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

Objectives: The strati ﬁ cation of individuals su ﬀ ering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to speci ﬁ cally monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metab-olomics isprogressivelyrecognizedasauseful tool todescribe the consequences of virus-host interactions considering also clinical metadata. Methods: The present study characterized the urinary metabolic pro ﬁ le of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC – MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently iden-ti ﬁ ed antiviral nucleosides and their association with other metabolites and clinical data. Results: Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD + ) and sirtuin 1 (SIRT1) pathway. Conclusions: Our results con ﬁ rm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we establishedfor the ﬁ rst time a casual link between kynurenine accumulation and deranged NAD + /SIRT1, o ﬀ ering a novel mechanism through which SARS-CoV-2 manipulates host physiology.


Introduction
Despite the proven efficacy of vaccinations in preventing COVID-19 development, thereby reducing the rate of fatal events and hospitalizations, the SARS-CoV-2 outbreak still poses health, social, and economic challenges worldwide.In fact, the number of confirmed cases is globally increasing [1], while a Nature survey conducted amongst immunologists, virologists, and infectious-disease experts indicated that SARS-CoV-2 will continue to circulate in the human population for years to come [2].In addition, about 10-15 % of patients who suffered from mild-to-moderate COVID-19 present persistent symptoms weeks or even months after the original infection [3].This emerging clinical condition, referred to as post-acute COVID-19 syndrome (PACS), has the potential to overwhelm health systems and economies.
Although COVID-19 preferentially affects the respiratory tract, it is now well-established that systemic manifestations are a major component of the clinical picture.While viral spreading to extrapulmonary organs is mechanistically explained by the ubiquitous expression of the angiotensinconverting enzyme 2 (ACE2), disease outcome largely depends on host factors, including age, sex, and pre-existing comorbidities, as well as on the effectiveness of individual immune responses mounted to combat the infection [4,5].The wide spectrum of the possible virus-host interactions result in a substantial degree of heterogeneity in symptoms, severity, recovery time, and response to treatments [5][6][7][8][9].
These observations collectively highlight the need to investigate the metabolic phenotype of COVID-19 patients further, as recognition of the specific metabotypes related to disease progression could have not only a descriptive value but also relevant diagnostic and prognostic significance.The present study used a combination of proton nuclear magnetic resonance ( 1 H NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) to investigate the urinary metabolic profiles associated with acute COVID-19 in a large cohort of patients that was previously characterized by serum metabolomics [11].Urine was selected as biological matrix based on its valuable properties as non-invasively available biospecimen, which have been already recognized in the context of COVID-19 [19,[33][34][35][36][37].The main focus of the present research was characterization of the acute COVID-19 urinary metabolic signature and recognition of the metabotypes specific to patient age, sex, and clinical deterioration.Further, we evaluated 4 ddhNs and their potential associations with either the other urinary metabolites or clinical parameters.In the final section of the study, we sought to expand knowledge on the SARS-CoV-2-induced changes in tryptophan metabolites and their involvement in the pathogenesis of the disease.More specifically, based on the crucial role of kynurenines in nicotinamide adenine dinucleotide (NAD) biosynthesis [38,39], we explored whether a dysregulation in this pathway could affect the activation of the NAD + -dependent deacetylases known as sirtuins, which show potent immunomodulatory and antiviral properties [40][41][42][43][44][45].

Study design
The present research investigated the metabolite profile of urine samples collected from Acute COVID-19 patients (AcuteCOV) (Heidelberg University Hospital).An independent pre-pandemic cohort of SARS-CoV-2-negative subjects was used as control (CTR) (data provided by Bruker BioSpin GmbH).
The overall study can be subdivided into 4 major aims: (1) Aim 1: Identification of the metabolite changes in AcuteCOV urine samples compared with CTR using 1 H NMR. This section analyzed the metabolite data obtained with 1 H NMR spectroscopy and Bruker IVDr method in urine samples to investigate differences between AcuteCOV (Werner Siemens Imaging Center, University Hospital Tübingen) patients and healthy CTR subjects (Data provided by Bruker BioSpin GmbH); (2) Aim 2: Characterization of the AcuteCOV urinary metabolic signature using 1 H NMR and LC-MS data.To enhance the characterization of AcuteCOV urinary metabolic phenotype, additional LC-MS experiments were carried out (Australian National Phenome Centre and Computational and Systems Medicine, Murdoch University).The information provided by this analysis integrated with the 1 H NMR data to derive more detailed metabolite profiles of AcuteCOV samples, which were then analyzed to identify potential disease severity, age, sex, BMI-dependent metabolic signatures; (3) Aim 3: Analysis of antiviral ddhNs and their associations with metabolite profiles and clinical variables.Here, we investigated whether the concentration of 4 antiviral ddhNs, namely 3′-deoxy-3′,4′-didehydro-cytidine (ddhC), 3′-deoxy-3′,4′-didehydrocytidine-5′-carboxylic acid (ddhC-5′CA), 3′-deoxy-3′,4′-didehydrouridine (ddhU), and 3′,5′-dideoxy-3′,4′-didehydrocytidine-5′-homocysteine (ddhC-5′ Hcy), measured in urine samples by LC-MS, was associated with either variations in urinary metabolite concentration or changes in clinical parameters; (4) Aim 4: Investigation of the potential association between SARS-CoV-2-induced kynurenine pathway dysregulation, NAD + biosynthesis, and sirtuins.In this section, we sought to provide a biological interpretation underlying the SARS-CoV-2-induced perturbations in the tryptophan metabolites, by investigating whether a dysregulation in this pathway could affect the activation of the NAD +dependent deacetylases sirtuins.To this aim, in addition to the results obtained by 1 H NMR and LC-MS in urine samples, we included the concentration of inflammatory mediators measured in matched serum samples of the same cohort of patients [11].

Study cohort information
Acute SARS-CoV-2 infected cohort (AcuteCOV): SARS-CoV-2 positive individuals were recruited from 7th September 2020 to 21st March 2021 within a prospective non-interventional study conducted by Heidelberg University Hospital [46].According to the World Health Organization (WHO) guidance, laboratory confirmation for SARS-CoV-2 was defined as a positive result of quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR) assay of nasal and pharyngeal swabs.Only subjects who were over 18 years of age and showing completed questionnaires were included.All participants provided written informed consent according to the Declaration of Helsinki and the local Ethics committee had approved biosample and data collection and analysis (reference number: S-324/2020).
Patient management was based on ambulatory monitoring with the "Coronataxi digital early warning" (CDEW) system, deployed in Rhein-Neckar County and Heidelberg, Germany.This approach is an outpatient care system consisting of remote digital monitoring via a mobile application (with symptom questionnaire and daily pulse oximetry), a medical doctor dashboard and medical care delivery to COVID-19 patients in home quarantine when indicated (Heidelberg Medical University Ethics commission approval: S-324/2020).
Blood and urine sample collection took place during home visits by nurses.Upon collection, both blood and urine specimens were kept at room temperature (RT) and transferred to the Heidelberg University Hospital.Samples were then further processed and stored at −80 °C until shipment to the University of Tübingen Hospital.
Clinical data were collected based on structured patient interviews.The recorded information included the following variables: age, sex, medical comorbidities, regular medication, height, weight, severity of acute COVID-19 disease, symptoms.For hospitalized patients, the mode of respiratory support (oxygen supply via nasal low-or high-flow, oxygen mask or invasive mechanical ventilation) was likewise recorded.
Control (CTR) cohort: Urine metabolite patterns in healthy subjects have been characterized by Bruker BioSpin GmbH using a well-established quantitative in vitro diagnostics research (IVDr) standardized approach in 1 H NMR spectroscopy [47].Healthy control urine samples were utilized for overall comparison and statistical investigations.In accordance with local requirements, informed permission was acquired from participants or their legal representatives.

Sample processing and quantitative 1 H NMR spectroscopy analysis
1 H NMR spectra were generated using the standardized Bruker Avance IVDr platform (Avance III HD spectrometer) and the quantification was performed using the different Bruker IVDr packages (https://www.bruker.com/de/products-and-solutions/mr/nmr-clinical-research-solutions/b-imethods.html), as previously described [48].Urine sample preparation was performed according to B.I.-embedded standard operation procedure (SOP) [49].For quality control (QC), the B.I. BioBank QC™ method; and for quantification B.I. Quant-UR eTM (eextended, up to 150 metabolites quantified) modules were utilized.Briefly, for each sample a 600 μL aliquot was transferred into an autoclaved 2 mL microcentrifuge tube (MCT) and centrifuged at 2000 RCF for 10 min at 4 °C (Heraeus Megafuge 8R, Thermo Electron LED GmbH, Osterode am Harz, Germany).Then, 585 μL of the supernatant were added with 65 μL of the standard Bruker urine buffer of pH 7.4 (order number AH0621-10, provided by Bruker BioSpin GmbH, Ettlingen, Germany; contains 1.5 M KH 2 PO 4 , 2 mM NaN 3 , 0.1 % TSP-d 4 ) and the mixture was vortexed for 30 s (VORTEX-GENIE 2, Scientific Industries, Inc., Bohemia, NY, USA).A volume of 600 μL of the processed sample was then transferred to a 5 mm Bruker NMR tube.
NMR spectroscopy was performed using a 5 mm triple resonance (TXI) RT probe that was controlled via TopSpin software, version 3.6.2.The sample jet was set to a cooling temperature of 5 °C, then sample spectral data were acquired at 300 K.The standard one-dimensional (1D) NMR experiment with solvent suppression was acquired with 32 scans (65,536 data points, 20.0186 ppm spectral width).total experimental time of 4 min per sample.The obtained spectra were uploaded to the Bruker data analysis server for automated quantification.The sample and shim quality of the experiment was tested by the full width at half maximum (fwhm) of the TSP signal (δ 0.00) being less than 1.30 Hz, sharp singlet symmetrical peak.The residual water signal after solvent suppression is less than 30.0 mmol/L.
A statistical total correlation spectroscopy (STOCSY) was performed on each metabolite to find the signal patterns and their chemical shift [50].Metabolites that could not be determined by its pattern and chemical shift by STOCSY were further investigated by 2D experiments ( 1 H-1 H COSY, 1 H-1 H TOCSY, 1 H- 13 C HSQC and 1 H-13 C HMBC).Each spectral region corresponding to the metabolites was integrated and correlated to the IVDr measurements to validate the quantification.Those that did not meet the criteria but the correct concentration is salvageable were done manually in R, otherwise removed from the analysis.Only metabolites passing the correlation of lineshape metabolite signals with calculated fit over 80 % were included in the study.Additionally, maleate quantification was excluded due to the recent report of urine NMR spectra in the same spectral region [32].
NMR metabolites were quantified via an external reference based on an ERETIC calibration [51].The calibration transfer to each individual sample is facilitated by the PULCON principle [52].An artificial reference peak representing a concentration equivalent of 10 mM can be added to the solvent suppressed 1D NMR spectrum in a region without metabolite signals.Quantitative calibrations need to be obtained and verified using a dedicated reference sample with known compound concentrations according to B.I. Methods QC procedures.Human body fluid sample preparation and NMR protocols were derived from an article of Dona et al. [49].Sample processing and targeted quantitative LC-MS Urine samples were thawed at 4 °C and prepared for analyses following previously reported metabolic phenotyping methods [53], with minor modifications.For the quantification of biogenic amines and amino acid metabolites, separation was performed by ultra-high-performance liquid chromatography (UHPLC) using an Acquity UPLC (Waters Corp, Milford, MA) coupled to a Bruker Impact II QToF mass spectrometer (Bruker, Daltonics, Billerica, MA).Resulting data files were processed for integrations and quantification using Target Analysis for Screening Quantification (TASQ) software v2.2 (Bruker, Daltonics, Bremen, Germany) where calibration curves were linearly fitted with a weighting factor 1/x.For the measurement of tryptophan and associated catabolites, separation was performed using Acquity UHPLC coupled to a Waters Xevo TQ-XS mass spectrometer (Waters Corp, Wilmslow, U.K.).Obtained raw files were processed for peak integrations and metabolite quantifications using the TargetLynx package within MassLynx v4.2, where calibration curves were linearly fitted for each metabolites using a weighting factor of 1/x.Resulting data matrices were quality controlled and combined prior to statistical analysis.

Enzyme-linked immunoassay (ELISA) based analysis to assess serum SIRT1
SIRT1 serum concentration was measured with a commercially available Human SIRT1 ELISA Kit (Thermo Fisher Scientific Inc., Germany.Inter assay coefficient of variation <10 %, Intra assay coefficient of variation <12 %, detection limit: 1.23 ng/mL), according to the manufacturer's instructions.Serum samples of 171 patients were used in this experiment.Briefly, each well was filled with 50 µL of undiluted serum and 50 µL of proprietary diluent.Seven different concentrations of a lyophilized human SIRT1 standard were prepared to build a calibration curve ranging from 300 ng/mL to 1.229 ng/mL.Absorbance was measured at 450 nm using a plate Reader (Tecan Trading AG, Switzerland).SIRT1 concentration was then calculated based on the standard curve and by adjusting for sample dilution.

Statistical analysis and data interpretation
Statistical analysis was performed using the online comprehensive tool Metaboanalyst (Version 5.0), the JMP Pro 15 software (©SAS Institute Inc., Cary, NC, USA), and SigmaPlot 11.0 (Systat Software Inc., San Jose, CA, USA).
Missing values were handled by the replacement by limit of detection (LoDs) method (1/5th of the positive value of each variable).All spectra were baseline corrected and normalized using probabilistic quotient normalization (PQN) [54].Both univariate and multivariate analysis approaches were applied.Unsupervised agglomerative hierarchical cluster analysis was performed to identify any potential specific signature of metabolite urine concentration (NA-chip analyzer program, https://sites.google.com/site/dchipsoft/).Unsupervised principal component analysis (PCA), supervised partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares -discriminant analysis (OPLS-DA) were also carried out as multivariate analysis.Differences across experimental groups were investigated by Mann-Whitney U test, Pearson's chi-squared test, One-way analysis of variance (ANOVA) or Kruskal-Wallis test on ranks.Two-way ANOVA was applied to investigate influences of two independent variables and their potential interaction.Tukey's test was used for post hoc multicomparison procedure.The false discovery rate (FDR) was controlled using the Benjamini-Hochberg correction to maximize statistical power [55].Linear correlation between variables was assessed based on Pearson's correlation coefficient.A p-value <0.05 and a fold change (FC) >1.2 were considered significant.

Baseline characteristics of the study cohorts and metabolic data included in the analysis
A schematic workflow diagram of the investigation is shown in Figure 1.

Study sample
A subset of 243 individuals from the Coronataxi study, for whom there complete metadata as well as urine and blood samples collected at the first follow-up timepoint, was used for the purpose of this study.Concerning the CTR group, 309 subjects were extracted from the Bruker cohort and included in the present research.Demographic data of the two study groups are reported in Table 1.Age ranges were defined according to the following criteria [56]: young adult, from 19 to 24 years; adult, from 25 to 44 years; middle aged, from 45 to 64 years; aged, from 65 to 79 years; senile, >80 years.Body mass index (BMI) categories were likewise assigned based on standard classification: (1) underweight, BMI≤18.5 kg/m 2 ; (2) normal weight, BMI≥18.5 and <24.9 kg/m 2 ; (3) overweight, BMI≥25 and ≤29.9 kg/m 2 ; (3) obesity I, BMI>30 and ≤34.9 kg/m 2 ; (4) obesity II, BMI>35 and ≤39.9 kg/m 2 ; (5) obesity III, BMI>40 kg/m 2 [57].Table 2 shows the baseline characteristics, clinical outcomes, laboratory data, and comorbidities of the AcuteCOV cohort.

Metabolic data
Up to 150 low-molecular-weight metabolite concentrations were obtained from the Bruker IVDr quantification in urine B.I.Quant-UR.The dataset was then subjected to internal QC analysis as described in the Methods: (1) Rho-sigma filtering; (2) metabolite detection in at least 70 % of the evaluated patient samples; (3) correlation of lineshape QC with calculated fit over 80 %.Based on these criteria, 44 metabolites were selected to be part of the final metabolite list for the comparison of AcuteCOV vs. CTR (Aim 1, Figure 1; please find the complete list in Supplementary Table S1).
With regard to aims 2, 3, and 4 the concentrations of alanine, glycine, methionine, taurine, and valine provided by NMR spectroscopy experiments were replaced with the corresponding LC-MS-based data (Figure 1, Supplementary Table S1, metabolites highlighted in green color).Moreover, 5 additional metabolites (3-indoxylsulfate, hypoxanthine, pseudouridine, urea, cis-aconitate) were manually analyzed using Chenomx NMRSuite 7.1.Therefore, the final list for aims 2, 3, and 4 included 92 metabolites, out of which 44 were measured by NMR and 48 by LC-MS (Figure 1; please find the complete list in Supplementary Table S1).
Aim 1: Identification of the metabolite changes in AcuteCOV urine samples compared with CTR PCA was carried out to investigate general group separation based on urinary metabolite concentrations assessed via 1 H NMR spectroscopy-based metabolomics approach (Figure 2A).Unsupervised hierarchical clustering revealed a good discrimination between the urinary metabolite profile of AcuteCOV and CTR groups (Supplementary Material 1.2.1).Associations between metabolite concentration profiles were explored by multivariate analysis that disclosed 13 main clusters of variables (Supplementary Material 1.2.1.2,Supplementary Table S2).Univariate analysis identified a total of 22 differentially expressed metabolites, of which 11 were increased in the AcuteCOV group compared with CTR, while 11 metabolites were decreased (Figure 2B).
Next, we investigated whether sex, age, and BMI factors can influence the metabolite changes associated with SARS-CoV-2 infection.Unsupervised hierarchical clustering analysis of metabolite concentrations disclosed a diseasedriven clusterization, indicating that the distinctive infection-dependent metabolic signature previously identified was sustained irrespective of age (Figure 2C, Supplementary Figure S1), sex (Figure 2D, Supplementary Figure S2), and BMI (Figure 2E).Consistently, for most of the differentially-expressed metabolites, the magnitude and pattern of modulation was similar to those observed within the whole sample analysis (Supplementary Table S3 and S4).

Association between urine metabolic profiles and disease severity
Differences in the urinary metabolite concentration between hospitalized and nonhospitalized patients We explored metabolite differences between non-hospitalized (n=186) and hospitalized (n=57) patients.Univariate analysis identified 12 up-regulated metabolites in the hospitalized group, among which there were the antiviral nucleosides and 4 metabolites involved in the tryptophan catabolic pathway, namely 3-hydroxykynurenine, kynurenine, 3-hydroxyanthranilic acid, and quinolinic acid (Supplementary Table S5).On the other hand, 8 metabolites, including citric acid, trigonelline, allantoin, and glycine were down-regulated in hospitalized patients compared with subjects who did not require hospital admission.
To depict the clinical significance of the identified metabolite differences, a volcano plot was created using both metabolites and clinical data.As shown in Figure 3, C-reactive protein (CRP) and ferritin serum concentrations showed similar fold change range and degree of statistical significance compared with the antiviral ddhNs, 3-hydroxykynurenine, and kynurenine.With regard to variables with reduced concentrations in the hospitalized compared with the nonhospitalized group, iron, citric acid, lymphocyte count, Transferrin saturation, glomerular filtration rate (GFR), platelets, and glycine, showed the most significant p-values.
practice to evaluate the main pathophysiological derangements leading to severe disease.Notably, based on previous studies [58,59], we elected to use the following clinical variables: CRP (inflammation), peripheral blood lymphocyte count (immune response), D-dimer (coagulation), and oxygen saturation (SpO 2 , lung function).We also included the Endothelial activation and stress index (EASIX), a predictor of endothelial complications recently validated in a cohort of COVID-19 hospitalized patients [60].As shown in the heatmap reported in Figure 4, two main clusters were identified (euclidean distance measure, ward clustering): (1) metabolites whose concentration tend to increase along with worsening of clinical parameters; (2) metabolites whose concentration tend to decrease along with worsening of clinical parameters.As highlighted in the dendogram of the hierarchical cluster analysis, each of the two main clusters included two sub-clusters, referred to as 1A (n=2 metabolites), 1B (n=11), 2A (n=7), and 2B (n=9).Of note, Cluster 1B includes the tryptophan-related metabolites neopterin, 3-hydroxy kynurenine, kynurenine, and 3-hydroxyanthranilic acid, together with the 4 antiviral ddhNs.

Influences of sex, age, and BMI factors on the AcuteCOV metabolomic profile
We investigated whether specific metabolite profiles could be identified across sex, age, and BMI patient subgroups.
Investigation of the metabolite differences across age groups disclosed increasing concentrations of neopterin, ddhC-5′CA, and taurine as the population increases in age, since lower levels of these metabolites were observed in younger subjects compared with the senile group (Supplementary Figure S4B).Of note, the same pattern of modulation was observed for the metabolites related to the tryptophan pathway, including kynurenine, quinolinic acid, 3-hydroxykynurenine, and kynurenic acid (Supplementary Figure S4B).

Aim 3: Analysis of the antiviral ddhNs and their associations with metabolite profiles and clinical variables
The 4 antiviral molecules ddhC, ddh-5′CA, ddhC-5′Hcy, ddhU accounted for a significant proportion of variability in the dataset, as indicated by PCA (Figure 5A).Thus, we explored whether these molecules were related to specific metabolite signatures and clinical manifestations.To this end, a combined factor, referred to as "Total ddhNs", was calculated for each patient by adding up the urinary concentration of the 4 antiviral nucleosides: As shown in Table 3, "Total ddhNs" was positively associated with markers of systemic inflammation, including CRP, EASIX, and LDH, whereas leukocytes and lymphocyte counts decreased along with increasing antiviral nucleoside concentrations.With regard to associations with the other metabolites, a strong positive correlation was found with neopterin and a number of molecules related to tryptophan metabolism, including quinolinic acid, kynurenine, 3-hydroxykynurenine, kynurenic acid, and 3-hydroxyanthranilic acid (Table 4).On the other hand, citric acid, glycine, and threonine showed the lowest correlation coefficients, indicating an inverse association with antiviral nucleosides.Moreover, an inverse correlation was observed between the nucleoside sum and different glucogenic amino acids entering the TCA cycle upstream to succinate, such as glycine, serine, threonine, alanine, arginine, glutamine, whereas aspartate, which is converted to oxaloacetate, resulted positively correlated.
To further understand the association between the antiviral nucleosides and the concentration of the other metabolites, we stratified the population according to quartiles (Q) of "Total ddhNs": (1) Q1, from 0.1 AU to 214.6 AU; (2) Q2, from 214.6 AU to 756.7 AU; (3) Q3, from 756.7 AU to 2,220.9AU; (4) Q4, from 2,220.9AU to 6,939.4AU.PLS-DA found an excellent discrimination between Q1 and Q4 (Figure 5B).These results were confirmed by univariate analysis performed to investigate differences in the metabolite concentrations across the quartiles (Figure 5C and Supplementary Table S6).Among the metabolites whose concentration increased along with higher "Total ddhNs", kynurenine, neopterin, 3-hydroxykynurenine, and quinolinic acid showed the highest FC between Q1 and Q4.Conversely, the reduced metabolites were citric acid and trigonelline.

Aim 4: Investigation of the potential association between SARS-CoV-2-induced kynurenines/trigonelline dysregulation, NAD + biosynthesis, and sirtuins
As schematized in Figure 6A, the kynurenine pathway mediates NAD + de novo synthesis from tryptophan.Since SARS-CoV-2 can inhibit NAD + generation through downregulation of the enzymes NAD Synthetase 1 (NADSYN1) and quinolate phosphoribosyltransferase (QPRT) [61], the accumulation of kynurenine metabolites could be a consequence of the virus-induced blockade of the downstream biochemical steps.In this view, the reduced urinary concentration of 1-methylnicotinamide and trigonelline could reflect an increased activation of the NAD + salvage pathways, in an effort to improve NAD + recycling.Therefore, a pilot study was performed to investigate whether the derangement in NAD + biosynthesis could have influences on the activation of sirtuins, NAD + -consuming enzymes with potent immunomodulatory and antiviral properties.
We assessed SIRT1 expression in blood samples and found detectable concentrations of this enzyme only in 49 patients (presence of SIRT1), while 122 subjects showed no serum Sirt1 (absence of SIRT1).Next, we investigated the potential association between SIRT1 and the metabolic perturbations and clinical deterioration observed in COVID-19.Univariate analysis revealed that absence of SIRT1 was associated with significantly higher kynurenic acid, quinolinic acid, and neopterin levels in both serum and urine samples (Figure 6B).Moreover, patients with no SIRT1 expression had higher concentration of the inflammatory indexes ferritin, CRP, EASIX, and LDH compared with those patients showing detectable SIRT1 amounts in their blood (Figure 6C).Consistently, absence of SIRT1 was associated with increased serum concentration of the chemokines IL-8 and MCP-1 (Figure 6D).

Discussion
The present study confirms and expands the crucial role of urinary metabolic profiling as a diagnostic and prognostic tool in COVID-19.We show that changes in metabolite urinary concentration can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers.We likewise provide further evidence of the importance of the kynurenine pathway in SARS-CoV-2-induced phenoconversion, by documenting a marked accumulation of these metabolites in deteriorated patients as well as in higher-risk subjects.The analysis of urinary ddhNs indicates that these antiviral nucleosides not only describe active infection but are also associated with the main COVID-19-related perturbations, suggesting a potential role of such molecules as a molecular link between viral load, host immune response, and metabolic disturbances.Finally, an association between kynurenine dysregulations and NAD + /sirtuins has been established for the first time, offering a new pathogenic mechanism potentially responsible for a variety of COVID-19 pathological hallmarks.By integrating 1 H NMR spectroscopy and LC-MS data, we provide a precise description of the changes in urine metabolite concentration associated with clinical deterioration and severe disease requiring patient hospitalization.More specifically, we identified 29 metabolites whose concentration significantly increases/decreases along with a worsening of clinical parameters routinely used to capture the main pathophysiological derangements of the disease [58,59], including, CRP, SpO 2 , d-dimer, and lymphocyte count.In addition, in our cohort, metabolite urinary concentration showed a distinctive pattern in hospitalized and nonhospitalized individuals, with a diagnostic performance comparable to that of clinical variables.These observations further confirm that assessment of metabolite levels in urine samples provides valuable diagnostic and prognostic information in COVID-19 [33,34,36,37] and could represent a potent mean for noninvasive screening in all the clinical conditions characterized by metabolic dysregulations, similarly to SARS-CoV-2 infection.
The greatest differences between higher-risk and lowerrisk patients were observed in the concentration of metabolites related to the tryptophan/kynurenine pathway and of the urinary ddhNs.SARS-CoV-2-induced dysregulation of the kynurenine pathway is widely described in both acute COVID-19 and PACS [17][18][19][20][21][22][23][24]62].The rise of kynurenines is positively associated with release of inflammatory mediators [18,20,23,63] and disease severity [20,23,24].Moreover, since high levels of kynurenic acid, 3-hydroxykynurenine, 3-hydroxyanthranilic acid, and quinolinic acid exert deleterious effects on the brain and the gut [18,39,62], the accumulation of these molecules can directly contribute to exacerbating patient health status.Consistently, we found higher kynurenine urinary concentration in hospitalized patients compared with non-hospitalized, as well as in subjects showing poorer clinical picture.Moreover, increased tryptophan metabolites were detected in urine samples of higher-risk patients, including elderly and men.This finding offers a rationale for the poorer clinical outcome associated with these classes of patients [20,23,63].
Concerning ddhNs, our study provides additional information regarding their role in SARS-CoV-2 infection.In our previous work [32], we linked the excretion of these endogenous antiviral agents to the activity of the virus inhibitory protein, endoplasmic reticulum-associated, IFN inducible (Viperin) protein, an enzyme induced by interferons to inhibit viral replication [64].A positive correlation between serum ddhNs and cytokine levels was also identified, together with higher ddhN urinary concentrations in hospitalized patients compared with nonhospitalized.Here, we confirm and expand these observations, by demonstrating that nucleoside concentration changes are associated with alterations in numerous clinical variables routinely used to assess patient health status in COVID-19, including biomarkers of inflammation, leukocyte count and molecules related to iron homeostasis.Moreover, the antiviral ddhNs showed a distinctive profile in hospitalized compared with nonhospitalized patients, with similar fold changes and statistical significance of CRP and ferritin.These findings clearly demonstrate that urinary ddhNs not only capture active viral infection but can also describe clinical deterioration, with a discrimination ability comparable with that of the markers currently used in clinical practice [58,59].With regard to associations with urinary metabolites, a strong positive correlation was found between ddhNs and neopterin, kynurenines, and taurine, and its connection to NAD + biosynthesis, which, in turn, is crucial to activate sirtuins.Metabolites typed in red fonts have increased urinary concentrations in patients with worst clinical condition, in males, in elderly, and/or in subjects with higher ddhN urinary release, whereas metabolites typed in blue showed an opposite modulation pattern, with lower urinary concentrations in at least one of the patient classes mentioned above.Enzymes involved in while different amino acids and molecules related to the urea cycle were inversely associated with the nucleosides.These results provide additional evidence supporting the connection between viral load, host antiviral response, and metabolic derangements in infectious diseases.
The comparison between AcuteCOV and controls, which relied exclusively on 1 H NMR data, further demonstrates that metabolic phenoconversion could have a causative role in the evolution of the main homeostatic perturbations observed in COVID-19 [18].Increased succinate and fumarate, together with reduced citrate, denote an impairment in the TCA cycle, in line with previous studies [11,33].In particular, the rise of succinate suggests a dysfunctional mitochondrial complex II, namely succinate dehydrogenase, which plays a crucial role in the generation of oxygen reactive species [65][66][67].Moreover, since succinate can act as a pro-inflammatory/ chemoattractant signaling molecule [26], its deregulation could represent one of the deleterious biological events leading to immunometabolic reprogramming, which was extensively documented in COVID-19 [5,27].Compared with noninfected individuals, the AcuteCOV group also showed reduced concentrations of dimethylamine, N,N-dimethylglycine, and glycine.In addition to being indicative of altered one-carbon metabolism, these findings could unveil a new-onset insulin resistance [68,69].The higher glucose excretion observed in SARS-CoV-2 patients further supports this concept [70,71].Consistent with previous papers [6,18], we revealed a marked increase in urinary taurine.Accumulation of this metabolite could derive from deranged one-carbon/cysteine metabolism or might reflect the activation of adaptive mechanisms to cope with oxidative stress.Of note, taurine appears to be particularly relevant for PACS development, as perturbations in its concentration were reported to be persistently sustained over months after the original SARS-CoV-2 infection [6,72].
The last section of our research sought to expand knowledge about SARS-CoV-2-induced changes in tryptophan metabolites and their involvement in the pathogenesis of COVID-19.From a mechanistic point of view, the kynurenine pathway leads to the production of quinolinic acid which, in turn, serves as a precursor in NAD + de novo biosynthesis [38,39,73].Besides being a key coenzyme in many redox reactions, NAD + has emerged as a master regulator of inflammation, leukocyte functions, and host antiviral responses [73][74][75][76].In fact, NAD + availability is essential to activate the NAD + -dependent enzymes sirtuins, CD38, and poly-ADP-ribose polymerases (PARPs), which exert broad immunomodulatory and defense effects [40][41][42][43][44]. Increasing evidence demonstrates that many viruses, including SARS-CoV-2, can deplete NAD + concentrations [5,77,78].This phenomenon could be part of the miscellaneous viral strategies to evade the host immune system [73,79,80].Recently, Heer and colleagues showed that SARS-CoV-2 infection is associated with down-regulation of genes involved in NAD + synthesis from tryptophan or nicotinic acid, namely QPRT and NADSYN [61].Based on these findings, we hypothesized that the kynurenine accumulation detected in our cohort and in other AcuteCOV populations reflects a virus-induced blockade of the downstream biochemical steps required to generate NAD + .Furthermore, the reduced excretion of the niacin metabolite trigonelline could be a consequence of enhanced NAD + recycling in the salvage pathways.Since NAD + depletion leads to decreased sirtuin activity [43], we tested the idea that such SARS-CoV-2-induced metabolic perturbations could be associated with SIRT1 deregulation.
Our pilot study discloses, for the first time, a potential association between kynurenines/trigonelline and SIRT1 in COVID-19.In fact, higher kynurenine concentrations in both serum and urine samples were associated with lower SIRT1 blood levels.After stratifying our cohort according to SIRT1 levels, we found that patients with impaired SIRT1 showed higher serum concentration of biomarkers of systemic inflammation and epithelial dysfunction, as well as an enhanced release of inflammatory mediators.Given the multiple detrimental consequences of SIRT1 deregulation on host homeostasis [41,42,81], our preliminary results not only provide a potential mechanistic explanation underlying the SARS-CoV-2-induced superactivation of the kynurenine pathway, but they also offer a new pathogenic mechanism potentially responsible for a variety of clinical manifestations in COVID-19, including excessive systemic cytokine release, metabolism alterations, and insulin resistance.
Some limitations of the study should be discussed.First, this is a single-center analysis which mainly involved patients suffering from mild disease, while a very small proportion of severe cases was included [46].Therefore, the results of the present research could not be generalized to the biochemical reactions presented here are typed in capital letters; the expression of enzymes framed in green is modulated by SARS-CoV-2 [61].Differences in the concentration of (B) selected metabolites, (C) clinical variables, and (D) serum inflammatory mediators between patient groups derived based on absence (n=122, white box) or presence (n=49, green box) of SIRT1 in serum samples.Box plots illustrate median , red lines denote the mean of each study group.Wilcoxon-Mann-Whitney test; p-values: * p<0.05; ** p<0.01; *** p<0.001.CRP, C-reactive protein; EASIX, endothelial activation and stress index; LDH, lactate dehydrogenase; IDO, 2,3-dioxygenase and indole 2,3-dioxygenase; NADSYN1, NAD synthetase 1; NAPRT, nicotinic acid phosphoribosyltransferase; NNMT, nicotinamide N-methyltransferase; QPRT, quinolate phosphoribosyltransferase; SIRT1, sirtuin 1.
the wide-spectrum of COVID-19 severity classes.On the other hand, our data specifically describe the metabolic perturbations underlying asymptomatic and mild diseases, which, due to the successful introduction of vaccination, represent the most common clinical manifestations of SARS-CoV-2 infection.Second, we exclusively analyzed spontaneous urine that could be less informative than 24 h-urine in describing a person's metabolic state as it is affected by external factors such as diet, hydration, and circadian rhythms.Nevertheless, compared to 24-h urine, spontaneous urine samples offer clear clinical advantages in terms of ease of collection and practicality.Hence, they were considered the optimal choice for analyzing a large number of patients in our real-world cohort.Still on sample collection, since the "Coronataxi" study did not involve scheduled regimen for collecting biological specimens, we cannot exclude the possibility of influences on the urinary metabolite profile due to variations in fasting condition among patients.Lastly, we acknowledge that evaluating SIRT1 concentration by ELISA may not represent the ideal method to investigate Sirtuin activation.Despite this, we were able to provide preliminary evidence suggesting a potential link between the kynurenine pathway and NAD + /sirtuins in SARS-CoV-2.Therefore, our results represent a useful starting point for further researches on the topic.
In conclusion, we demonstrated that urinary metabolomics assumes similar diagnostic/prognostic power to clinical pathology in COVID-19 as it can fully capture patient clinical deterioration and aberrant immune activation.Therefore, the description of metabolite changes in urine samples appears extremely useful not only to optimize patient stratification, but also to assess the effectiveness of novel treatments in preventing severe COVID-19 or PACS development.In this regard, the urinary ddhNs emerge as promising biomarkers of SARS-CoV-2 and possibly other viral infections.In fact, unlike the nonspecific markers currently used in the clinical practice, ddhNs concentration is not influenced by pre-existing comorbidities.To facilitate the clinical usability of our results, Table 5 provides a list of the most discriminant metabolites for each of our study aims, along with their absolute concentrations.The combined use of serum and urine metabolomics can further improve diagnostic accuracy, fostering the application of precision medicine in the context of infectious diseases [82], as well as in other clinical conditions marked by deranged metabolism.
A substantial novelty of the present research resides in the recognition of a potential association between kynurenine pathway superactivation and impaired NAD + biosynthesis, which could lead to inefficient sirtuin response.Our observations could be of paramount clinical relevancefor the development of innovative therapeutic strategies aimed at restoring the physiological NAD + turnover in COVID-19 patients [75,83,84].

Figure 2 :
Figure 2: Identification of the metabolite changes in AcuteCOV urine samples compared with CTR.(A) Principal component analysis (PCA) scores plot illustrating general group separation based on urinary metabolite concentrations assessed via 1 H NMR spectroscopy-based metabolomics approach.The analysis excluded 4 patients based on extremely different glucose and creatinine urinary concentrations.Out of the 4 outliers, 3 were controls, while 1 subject was from the AcuteCOV group.(B) List of metabolites increased or decreased in AcuteCOV compared with CTR.Only molecules showing log2 fold change (FC) >1.2 are included.A two-color scale is used to illustrate FC: deep red denotes +1.6, deep blue denotes −1.Wilcoxon-Mann-Whitney test.Unsupervised hierarchical clustering reveals an overall SARS-CoV-2 infection-driven clusterization irrespective of (C) age, (D) sex, and (E) BMI groups.

Figure 3 :
Figure 3: Differences in urinary metabolite concentration and serum clinical parameters between hospitalized and nonhospitalized patients.(A) Combined volcano plot showing the metabolites and the clinical variables significantly different in the comparison of hospitalized (n=57) vs. nonhospitalized (n=186) patients, with a fold change >1.2 and p-value <0.05.Increased variables are typed with red letters, while decreased with blue letters.Metabolites are written in bold, while clinical parameters are diplayed in a regular typeface.* Denotes serum variables assessed on the same day of urine collection.BMI, body mass index; BT, body temperature; CRP, C-reactive protein; EASIX, endothelial activation and stress index; GFR, glomerular filtration rate; RR, respiratory rate; Tf, transferrin.

Figure 5 :
Figure 5: Analysis of the antiviral ddhNs and their associations with metabolic urinary profiles.(A) Principal component analysis (PCA) loading plot showing a strong influence of the 4 ddhNs on PC1.(B) PLS-DA reveals no overlap between the quartile Q1 and Q4 of "Total ddhNs", which was calculated for each patient by adding up the urinary concentrations of the 4 antiviral nucleosides: [ddhC] + [ddh-5′ CA] + [ddhC-5′Hcy] + [ddhU].(C) Box plots illustrating the metabolites with the highest fold change between "Total ddhNs" Q1 and Q4.Kruskal-Wallis One-way ANOVA on Ranks.Normalized concentrations were obtained by applying the probabilistic quotient normalization (PQN).All the metabolites displayed here had a p-value<0.001.For each experimental group, box plots show the median, while the red line denotes the mean.LDA, linear discriminant analysis; PCA, principal component analysis; Q, quartile.

Figure 6 :
Figure 6: Association between kynurenine/trigonelline dysregulation, NAD + biosynthesis, and sirtuins.(A) Simplified diagram of the tryptophan pathway and its connection to NAD + biosynthesis, which, in turn, is crucial to activate sirtuins.Metabolites typed in red fonts have increased urinary concentrations in patients with worst clinical condition, in males, in elderly, and/or in subjects with higher ddhN urinary release, whereas metabolites typed in blue showed an opposite modulation pattern, with lower urinary concentrations in at least one of the patient classes mentioned above.Enzymes involved in

Table  :
Demographic information of the AcuteCOV and CTR study cohorts.

Table  :
Association between "Total ddhNs" and clinical variables.

Table  :
Association between "Total ddhNs" and the other urinary metabolites.