic and clinical features, supporting a potential role of COVID-19 in the development of diabetes.

Metabolomics reveals sex-specific metabolic shifts and predicts the duration from positive to negative in non-severe COVID-19 patients during recovery process

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.

High-throughput approaches of diagnosis and therapies for COVID-19: antibody panels, proteomics and metabolomics

The urgent need for diagnostics and therapeutics against the COVID-19 pandemic has shown the great potential of antibodies, proteomics and metabolomics in this direction. Several clinical trials are underway using antibodies from COVID-19 patients that show very specific and strong binding to viral proteins leading to neutralization. On the other hand, proteomic and metabolomic profiles of COVID-19 patients present novel diagnostic biomarkers to predict patient outcomes and enable the development of personalized therapeutics to target the dysregulated pathways, as revealed by those profiles. Here, we discuss how studies based on antibodies, proteomics and metabolomics contribute to the development of diagnostics and therapeutics against COVID-19. The elegant technology can extend to high-throughput, rapid and reliable drug discovery strategies of the future.

In order to prevent and treat the ongoing COVID-19 pandemic, several groups around the world are focused on a detailed understanding of the biology of SARS-CoV-2 infection, the biological events occurring inside the patients and the response of the patients to the infection. SARS-CoV-2 is the coronavirus that causes COVID-19. Some of the approaches to combat the pandemic have provided important results that can help toward therapeutic developments against COVID-19. This review discusses three such areas – antibody treatment to prevent COVID-19 infection, analysis of changes in protein profiles of COVID-19 patients, and analysis of metabolism or energy-related changes in COVID-19 patients. Antibodies are molecules produced in the host’s body as a defense response to infection. Antibodies extracted from patients who recovered from COVID-19 have been used to successfully manufacture large amounts of antibodies to treat COVID-19 patients. The analysis of protein and metabolism profiles of COVID-19 patients has shown that several proteins and metabolism-related entities in the body are either upregulated or downregulated in COVID-19. These abnormal levels can either be attenuated by medical intervention or can be monitored as indicators of COVID-19 diagnosis. Overall, antibodies, protein and metabolism profiles are three important tools, among several others, that are helpful in combating the COVID-19 pandemic.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

SARS-CoV-2 infection: molecular mechanisms of severe outcomes to suggest therapeutics

Introduction:The year 2020 was defined by the 29,903 base pairs of RNA that codes for the SARS-CoV-2 genome. SARS-CoV-2 infects humans to cause COVID-19, spreading from patient-to-patient yet impacts patients very divergently.Areas covered: Within this review, we address the known molecular mechanisms and supporting data for COVID-19 clinical course and pathology, clinical risk factors and molecular signatures, therapeutics of severe COVID-19, and reinfection/vaccination. Literature and published datasets were reviewed using PubMed, Google Scholar, and NCBI SRA tools. The combination of exaggerated cytokine signaling, pneumonia, NETosis, pyroptosis, thrombocytopathy, endotheliopathy, multiple organ dysfunction syndrome (MODS), and acute respiratory distress syndrome (ARDS) create a positive feedback loop of severe damage in patients with COVID-19 that impacts the entire body and may persist for months following infection. Understanding the molecular pathways of severe COVID-19 opens the door for novel therapeutic design. We summarize the current insights into pathology, risk factors, secondary infections, genetics, omics, and drugs being tested to treat severe COVID-19.Expert opinion: A growing level of support suggests the need for stronger integration of biomarkers and precision medicine to guide treatment strategies of severe COVID-19, where each patient has unique outcomes and thus require guided treatment.

Large-Scale Multi-omic Analysis of COVID-19 Severity

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

A contemporary insight of metabolomics approach for COVID-19: Potential for novel therapeutic and diagnostic targets

The Coronavirus disease 2019 (COVID-19) pandemic is caused by rapidly spreading pathogenic virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that affects vast majority of population worldwide. Although, around 80% of the cases had mild infection but still remaining 20% had developed respiratory failure and dysfunction of other organs that necessitate urgent oxygen therapy or specific interventions. Therefore, it is imperative to establish novel prognostic approaches to screen patients at high-risk of developing severe complications. The primary focus of current research for COVID-19 is to discover safe and efficacious vaccine for prevention and effective treatment for better management of the patients to overcome the pandemic. To achieve this goal, it is imperative to have better understanding of the molecular pathways involved in the pathophysiology and progression of severe COVID-19. The surge for reliable diagnostics and therapeutics targets for COVID-19 highlighted the great potential of high-throughput approach like metabolomics which may enable the development of personalized medicine.

Mass Spectrometry Techniques in Emerging Pathogens Studies: COVID-19 Perspectives

As corona virus disease 2019 (COVID-19) is a rapidly growing public health crisis across the world, our knowledge of meaningful diagnostic tests and treatment for severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is still evolving. This novel coronavirus disease COVID-19 can be diagnosed using RT-PCR, but inadequate access to reagents, equipment, and a nonspecific target has slowed disease detection and management. Precision medicine, individualized patient care, requires suitable diagnostics approaches to tackle the challenging aspects of viral outbreaks where many tests are needed in a rapid and deployable approach. Mass spectrometry (MS)-based technologies such as proteomics, glycomics, lipidomics, and metabolomics have been applied in disease outbreaks for identification of infectious disease agents such as virus and bacteria and the molecular phenomena associated with pathogenesis. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is widely used in clinical diagnostics in the United States and Europe for bacterial pathogen identification. Paper spray ionization mass spectrometry (PSI-MS), a rapid ambient MS technique, has recently open a new opportunity for future clinical investigation to diagnose pathogens. Ultra-high-pressure liquid chromatography coupled high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics have been employed in large-scale biomedical research to discriminate infectious pathogens and uncover biomarkers associated with pathogenesis. PCR-MS has emerged as a new technology with the capability to directly identify known pathogens from the clinical specimens and the potential to identify genetic evidence of undiscovered pathogens. Moreover, miniaturized MS offers possible applications with relatively fast, highly sensitive, and potentially portable ways to analyze for viral compounds. However, beneficial aspects of these rapidly growing MS technologies in pandemics like COVID-19 outbreaks has been limited. Hence, this perspective gives a brief of the existing knowledge, current challenges, and opportunities for MS-based techniques as a promising avenue in studying emerging pathogen outbreaks such as COVID-19.

Large-scale Multi-omic Analysis of COVID-19 Severity

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.