Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication

SARS-CoV-2 mitochondrial metabolic and epigenomic reprogramming in COVID-19

To determine the effects of SARS-CoV-2 infection on cellular metabolism, we conducted an exhaustive survey of the cellular metabolic pathways modulated by SARS-CoV-2 infection and confirmed their importance for SARS-CoV-2 propagation by cataloging the effects of specific pathway inhibitors. This revealed that SARS-CoV-2 strongly inhibits mitochondrial oxidative phosphorylation (OXPHOS) resulting in increased mitochondrial reactive oxygen species (mROS) production. The elevated mROS stabilizes HIF-1α which redirects carbon molecules from mitochondrial oxidation through glycolysis and the pentose phosphate pathway (PPP) to provide substrates for viral biogenesis. mROS also induces the release of mitochondrial DNA (mtDNA) which activates innate immunity. The restructuring of cellular energy metabolism is mediated in part by SARS-CoV-2 Orf8 and Orf10 whose expression restructures nuclear DNA (nDNA) and mtDNA OXPHOS gene expression. These viral proteins likely alter the epigenome, either by directly altering histone modifications or by modulating mitochondrial metabolite substrates of epigenome modification enzymes, potentially silencing OXPHOS gene expression and contributing to long-COVID.

Infection with SARS-CoV-2 can cause pancreatic impairment

Evidence suggests associations between COVID-19 patients or vaccines and glycometabolic dysfunction and an even higher risk of the occurrence of diabetes. Herein, we retrospectively analyzed pancreatic lesions in autopsy tissues from 67 SARS-CoV-2 infected non-human primates (NHPs) models and 121 vaccinated and infected NHPs from 2020 to 2023 and COVID-19 patients. Multi-label immunofluorescence revealed direct infection of both exocrine and endocrine pancreatic cells by the virus in NHPs and humans. Minor and limited phenotypic and histopathological changes were observed in adult models. Systemic proteomics and metabolomics results indicated metabolic disorders, mainly enriched in insulin resistance pathways, in infected adult NHPs, along with elevated fasting C-peptide and C-peptide/glucose ratio levels. Furthermore, in elder COVID-19 NHPs, SARS-CoV-2 infection causes loss of beta (β) cells and lower expressed-insulin in situ characterized by islet amyloidosis and necrosis, activation of α-SMA and aggravated fibrosis consisting of lower collagen in serum, an increase of pancreatic inflammation and stress markers, ICAM-1 and G3BP1, along with more severe glycometabolic dysfunction. In contrast, vaccination maintained glucose homeostasis by activating insulin receptor α and insulin receptor β. Overall, the cumulative risk of diabetes post-COVID-19 is closely tied to age, suggesting more attention should be paid to blood sugar management in elderly COVID-19 patients.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

The potential role of ferroptosis in COVID-19-related cardiovascular injury

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a global health threat in 2019. An important feature of the disease is that multiorgan symptoms of SARS-CoV-2 infection persist after recovery. Evidence indicates that people who recovered from COVID-19, even those under the age of 65 years without cardiovascular risk factors such as smoking, obesity, hypertension, and diabetes, had a significantly increased risk of cardiovascular disease for up to one year after diagnosis. Therefore, it is important to closely monitor individuals who have recovered from COVID-19 for potential cardiovascular damage that may manifest at a later stage. Ferroptosis is an iron-dependent form of non-apoptotic cell death characterized by the production of reactive oxygen species (ROS) and increased lipid peroxide levels. Several studies have demonstrated that ferroptosis plays an important role in cancer, ischemia/reperfusion injury (I/RI), and other cardiovascular diseases. Altered iron metabolism, upregulation of reactive oxygen species, and glutathione peroxidase 4 inactivation are striking features of COVID-19-related cardiovascular injury. SARS-CoV-2 can cause cardiovascular ferroptosis, leading to cardiovascular damage. Understanding the mechanism of ferroptosis in COVID-19-related cardiovascular injuries will contribute to the development of treatment regimens for preventing or reducing COVID-19-related cardiovascular complications. In this article, we go over the pathophysiological underpinnings of SARS-CoV-2-induced acute and chronic cardiovascular injury, the function of ferroptosis, and prospective treatment approaches.

A ketogenic diet can mitigate SARS-CoV-2 induced systemic reprogramming and inflammation

The ketogenic diet (KD) has demonstrated benefits in numerous clinical studies and animal models of disease in modulating the immune response and promoting a systemic anti-inflammatory state. Here we investigate the effects of a KD on systemic toxicity in mice following SARS-CoV-2 infection. Our data indicate that under KD, SARS-CoV-2 reduces weight loss with overall improved animal survival. Muted multi-organ transcriptional reprogramming and metabolism rewiring suggest that a KD initiates and mitigates systemic changes induced by the virus. We observed reduced metalloproteases and increased inflammatory homeostatic protein transcription in the heart, with decreased serum pro-inflammatory cytokines (i.e., TNF-α, IL-15, IL-22, G-CSF, M-CSF, MCP-1), metabolic markers of inflammation (i.e., kynurenine/tryptophane ratio), and inflammatory prostaglandins, indicative of reduced systemic inflammation in animals infected under a KD. Taken together, these data suggest that a KD can alter the transcriptional and metabolic response in animals following SARS-CoV-2 infection with improved mice health, reduced inflammation, and restored amino acid, nucleotide, lipid, and energy currency metabolism.

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

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

Neuroproteomic Analysis after SARS-CoV-2 Infection Reveals Overrepresented Neurodegeneration Pathways and Disrupted Metabolic Pathways

Besides respiratory illness, SARS-CoV-2, the causative agent of COVID-19, leads to neurological symptoms. The molecular mechanisms leading to neuropathology after SARS-CoV-2 infection are sparsely explored. SARS-CoV-2 enters human cells via different receptors, including ACE-2, TMPRSS2, and TMEM106B. In this study, we used a human-induced pluripotent stem cell-derived neuronal model, which expresses ACE-2, TMPRSS2, TMEM106B, and other possible SARS-CoV-2 receptors, to evaluate its susceptibility to SARS-CoV-2 infection. The neurons were exposed to SARS-CoV-2, followed by RT-qPCR, immunocytochemistry, and proteomic analyses of the infected neurons. Our findings showed that SARS-CoV-2 infects neurons at a lower rate than other human cells; however, the virus could not replicate or produce infectious virions in this neuronal model. Despite the aborted SARS-CoV-2 replication, the infected neuronal nuclei showed irregular morphology compared to other human cells. Since cytokine storm is a significant effect of SARS-CoV-2 infection in COVID-19 patients, in addition to the direct neuronal infection, the neurons were treated with pre-conditioned media from SARS-CoV-2-infected lung cells, and the neuroproteomic changes were investigated. The limited SARS-CoV-2 infection in the neurons and the neurons treated with the pre-conditioned media showed changes in the neuroproteomic profile, particularly affecting mitochondrial proteins and apoptotic and metabolic pathways, which may lead to the development of neurological complications. The findings from our study uncover a possible mechanism behind SARS-CoV-2-mediated neuropathology that might contribute to the lingering effects of the virus on the human brain.

Understanding COVID-19 progression with longitudinal peripheral blood mononuclear cell proteomics: Changes in the cellular proteome over time

The clinical presentation of COVID-19 is highly variable, and understanding the underlying biological processes is crucial. This study utilized a proteomic analysis to investigate dysregulated processes in the peripheral blood mononuclear cells of patients with COVID-19 compared to healthy volunteers. Samples were collected at different stages of the disease, including hospital admission, after 7 days of hospitalization, and 30 days after discharge. Metabolic pathway alterations and increased abundance of neutrophil-related proteins were observed in patients. Patients progressing to critical illness had significantly low-abundance proteins in the pentose phosphate and glycolysis pathways compared with those presenting clinical recovery. Important biological processes, such as fatty acid concentration and glucose metabolism disorder, remained altered even after 30 days of hospital discharge. Temporal proteomic changes revealed distinct pathways in critically ill and non-critically ill patients. Our study emphasizes the significance of longitudinal cellular proteomic studies in identifying disease progression-related pathways and persistent protein changes post-hospitalization.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Glucose metabolism disorder: a potential accomplice of SARS-CoV-2

Globally, 265,713,467 confirmed cases of SARS-CoV-2 (CoV-2), including 5,260,888 deaths, have been reported by the WHO. It is important to study the mechanism of this infectious disease. A variety of evidences show the potential association between CoV-2 and glucose metabolism. Notably, people with type 2 diabetes mellitus (T2DM) and other metabolic complications were prone to have a higher risk of developing a more severe infection course than people who were metabolically normal. The correlations between glucose metabolism and CoV-2 progression have been widely revealed. This review will discuss the association between glucose metabolism disorders and CoV-2 progression, showing the promoting effect of diabetes and other diseases related to glucose metabolism disorders on the progression of CoV-2. We will further conclude the effects of key proteins and pathways in glucose metabolism regulation on CoV-2 progression and potential interventions by targeting glucose metabolism disorders for CoV-2 treatment. Therefore, this review will provide systematic insight into the treatment of CoV-2 from the perspective of glucose metabolism.

microRNA-185 Inhibits SARS-CoV-2 Infection through the Modulation of the Host's Lipid Microenvironment

With the emergence of the novel betacoronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there has been an urgent need for the development of fast-acting antivirals, particularly in dealing with different variants of concern (VOC). SARS-CoV-2, like other RNA viruses, depends on host cell machinery to propagate and misregulate metabolic pathways to its advantage. Herein, we discovered that the immunometabolic microRNA-185 (miR-185) restricts SARS-CoV-2 propagation by affecting its entry and infectivity. The antiviral effects of miR-185 were studied in SARS-CoV-2 Spike protein pseudotyped virus, surrogate virus (HCoV-229E), as well as live SARS-CoV-2 virus in Huh7, A549, and Calu-3 cells. In each model, we consistently observed microRNA-induced reduction in lipid metabolism pathways-associated genes including SREBP2, SQLE, PPARG, AGPAT3, and SCARB1. Interestingly, we also observed changes in angiotensin-converting enzyme 2 (ACE2) levels, the entry receptor for SARS-CoV-2. Taken together, these data show that miR-185 significantly restricts host metabolic and other pathways that appear to be essential to SAR-CoV-2 replication and propagation. Overall, this study highlights an important link between non-coding RNAs, immunometabolic pathways, and viral infection. miR-185 mimics alone or in combination with other antiviral therapeutics represent possible future fast-acting antiviral strategies that are likely to be broadly antiviral against multiple variants as well as different virus types of potential pandemics.

Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins bind to host mitochondrial proteins, likely inhibiting oxidative phosphorylation (OXPHOS) and stimulating glycolysis. We analyzed mitochondrial gene expression in nasopharyngeal and autopsy tissues from patients with coronavirus disease 2019 (COVID-19). In nasopharyngeal samples with declining viral titers, the virus blocked the transcription of a subset of nuclear DNA (nDNA)-encoded mitochondrial OXPHOS genes, induced the expression of microRNA 2392, activated HIF-1α to induce glycolysis, and activated host immune defenses including the integrated stress response. In autopsy tissues from patients with COVID-19, SARS-CoV-2 was no longer present, and mitochondrial gene transcription had recovered in the lungs. However, nDNA mitochondrial gene expression remained suppressed in autopsy tissue from the heart and, to a lesser extent, kidney, and liver, whereas mitochondrial DNA transcription was induced and host-immune defense pathways were activated. During early SARS-CoV-2 infection of hamsters with peak lung viral load, mitochondrial gene expression in the lung was minimally perturbed but was down-regulated in the cerebellum and up-regulated in the striatum even though no SARS-CoV-2 was detected in the brain. During the mid-phase SARS-CoV-2 infection of mice, mitochondrial gene expression was starting to recover in mouse lungs. These data suggest that when the viral titer first peaks, there is a systemic host response followed by viral suppression of mitochondrial gene transcription and induction of glycolysis leading to the deployment of antiviral immune defenses. Even when the virus was cleared and lung mitochondrial function had recovered, mitochondrial function in the heart, kidney, liver, and lymph nodes remained impaired, potentially leading to severe COVID-19 pathology.

Direct and indirect impact of SARS-CoV-2 on the brain

Although COVID-19 is mostly a pulmonary disease, it is now well accepted that it can cause a much broader spectrum of signs and symptoms and affect many other organs and tissue. From mild anosmia to severe ischemic stroke, the impact of SARS-CoV-2 on the central nervous system is still a great challenge to scientists and health care practitioners. Besides the acute and severe neurological problems described, as encephalopathies, leptomeningitis, and stroke, after 2 years of pandemic, the chronic impact observed during long-COVID or the post-acute sequelae of COVID-19 (PASC) greatly intrigues scientists worldwide. Strikingly, even asymptomatic, and mild diseased patients may evolve with important neurological and psychiatric symptoms, as confusion, memory loss, cognitive decline, chronic fatigue, associated or not with anxiety and depression. Thus, the knowledge on the correlation between COVID-19 and the central nervous system is of great relevance. In this sense, here we discuss some important mechanisms obtained from in vitro and in vivo investigation regarding how SARS-CoV-2 impacts the brain and its cells and function.

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.

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

The immune mechanism of the nasal epithelium in COVID-19-related olfactory dysfunction

During the first waves of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, olfactory dysfunction (OD) was reported as a frequent clinical sign. The nasal epithelium is one of the front-line protections against viral infections, and the immune responses of the nasal mucosa may be associated with OD. Two mechanisms underlying OD occurrence in COVID-19 have been proposed: the infection of sustentacular cells and the inflammatory reaction of the nasal epithelium. The former triggers OD and the latter likely prolongs OD. These two alternative mechanisms may act in parallel; the infection of sustentacular cells is more important for OD occurrence because sustentacular cells are more likely to be the entry point of SARS-CoV-2 than olfactory neurons and more susceptible to early injury. Furthermore, sustentacular cells abundantly express transmembrane protease, serine 2 (TMPRSS2) and play a major role in the olfactory epithelium. OD occurrence in COVID-19 has revealed crucial roles of sustentacular cells. This review aims to elucidate how immune responses of the nasal epithelium contribute to COVID-19-related OD. Understanding the underlying immune mechanisms of the nasal epithelium in OD may aid in the development of improved medical treatments for COVID-19-related OD.

Role of myeloid cells in system-level immunometabolic dysregulation during prolonged successful HIV-1 treatment

OBJECTIVE

Why people with HIV-1 on ART (PWH ART ) display convoluted metabolism and immune cell functions during prolonged suppressive therapy is not well evaluated. In this study, we aimed to address this question using multiomics methodologies to investigate immunological and metabolic differences between PWH ART and HIV-1 negative individuals (HC).

DESIGN

Cross-sectional study.

METHODS

Untargeted and targeted metabolomics was performed using gas and liquid chromatography/mass spectrometry, and targeted proteomics using Olink inflammation panel on plasma samples. The cellular metabolic state was further investigated using flow cytometry and intracellular metabolic measurement in single-cell populations isolated by EasySep cell isolation. Finally, flow cytometry was performed for deep-immunophenotyping of mononuclear phagocytes.

RESULTS

We detected increased levels of glutamate, lactate, and pyruvate by plasma metabolomics and increased inflammatory markers (e.g. CCL20 and CCL7) in PWH ART compared to HC. The metabolite transporter detection by flow cytometry in T cells and monocytes indicated an increased expression of glucose transporter 1 (Glut1) and monocarboxylate transporter 1 (MCT-1) in PWH ART . Single cell-type metabolite measurement identified decreased glucose, glutamate, and lactate in monocytic cell populations in PWH ART . Deep-immunophenotyping of myeloid cell lineages subpopulations showed no difference in cell frequency, but expression levels of CCR5 were increased on classical monocytes and some dendritic cells.

CONCLUSIONS

Our data thus suggest that the myeloid cell populations potentially contribute significantly to the modulated metabolic environment during suppressive HIV-1 infection.

Pathophysiological involvement of host mitochondria in SARS-CoV-2 infection that causes COVID-19: a comprehensive evidential insight

SARS-CoV-2 is a positive-strand RNA virus that infects humans through the nasopharyngeal and oral route causing COVID-19. Scientists left no stone unturned to explore a targetable key player in COVID-19 pathogenesis against which therapeutic interventions can be initiated. This article has attempted to review, coordinate and accumulate the most recent observations in support of the hypothesis predicting the altered state of mitochondria concerning mitochondrial redox homeostasis, inflammatory regulations, morphology, bioenergetics and antiviral signalling in SARS-CoV-2 infection. Mitochondria is extremely susceptible to physiological as well as pathological stimuli, including viral infections. Recent studies suggest that SARS-CoV-2 pathogeneses alter mitochondrial integrity, in turn mitochondria modulate cellular response against the infection. SARS-CoV-2 M protein inhibited mitochondrial antiviral signalling (MAVS) protein aggregation in turn hinders innate antiviral response. Viral open reading frames (ORFs) also play an instrumental role in altering mitochondrial regulation of immune response. Notably, ORF-9b and ORF-6 impair MAVS activation. In aged persons, the NLRP3 inflammasome is over-activated due to impaired mitochondrial function, increased mitochondrial reactive oxygen species (mtROS), and/or circulating free mitochondrial DNA, resulting in a hyper-response of classically activated macrophages. This article also tries to understand how mitochondrial fission-fusion dynamics is affected by the virus. This review comprehends the overall mitochondrial attribute in pathogenesis as well as prognosis in patients infected with COVID-19 taking into account pertinent in vitro, pre-clinical and clinical data encompassing subjects with a broad range of severity and morbidity. This endeavour may help in exploring novel non-canonical therapeutic strategies to COVID-19 disease and associated complications.

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

Background

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

Methods

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

Results

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

Conclusion

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

Metabolomics meets systems immunology

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

A metabolic blueprint of COVID-19 and long-term vaccine efficacy

Viruses are obligatory protein-coated units and often utilize the metabolic functions of the cells they infect. Viruses hijack cellular metabolic functions and cause consequences that can range from minor to devastating, as we have all witnessed during the COVID-19 pandemic. For understanding the virus-driven pathogenesis and its implications on the host, the cellular metabolism needs to be elucidated. How SARS-CoV-2 triggers metabolic functions and rewires the metabolism remains unidentified but the implications of the metabolic patterns are under investigation by several researchers. In this review, we have described the SARS-CoV-2-mediated metabolic alterations from in vitro studies to metabolic changes reported in victims of COVID-19. We have also discussed potential therapeutic targets to diminish the viral infection and suppress the inflammatory response, with respect to evidenced studies based on COVID-19 research. Finally, we aimed to explain how we could extend vaccine-induced immunity in people by targeting the immunometabolism.

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.

Ectopic expression of SARS-CoV-2 S and ORF-9B proteins alters metabolic profiles and impairs contractile function in cardiomyocytes

Coronavirus disease 2019 (COVID-19) is associated with adverse impacts in the cardiovascular system, but the mechanisms driving this response remain unclear. In this study, we conducted "pseudoviral infection" of SARS-CoV-2 subunits to evaluate their toxic effects in cardiomyocytes (CMs), that were derived from human induced pluripotent stem cells (hiPSCs). We found that the ectopic expression of S and ORF-9B subunits significantly impaired the contractile function and altered the metabolic profiles in human cardiomyocytes. Further mechanistic study has shown that the mitochondrial oxidative phosphorylation (OXPHOS), membrane potential, and ATP production were significantly decreased two days after the overexpression of S and ORF-9B subunits, while S subunits induced higher level of reactive oxygen species (ROS). Two weeks after overexpression, glycolysis was elevated in the ORF-9B group. Based on the transcriptomic analysis, both S and ORF-9B subunits dysregulated signaling pathways associated with metabolism and cardiomyopathy, including upregulated genes involved in HIF-signaling and downregulated genes involved in cholesterol biosynthetic processes. The ORF-9B subunit also enhanced glycolysis in the CMs. Our results collectively provide an insight into the molecular mechanisms underlying SARS-CoV-2 subunits-induced metabolic alterations and cardiac dysfunctions in the hearts of COVID-19 patients.

An integrative analysis of Qingfei Paidu Decoction for its anti-HCoV-229E mechanism in cold and damp environment based on the pharmacokinetics, metabolomics and molecular docking technology

BACKGROUND

The novel coronavirus pneumonia (COVID-19) has spread rapidly around the world. As a member against the epidemic, Qingfei Paidu Decoction (QFPDD) has been approved for the treatment of COVID-19 in China. However, its antiviral mechanism was still largely unclear.

PURPOSE

An integrated strategy was used to explore the antiviral mechanisms of QFPDD in cold and damp environment, including pharmacokinetic (PK), network pharmacology, metabolomics and protein verification.

METHODS

Firstly, the pharmacokinetic study of the prototype absorbed ingredients were analyzed by UHPLC-QqQ-MS. Secondly, the metabolomics analysis of the endogenous constituents was carried out. Based on the aforementioned results, an integrated network was constructed to identify the curative components, crucial endogenous differential metabolites and related pathways. Finally, the validation tests were implemented by molecular docking and western blotting (WB).

RESULTS

According to the pharmacokinetic behaviors analysis of 31 components in vivo, the flavonoids presented more longer residence time and higher exposure compared with the other compounds. The efficacy and antiviral mechanism of QFPDD were verified by the poly-pharmacology, metabolomics, molecular docking and WB. For the occurrence of metabolic disorder, the change of amino acid transporters should not be neglected. Afterward, 8 curative compounds, 6 key genes and corresponding metabolic pathways were filtered by compound-reaction-enzyme-gene network. The molecular docking verified that the active ingredients bound to the relevant targets well.

CONCLUSION

In the present study, an in vivo comprehensive pharmacokinetic behaviors of QFPDD was analyzed for the first time. The results illustrated that QFPDD could exhibit immune regulation, anti-infection, anti-inflammation and metabolic disorder to perform a corresponding therapeutic effect. Moreover, our findings highlighted the roles of amino acid transporters in the coronavirus infection situation.

Abnormal glucose metabolism in virus associated sepsis

Sepsis is identified as a potentially lethal organ impairment triggered by an inadequate host reaction to infection (Sepsis-3). Viral sepsis is a potentially deadly organ impairment state caused by the host's inappropriate reaction to a viral infection. However, when a viral infection occurs, the metabolism of the infected cell undergoes a variety of changes that cause the host to respond to the infection. But, until now, little has been known about the challenges faced by cellular metabolic alterations that occur during viral infection and how these changes modulate infection. This study concentrates on the alterations in glucose metabolism during viral sepsis and their impact on viral infection, with a view to exploring new potential therapeutic targets for viral sepsis.

Olfactory dysfunction in COVID-19: new insights into the underlying mechanisms

The mechanisms of olfactory dysfunction in COVID-19 are still unclear. In this review, we examine potential mechanisms that may explain why the sense of smell is lost or altered. Among the current hypotheses, the most plausible is that death of infected support cells in the olfactory epithelium causes, besides altered composition of the mucus, retraction of the cilia on olfactory receptor neurons, possibly because of the lack of support cell-derived glucose in the mucus, which powers olfactory signal transduction within the cilia. This mechanism is consistent with the rapid loss of smell with COVID-19, and its rapid recovery after the regeneration of support cells. Host immune responses that cause downregulation of genes involved in olfactory signal transduction occur too late to trigger anosmia, but may contribute to the duration of the olfactory dysfunction.

Understanding Long COVID; Mitochondrial Health and Adaptation-Old Pathways, New Problems

Many people infected with the SARS-CoV-2 suffer long-term symptoms, such as "brain fog", fatigue and clotting problems. Explanations for "long COVID" include immune imbalance, incomplete viral clearance and potentially, mitochondrial dysfunction. As conditions with sub-optimal mitochondrial function are associated with initial severity of the disease, their prior health could be key in resistance to long COVID and recovery. The SARs virus redirects host metabolism towards replication; in response, the host can metabolically react to control the virus. Resolution is normally achieved after viral clearance as the initial stress activates a hormetic negative feedback mechanism. It is therefore possible that, in some individuals with prior sub-optimal mitochondrial function, the virus can "tip" the host into a chronic inflammatory cycle. This might explain the main symptoms, including platelet dysfunction. Long COVID could thus be described as a virally induced chronic and self-perpetuating metabolically imbalanced non-resolving state characterised by mitochondrial dysfunction, where reactive oxygen species continually drive inflammation and a shift towards glycolysis. This would suggest that a sufferer's metabolism needs to be "tipped" back using a stimulus, such as physical activity, calorie restriction, or chemical compounds that mimic these by enhancing mitochondrial function, perhaps in combination with inhibitors that quell the inflammatory response.

Whole-transcriptome analyses of sheep embryonic testicular cells infected with the bluetongue virus

Introduction

bluetongue virus (BTV) infection triggers dramatic and complex changes in the host's transcriptional profile to favor its own survival and reproduction. However, there is no whole-transcriptome study of susceptible animal cells with BTV infection, which impedes the in-depth and systematical understanding of the comprehensive characterization of BTV-host interactome, as well as BTV infection and pathogenic mechanisms.

Methods

to systematically understand these changes, we performed whole-transcriptome sequencing in BTV serotype 1 (BTV-1)-infected and mock-infected sheep embryonic testicular cells, and subsequently conducted bioinformatics differential analyses.

Results

there were 1504 differentially expressed mRNAs, 78 differentially expressed microRNAs, 872 differentially expressed long non-coding RNAs, and 59 differentially expressed circular RNAs identified in total. Annotation from the Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of competing endogenous RNA networks revealed differentially expressed RNAs primarily related to virus-sensing and signaling transduction pathways, antiviral and immune responses, inflammation, and development and metabolism related pathways. Furthermore, a protein-protein interaction network analysis found that BTV may contribute to abnormal spermatogenesis by reducing steroid biosynthesis. Finally, real-time quantitative PCR and western blotting results showed that the expression trends of differentially expressed RNAs were consistent with the whole-transcriptome sequencing data.

Discussion

this study provides more insights of comprehensive characterization of BTV-host interactome, and BTV infection and pathogenic mechanisms.

A Time-Series Metabolomic Analysis of SARS-CoV-2 Infection in a Ferret Model

The global threat of COVID-19 has led to an increased use of metabolomics to study SARS-CoV-2 infections in animals and humans. In spite of these efforts, however, understanding the metabolome of SARS-CoV-2 during an infection remains difficult and incomplete. In this study, metabolic responses to a SAS-CoV-2 challenge experiment were studied in nasal washes collected from an asymptomatic ferret model (n = 20) at different time points before and after infection using an LC-MS-based metabolomics approach. A multivariate analysis of the nasal wash metabolome data revealed several statistically significant features. Despite no effects of sex or interaction between sex and time on the time course of SARS-CoV-2 infection, 16 metabolites were significantly different at all time points post-infection. Among these altered metabolites, the relative abundance of taurine was elevated post-infection, which could be an indication of hepatotoxicity, while the accumulation of sialic acids could indicate SARS-CoV-2 invasion. Enrichment analysis identified several pathways influenced by SARS-CoV-2 infection. Of these, sugar, glycan, and amino acid metabolisms were the key altered pathways in the upper respiratory channel during infection. These findings provide some new insights into the progression of SARS-CoV-2 infection in ferrets at the metabolic level, which could be useful for the development of early clinical diagnosis tools and new or repurposed drug therapies.

The oldest unvaccinated Covid-19 survivors in South America

BACKGROUND

Although older adults are at a high risk of severe or critical Covid-19, there are many cases of unvaccinated centenarians who had a silent infection or recovered from mild or moderate Covid-19. We studied three Brazilian supercentenarians, older than 110 years, who survived Covid-19 in 2020 before being vaccinated.

RESULTS

Despite their advanced age, humoral immune response analysis showed that these individuals displayed robust levels of IgG and neutralizing antibodies (NAbs) against SARS-CoV-2. Enrichment of plasma proteins and metabolites related to innate immune response and host defense was also observed. None presented autoantibodies (auto-Abs) to type I interferon (IFN). Furthermore, these supercentenarians do not carry rare variants in genes underlying the known inborn errors of immunity, including particular inborn errors of type I IFN.

CONCLUSION

These observations suggest that their Covid-19 resilience might be a combination of their genetic background and their innate and adaptive immunity.

Longitudinal characterization of circulating neutrophils uncovers phenotypes associated with severity in hospitalized COVID-19 patients

Mechanisms of neutrophil involvement in severe coronavirus disease 2019 (COVID-19) remain incompletely understood. Here, we collect longitudinal blood samples from 306 hospitalized COVID-19+ patients and 86 controls and perform bulk RNA sequencing of enriched neutrophils, plasma proteomics, and high-throughput antibody profiling to investigate relationships between neutrophil states and disease severity. We identify dynamic switches between six distinct neutrophil subtypes. At days 3 and 7 post-hospitalization, patients with severe disease display a granulocytic myeloid-derived suppressor cell-like gene expression signature, while patients with resolving disease show a neutrophil progenitor-like signature. Humoral responses are identified as potential drivers of neutrophil effector functions, with elevated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific immunoglobulin G1 (IgG1)-to-IgA1 ratios in plasma of severe patients who survived. In vitro experiments confirm that while patient-derived IgG antibodies induce phagocytosis in healthy donor neutrophils, IgA antibodies predominantly induce neutrophil cell death. Overall, our study demonstrates a dysregulated myelopoietic response in severe COVID-19 and a potential role for IgA-dominant responses contributing to mortality.

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.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.

Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity

The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.

A Meta-summary and Bioinformatic Analysis Identified Interleukin 6 as a Master Regulator of COVID-19 Severity Biomarkers

With the rising demand for improved COVID-19 disease monitoring and prognostic markers, studies have aimed to identify biomarkers using a range of screening methods. However, the selection of biomarkers for validation from large datasets may result in potentially important biomarkers being overlooked when datasets are considered in isolation. Here, we have utilized a meta-summary approach to investigate COVID-19 biomarker datasets to identify conserved biomarkers of COVID-19 severity. This approach identified a panel of 17 proteins that showed a consistent direction of change across two or more datasets. Furthermore, bioinformatics analysis of these proteins highlighted a range of enriched biological processes that include inflammatory responses and compromised integrity of physiological systems including cardiovascular, neurological, and metabolic. A panel of upstream regulators of the COVID-19 severity biomarkers were identified, including chemical compounds currently under investigation for COVID-19 treatment. One of the upstream regulators, interleukin 6 (IL6), was identified as a “master regulator” of the severity biomarkers. COVID-19 disease severity is intensified due to the extreme viral immunological reaction that results in increased inflammatory biomarkers and cytokine storm. Since IL6 is the primary stimulator of cytokines, it could be used independently as a biomarker in determining COVID-19 disease progression, in addition to a potential therapeutic approach targeting IL6. The array of upstream regulators of the severity biomarkers identified here serve as attractive candidates for the development of new therapeutic approaches to treating COVID-19. In addition, the findings from this study highlight COVID-19 severity biomarkers which represent promising, robust biomarkers for future validation studies for their use in defining and monitoring disease severity and patient prognosis.

Ferroptosis in COVID-19-related liver injury: A potential mechanism and therapeutic target

The outbreak and worldwide spread of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a threat to global public health. SARS-CoV-2 infection not only impacts the respiratory system but also causes hepatic injury. Ferroptosis, a distinct iron-dependent form of non-apoptotic cell death, has been investigated in various pathological conditions, such as cancer, ischemia/reperfusion injury, and liver diseases. However, whether ferroptosis takes part in the pathophysiological process of COVID-19-related liver injury has not been evaluated yet. This review highlights the pathological changes in COVID-19-related liver injury and presents ferroptosis as a potential mechanism in the pathological process. Ferroptosis, as a therapeutic target for COVID-19-related liver injury, is also discussed. Discoveries in these areas will improve our understanding of strategies to prevent and treat hepatic injuries caused by COVID-19.

SARS-CoV-2 Infection Impacts Carbon Metabolism and Depends on Glutamine for Replication in Syrian Hamster Astrocytes

COVID‐19 causes more than million deaths worldwide. Although much is understood about the immunopathogenesis of the lung disease, a lot remains to be known on the neurological impact of COVID‐19. Here we evaluated immunometabolic changes using astrocytes in vitro and dissected brain areas of SARS‐CoV‐2 infected Syrian hamsters. We show that SARS‐CoV‐2 alters proteins of carbon metabolism, glycolysis, and synaptic transmission, many of which are altered in neurological diseases. Real‐time respirometry evidenced hyperactivation of glycolysis, further confirmed by metabolomics, with intense consumption of glucose, pyruvate, glutamine, and alpha ketoglutarate. Consistent with glutamine reduction, the blockade of glutaminolysis impaired viral replication and inflammatory response in vitro. SARS‐CoV‐2 was detected in vivo in hippocampus, cortex, and olfactory bulb of intranasally infected animals. Our data evidence an imbalance in important metabolic molecules and neurotransmitters in infected astrocytes. We suggest this may correlate with the neurological impairment observed during COVID‐19, as memory loss, confusion, and cognitive impairment.

Proteomic Profile of Procoagulant Extracellular Vesicles Reflects Complement System Activation and Platelet Hyperreactivity of Patients with Severe COVID-19

Background

Extracellular vesicles (EVs) are a valuable source of biomarkers and display the pathophysiological status of various diseases. In COVID-19, EVs have been explored in several studies for their ability to reflect molecular changes caused by SARS-CoV-2. Here we provide insights into the roles of EVs in pathological processes associated with the progression and severity of COVID-19.

Methods

In this study, we used a label-free shotgun proteomic approach to identify and quantify alterations in EV protein abundance in severe COVID-19 patients. We isolated plasma extracellular vesicles from healthy donors and patients with severe COVID-19 by size exclusion chromatography (SEC). Then, flow cytometry was performed to assess the origin of EVs and to investigate the presence of circulating procoagulant EVs in COVID-19 patients. A total protein extraction was performed, and samples were analyzed by nLC-MS/MS in a Q-Exactive HF-X. Finally, computational analysis was applied to signify biological processes related to disease pathogenesis.

Results

We report significant changes in the proteome of EVs from patients with severe COVID-19. Flow cytometry experiments indicated an increase in total circulating EVs and with tissue factor (TF) dependent procoagulant activity. Differentially expressed proteins in the disease groups were associated with complement and coagulation cascades, platelet degranulation, and acute inflammatory response.

Conclusions

The proteomic data reinforce the changes in the proteome of extracellular vesicles from patients infected with SARS-CoV-2 and suggest a role for EVs in severe COVID-19.

Hypoxia-inducible factor 1 alpha (HIF-1α) stimulated and P2X7 receptor activated by COVID-19, as a potential therapeutic target and risk factor for epilepsy

Based on available evidence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a neuroinvasive virus. According to the centers for disease control and prevention (CDC), coronavirus disease 2019 (COVID-19) may cause epilepsy. In this line, COVID-19 can stimulate hypoxia-inducible factor-1 alpha (HIF-1α) and activate P2X7 receptor. Both HIF-1α and P2X7 receptors are linked to epileptogenesis and seizures. Therefore, in the current study, we suggested that COVID-19 may have a role in epileptogenesis and seizure through HIF-1α stimulation and P2X7 receptor activation. Consequently, pharmacological targeting of these factors could be a promising therapeutic approach for such patients.

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.

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.

Elevated plasma level of the glycolysis byproduct methylglyoxal on admission is an independent biomarker of mortality in ICU COVID-19 patients

Biomarkers to identify ICU COVID-19 patients at high risk for mortality are urgently needed for therapeutic care and management. Here we found plasma levels of the glycolysis byproduct methylglyoxal (MG) were 4.4-fold higher in ICU patients upon admission that later died (n = 33), and 1.7-fold higher in ICU patients that survived (n = 32),compared to uninfected controls (n = 30). The increased MG in patients that died correlated inversely with the levels of the MG-degrading enzyme glyoxalase-1 (r² = - 0.50), and its co-factor glutathione (r² = - 0.63), and positively with monocytes (r² = 0.29). The inflammation markers, SSAO (r² = 0.52), TNF-α (r² = 0.41), IL-1β (r² = 0.25), CRP (r² = 0.26) also correlated positively with MG. Logistic regression analysis provides evidence of a significant relationship between the elevated MG upon admission into ICU and death (P < 0.0001), with 42% of the death variability explained. From these data we conclude that elevated plasma MG on admission is a novel independent biomarker that predicts mortality in ICU COVID-19 patients.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

The Impact of Hyper-Acute Inflammatory Response on Stress Adaptation and Psychological Symptoms of COVID-19 Patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection induces a significant inflammatory response that are amplified by persistent stress. The pathophysiology of mental illnesses is explored in terms of inflammatory processes. Thus, anxious, depressed, or psychotic episodes may occur as a result of metabolic and immunological imbalances, as a direct result of their effect on the central nervous system, or as a side effect of the COVID-19 medication protocols. As such, the primary objective of this research is to establish if the psychological profiles of COVID-19 patients change substantially according to illness severity. The secondary objective is to determine if particular biological inflammatory indicators are associated with anxiety, sadness, psychoticism, and paranoid ideation. A cross-sectional study was performed on 90 hospitalized patients admitted during a 3-month period in the COVID-19 unit. All patients received the COPE-60 and SCL-90R questionnaires. Clinical and paraclinical data were collected and the information was classified according to the severity of COVID-19.The hyper-acute inflammation encountered in patients with severe COVID-19 infection characterized 80.0% of patients using disengagement coping methods, significantly more than patients with mild or moderate SARS-CoV-2 infection severity (p-value = 0.012), respectively, 73.3% severe COVID-19 patients engaging in emotion-focused coping strategies based on the COPE-60 scale (p-value = 0.037). Additionally, it was determined that negative coping mechanisms (disengagement) and emotion-focused methods are independent risk factors for developing psychoticism symptoms following acute SARS-CoV-2 infection, based on the SCL-90 questionnaire (OR = 2.07; CI = 1.44–3.01), respectively (OR = 2.92; CI = 1.44–3.01). Elevated white blood cells and monocytes and inflammatory markers, such as fibrinogen, procalcitonin, IL-6, and D-dimers, were also identified as risk factors for psychoticism symptoms in multivariate analysis. It is particularly important to consider the constant mental-state evaluation in patients with severe COVID-19 that might benefit from early intervention before psychotic symptoms onset.

The Lactate and the Lactate Dehydrogenase in Inflammatory Diseases and Major Risk Factors in COVID-19 Patients

Lactate dehydrogenase (LDH) is a terminating enzyme in the metabolic pathway of anaerobic glycolysis with end product of lactate from glucose. The lactate formation is crucial in the metabolism of glucose when oxygen is in inadequate supply. Lactate can also be formed and utilised by different cell types under fully aerobic conditions. Blood LDH is the marker enzyme, which predicts mortality in many conditions such as ARDS, serious COVID-19 and cancer patients. Lactate plays a critical role in normal physiology of humans including an energy source, a signaling molecule and a pH regulator. Depending on the pH, lactate exists as the protonated acidic form (lactic acid) at low pH or as sodium salt (sodium lactate) at basic pH. Lactate can affect the immune system and act as a signaling molecule, which can provide a “danger” signal for life. Several reports provide evidence that the serum lactate represents a chemical marker of severity of disease similar to LDH under inflammatory conditions. Since the mortality rate is much higher among COVID-19 patients, associated with high serum LDH, this article is aimed to review the LDH as a therapeutic target and lactate as potential marker for monitoring treatment response of inflammatory diseases. Finally, the review summarises various LDH inhibitors, which offer potential applications as therapeutic agents for inflammatory diseases, associated with high blood LDH. Both blood LDH and blood lactate are suggested as risk factors for the mortality of patients in serious inflammatory diseases.

A high-risk gut microbiota configuration associates with fatal hyperinflammatory immune and metabolic responses to SARS-CoV-2

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. However, the role of the gut microbiota in regulating these responses has not been thoroughly investigated. In order to identify mechanisms underpinning microbiota interactions with host immune and metabolic systems that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalized COVID-19 patients and compared those with the most severe outcome (i.e. death, n = 41) to those with severe non-fatal disease (n = 89), or mild/moderate disease (n = 42), that recovered. A distinct subset of 8 cytokines (e.g. TSLP) and 140 metabolites (e.g. quinolinate) in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the fecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts, such as Enterococcus. In contrast, less severe clinical outcomes are associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

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.

Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2–6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the “healthy” state faster than the lipoproteome.