Increased Kynurenine Indicates a Fatal Course of COVID-19

The roles of the kynurenine pathway in COVID-19 neuropathogenesis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the highly contagious respiratory disease Corona Virus Disease 2019 (COVID-19) that may lead to various neurological and psychological disorders that can be acute, lasting days to weeks or months and possibly longer. The latter is known as long-COVID or more recently post-acute sequelae of COVID (PASC). During acute COVID-19 infection, a strong inflammatory response, known as the cytokine storm, occurs in some patients. The levels of interferon-γ (IFN-γ), interferon-β (IFN-β), interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) are particularly increased. These cytokines are known to activate the enzyme indoleamine 2,3-dioxygenase 1 (IDO-1), catalysing the first step of tryptophan (Trp) catabolism through the kynurenine pathway (KP) leading to the production of several neurotoxic and immunosuppressive metabolites. There is already data showing elevation in KP metabolites both acutely and in PASC, especially regarding cognitive impairment. Thus, it is likely that KP involvement is significant in SARS-CoV-2 pathogenesis especially neurologically.

A supervised Bayesian factor model for the identification of multi-omics signatures

MOTIVATION

Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful.

RESULTS

We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes.

AVAILABILITY AND IMPLEMENTATION

SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

Nutritional and Wellness Strategies for Neurological and Psychiatric Recovery From Post-COVID Syndrome and Post-acute Sequelae of COVID-19

The post-COVID syndrome was officially recognized as a disability under the Americans with Disabilities Act, indicating that this syndrome has made a significant impact on our populace. Also, post-acute sequelae of COVID-19 (PASC) is a term that describes the long-term health problems that some people experience after being infected with the virus that causes COVID-19. These problems can last for weeks, months, or even years, and can affect various parts of the body, such as the heart, lungs, brain, and blood vessels. This narrative review paper utilized the PubMed database to explore the pathophysiology of post-COVID syndrome's neurological and psychiatric symptoms and PASC and make therapeutic connections to the known mechanisms of various nutritional, supplemental, and wellness approaches. Searches were queried on the PubMed database between March 29 and April 16, 2022, using the phrases "long-covid," "post-COVID syndrome," "Vitamin D covid," "vitamin C covid," "omega-3 covid," "kynurenine covid," "whole-body hyperthermia," "mushrooms immunity," "n-acetyl cysteine covid," "mushrooms cognition," "sugar consumption inflammation," and "covid microbiome." Articles were screened for their relevance to the discussion of post-COVID syndrome's neurological and psychiatric pathophysiology at the discretion of the principal researcher. There were no limitations regarding publication years, but articles from 2005 to April 2022 were cited. Micro-ischemic disease, neuropathy, autoimmune processes, mast-cell activation, and impaired blood-brain barriers have all been implicated in the pathological processes of this syndrome with varying degrees of supportive evidence. The common denominators, however, are inflammation and oxidative stress. Therefore, a beneficial approach to dealing with the complications of post-COVID syndrome would be to reduce the exacerbations of these common denominators with lifestyle and nutritional changes. Replenishing nutritional deficiencies, supplementing with N-acetylcysteine, decreasing consumption of refined sugars, preventing dysbiosis of the microbiome, performing exercises, increasing dietary intake of mushrooms, utilizing beneficial herbs such as rosemary, and increasing the core body temperature through whole-body hyperthermia seem to show potential for efficacy in this pursuit. Considering the safety and evidence-based connections of the therapies explored for dealing with the post-Covid syndrome, it could be of great benefit and of little harm to our patients to include these considerations in formulating post-Covid treatment plans.

HDL-Related Parameters and COVID-19 Mortality: The Importance of HDL Function

COVID-19, caused by the SARS-CoV-2 coronavirus, emerged as a global pandemic in late 2019, resulting in significant global public health challenges. The emerging evidence suggests that diminished high-density lipoprotein (HDL) cholesterol levels are associated with the severity of COVID-19, beyond inflammation and oxidative stress. Here, we used nuclear magnetic resonance spectroscopy to compare the lipoprotein and metabolic profiles of COVID-19-infected patients with non-COVID-19 pneumonia. We compared the control group and the COVID-19 group using inflammatory markers to ensure that the differences in lipoprotein levels were due to COVID-19 infection. Our analyses revealed supramolecular phospholipid composite (SPC), phenylalanine, and HDL-related parameters as key discriminators between COVID-19-positive and non-COVID-19 pneumonia patients. More specifically, the levels of HDL parameters, including apolipoprotein A-I (ApoA-I), ApoA-II, HDL cholesterol, and HDL phospholipids, were significantly different. These findings underscore the potential impact of HDL-related factors in patients with COVID-19. Significantly, among the HDL-related metrics, the cholesterol efflux capacity (CEC) displayed the strongest negative association with COVID-19 mortality. CEC is a measure of how well HDL removes cholesterol from cells, which may affect the way SARS-CoV-2 enters cells. In summary, this study validates previously established markers of COVID-19 infection and further highlights the potential significance of HDL functionality in the context of COVID-19 mortality.

The possible role of quinolinic acid as a predictive marker in patients with SARS-CoV-2

BACKGROUND AND AIMS

Quinolinic acid (QA) is a metabolite of the kynurenine pathway, which is activated by inflammatory stimuli during viral infection. We investigated the role of QA in patients infected with SARS-CoV-2, particularly its prognostic value for survival.

METHODS

Overall, 104 unvaccinated inpatients were included, divided into a survival (N = 80) and a deceased group (N = 24). Plasma levels of tryptophan, kynurenine, QA, C-reactive protein (CRP) and procalcitonin (PCT) were measured on admission and after seven days. The QA/TRP ratio and the relative differences between the measurements for QA (QA-Diff) and QA/TRP (Diff-QA/TRP) were calculated.

RESULTS

Among the kynurenine pathway markers, QA-Diff showed the highest discriminatory power for the survival prognosis (Youden index 0.467, cut-off -1.3 %, AUC 0.733, p < 0.001, sensitivity 0.79, specificity 0.675). Among the inflammatory markers, CRP showed the highest discriminatory power (Youden index 0.533, cut-off 25.0 mg/L, AUC 0.794, p < 0.001, sensitivity 0.958, specificity 0.575). A significant correlation between QA and PCT was found on admission and after one week (Spearman's rho 0.455 and 0.539, all p-values < 0.001).

CONCLUSIONS

QA may serve as prognostic marker for survival in patients with SARS-CoV-2. The repeated measurements during the first week of the disease may enhance the prognostic power.

A supervised Bayesian factor model for the identification of multi-omics signatures

MOTIVATION

Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are iden-tified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive model-ing. However, multi-omics integration and predictive modeling are generally performed independent-ly in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful.

RESULTS

We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the recon-struction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes.

AVAILABILITY

SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

The kynurenine pathway of tryptophan metabolism: a neglected therapeutic target of COVID-19 pathophysiology and immunotherapy

SARS-CoV-2 (COVID-19) exerts profound changes in the kynurenine (Kyn) pathway (KP) of tryptophan (Trp) metabolism that may underpin its pathophysiology. The KP is the main source of the vital cellular effector NAD+ and intermediate metabolites that modulate immune and neuronal functions. Trp metabolism is the top pathway influenced by COVID-19. Sixteen studies established virus-induced activation of the KP mediated mainly by induction of indoleamine 2,3-dioxygenase (IDO1) in most affected tissues and of IDO2 in lung by the increased release of proinflammatory cytokines but could additionally involve increased flux of plasma free Trp and induction of Trp 2,3-dioxygenase (TDO) by cortisol. The major Kyn metabolite targeted by COVID-19 is kynurenic acid (KA), the Kyn metabolite with the greatest affinity for the aryl hydrocarbon receptor (AhR), which is also activated by COVID-19. AhR activation initiates two important series of events: a vicious circle involving IDO1 induction, KA accumulation and further AhR activation, and activation of poly (ADP-ribose) polymerase (PARP) leading to NAD+ depletion and cell death. The virus further deprives the host of NAD+ by inhibiting its main biosynthetic pathway from quinolinic acid, while simultaneously acquiring NAD+ by promoting its synthesis from nicotinamide in the salvage pathway. Additionally, the protective effects of sirtuin 1 are minimised by the PARP activation. KP dysfunction may also underpin the mood and neurological disorders acutely and during 'long COVID'. More studies of potential effects of vaccination therapy on the KP are required and exploration of therapeutic strategies involving modulation of the KP changes are proposed.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

Advances in kynurenine analysis

Kynurenine, the first product of tryptophan degradation via the kynurenine pathway, has become one of the most frequently mentioned biomarkers in recent years. Its levels in the body indicate the state of the human physiology. Human serum and plasma are the main matrixes used to evaluate kynurenine levels and liquid chromatography is the dominant technique for its determination. However, their concentrations in blood do not always correspond to the levels in other matrixes obtained from the affected individuals. It is therefore important to decide when it is appropriate to analyse kynurenine in alternative matrices. However, liquid chromatography may not be the best option for the analysis. This review presents alternatives that can be used and summarizes the features that need to be considered prior to kynurenine determination. Possible approaches to kynurenine analysis in a variety of human matrixes, their challenges, and limitations are critically discussed.

Is COVID-19 severity associated with telomere length? A systematic review and meta-analysis

Telomere shortening, a marker of cellular aging, has been linked to hospitalization and the severity of COVID-19. In this systematic review and meta-analysis, the mean difference in telomere length between non-severe and severe COVID-19 individuals was pooled to determine the association between short telomeres and COVID-19 severity. Relevant studies were retrieved through searches conducted in PubMed-Medline, Scopus, EMBASE, Medrxiv, Biorxiv, EuroPMC, and SSRN databases up to November 2022. Selected studies were systematically reviewed and assessed for risk of bias using AXIS tool. The standardized mean difference in telomere length between non-severe and severe COVID-19 was pooled using random-effects model. A total of thirteen studies were included in the review, out of which seven (1332 patients with the severe COVID-19 disease and 6321 patients with non-severe COVID-19) were eligible for meta-analysis. The estimated pooled mean difference in Leukocyte telomere length between severe COVID-19 and non-severe COVID-19 was 0.39 (95% CI - 0.02 to 0.81, I² = 93.5%) with substantial heterogeneity. Our findings do not provide clear evidence for association of shorter telomere length and severe COVID-19 disease. More extensive studies measuring absolute telomere length with age and gender adjustments are needed to draw definitive conclusions on the potential causal association between telomere shortening and COVID-19 severity.

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2

Introduction

The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection.

Methods

We studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS–MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance.

Results

A total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95%CI 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95).

Discussion

This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.

Alteration of the kynurenine pathway is inversely associated with the humoral immune response in patients with SARS-CoV-2

Background

The scale and the course of antibody production in patients with SARS-CoV-2 is highly variable. Factors involved in the immune regulation during the infection may play a major role in the antibody response. We investigated the relationship between the inflammatory markers of the kynurenine pathway and the concentration of antibodies against SARS-CoV-2 in infected patients 8 – 11 days after admission.

Methods

The study included 72 SARS-CoV-2 - positive inpatients hospitalized between August 2020 and April 2021. The plasma concentrations of tryptophan, kynurenine, anti-SARS-CoV-2 antibodies and the leucocyte count were measured 8 – 11 days after admission. The kynurenine/tryptophan ratio (KYN/TRP ratio) was calculated. Tertiles based on the values for tryptophan, kynurenine, KYN/TRP ratio and the leucocytes were generated.

Results

Statistically significant correlations were observed between anti-SARS-CoV-2 antibodies and tryptophan, kynurenine, KYN/TRP ratio and the leucocytes (p-values < 0.001–0.007). The high kynurenine and KYN/TRP ratio tertiles showed significantly lower antibody titers compared to the low tertiles (p-values 0.017 and < 0.001). The low tryptophan and leucocytes tertiles showed significantly lower antibody titers compared to the high tertiles (p-values 0.001 and 0.008).

Conclusion

Patients with higher activation levels of the kynurenine pathway tended to develop lower anti-SARS-CoV-2 antibody titers.

Untargeted plasma metabolomic fingerprinting highlights several biomarkers for the diagnosis and prognosis of coronavirus disease 19

Objectives

The COVID-19 pandemic has been a serious worldwide public health crisis since 2020 and is still challenging healthcare systems. New tools for the prognosis and diagnosis of COVID-19 patients remain important issues.

Design

Here, we studied the metabolome of plasma samples of COVID-19 patients for the identification of prognosis biomarkers.

Patients

Plasma samples of eighty-six SARS-CoV-2-infected subjects and 24 healthy controls were collected during the first peak of the COVID-19 pandemic in France in 2020.

Main results

Plasma metabolome fingerprinting allowed the successful discrimination of healthy controls, mild SARS-CoV-2 subjects, and moderate and severe COVID-19 patients at hospital admission. We found a strong effect of SARS-CoV-2 infection on the plasma metabolome in mild cases. Our results revealed that plasma lipids and alterations in their saturation level are important biomarkers for the detection of the infection. We also identified deoxy-fructosyl-amino acids as new putative plasma biomarkers for SARS-CoV-2 infection and COVID-19 severity. Finally, our results highlight a key role for plasma levels of tryptophan and kynurenine in the symptoms of COVID-19 patients.

Conclusion

Our results showed that plasma metabolome profiling is an efficient tool for the diagnosis and prognosis of SARS-CoV-2 infection.

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes

Background

Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness.

Methods

We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays.

Results

Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p&lt;0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p&lt;0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p&lt;0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p&lt;0.001) as an early criterion to accurately classify patients with critical outcomes.

Conclusion

Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.

Tryptophan: Its Metabolism along the Kynurenine, Serotonin, and Indole Pathway in Malignant Melanoma

(1) Background: Tryptophan metabolism is known to be one of the important mechanisms used by cancer to evade immune surveillance. Altered tryptophan metabolism was studied in patients with pigmented malignant melanoma confirmed histologically by the anatomic stage grouping for cutaneous melanoma using clinical staging on the basis of the Breslow thickness of the melanoma, the degree of spread to regional lymph nodes, and by the presence of distant metastasis. (2) Methods: Urinary tryptophan metabolites were detected by RP-HPLC method. (3) Results: In the present work, we provided evidence of altered metabolism of all tryptophan pathways in melanoma patients. (4) Conclusions: Knowledge of the shifted serotonin pathway toward DHICA formation and kynurenine pathway shifted toward NAD+ production could serve in the early detection of the disease and the initiation of early treatment of malignant melanoma.

The tryptophan catabolite or kynurenine pathway in COVID-19 and critical COVID-19: a systematic review and meta-analysis

Background

Coronavirus disease 2019 (COVID-19) is accompanied by activated immune-inflammatory pathways and oxidative stress, which both induce indoleamine-2,3-dioxygenase (IDO), a key enzyme of the tryptophan (TRP) catabolite (TRYCAT) pathway. The aim of this study was to systematically review and meta-analyze the status of the TRYCAT pathway, including the levels of TRP and kynurenine (KYN) and the activity of IDO, as measured by the ratio of KYN/TRP.

Methods

This systematic review searched PubMed, Google Scholar, and Web of Sciences and included 14 articles that compared TRP and tryptophan catabolites (TRYCATs) in COVID-19 patients versus non-COVID-19 controls, as well as severe/critical versus mild/moderate COVID-19. The analysis was done on a total of 1269 people, including 794 COVID-19 patients and 475 controls.

Results

The results show a significant (p < 0.0001) increase in the KYN/TRP ratio (standardized mean difference, SMD = 1.099, 95% confidence interval, CI: 0.714; 1.484) and KYN (SMD = 1.123, 95% CI: 0.730; 1.516) and significantly lower TRP (SMD = − 1.002, 95%CI: − 1.738; − 0.266) in COVID-19 versus controls. The KYN/TRP ratio (SMD = 0.945, 95%CI: 0.629; 1.262) and KYN (SMD = 0.806, 95%CI: 0.462; 1.149) were also significantly (p < 0.0001) higher and TRP lower (SMD = − 0.909, 95% CI: − 1.569; − 0.249) in severe/critical versus mild/moderate COVID-19. No significant difference was detected in kynurenic acid (KA) and the KA/KYN ratio between COVID-19 patients and controls.

Conclusions

Our results indicate increased activity of the IDO enzyme in COVID-19 and severe/critical patients. The TRYCAT pathway is implicated in the pathophysiology and progression of COVID-19 and may signal a worsening outcome of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07582-1.

Psychological outcomes of COVID-19 survivors at sixth months after diagnose: the role of kynurenine pathway metabolites in depression, anxiety, and stress

Coronavirus disease 2019 (COVID-19) has resulted in long-term psychiatric symptoms because of the immunologic response to the virus itself as well as fundamental life changes related to the pandemic. This immune response leads to altered tryptophan (TRP)-kynurenine (KYN) pathway (TKP) metabolism, which plays an essential role in the pathophysiology of mental illnesses. We aimed to define TKP changes as a potential underlying mechanism of psychiatric disorders in post-COVID-19 patients. We measured plasma levels of several TKP markers, including KYN, TRP, kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), and quinolinic acid (QUIN), as well as the TRP/KYN, KYNA/3-HK, and KYNA/QUIN ratios, in 90 post-COVID-19 patients (on the first day of hospitalization) and 59 healthy controls (on the first admission to the Check-Up Center). An online questionnaire that included the Depression, Anxiety and Stress Scale-21 (DASS-21) was used 6 months after the initial assessment in both groups. A total of 32.2% of participants with COVID-19 showed depressive symptoms, 21.1% exhibited anxiety, and 33.3% had signs of stress at follow-up, while 6.6% of healthy controls exhibited depressive and anxiety symptoms and 18.6% had signs of stress. TRP and 3-HK were negative predictors of anxiety and stress, but KYN positively predicted anxiety and stress. Moreover, TRP negatively predicted depression, while KYNA/3-HK was a negative predictor of anxiety. The correlation between depression, anxiety, and stress and TKP activation in COVID-19 could provide prospective biomarkers, especially the reduction in TRP and 3HK levels and the increase in KYN. Our results suggest that the alteration of TKP is not only a potential biomarker of viral infection-related long-term psychiatric disorders but also that the therapy targets future viral infections related to depression and anxiety.

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.

Dramatic Decrease of Vitamin K2 Subtype Menaquinone-7 in COVID-19 Patients

(1) Background: Vitamin K (VK) is a fat-soluble compound with a common chemical structure, a 2-methyl-1,4-naphthoquinone ring, and a variable aliphatic side-chain. VK is involved in the synthesis of blood-clotting proteins, bone stability, anti-oxidative, and immune inflammatory-modulatory functions. Vitamin K also activates protein S, which acts as an antioxidant and anti-inflammatory. The fact that cytokine overproduction, oxidative stress, and disturbed microcirculation by thrombogenicity play a central role in severe COVID-19 prompted us to analyze this vitamin. (2) Methods: We analyzed by a validated liquid-chromatography tandem mass-spectrometry method serum vitamin K1, MK4, MK7, and VK epoxide levels in 104 healthy controls, 77 patients with non-COVID-19 pneumonia, and 135 hospitalized COVID-19 patients with potentially fatal outcomes admitted to our University Hospital between April and November 2020. We included the quotient between VK and triglyceride (TG, nmol/mmol/L) values in the analyses with respect to the TG transporter function for all VK subtypes. Additionally, we assessed anthropometric, routine laboratory, and clinical data from the laboratory and hospital information systems. (3) Results: The COVID-19 patients had significantly lower MK7 levels than non-COVID-19 pneumonia patients and healthy controls. COVID-19 and non-COVID-19 pneumonia patients had significantly lower vitamin K1 and significantly higher MK4 compared to healthy controls, but did not differ significantly from each other. Between COVID-19 non-survivors (n = 30) and survivors (n = 105) no significant differences were seen in all vitamin K subtypes, despite the fact that non-survivors had higher peak concentrations of IL-6, CRP, d-dimer, and higher oxygen needs, respectively. (4) Conclusions: The present data identified significantly decreased vitamin K1, K2 (MK7), and increased MK4 levels in patients with COVID-19 compared to healthy controls. Vitamin K2 (MK7) was lowest in COVID-19 patients irrespective of potentially fatal courses, indicating consumption of this VK subtype by COVID-19 immanent effects, most probably inflammatory and oxidative stress factors.

Unbalanced IDO1/IDO2 Endothelial Expression and Skewed Keynurenine Pathway in the Pathogenesis of COVID-19 and Post-COVID-19 Pneumonia

Despite intense investigation, the pathogenesis of COVID-19 and the newly defined long COVID-19 syndrome are not fully understood. Increasing evidence has been provided of metabolic alterations characterizing this group of disorders, with particular relevance of an activated tryptophan/kynurenine pathway as described in this review. Recent histological studies have documented that, in COVID-19 patients, indoleamine 2,3-dioxygenase (IDO) enzymes are differentially expressed in the pulmonary blood vessels, i.e., IDO1 prevails in early/mild pneumonia and in lung tissues from patients suffering from long COVID-19, whereas IDO2 is predominant in severe/fatal cases. We hypothesize that IDO1 is necessary for a correct control of the vascular tone of pulmonary vessels, and its deficiency in COVID-19 might be related to the syndrome’s evolution toward vascular dysfunction. The complexity of this scenario is discussed in light of possible therapeutic manipulations of the tryptophan/kynurenine pathway in COVID-19 and post-acute COVID-19 syndromes.

Tryptophan regulates Drosophila zinc stores

SignificanceZinc deficiency in the human population, a major public health concern, can also be a consequence of nutritional deficiency in protein uptake. The discovery that tryptophan metabolites 3-hydroxykynurenine and xanthurenic acid are major zinc-binding ligands in insect cells establishes the kynurenine pathway as a regulator of systemic zinc homeostasis. Many biological processes influenced by zinc and the kynurenine pathway, including the regulation of innate and acquired immune responses to viral infections, have not been studied in light of the direct molecular links revealed in this study.

Peculiarities of the T Cell Immune Response in COVID-19

Understanding the T cell response to SARS-CoV-2 is critical to vaccine development, epidemiological surveillance, and control strategies for this disease. This review provides data from studies of the immune response in coronavirus infections. It describes general mechanisms of immunity, its T cell components, and presents a detailed scheme of the T cell response in SARS-CoV-2 infection, including from the standpoint of determining the most promising targets for assessing its level. In addition, we reviewed studies investigating post-vaccination immunity in the development of vaccines against COVID-19. This review also includes the peculiarities of immunity in different age and gender groups, and in the presence of a number of factors, for example, comorbidity or disease severity. This study summarizes the most informative methods for assessing the immune response to SARS-CoV-2 infection.

Cross-talk between red blood cells and plasma influences blood flow and omics phenotypes in severe COVID-19

Coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and can affect multiple organs, among which is the circulatory system. Inflammation and mortality risk markers were previously detected in COVID-19 plasma and red blood cells (RBCs) metabolic and proteomic profiles. Additionally, biophysical properties, such as deformability, were found to be changed during the infection. Based on such data, we aim to better characterize RBC functions in COVID-19. We evaluate the flow properties of RBCs in severe COVID-19 patients admitted to the intensive care unit by using microfluidic techniques and automated methods, including artificial neural networks, for an unbiased RBC analysis. We find strong flow and RBC shape impairment in COVID-19 samples and demonstrate that such changes are reversible upon suspension of COVID-19 RBCs in healthy plasma. Vice versa, healthy RBCs resemble COVID-19 RBCs when suspended in COVID-19 plasma. Proteomics and metabolomics analyses allow us to detect the effect of plasma exchanges on both plasma and RBCs and demonstrate a new role of RBCs in maintaining plasma equilibria at the expense of their flow properties. Our findings provide a framework for further investigations of clinical relevance for therapies against COVID-19 and possibly other infectious diseases.