The Serum Metabolome of Moderate and Severe COVID-19 Patients Reflects Possible Liver Alterations Involving Carbon and Nitrogen Metabolism

SARS-CoV-2 infection unevenly impacts metabolism in the coronal periphery of the lungs

COVID-19 significantly decreases amino acids, fatty acids, and most eicosanoidsSARS-CoV-2 preferentially localizes to central lung tissueMetabolic disturbance is highest in peripheral tissue, not central like viral loadSpatial metabolomics allows detection of metabolites not altered overallSARS-CoV-2, the virus responsible for COVID-19, is a highly contagious virus that can lead to hospitalization and death. COVID-19 is characterized by its involvement in the lungs, particularly the lower lobes. To improve patient outcomes and treatment options, a better understanding of how SARS-CoV-2 impacts the body, particularly the lower respiratory system, is required. In this study, we sought to understand the spatial impact of COVID-19 on the lungs of mice infected with mouse-adapted SARS2-N501Y MA30 . Overall, infection caused a decrease in fatty acids, amino acids, and most eicosanoids. When analyzed by segment, viral loads were highest in central lung tissue, while metabolic disturbance was highest in peripheral tissue. Infected peripheral lung tissue was characterized by lower levels of fatty acids and amino acids when compared to central lung tissue. This study highlights the spatial impacts of SARS-CoV-2 and helps explain why peripheral lung tissue is most damaged by COVID-19.

Starve a cold or feed a fever? Identifying cellular metabolic changes following infection and exposure to SARS-CoV-2

Viral infections induce major shifts in cellular metabolism elicited by active viral replication and antiviral responses. For the virus, harnessing cellular metabolism and evading changes that limit replication are essential for productive viral replication. In contrast, the cellular response to infection disrupts metabolic pathways to prevent viral replication and promote an antiviral state in the host cell and neighboring bystander cells. This competition between the virus and cell results in measurable shifts in cellular metabolism that differ depending on the virus, cell type, and extracellular environment. The resulting metabolic shifts can be observed and analyzed using global metabolic profiling techniques to identify pathways that are critical for either viral replication or cellular defense. SARS-CoV-2 is a respiratory virus that can exhibit broad tissue tropism and diverse, yet inconsistent, symptomatology. While the factors that determine the presentation and severity of SARS-CoV-2 infection remain unclear, metabolic syndromes are associated with more severe manifestations of SARS-CoV-2 disease. Despite these observations a critical knowledge gap remains between cellular metabolic responses and SARS-CoV-2 infection. Using a well-established untargeted metabolomics analysis workflow, we compared SARS-CoV-2 infection of human lung carcinoma cells. We identified significant changes in metabolic pathways that correlate with either productive or non-productive viral infection. This information is critical for characterizing the factors that contribute to SARS-CoV-2 replication that could be targeted for therapeutic interventions to limit viral disease.

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.

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.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Using a targeted metabolomics approach to explore differences in ARDS associated with COVID-19 compared to ARDS caused by H1N1 influenza and bacterial pneumonia

RATIONALE

Acute respiratory distress syndrome (ARDS) is a life-threatening critical care syndrome commonly associated with infections such as COVID-19, influenza, and bacterial pneumonia. Ongoing research aims to improve our understanding of ARDS, including its molecular mechanisms, individualized treatment options, and potential interventions to reduce inflammation and promote lung repair.

OBJECTIVE

To map and compare metabolic phenotypes of different infectious causes of ARDS to better understand the metabolic pathways involved in the underlying pathogenesis.

METHODS

We analyzed metabolic phenotypes of 3 ARDS cohorts caused by COVID-19, H1N1 influenza, and bacterial pneumonia compared to non-ARDS COVID-19-infected patients and ICU-ventilated controls. Targeted metabolomics was performed on plasma samples from a total of 150 patients using quantitative LC-MS/MS and DI-MS/MS analytical platforms.

RESULTS

Distinct metabolic phenotypes were detected between different infectious causes of ARDS. There were metabolomics differences between ARDSs associated with COVID-19 and H1N1, which include metabolic pathways involving taurine and hypotaurine, pyruvate, TCA cycle metabolites, lysine, and glycerophospholipids. ARDSs associated with bacterial pneumonia and COVID-19 differed in the metabolism of D-glutamine and D-glutamate, arginine, proline, histidine, and pyruvate. The metabolic profile of COVID-19 ARDS (C19/A) patients admitted to the ICU differed from COVID-19 pneumonia (C19/P) patients who were not admitted to the ICU in metabolisms of phenylalanine, tryptophan, lysine, and tyrosine. Metabolomics analysis revealed significant differences between C19/A, H1N1/A, and PNA/A vs ICU-ventilated controls, reflecting potentially different disease mechanisms.

CONCLUSION

Different metabolic phenotypes characterize ARDS associated with different viral and bacterial infections.

Developmental Impacts of Epigenetics and Metabolism in COVID-19

Developmental biology is intricately regulated by epigenetics and metabolism but the mechanisms are not completely understood. The situation becomes even more complicated during diseases where all three phenomena are dysregulated. A salient example is COVID-19, where the death toll exceeded 6.96 million in 4 years, while the virus continues to mutate into different variants and infect people. Early evidence during the pandemic showed that the host's immune and inflammatory responses to COVID-19 (like the cytokine storm) impacted the host's metabolism, causing damage to the host's organs and overall physiology. The involvement of angiotensin-converting enzyme 2 (ACE2), the pivotal host receptor for the SARS-CoV-2 virus, was identified and linked to epigenetic abnormalities along with other contributing factors. Recently, studies have revealed stronger connections between epigenetics and metabolism in COVID-19 that impact development and accelerate aging. Patients manifest systemic toxicity, immune dysfunction and multi-organ failure. Single-cell multiomics and other state-of-the-art high-throughput studies are only just beginning to demonstrate the extent of dysregulation and damage. As epigenetics and metabolism directly impact development, there is a crucial need for research implementing cutting-edge technology, next-generation sequencing, bioinformatics analysis, the identification of biomarkers and clinical trials to help with prevention and therapeutic interventions against similar threats in the future.

Longitudinal NMR-Based Metabolomics Study Reveals How Hospitalized COVID-19 Patients Recover: Evidence of Dyslipidemia and Energy Metabolism Dysregulation

Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), can manifest as long-term symptoms in multiple organ systems, including respiratory, cardiovascular, neurological, and metabolic systems. In patients with severe COVID-19, immune dysregulation is significant, and the relationship between metabolic regulation and immune response is of great interest in determining the pathophysiological mechanisms. We aimed to characterize the metabolomic footprint of recovering severe COVID-19 patients at three consecutive timepoints and compare metabolite levels to controls. Our findings add proof of dysregulated amino acid metabolism in the acute phase and dyslipidemia, glycoprotein level alterations, and energy metabolism disturbances in severe COVID-19 patients 3-4 months post-hospitalization.

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.

Circulating Inflammatory Factor Levels in the Early Phase of COVID-19 are Associated with the Progression of Respiratory Failure: A Single-Center Retrospective Study

Purpose

To evaluate the potential relationships between serum interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α levels and occurrence of respiratory failure in patients with early-stage COVID-19 disease.

Patients and Methods

We analyzed clinical characteristics, laboratory parameters, and immunoinflammatory markers in 302 patients diagnosed with SARS-CoV-2 infection who required hospitalization at Changshu Hospital of Nantong University. IL-2, IL-4, IL-6, IL-10, IL-17, IFN-γ, and TNF-α levels in the peripheral blood of patients hospitalized five days after disease onset were measured using multiplex bead-based flow fluorescent immunoassay (MBFFI).

Results

Patients with respiratory failure had higher serum IL-4 [0 (0, 0.54) pg/mL], IL-6 [40.76 (12.33, 90.28) pg/mL], IL-10 [6.65 (4.12, 11.34) pg/mL], and IL-17 [9.48 (4.31, 12.13) pg/mL] levels than patients without respiratory failure (P=0.042, P<0.0001, P=0.012, and P=0.036, respectively). Serum IL-2, IFN-γ, and TNF-α levels were not significantly different between the two groups. The occurrence of respiratory failure was positively correlated with sex (R=0.122, P=0.034), lactic acid (R=0.193, P=0.007), white blood cell count (R=0.121, P=0.038), erythrocyte distribution width (R=0.131, P=0.024), thyrocalcitonin (R=0.280, P<0.0001), and D-dimer levels (R=0.214, P<0.0001) but negatively correlated with oxygen partial pressure (R=-0.208, P=0.004), oxygen saturation (R=-0.220, P=0.002), lymphocyte count (R=-0.129, P=0.026), and calcium (R=-0.152, P=0.042). Among the immunoinflammatory biomarkers, the occurrence of respiratory failure was positively correlated with IL-4 (R=-0.117, P=0.042), IL-6 (R=0.206, P<0.0001), IL-10 (R=0.145, P=0.012), and IL-17 (R=0.121, P=0.036) levels.

Conclusion

Serum levels of pro-inflammatory cytokines IL-6 and IL-17 and anti-inflammatory cytokines IL-4 and IL-10 were significantly elevated in patients with respiratory failure and weakly positively correlated with the occurrence of respiratory failure. Further studies are required to explore these key immune mechanisms to help clinicians better manage acute complications, long-term sequelae, and possible future COVID-19 variants and be flexible in managing future epidemics and similar public health threats.

Human wild-type and D76N β2-microglobulin variants are significant proteotoxic and metabolic stressors for transgenic C. elegans

β2-microglobulin (β2-m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild-type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2-m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2-m at high concentrations, mimicking the condition that underlies dialysis-related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2-m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2-m levels rather than with the presence of mutations, being more pronounced in WT β2-m worms. β2-m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2-m at high concentration compared to D76N β2-m worms. Altogether, these data show that β2-m is a proteotoxic protein in vivo also in its wild-type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2-m amyloidosis (high levels of non-mutated β2-m vs. normal levels of variant β2-m) and provide important clues on the molecular bases of these human diseases.

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.

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.

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.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Multiplatform analyses reveal distinct drivers of systemic pathogenesis in adult versus pediatric severe acute COVID-19

The pathogenesis of multi-organ dysfunction associated with severe acute SARS-CoV-2 infection remains poorly understood. Endothelial damage and microvascular thrombosis have been identified as drivers of COVID-19 severity, yet the mechanisms underlying these processes remain elusive. Here we show alterations in fluid shear stress-responsive pathways in critically ill COVID-19 adults as compared to non-COVID critically ill adults using a multiomics approach. Mechanistic in-vitro studies, using microvasculature-on-chip devices, reveal that plasma from critically ill COVID-19 adults induces fibrinogen-dependent red blood cell aggregation that mechanically damages the microvascular glycocalyx. This mechanism appears unique to COVID-19, as plasma from non-COVID sepsis patients demonstrates greater red blood cell membrane stiffness but induces less significant alterations in overall blood rheology. Multiomics analyses in pediatric patients with acute COVID-19 or the post-infectious multi-inflammatory syndrome in children (MIS-C) demonstrate little overlap in plasma cytokine and metabolite changes compared to adult COVID-19 patients. Instead, pediatric acute COVID-19 and MIS-C patients show alterations strongly associated with cytokine upregulation. These findings link high fibrinogen and red blood cell aggregation with endotheliopathy in adult COVID-19 patients and highlight differences in the key mediators of pathogenesis between adult and pediatric populations.

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

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

New Insights into the Identification of Metabolites and Cytokines Predictive of Outcome for Patients with Severe SARS-CoV-2 Infection Showed Similarity with Cancer

SARS-CoV-2 infection is characterized by several clinical manifestations, ranging from the absence of symptoms to severe forms that necessitate intensive care treatment. It is known that the patients with the highest rate of mortality develop increased levels of proinflammatory cytokines, called the "cytokine storm", which is similar to inflammatory processes that occur in cancer. Additionally, SARS-CoV-2 infection induces modifications in host metabolism leading to metabolic reprogramming, which is closely linked to metabolic changes in cancer. A better understanding of the correlation between perturbed metabolism and inflammatory responses is necessary. We evaluated untargeted plasma metabolomics and cytokine profiling via ¹H-NMR (proton nuclear magnetic resonance) and multiplex Luminex assay, respectively, in a training set of a limited number of patients with severe SARS-CoV-2 infection classified on the basis of their outcome. Univariate analysis and Kaplan-Meier curves related to hospitalization time showed that lower levels of several metabolites and cytokines/growth factors, correlated with a good outcome in these patients and these data were confirmed in a validation set of patients with similar characteristics. However, after the multivariate analysis, only the growth factor HGF, lactate and phenylalanine retained a significant prediction of survival. Finally, the combined analysis of lactate and phenylalanine levels correctly predicted the outcome of 83.3% of patients in both the training and the validation set. We highlighted that the cytokines and metabolites involved in COVID-19 patients' poor outcomes are similar to those responsible for cancer development and progression, suggesting the possibility of targeting them by repurposing anticancer drugs as a therapeutic strategy against severe SARS-CoV-2 infection.

COVID-19 and non-alcoholic fatty liver disease: Biological insights from multi-omics data

We explored the shared pathophysiological mechanisms between COVID-19 and non-alcoholic fatty liver disease (NAFLD) by integrating multi-omics data. We studied common genetic risk factors and underlying biological processes using functional enrichment analysis. To understand the sex-specific pathways involved in the clinical course of SARS-CoV-2 infection, we processed sex-stratified data from COVID-19 genome-wide association datasets. We further explored the transcriptional signature of the liver cells in healthy and COVID-19 tissue specimens. We also integrated genetic and metabolomic information. We found that COVID-19 and NAFLD share biological disease mechanisms, including pathways that regulate the inflammatory and lipopolysaccharide response. Single-cell transcriptomics revealed enrichment of complement-related pathways in Kupffer cells, syndecan-mediated signalling in plasma cells, and epithelial-to-mesenchymal transition in hepatic stellate cells. The strategy of pathway-level analysis of genomic and metabolomic data uncovered l-lactic acid, Krebs cycle intermediate compounds, arachidonic acid and cortisol among the most prominent shared metabolites.

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.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Association between Circulating Amino Acids and COVID-19 Severity

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

Lipidomic alterations in human saliva from cystic fibrosis patients

Cystic fibrosis is a hereditary metabolic disorder characterized by impaired traffic of chloride ions and water through membranes of the respiratory and gastrointestinal, that causes inadequate hydration of airway surfaces, dehydrated mucous secretions and a high-sodium chloride sweat. Although the classical presentation of the condition is well known, a better characterization of metabolic alterations related is need. In particular, the metabolic composition alterations of biological fluids may be influence by the disease state and could be captured as putative signature to set targeted therapeutic strategies. A targeted comprehensive mass spectrometry-based platform was employed to dissect the lipid content of saliva samples form CF patients, in order to investigate alterations in the lipid metabolic homeostasis related to the pathology, chronic obstructive pulmonary disease, Pseudomonas Aeruginosa infection, pancreatic insufficiency, liver disfunction and diabetes-related complications.

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.

Serum metabolomics-based heterogeneities and screening strategy for metabolic dysfunction-associated fatty liver disease (MAFLD)

BACKGROUND AND AIMS

Metabolic dysfunction-associated fatty liver disease (MAFLD) brings heavy clinical and economic burdens to society, while understandings on heterogeneities are limited.

MATERIALS AND METHODS

We conducted a serum metabolomics study to reveal the metabolic heterogeneities and develop a diagnostic strategy for MAFLD using a discovery set consisting of 122 biopsy-proven MAFLD patients [lean (n = 12), overweight (n = 20), obese (n = 74), type 2 diabetes mellitus (T2DM, n = 16)] and 35 controls, and a validation set containing 60 biopsy-proven MAFLD patients (20 lean, 20 obese and 20 T2DM) and 20 controls.

RESULTS

Mitochondrial dysfunction, destructed phospholipids homeostasis, and folate deficiency were most severe in MAFLD concurrent T2DM patients. Formiminoglutamate, sphinganine and sphingosine correlated positively with HbA1c, while glycoursodeoxycholicacidsulfate correlated positively with AST. Additionally, the linear discriminant analysis (LDA) model using metabolites 5-hydroxyhexanoate, ribitol and formiminoglutamate demonstrated pretty good performance in screening for MAFLD patients, with AUC for validation samples being 0.94 (CI: 0.88-1.0). For easier clinical applications, an M-index based on the three metabolites was further designed.

CONCLUSION

Our study supports that MAFLD concurrent T2DM patients deserve particular attentions in clinical follow-up, and paves the way for developing more effective diagnostic options in future studies.

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.

Endometrial gene expression differences in women with coronavirus disease 2019

OBJECTIVE

To study the potential effect of coronavirus disease (COVID-19) on the endometrial transcriptome of affected, symptomatic women for the detection of altered gene expression.

DESIGN

Pilot study of the endometrial transcriptomes of women manifesting COVID-19 compared with those of women without COVID-19 undergoing hysteroscopic procedures for benign gynecologic disorders using RNA sequencing.

SETTING

Hospital and university laboratories.

PATIENT(S)

Women with (n = 14) and without a COVID-19 (n = 10) diagnosis based on a nasopharyngeal swab analysis using quantitative reverse-transcription polymerase chain reaction. The endometrium of the patients with COVID-19 had previously been tested for severe acute respiratory syndrome coronavirus 2 infection, revealing the absence of the virus in this tissue.

INTERVENTION(S)

Endometrial biopsy sample collection.

MAIN OUTCOMES MEASURE(S)

Endometrial gene expression and functional analysis of symptomatic patients with COVID-19 vs. individuals without the infection.

RESULT(S)

The systemic disease COVID-19 altered endometrial gene expression in 75% of the women, with the patients exhibiting a preponderance of 163 up-regulated (e.g., UTS2, IFI6, IFIH1, and BNIP3) and 72 down-regulated genes (e.g., CPZ, CDH3, and IRF4) (false discovery rate<0.05). A total of 161 dysregulated functions (36 up-regulated and 125 down-regulated) were typically enriched in the endometria of the patients with COVID-19, including up-regulation in pathways involved in the development of immune responses to viruses and cytokine inflammation, reflecting elicitation of a COVID-19 response pathway.

CONCLUSION(S)

Coronavirus disease 2019 affects endometrial gene expression despite the absence of severe acute respiratory syndrome coronavirus 2 RNA in endometrial tissues.

An NMR-Based Model to Investigate the Metabolic Phenoreversion of COVID-19 Patients throughout a Longitudinal Study

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.

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.

Association of COVID-19 with hepatic metabolic dysfunction

The coronavirus disease 2019 (COVID-19) pandemic continues to be a global problem with over 438 million cases reported so far. Although it mostly affects the respiratory system, the involvement of extrapulmonary organs, including the liver, is not uncommon. Since the beginning of the pandemic, metabolic com-orbidities, such as obesity, diabetes, hypertension, and dyslipidemia, have been identified as poor prognostic indicators. Subsequent metabolic and lipidomic studies have identified several metabolic dysfunctions in patients with COVID-19. The metabolic alterations appear to be linked to the course of the disease and inflammatory reaction in the body. The liver is an important organ with high metabolic activity, and a significant proportion of COVID-19 patients have metabolic comorbidities; thus, this factor could play a key role in orchestrating systemic metabolic changes during infection. Evidence suggests that metabolic dysregulation in COVID-19 has both short- and long-term metabolic implications. Furthermore, COVID-19 has adverse associations with metabolic-associated fatty liver disease. Due to the ensuing effects on the renin-angiotensin-aldosterone system and ammonia metabolism, COVID-19 can have significant implications in patients with advanced chronic liver disease. A thorough understanding of COVID-19-associated metabolic dysfunction could lead to the identification of important plasma biomarkers and novel treatment targets. In this review, we discuss the current understanding of metabolic dysfunction in COVID-19, focusing on the liver and exploring the underlying mechanistic pathogenesis and clinical implications.

Serum metabolomic abnormalities in survivors of non-severe COVID-19

Metabolic reprogramming is a distinctive characteristic of SARS-CoV-2 infection, which refers to metabolic changes in hosts triggered by viruses for their survival and spread. It is current urgent to understand the metabolic health status of COVID-19 survivors and its association with long-term health consequences of infection, especially for the predominant non-severe patients. Herein, we show systemic metabolic signatures of survivors of non-severe COVID-19 from Wuhan, China at six months after discharge using metabolomics approaches. The serum amino acids, organic acids, purine, fatty acids and lipid metabolism were still abnormal in the survivors, but the kynurenine pathway and the level of itaconic acid have returned to normal. These metabolic abnormalities are associated with liver injury, mental health, energy production, and inflammatory responses. Our findings identify and highlight the metabolic abnormalities in survivors of non-severe COVID-19, which provide information on biomarkers and therapeutic targets of infection and cues for post-hospital care and intervention strategies centered on metabolism reprogramming.

Multi-Omics Studies Unveil Extraciliary Functions of BBS10 and Show Metabolic Aberrations Underlying Renal Disease in Bardet-Biedl Syndrome

Bardet–Biedl syndrome (BBS) is a rare autosomal recessive ciliopathy resulting in multiple organ dysfunctions, including chronic kidney disease (CKD). Despite the recent progress in the ’ciliopathy’ field, there is still little information on the mechanisms underlying renal disease. To elucidate these pathomechanisms, we conducted a translational study, including (i) the characterization of the urine metabolomic pattern of BBS patients and controls in a pilot and confirmation study and (ii) the proteomic analysis of the BBS10 interactome, one of the major mutated BBS genes in patients, in a renal-epithelial-derived cell culture model. The urine metabolomic fingerprinting of BBS patients differed from controls in both pilot and confirmation studies, demonstrating an increased urinary excretion of several monocarboxylates, including lactic acid (LA), at both early and late CKD stages. Increased urine LA was detected in the absence of both increased plasmatic LA levels and generalized proximal tubular dysfunction, suggesting a possible renal-specific defective handling. The inner medulla renal epithelial (IMCD3) cell line, where Bbs10 was stably invalidated, displayed an increased proliferative rate, increased ATP production, and an up-regulation of aerobic glycolysis. A mass spectrometry-based analysis detected several putative BBS10 interactors in vitro, indicating a potential role of BBS10 in several biological processes, including renal metabolism, RNA processing, and cell proliferation. The present study suggests that the urine metabolomic pattern of BBS patients may reflect intra-renal metabolic aberrations. The analysis of BBS10 interactors unveils possible novel functions, including cell metabolism.

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

OBJECTIVE

Since the COVID-19 pandemic began in early 2020, SARS-CoV2 has claimed more than six million lives world-wide, with over 510 million cases to date. To reduce healthcare burden, we must investigate how to prevent non-acute disease from progressing to severe infection requiring hospitalization.

METHODS

To achieve this goal, we investigated metabolic signatures of both non-acute (out-patient) and severe (requiring hospitalization) COVID-19 samples by profiling the associated plasma metabolomes of 84 COVID-19 positive University of Virginia hospital patients. We utilized supervised and unsupervised machine learning and metabolic modeling approaches to identify key metabolic drivers that are predictive of COVID-19 disease severity. Using metabolic pathway enrichment analysis, we explored potential metabolic mechanisms that link these markers to disease progression.

RESULTS

Enriched metabolites associated with tryptophan in non-acute COVID-19 samples suggest mitigated innate immune system inflammatory response and immunopathology related lung damage prevention. Increased prevalence of histidine- and ketone-related metabolism in severe COVID-19 samples offers potential mechanistic insight to musculoskeletal degeneration-induced muscular weakness and host metabolism that has been hijacked by SARS-CoV2 infection to increase viral replication and invasion.

CONCLUSIONS

Our findings highlight the metabolic transition from an innate immune response coupled with inflammatory pathway inhibition in non-acute infection to rampant inflammation and associated metabolic systemic dysfunction in severe COVID-19.

Metabolic Profiling at COVID-19 Onset Shows Disease Severity and Sex-Specific Dysregulation

Background

metabolic changes through SARS-CoV-2 infection has been reported but not fully comprehended. This metabolic dysregulation affects multiple organs during COVID-19 and its early detection can be used as a prognosis marker of severity. Therefore, we aimed to characterize metabolic and cytokine profile at COVID-19 onset and its relationship with disease severity to identify metabolic profiles predicting disease progression.

Material and Methods

we performed a retrospective cross-sectional study in 123 COVID-19 patients which were stratified as asymptomatic/mild, moderate and severe according to the highest COVID-19 severity status, and a group of healthy controls. We performed an untargeted plasma metabolic profiling (gas chromatography and capillary electrophoresis-mass spectrometry (GC and CE-MS)) and cytokine evaluation.

Results

After data filtering and identification we observed 105 metabolites dysregulated (66 GC-MS and 40 CE-MS) which shown different expression patterns for each COVID-19 severity status. These metabolites belonged to different metabolic pathways including amino acid, energy, and nitrogen metabolism among others. Severity-specific metabolic dysregulation was observed, as an increased transformation of L-tryptophan into L-kynurenine. Thus, metabolic profiling at hospital admission differentiate between severe and moderate patients in the later phase of worse evolution. Several plasma pro-inflammatory biomarkers showed significant correlation with deregulated metabolites, specially with L-kynurenine and L-tryptophan. Finally, we describe a strong sex-related dysregulation of metabolites, cytokines and chemokines between severe and moderate patients. In conclusion, metabolic profiling of COVID-19 patients at disease onset is a powerful tool to unravel the SARS-CoV-2 molecular pathogenesis.

Conclusions

This technique makes it possible to identify metabolic phenoconversion that predicts disease progression and explains the pronounced pathogenesis differences between sexes.

Sex differences in the human metabolome

BACKGROUND

The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved.

MAIN BODY

This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account.

CONCLUSIONS

The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.

Thyroid-Stimulating Hormone Predicts Total Cholesterol and Low-Density Lipoprotein Cholesterol Reduction during the Acute Phase of COVID-19

A complex dysregulation of lipid metabolism occurs in COVID-19, leading to reduced total cholesterol (TC), LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) levels, along with a derangement of thyroid function, leading to reduced thyroid-stimulating hormone (TSH) levels. This study aimed to explore the association between TSH levels during COVID-19 and the variation (Δ) of lipid profile parameters in the period preceding (from 1 month up to 1 year) hospital admission due to COVID-19. Clinical data of 324 patients (mean age 76 ± 15 years, 54% males) hospitalized due to COVID-19 between March 2020 and March 2022 were retrospectively analyzed. The association between TSH levels at hospital admission and either Δ-TC, Δ-LDL-C, or Δ-HDL-C over the selected time frame was assessed through univariable and multivariable analyses. TSH levels were below the lower reference limit of 0.340 μUI/mL in 14% of COVID-19 patients. A significant reduction of plasma TC, LDL-C, and HDL-C was recorded between the two time points (p < 0.001 for all the comparisons). TSH was directly associated with Δ-TC (rho = 0.193, p = 0.001), Δ-LDL-C (rho = 0.201, p = 0.001), and Δ-HDL-C (rho = 0.160, p = 0.008), and inversely associated with C-reactive protein (CRP) (rho = −0.175, p = 0.004). Moreover, TSH decreased with increasing COVID-19 severity (p < 0.001). CRP and COVID-19 severity were inversely associated with Δ-TC, Δ-LDL-C, and Δ-HDL-C (p < 0.05 for all associations). A significant independent association was found between TSH and either Δ-TC (β = 0.125, p = 0.044) or Δ-LDL-C (β = 0.131, p = 0.036) after adjusting for multiple confounders including CRP and COVID-19 severity. In conclusion, lower levels of TSH may contribute to explain TC and LDL-C reduction in the acute phase of COVID-19.

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.

Variations in Biochemical Values under Stress in Children with SARS-CoV-2 Infection

In the case of SARS-CoV-2 infection, children seem to be less affected than adults, but data regarding epidemiologic characteristics and biochemical values are poor and essentially based on limited case series. The aim of our study is to highlight the predictive value of some biochemical markers at hospitalization, for the correct classification of the patient in the form of disease. Methods: We performed an analytical retrospective study on 82 pediatric patients diagnosed with COVID-19 in the emergency department, with moderate or severe form of disease, and treated in our tertiary hospital. We analyzed the epidemiologic characteristics, symptomatology and biochemical values and compare the data according to the form of disease. Results: The mean age at admission was 4.5 years (median 1 year) and the masculine/feminine ratio was 1.5. Comparing the data between the two groups of patients (42 severe/40 moderate), we observed that the severe form presented with a lower pH at admission (p = 0.02), hyperglycemia (p = 0.01), increased values of transaminases (p = 0.01 and 0.02) and hypoproteinemia (p = 0.01). Also, the severe form was statistically significantly associated with comorbidities, acute respiratory distress, rising of the inflammatory markers during hospitalization. Hyperlactatemia (Lactate > 1.5 mmol/L) was significantly associated with the age under one year (p < 0.001). Mortality rate was 9.75% and the median age at death was 3 months. Univariate logistic regression model shows that the presence of anemia increased the probability of death 88 times, comorbidities 23.3 times and ketoacidosis 16.4 times. Conclusions: Metabolic acidosis, hyperlactatemia, hyperglycemia, modified hepatic values and hypoproteinemia are biochemical markers associated with the severe form of disease in SARS-CoV-2 infection in children. Presence of anemia, comorbidities and ketoacidosis are important risk factors for death of pediatric patients with SARS-CoV-2 infection.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Skeletal Muscle and COVID-19: The Potential Involvement of Bioactive Sphingolipids

SARS-CoV-2 virus infection is the cause of the coronavirus disease 2019 (COVID-19), which is still spreading over the world. The manifestation of this disease can range from mild to severe and can be limited in time (weeks) or persist for months in about 30–50% of patients. COVID-19 is considered a multiple organ dysfunction syndrome and the musculoskeletal system manifestations are beginning to be considered of absolute importance in both COVID-19 patients and in patients recovering from the SARS-CoV-2 infection. Musculoskeletal manifestations of COVID-19 and other coronavirus infections include loss of muscle mass, muscle weakness, fatigue or myalgia, and muscle injury. The molecular mechanisms by which SARS-CoV-2 can cause damage to skeletal muscle (SkM) cells are not yet well understood. Sphingolipids (SLs) represent an important class of eukaryotic lipids with structural functions as well as bioactive molecules able to modulate crucial processes, including inflammation and viral infection. In the last two decades, several reports have highlighted the role of SLs in modulating SkM cell differentiation, regeneration, aging, response to insulin, and contraction. This review summarizes the consequences of SARS-CoV-2 infection on SkM and the potential involvement of SLs in the tissue responses to virus infection. In particular, we highlight the role of sphingosine 1-phosphate signaling in order to aid the prediction of novel targets for preventing and/or treating acute and long-term musculoskeletal manifestations of virus infection in 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.

Intravital Imaging with Two-Photon Microscopy: A Look into the Kidney

Fluorescence microscopy has represented a crucial technique to explore the cellular and molecular mechanisms in the field of biomedicine. However, the conventional one-photon microscopy exhibits many limitations when living samples are imaged. The new technologies, including two-photon microscopy (2PM), have considerably improved the in vivo study of pathophysiological processes, allowing the investigators to overcome the limits displayed by previous techniques. 2PM enables the real-time intravital imaging of the biological functions in different organs at cellular and subcellular resolution thanks to its improved laser penetration and less phototoxicity. The development of more sensitive detectors and long-wavelength fluorescent dyes as well as the implementation of semi-automatic software for data analysis allowed to gain insights in essential physiological functions, expanding the frontiers of cellular and molecular imaging. The future applications of 2PM are promising to push the intravital microscopy beyond the existing limits. In this review, we provide an overview of the current state-of-the-art methods of intravital microscopy, focusing on the most recent applications of 2PM in kidney physiology.

Nucleic Acids as Biotools at the Interface between Chemistry and Nanomedicine in the COVID-19 Era

The recent development of mRNA vaccines against the SARS-CoV-2 infection has turned the spotlight on the potential of nucleic acids as innovative prophylactic agents and as diagnostic and therapeutic tools. Until now, their use has been severely limited by their reduced half-life in the biological environment and the difficulties related to their transport to target cells. These limiting aspects can now be overcome by resorting to chemical modifications in the drug and using appropriate nanocarriers, respectively. Oligonucleotides can interact with complementary sequences of nucleic acid targets, forming stable complexes and determining their loss of function. An alternative strategy uses nucleic acid aptamers that, like the antibodies, bind to specific proteins to modulate their activity. In this review, the authors will examine the recent literature on nucleic acids-based strategies in the COVID-19 era, focusing the attention on their applications for the prophylaxis of COVID-19, but also on antisense- and aptamer-based strategies directed to the diagnosis and therapy of the coronavirus pandemic.

COVID-19 and One-Carbon Metabolism

Dysregulation of one-carbon metabolism affects a wide range of biological processes and is associated with a number of diseases, including cardiovascular disease, dementia, neural tube defects, and cancer. Accumulating evidence suggests that one-carbon metabolism plays an important role in COVID-19. The symptoms of long COVID-19 are similar to those presented by subjects suffering from vitamin B₁₂ deficiency (pernicious anemia). The metabolism of a cell infected by the SARS-CoV-2 virus is reshaped to fulfill the need for massive viral RNA synthesis, which requires de novo purine biosynthesis involving folate and one-carbon metabolism. Many aspects of host sulfur amino acid metabolism, particularly glutathione metabolism underlying antioxidant defenses, are also taken over by the SARS-CoV-2 virus. The purpose of this review is to summarize recent findings related to one-carbon metabolism and sulfur metabolites in COVID-19 and discuss how they inform strategies to combat the disease.

Network Pharmacology Study to Elucidate the Key Targets of Underlying Antihistamines against COVID-19

Antihistamines have potent efficacy to alleviate COVID-19 (Coronavirus disease 2019) symptoms such as anti-inflammation and as a pain reliever. However, the pharmacological mechanism(s), key target(s), and drug(s) are not documented well against COVID-19. Thus, we investigated to decipher the most significant components and how its research methodology was utilized by network pharmacology. The list of 32 common antihistamines on the market were retrieved via drug browsing databases. The targets associated with the selected antihistamines and the targets that responded to COVID-19 infection were identified by the Similarity Ensemble Approach (SEA), SwissTargetPrediction (STP), and PubChem, respectively. We described bubble charts, the Pathways-Targets-Antihistamines (PTA) network, and the protein–protein interaction (PPI) network on the RPackage via STRING database. Furthermore, we utilized the AutoDock Tools software to perform molecular docking tests (MDT) on the key targets and drugs to evaluate the network pharmacological perspective. The final 15 targets were identified as core targets, indicating that Neuroactive ligand–receptor interaction might be the hub-signaling pathway of antihistamines on COVID-19 via bubble chart. The PTA network was constructed by the RPackage, which identified 7 pathways, 11 targets, and 30 drugs. In addition, GRIN2B, a key target, was identified via topological analysis of the PPI network. Finally, we observed that the GRIN2B-Loratidine complex was the most stable docking score with −7.3 kcal/mol through molecular docking test. Our results showed that Loratadine might exert as an antagonist on GRIN2B via the neuroactive ligand–receptor interaction pathway. To sum up, we elucidated the most potential antihistamine, a key target, and a key pharmacological pathway as alleviating components against COVID-19, supporting scientific evidence for further research.

Oxylipin profile in saliva from patients with cystic fibrosis reveals a balance between pro-resolving and pro-inflammatory molecules

Oxylipins are signaling molecules originated by fatty acids that modulate vascular and bronchial tone, bronchial secretion, cytokine production and immune cell activity. The unbalanced production of pro-inflammatory and pro-resolving (i.e., anti-inflammatory) oxylipins has a relevant role in the pathogenesis of pulmonary inflammation like in cystic fibrosis (CF). We analyzed by LC-MRM/MS 65 oxylipins and 4 fatty acids in resting saliva from 69 patients with CF and 50 healthy subjects (controls). The salivary levels of 48/65 oxylipins were significantly different between CF patients and controls. Among these, EpETE, DHET, 6ketoPGE1 and HDHA were significantly higher in saliva from CF patients than in controls. All these molecules display anti-inflammatory effects, i.e., releasing of bronchial and vascular tone, modulation of cytokine release. While 20-hydroxyPGF2A, PGB2, EpDPE, 9 K-12-ELA, bicyclo-PGE2, oleic acid, LTC4, linoleic acid, 15oxoEDE, 20 hydroxyPGE2 and DHK-PGD2/PGE2 (mostly associated to pro-inflammatory effects) resulted significantly lower in CF patients than in controls. Our data suggest that the salivary oxylipins profile in CF patients is addressed toward a global anti-inflammatory effect. Although these findings need be confirmed on larger populations in prospective studies, they will contribute to better understand the pathogenesis of CF chronic inflammation and to drive targeted therapies based on the modulation of oxylipins synthesis and degradation.

Proteome data of neuroblastoma cells overexpressing Neuroglobin

In this article, we present data on the proteome of human neuroblastoma cells stably overexpressing Neuroglobin (NGB). The neuroprotective role of NGB is clearly established, nevertheless the related mechanistic processes, which are dependent on NGB overexpression, are not known. To address this question, we performed shotgun label-free quantification (LFQ) proteomics using an SH-SY5Y cell model of neuroblastoma that overexpresses an NGB-FLAG construct, and wild type cells transfected with an empty vector as control (CTRL). The proteomes from six biological samples per condition were digested using the S-Trap sample preparation followed by LC-MS/MS analysis with a LTQ-Orbitrap XL mass spectrometer. The quantitative analysis was performed using the LFQ algorithm of MaxQuant, leading to 1654 correctly quantified proteins over 2580 identified proteins. Finally, the statistic comparison of the two analyzed groups within Perseus platform identified 178 differential proteins (107 up- and 71 down-regulated). In addition, multivariate statistical analysis was carried out using MetaboAnalyst 5.0 software. MS proteomics data are available via ProteomeXchange with the dataset identifier PXD029012.

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.

Rapidity and Precision of Steroid Hormone Measurement

Steroids are present in all animals and plants, from mammals to prokaryotes. In the medical field, steroids are commonly classified as glucocorticoids, mineralocorticoids, and gonadal steroid hormones. Monitoring of hormones is useful in clinical and research fields for the assessment of physiological changes associated with aging, disease risk, and the diagnostic and therapeutic effects of various diseases. Since the discovery and isolation of steroid hormones, measurement methods for steroid hormones in biological samples have advanced substantially. Although immunoassays (IAs) are widely used in daily practice, mass spectrometry (MS)-based methods have been reported to be more specific. Steroid hormone measurement based on MS is desirable in clinical practice; however, there are several drawbacks, including the purchase and maintenance costs of the MS instrument and the need for specialized training of technicians. In this review, we discuss IA- and MS-based methods currently in use and briefly present the history of steroid hormone measurement. In addition, we describe recent advances in IA- and MS-based methods and future applications and considerations.