Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

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

  • José C. Páez-Franco ,

    Roles Formal analysis, Investigation, Methodology, Writing – original draft (JCP-F); (AU-A)

    Affiliation Red de Apoyo a la Investigación (RAI), Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • José L. Maravillas-Montero,

    Roles Investigation

    Affiliation Red de Apoyo a la Investigación (RAI), Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Nancy R. Mejía-Domínguez,

    Roles Data curation, Formal analysis, Methodology

    Affiliation Red de Apoyo a la Investigación (RAI), Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Jiram Torres-Ruiz,

    Roles Investigation

    Affiliations Emergency Medicine Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico, Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Karla M. Tamez-Torres,

    Roles Investigation, Resources

    Affiliation Department of Infectology and Microbiology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Alfredo Pérez-Fragoso,

    Roles Investigation, Resources

    Affiliation Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Juan Manuel Germán-Acacio,

    Roles Methodology

    Affiliation Red de Apoyo a la Investigación (RAI), Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Alfredo Ponce-de-León,

    Roles Conceptualization, Investigation, Resources

    Affiliation Department of Infectology and Microbiology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Diana Gómez-Martín,

    Roles Conceptualization, Investigation, Resources

    Affiliation Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

  • Alfredo Ulloa-Aguirre

    Roles Conceptualization, Investigation, Supervision, Writing – review & editing (JCP-F); (AU-A)

    Affiliation Red de Apoyo a la Investigación (RAI), Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico


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.


Efforts to unravel novel therapies to treat COVID-19 and post-COVID-19 manifestations are crucial due to the emergence of novel SARS-CoV-2 variants with augmented infectivity and/or pathogenicity. Clinical tests combining integral snapshots from the metabolic status of patients with COVID-19, have provided important information on the metabolic abnormalities provoked by SARS-CoV-2 infection [13], which may be useful for the design of new therapeutic approaches to counteract the negative effects of COVID-19 on the metabolic homeostasis. Alterations in the latter include the harmful effects of hypoxia and excessive reactive oxygen species (ROS) production that lead to severe cellular damage [4,5].

It is well known that decreased oxygen supply, incremented glycolysis, and activation of immune responses promote an altered redox state in COVID-19 and several malignancies [68]. These metabolic changes must be accompanied by a physiological response to support redox homeostasis. Synthesis, recycling, and/or supplementation of molecules with antioxidant activity [such as glutathione (GSH) or ascorbic acid] are essential to achieve ROS clearance [9], and in fact, several studies have shown that glutamine, GSH, and proline metabolism have a direct role on altered glutamic acid and cystine levels [1013], which are amino acids that influence the synthesis of GSH, a molecule with antioxidant activity that has been recently revisited in the context of COVID-19 pathogenesis [4,14].

Several clinical studies focused on specific therapeutic interventions that increase the antioxidant capacity of COVID-19 patients have shown variable outcomes [1518]. For example, administration of combined metabolic activators (e.g. glutathione and NAD+ precursors) or N-acetyl-cysteine (NAC) to a limited number of subjects, reduced the risk of mechanical ventilation and mortality as well as the plasma levels of metabolites associated with inflammation and inflammatory cytokines [15,16,18]. In contrast, administration of high dose zinc gluconate and/or ascorbic acid, which may positively impact on the immune response to infection, resulted in marginal effects on the duration of symptoms in mild to moderate COVID-19 [17]. On the other hand, it is well-known that the presence of comorbidities (e.g. metabolic syndrome, obesity, diabetes, and hypertension, among others) and age-related issues may hide important biochemical and metabolic characteristics derived from SARS-CoV-2 infection [19,20], as they may predispose to or are, per se, chronic inflammatory conditions that may alter the metabolic equilibrium as part of their pathophysiology and thereby significantly impact COVID-19 progression.

In the present study, we employed an untargeted metabolomic approximation to explore the main metabolic changes exhibited by a population of male patients less than 50 years of age with COVID-19, without pre-existing comorbidities (in particular, metabolic and cardiovascular diseases) that may convey greater risk of life-threatening complications from this infection and introduce confounding variables to our study findings [2126]. In addition, we focused only in men considering the particular role of male endocrine environment on COVID-19 severity, including death [2729]. Further, the metabolomic profile in women varies significantly with the menstrual cycle, which also may introduce confounding variables that prevent an accurate estimation of the relationship between changes in metabolomic profile and disease severity [30,31]. Under these considerations, the purpose of this analysis was to unveil those metabolic abnormalities inherent to COVID-19 respiratory illness that characterize patients with mild/moderate and severe disease, emphasizing on the metabolic alterations provoked by hypoxia in the context of redox homeostasis. We identified increased levels of glutamic acid, and phenylalanine and reduced cystine, glutamine, proline, threonic acid, and cholesterol levels. Our results highlight the value of amino acid metabolism in ROS recycling pathways and provide insights for new supportive therapies that may lead to better outcomes in this potentially catastrophic disease.



To minimize the influence of gender, age and/or comorbidities on the metabolomics analysis performed [2126,30,31], serum samples from twenty-two male COVID-19 patients (16 mild/moderate and 6 severe disease) with a positive RT-PCR test for SARS-CoV-2 as well as from sixteen RT-PCR negative healthy male volunteers (non-professional healthcare workers at our institution), less than 50 years of age (range 19 to 49 years), with no major pre-existing health conditions (e.g. metabolic syndrome, obesity, type-2 diabetes mellitus, cardiovascular diseases and/or other chronic diseases known to be associated with a poor prognosis of SARS-CoV-2 infection, as assessed by clinical history, physical examination, and/or routine biochemical tests [32]) were selected for the present metabolomic analysis. All subjects attended a third level referral center in Mexico City (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán) from March to October 2020. Sixteen (72.7%) donor patients were classified as mild/moderate on the basis of previously published criteria [33] (fever, upper respiratory infection symptoms, with or without pneumonia, O2 saturation at rest >93% and respiratory rate <30 breaths per minute) and followed-up at home, whereas six (27.2%) patients were classified as severe [either respiratory failure, O2 saturation <93%, respiratory rate >30 breaths per minute or PaFi (PaO2/FiO2) ≤300 mmHg] and received in-hospital treatment. In subjects that met the inclusion criteria, blood samples were collected from an antecubital vein and placed in tubes containing clot-activating gel (BD Vacutainer, Becton Dickinson, Mexico). Tubes were inverted five times and the mixed blood was allowed to clot for 30 minutes at room temperature, centrifuged at 1300 x g for 15 minutes, and the resulting serum was then collected with a sterile disposable Pasteur pipette, aliquoted in cryogenic vials, and stored at -80°C until GC/MS analysis. All included samples were drawn upon admission to the triage area at the emergency department for the patients or at the epidemiology unit for the controls, where hospital staff members attended for randomized nasopharyngeal PCR testing during the morning shift and in a fasting state (minimum 8 h). The study was approved by the institutional ethics and research committees of our institute (Ref. RAI3688/21/22/1). Written informed consent to participate in the study was obtained from all participants. All analytical methods were carried out in accordance with relevant and updated guidelines and regulations.

Gas chromatography/mass spectrometry (GC/MS) analysis

Metabolomics analysis was performed by GC/MS as previously described, with minor modifications [34]. Briefly, 35 μL of serum were mixed with 150 μL of 1:3 chloroform-methanol and 5 μL of internal standard (tridecanoic acid, methyl tricosanoate, and 5α-cholestane, 0.18 mg/mL each) and vortexed for 2 min. The mix was incubated for 20 min at -20°C and then centrifuged at 16000 x g during 10 min at 4°C. The supernatant unique phase was collected, evaporated in a constant nitrogen flux, resuspended in 40 μL of methoxyamine in pyridine and incubated for 90 min at 37°C. Thereafter, 40 μL of N-methyltrifluoroacetamide (MBSTFA) plus 1% trimethylchlorosilane (TMCS) was added to the final solution and incubated for 30 min at 37°C. Finally, one microliter was injected (splitless) to a GC/MS system (Agilent 5977A/7890B, Santa Clara, CA, USA) with an autosampler (G4513A, Agilent) and run in a HP-5ms column (30 m × 250 μm × 0.25 μm; Agilent) with helium 99.9999% as a mobile phase and at 1 ml/min flow, 200°C inlet temperature, 200°C source temperature, and 250°C interface temperature. The running method consisted of 1 min hold at 60°C with an increased ramp of 10°C/min to 325°C, with a final held time of 10 min. Samples were injected aleatorily and a quality control sample (a mix of all the samples) was included every six samples to monitor changes in instrument acquisition and to ensure reproducibility. Only those peaks with less than 30% relative standard deviation (RSD) in quality control samples were included [35]. All reagents and solvents employed were pure analytical grade materials purchased from commercial sources.

Chromatogram deconvolution

Raw GC/MS data was transformed using the Agilent Chemstation software (Agilent), and the chromatogram deconvolution and alignment were processed with the Mzmine 2 software [36] using the following parameters: retention time (RT) range, 5.5–27.5 min; m/z range, 50–500; m/z tolerance, 0.5; noise level, 1×103; and peak duration range, 0.01–0.2 min. Only those peaks that appeared in 80% of all the chromatograms were included. Thereafter, the identification was achieved using the National Institute of Standards and Technology (NIST) spectral library. Only those metabolites with a matching value >70% were included in the final analysis. The raw data has been submitted to MetaboLights data base with the accession number MTBLS5011.

Statistical analysis

To analyze for differences among control and patients groups, the U-Mann-Whitney test or Kruskal-Wallis test were applied using Graphpad Prism software 8.0 (GraphPad Software Inc., San Diego, CA) and adjusted by False Discovery Rate analysis. A probability value of p<0.05 was considered statistically significant. Spearman´s rank correlation analysis, heatmaps with hierarchical clustering, and fold change analysis were performed in Metaboanalyst 5.0 and R project software [37,38] using data with sum normalization. Changes in those metabolites with a fold change (FC) >1.5 and p-value <0.05 were considered significant. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) also were performed through Metaboanalyst 5.0 previous sum normalization, data log transformation, and mean centering. Data normalization through internal standard (tridecanoic acid) showed similar results. Metabolite set enrichment analysis was generated in Metaboanalyst 5.0, employing the KEGG human metabolic pathways database (


The main characteristics of the cohort studied are listed in Table 1. Further biochemical tests (shown in S1 Table) were performed in the patients included in the severe COVID-19 group. All patients in the mild/moderate group were followed-up through daily phone calls for 2 weeks since the time of COVID-19 diagnosis and none progressed to severe or critical forms of the disease. None of the patients included in mild/moderate or the severe groups progressed to a more advanced condition, and all were followed until their discharge from the hospital.

Table 1. Main demographic features of the patients included in the analysis.

Our untargeted metabolomic approach consistently identified 33 different metabolites (S2 Table). To ensure that our technique was reproducible throughout the study, Principal Component Analysis (PCA) was applied to all samples; a well-defined QC cluster was noticed, indicating that our method was robust enough to analyze the cohort’s metabolomic profile (S1 Fig). Increased glutamic acid, and reduced glutamine, cystine, and threonic acid levels were detected in both the mild/moderate and severe COVID-19 patients, whereas proline levels significantly decreased only in the severe COVID-19 group. The majority of these changes were more marked in patients with severe disease. The statistical analysis for those metabolites that exhibited a FC>1.5 is shown in Table 2 and the complete set of all metabolites in S2 Table. To visualize these differences, heatmap and hierarchical clustering analyses were applied (Fig 1A). As shown, our patients segregated with good performance and according with disease severity.

Fig 1. Untargeted serum metabolomics of COVID-19 patients.

A. Heatmap and hierarchical clustering analysis of COVID-19 and control individuals with all the metabolites included in the analysis. Different intensities in red and blue colors in the heatmap and scale bar at the right side of the figure, denotes increased or reduced levels, respectively. B. Spearman correlation analysis of serum metabolites in COVID-19 and control groups. H, healthy controls, M, mild/moderate disease; S, severe disease. Blue numbers indicate a positive association. Red numbers indicate a negative association. Asterisks denote statistical significance with p-value < 0.05. The exact p-values of Spearman´s correlation analysis is depicted in S3 Table.

Table 2. Metabolites with significant changes between the groups.

To identify potential associations between the metabolites and disease severity, we performed Spearman correlation analysis, which revealed that glutamic acid correlated negatively with cystine, threonic acid, and glutamine (Fig 1B). Likewise, cystine correlated positively with threonic acid and glutamine. The family of branched chains amino acids (valine, leucine, and isoleucine) correlated positively among each other, albeit these changes did not reach statistical significance when analyzed by group severity (S2 Table).

To further discriminate between disease severity, PLS-DA was applied to the data, showing a good performance (Accuracy = R2 = 0.81, Q2 = 0.73, p<0.05) (Fig 2A). The variable importance in projection (VIP) scores are summarized in Fig 2B and corresponds to the metabolites that better discriminate the groups according to disease severity. Employing this set of metabolites, we implemented pathway enrichment analysis in Metaboanalyst 5.0 and observed statistical significance (p<0.05) and enrichment ratio of glutamine/glutamate metabolism (Fig 2C).

Fig 2. Partial least squares-discriminant analysis of COVID-19 patients.

A. Score plot of PLS-DA analysis of healthy controls (H), mild/moderate (M), and severe (S) patients. B. Variable importance in the projection (VIP) values that better explain the differences between the groups; C. Enriched metabolite data sets (p<0.05) for the VIP metabolites involved in COVID-19 severity clustering.

Metabolite association (VIP>1.2) with the clinical features in the severe group is depicted in Fig 3A. Glutamine positively correlated with LDH, CPK, PCO2, and HCO3, and negatively with PaFi. On the other hand, glutamic acid showed negative associations with prothrombin time (PT), International Normalized Ratio (INR), and FiO2. Moreover, cystine and threonic acid showed a positive correlation with alanine aminotransferase (ALT) and aspartate aminotransferase (AST), both markers of hepatic function. Conversely, cystine and threonic acid showed negative associations with sodium levels. Phenylalanine correlated negatively with SpO2, albumin, pH, and anion gap markers. The main changes observed in several amino acids and other metabolites are shown in Fig 3B. Glutamine, cystine, threonic acid (a sugar acid derived from threose), and proline, were progressively lower in patients with mild/moderate and severe disease, whereas glutamic acid and serine were significantly higher as compared with the healthy controls; the latter amino acid, albeit elevated in both COVID-19 groups, did not reach statistical significance in the severe group probably due to the reduced number of patients included in this group. The impact of the main changes observed in our cohort on ROS scavenging is also depicted in Fig 3B.

Fig 3. Correlation analysis of the metabolites and the clinical tests in the severe group.

A. Spearman correlations analysis of metabolites and clinical tests. SpO2 = Oxygen saturation; LDH = Lactate dehydrogenase; Art pH = Arterial pH; PaO2 = Partial pressure of oxygen; PaCO2 = Partial pressure of CO2; Art HCO3 = Arterial bicarbonate; PaFi = PaO2/FiO2; TB = Total bilirubin; DB = Direct bilirubin; IB = Indirect bilirubin; ALT = Alanine aminotransferase; AST = Aspartate aminotransferase; AP = Alkaline phosphatase; Alb = Albumin; INR = International Normalized Ratio; CRP = C reactive protein; CPK = Creatine phosphokinase. Different intensities in blue and red numbers indicate a positive or negative association, respectively (see also the gradient scale at the right side of the figure). B. Proposed model for the main changes identified in our cohort. The graphs were constructed with normalized values. H, healthy controls; M, mild/moderate disease; S, severe disease. ‡Direct involvement of cystine over glutamic acid levels through xCT antiporting system. †Enzymatic involvement over glutamic levels through proline dehydrogenase activity. Statistical differences were calculated employing Kruskal-Wallis and Dunn tests. P*<0.05 **<0.01, ***<0.001, ****<0.0001.


The main complications derived from SARS-CoV-2 infection are related to systemic oxygen shortage due to lung tissue damage as a result of exacerbated immune activation and virus replication, which lead to metabolic changes that prepare target organs and tissues to reduced oxygen supply [39]. Leaving aside the metabolic reshaping provoked by the virus on infected cells [40,41], it has been proposed that critical metabolic changes occur in endothelial and immune cells in the early stages of lung damage [40]. Moreover, if oxygen levels do not recover, the decrement in oxygen supply may lead to further damage to critical organs such as brain, kidneys, and liver [42,43], as observed in critical COVID-19 patients.

In the present study, we identified several differences in serum levels of particular metabolites among healthy controls and patients with mild/moderate or severe forms of COVID-19, which presumably were not related to any co-morbidity associated to a negative prognosis [32]. Immune-activated and oxygen-deprived cells show metabolic signatures associated with the increase in glycolytic pathways and glutamine metabolism that feed energetic and biosynthetic processes [4447]. In this vein and in line with other studies [4853] we observed decreased serum glutamine and high glutamic acid levels in patients with severe COVID-19. Further, applying pathway enrichment analysis, we detected statistical significance and enrichment ratio of glutamine/glutamate metabolism. Here is important to emphasize in that only 33 metabolites were identified from the sera by the GC/MS method employed of which only 15 were used in the enrichment pathway analysis. Therefore, it should not be surprising that the D-glutamine and D-glutamate metabolic pathways resulted highly enriched by this analysis. Although the results of this analysis fitted very well with the analytic data (Fig 3B), further studies including a larger number of metabolites are needed to confirm the results from the pathway enrichment analysis performed. Although different metabolomics analyses of samples from COVID-19 patients have detected increased levels of glutamate, this change still is poorly understood and underestimated despite that this amino acid acts as a neurotransmitter that when unbalanced, may lead to excitotoxicity and neurological damage, both observed in COVID-19 pathophysiology [54]. In fact, it is known that hypoxic-ischemic conditions may promote cognitive alterations [55,56] that to some extent, could resemble COVID-19 and long COVID-19 neurological disabilities [57,58]. The importance of glutamine metabolism in patients and experimental animals with COVID-19 is well documented [10,59,60] and in fact a recent analysis concluded that glutamine supplementation reduced time of hospitalization and the necessity of intensive care of these patients [61]. Further, the markedly decreased plasma levels of glutamine in patients with severe inflammatory condition due to COVID-19, rapidly reverted in those with a favorable outcome [62]. Glutamine-derived glutamate can feed several pathways like the Krebs cycle, gluconeogenesis, and GSH synthesis (Fig 3B), and its role in hypoxia is critical as it maintains energy and redox homeostasis in stress conditions [12]. In this regard, it has been observed that GSH levels are decreased in COVID-19 [4] and that supplementation with substrates that regenerate GSH (like N-acetylcysteine) may be favorable to reduce the risk for mechanical ventilation [15].

The endogenous source of glutamic acid derives from glutaminase activity, an enzyme that produces glutamic acid from glutamine in a process called glutaminolysis. A different source of glutamic acid is the gamma-glutamyl cycle, which is the synthetic pathway of GSH, an important tripeptide with antioxidant activity. Dysregulation of this cycle promotes the increment of glutamic and pyroglutamic acid [63]. Individuals with glutathione synthetase deficiency or paracetamol overdose show increased pyroglutamic acid levels, which when severe, may lead to metabolic acidosis and liver failure [63,64]. In the present study, we detected a slight increase (<1.5 fold change, p<0.05) in pyroglutamic acid levels in mild/moderate COVID-19 patients, a change that has been identified in other COVID-19 cohorts [59,65,66]. In our study, pyroglutamic changes were apparently unrelated to altered hepatic function, as disclosed by biochemical tests (S1 Table). Pyroglutamic acid modest increment in mild/moderate COVID-19 patients have been associated with alterations in GSH synthesis provoked by decreased supply of substrates like ATP, glycine or cysteine in environments with ROS excess [67]. In this regard, we observed decreased cystine levels, which is the predominant extracellular form of cysteine.

Cysteine or cystine imbalance has been reported in other COVID-19 cohorts with variable changes according to disease severity, immune activity and presence of comorbidities [1,33,51,6870]. In fact, cystine levels are critical to contend with ROS in COVID-19 as well as in several malignancies [1]. In the present study, reduced cystine levels correlated positively with ALT and AST levels in patients with severe COVID-19. In this vein, a potential hepatic involvement in cysteine and cystine turnover in COVID-19 seems plausible as this organ, which is frequently compromised in the severe form of this disease, is the main place of GSH synthesis [71,72]. Although cystine correlates negatively with glutamic acid, the counterion implied in the xCT antiporting system of cystine [73], the influence of this system on increased serum glutamic acid levels is not clear yet. Recently, a glutamate/cystine antiporter system that favors GSH synthesis in ROS was described in malignant lung cells [74] and it has been found that the transporter xCT is increased in monocytes from COVID-19 patients [51], suggesting that cystine has probably a role in immunity [75]. Interestingly, in an example of metabolic flexibility, cystine and glutamic acid levels influence glutamine anaplerotic usage in malignant cells through the xCT transporter [76]. Although the occurrence of this effect during hypoxia still remains unexplored, it is known that serum glutamine levels and its anaplerotic reactions are essential to reduce SARS-Cov-2 pathogenicity [10].

On the other hand, it was previously shown that reduced cystine levels correlated positively with threonic acid, a catabolite of ascorbic acid, the primary antioxidant obtained from food [77]. This relationship indicates that redox homeostasis was probably altered in our cohort. In this regard, it has been observed that ascorbic acid levels tend to be lower in severe COVID-19 patients [78]. Although our metabolomic analysis suggests that ascorbate supply might surpass redox imbalances, its exogenous supplementation has not shown any benefit, at least in ambulatory patients with COVID-19 [17].

In agreement with findings from other studies [1,2,7981], we detected decreased proline levels in those patients with severe COVID-19 studies. In this vein, it has been shown that decreased proline levels are associated with an unfavorable prognosis in patients with the severe form of this disease [80,82]. Further, in a genomewide association study of severe COVID-19 a locus spanning six genes showing cross replicating associations with disease severity was identified and, interestingly, one of these genes codes the transporter SIT-1, a protein that interacts with angiotensin converting enzyme 2 (ACE), the SARS-CoV-2 cell receptor [83] and that is directly involved in the transport of proline, an amino acid associated to protein folding. These data suggest that proline metabolism could be related with a stress response in COVID-19, more specifically to an exaggerated unfolded protein response in helper T-lymphocytes and macrophages, thereby contributing to intracellular hypoxia and a pathogenic cytokine response. In this regard, in vitro studies with cardiomyocytes expressing higher levels of proline dehydrogenase revealed that enhanced proline metabolism reduced apoptosis and decreased hypoxia derived from ROS [84]. In this same line of evidence, it has been shown that the activity of proline dehydrogenase increases the protection to ROS in malignant melanoma cells and also promotes release of glutamic acid and alpha-ketoglutarate as reaction byproducts [85]. This reaction can feed anaplerotic reactions in stress conditions and could partially explain some of the increased glutamic acid levels in COVID-19 [11].

Finally, we observed a modest, but significant increase (FC<1.5, p-value<0.05) in levels of serine in patients with mild/moderate and severe COVID-19, albeit to a lower extent in the latter group. Altered levels of serine associated with variable outcomes have been documented in other metabolomic analyses [68,86,87]. In this regard, it was shown that serine supplementation decreased ROS damage in mice with oxidative stress through direct involvement in GSH synthesis [88]; the property of promoting glycolysis and GSH production is apparently unique to this amino acid [89].

We recognize that the number of patients included in this metabolomic analysis was limited, given that the study design did not consider subjects with co-morbidities associated with high COVID-19 mortality and which may potentially confound the metabolomic analysis [2126,31]. Frequently associated COVID-19 co-morbidities including obesity, diabetes mellitus and cardiovascular diseases, all comprise ~80% of the patients attended in our third level health center, which made quite difficult to recruit for the present metabolomics study patients free of co-morbidities. In this vein, our findings are comparable with other studies including larger numbers of patients and presence of comorbidities in which altered levels of branched-chain amino acids, glutamine, glutamate, and proline also were identified, suggesting that neither differences in patient number or abscence of comorbidities influenced on the general metabolic reshaping provoked by SARS-CoV2 infection [1,79,81,9093]. Another limitation is that we did not include additional controls with other viral pneumonias in which hypoxia and respiratory failure may lead to altered metabolomics, particularly when associated to hypoxic liver injury [94,95]. Nevertheless, in this vein it should be emphasized that during the COVID-19 pandemic, the incidence of other opportunistic and/or viral pneumonias dramatically decreased in several regions, including ours [9699] most probably due to the mandatory measures imposed for COVID-19 prevention [99]. Finally, our metabolomics study was limited to males as it is well known that the metabolomic profile in premenopausal women varies with the menstrual cycle, particularly during the luteal phase. In this vein, studies employing GC/MS and nuclear magnetic resonance detected significant changes in a number of amino acids and derivatives, biogenic amines, and lipid species during the luteal phase of the menstrual cycle associated with the increased levels of serum progesterone that characterize this cycle phase [30,31]. Thus, we decided to include only males in our study given that for a mixed gender cohort it would have been necessary to include a relatively large number of women during at least the early, late follicular/preovulatory, and luteal phases of the menstrual cycle in order to statistically analyze the impact of each cycle phase on the metabolic changes exhibited by control and COVID-19 women, at least by multivariate analysis.

In conclusion, this study documents the existence of altered levels of several metabolites in COVID-19 patients in the absence of frequently associated co-morbidities. Assessing the magnitude of these alterations in both mild/moderate and severe COVID-19 patients is important as they seem to play a pivotal role in immune response and ROS scavenging. Our data emphasize the potential importance of amino acid supplementation in COVID-19 patients to prevent not only further progression of the disease but also probably the development of the chronic form of this infection.

Supporting information

S1 Fig. Principal component analysis of all patients included in the analysis.

Pooled samples (blue intense) were used as a quality control and shows a compact well-defined cluster, ensuring a good reproducibility of our GC/MS analysis.


S1 Table. Entire set of clinical test performed in the severe group.


S2 Table. List of metabolites included in our GC/MS analysis.


S3 Table. p-values of Spearman´s correlation analysis in Fig 1B.



  1. 1. Thomas T, Stefanoni D, Reisz JA, Nemkov T, Bertolone L, Francis RO, et al. COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. JCI Insight. 2020;5. pmid:32559180
  2. 2. Song JW, Lam SM, Fan X, Cao WJ, Wang SY, Tian H, et al. Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis. Cell Metab. 2020;32: 188–202.e5. pmid:32610096
  3. 3. Shen B, Yi X, Sun Y, Bi X, Du J, Zhang C, et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. SSRN Electron J. 2020. pmid:32492406
  4. 4. Karkhanei B, Talebi Ghane E, Mehri F. Evaluation of oxidative stress level: total antioxidant capacity, total oxidant status and glutathione activity in patients with COVID-19. New Microbes New Infect. 2021;42: 100897. pmid:34026228
  5. 5. Samir D. Oxidative Stress Associated with SARS-Cov-2 (COVID-19) Increases the Severity of the Lung Disease—A Systematic Review. J Infect Dis Epidemiol. 2020;6.
  6. 6. Kalyanaraman B. Reactive oxygen species, proinflammatory and immunosuppressive mediators induced in COVID-19: Overlapping biology with cancer. RSC Chem Biol. 2021;2: 1402–1414. pmid:34704045
  7. 7. Laforge M, Elbim C, Frère C, Hémadi M, Massaad C, Nuss P, et al. Tissue damage from neutrophil-induced oxidative stress in COVID-19. Nat Rev Immunol. 2020;20: 515–516. pmid:32728221
  8. 8. Alfadda AA, Sallam RM. Reactive oxygen species in health and disease. J Biomed Biotechnol. 2012;2012. pmid:22927725
  9. 9. Forcados GE, Muhammad A, Oladipo OO, Makama S, Meseko CA. Metabolic Implications of Oxidative Stress and Inflammatory Process in SARS-CoV-2 Pathogenesis: Therapeutic Potential of Natural Antioxidants. Front Cell Infect Microbiol. 2021;11: 1–11. pmid:34123871
  10. 10. Matsuyama T, Yoshinaga SK, Shibue K, Mak TW. Comorbidity-associated glutamine deficiency is a predisposition to severe COVID-19. Cell Death Differ. 2021. pmid:34663907
  11. 11. Pandhare J, Donald SP, Cooper SK, Phang JM. Regulation and function of proline oxidase under nutrient stress. J Cell Biochem. 2009;107: 759–768. pmid:19415679
  12. 12. Newsholme P., Lima M.M.R., Procopio J., Pithon-Curi T.C., Doi S.Q., Bazotte R.B., et al. Glutamine and glutamate as vital metabolites. Brazilian J Med Biol Res. 2003;36: 153–163. pmid:12563517
  13. 13. Valencia E, Marin A, Hardy G. Impact of oral L-glutamine on glutathione, glutamine, and glutamate blood levels in volunteers. Nutrition. 2002;18: 367–370. pmid:11985937
  14. 14. Silvagno F, Vernone A, Pescarmona GP. The role of glutathione in protecting against the severe inflammatory response triggered by covid-19. Antioxidants. 2020;9: 1–16. pmid:32708578
  15. 15. Assimakopoulos SF, Aretha D, Komninos D, Dimitropoulou D, Lagadinou M, Leonidou L, et al. N-acetyl-cysteine reduces the risk for mechanical ventilation and mortality in patients with COVID-19 pneumonia: a two-center retrospective cohort study. Infect Dis (Auckl). 2021;53: 847–854. pmid:34182881
  16. 16. Altay O, Arif M, Li X, Yang H, Aydın M, Alkurt G, et al. Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19. Adv Sci. 2021;8: 1–16. pmid:34180141
  17. 17. Thomas S, Patel D, Bittel B, Wolski K, Wang Q, Kumar A, et al. Effect of High-Dose Zinc and Ascorbic Acid Supplementation vs Usual Care on Symptom Length and Reduction among Ambulatory Patients with SARS-CoV-2 Infection: The COVID A to Z Randomized Clinical Trial. JAMA Netw Open. 2021;4: 1–10. pmid:33576820
  18. 18. Abulmeaty MMA, Aljuraiban GS, Shaikh SM, Aleid NE, Al Mazrou LR, Turjoman AA, et al. The efficacy of antioxidant oral supplements on the progression of COVID-19 in non-critically ill patients: A randomized controlled trial. Antioxidants. 2021;10. pmid:34069549
  19. 19. Cornillet M, Strunz B, Rooyackers O, Ponzetta A, Chen P, Muvva JR, et al. COVID‐19‐specific metabolic imprint yields insights into multiorgan system perturbations. Eur J Immunol. 2021. pmid:34837225
  20. 20. Wu P, Chen D, Ding W, Wu P, Hou H, Bai Y, et al. The trans-omics landscape of COVID-19. Nat Commun. 2021;12: 1–16. pmid:34315889
  21. 21. Lu J, Xie G, Jia W, Jia W. Metabolomics in human type 2 diabetes research. Front Med China. 2013;7: 4–13. pmid:23377891
  22. 22. Kordalewska M, Markuszewski MJ. Metabolomics in cardiovascular diseases. J Pharm Biomed Anal. 2015;113: 121–136. pmid:25958299
  23. 23. Kondoh H, Kameda M, Yanagida M. Whole blood metabolomics in aging research. Int J Mol Sci. 2021;22: 1–13. pmid:33375345
  24. 24. Lu Y, Wang Y, Ong CN, Subramaniam T, Choi HW, Yuan JM, et al. Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS. Diabetologia. 2016;59: 2349–2359. pmid:27514531
  25. 25. Rangel-Huerta OD, Pastor-Villaescusa B, Gil A. Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies. Metabolomics. Springer US; 2019. pmid:31197497
  26. 26. Cirulli ET, Guo L, Leon Swisher C, Shah N, Huang L, Napier LA, et al. Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk. Cell Metab. 2019;29: 488–500.e2. pmid:30318341
  27. 27. Brandi ML, Giustina A. Sexual Dimorphism of Coronavirus 19 Morbidity and Lethality. Trends Endocrinol Metab. 2020;31: 918–927. pmid:33082024
  28. 28. Bwire GM. Coronavirus: Why Men are More Vulnerable to Covid-19 Than Women? SN Compr Clin Med. 2020;2: 874–876. pmid:32838138
  29. 29. Olivas-Martínez A, Cárdenas-Fragoso JL, Jiménez JV, Lozano-Cruz OA, Ortiz-Brizuela E, Tovar-Méndez VH, et al. In-hospital mortality from severe COVID-19 in a tertiary care center in Mexico City; causes of death, risk factors and the impact of hospital saturation. PLoS One. 2021;16: 1–17. pmid:33534813
  30. 30. Wallace M, Hashim YZHY, Wingfield M, Culliton M, McAuliffe F, Gibney MJ, et al. Effects of menstrual cycle phase on metabolomic profiles in premenopausal women. Hum Reprod. 2010;25: 949–956. pmid:20150174
  31. 31. Draper CF, Duisters K, Weger B, Chakrabarti A, Harms AC, Brennan L, et al. Menstrual cycle rhythmicity: metabolic patterns in healthy women. Sci Rep. 2018;8: 1–15. pmid:30275458
  32. 32. Ejaz H, Alsrhani A, Zafar A, Javed H, Junaid K, Abdalla AE, et al. COVID-19 and comorbidities: Deleterious impact on infected patients. J Infect Public Health. 2020;13: 1833–1839. pmid:32788073
  33. 33. Torres-Ruiz J, Pérez-Fragoso A, Maravillas-Montero JL, Llorente L, Mejía-Domínguez NR, Páez-Franco JC, et al. Redefining COVID-19 Severity and Prognosis: The Role of Clinical and Immunobiotypes. Front Immunol. 2021;12: 1–13. pmid:34566957
  34. 34. Páez-Franco JC, Torres-Ruiz J, Sosa-Hernández VA, Cervantes-Díaz R, Romero-Ramírez S, Pérez-Fragoso A, et al. Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients. Sci Rep. 2021;11: 6350. pmid:33737694
  35. 35. Fiehn O. Metabolomics by gas chromatography-mass spectrometry: Combined targeted and untargeted profiling. Current Protocols in Molecular Biology. 2016. pmid:27038389
  36. 36. Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11. pmid:20650010
  37. 37. Pang Z, Chong J, Zhou G, De Lima Morais DA, Chang L, Barrette M, et al. MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49: W388–W396. pmid:34019663
  38. 38. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
  39. 39. Shen T, Wang T. Metabolic reprogramming in covid-19. Int J Mol Sci. 2021;22: 1–14. pmid:34768906
  40. 40. Icard P, Lincet H, Wu Z, Coquerel A, Forgez P, Alifano M, et al. The key role of Warburg effect in SARS-CoV-2 replication and associated inflammatory response. Biochimie. 2021;180: 169–177. pmid:33189832
  41. 41. Oliveira LG, Angelo Y de S, Yamamoto P, Carregari VC, Crunfli F, Reis-de-Oliveira G, et al. SARS-CoV-2 Infection Impacts Carbon Metabolism and Depends on Glutamine for Replication in Syrian Hamster Astrocytes. bioRxiv. 2021; 2021.10.23.465567. Available:
  42. 42. Leach RM, Treacher DF. Oxygen transport-2. Tissue hypoxia. BMJ. 1998;317: 1370–1373. pmid:9812940
  43. 43. Alayash AI. The Impact of COVID-19 Infection on Oxygen Homeostasis: A Molecular Perspective. Front Physiol. 2021;12: 1–8. pmid:34690793
  44. 44. Santos AF, Póvoa P, Paixão P, Mendonça A, Taborda-Barata L. Changes in Glycolytic Pathway in SARS-COV 2 Infection and Their Importance in Understanding the Severity of COVID-19. Front Chem. 2021;9: 1–9. pmid:34568275
  45. 45. Hugo‐Wissemann D, Anundi I, Lauchart W, Viebahn R, de Groot H. Differences in glycolytic capacity and hypoxia tolerance between hepatoma cells and hepatocytes. Hepatology. 1991;13: 297–303. pmid:1847350
  46. 46. Cruzat V, Rogero MM, Keane KN, Curi R, Newsholme P. Glutamine: Metabolism and immune function, supplementation and clinical translation. Nutrients. 2018;10: 1–31. pmid:30360490
  47. 47. Kumar V. How could we forget immunometabolism in SARS-CoV2 infection or COVID-19? Int Rev Immunol. 2021;40: 72–107. pmid:33155525
  48. 48. Schmelter F, Foeh B, Mallagaray A, Rahmoeller J, Ehlers M, Lehrian S, et al. Metabolic markers distinguish COVID-19 from other intensive care patients and show potential to stratify for disease risk. medRxiv. 2021; 2021.01.13.21249645.
  49. 49. Kimhofer T, Lodge S, Whiley L, Gray N, Loo RL, Lawler NG, et al. Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection. J Proteome Res. 2020;19: 4442–4454. pmid:32806897
  50. 50. Holmes E, Wist J, Masuda R, Lodge S, Nitschke P, Kimhofer T, et al. Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome. J Proteome Res. 2021;20: 3315–3329. pmid:34009992
  51. 51. Krishnan S, Nordqvist H, Ambikan AT, Gupta S, Sperk M, Svensson-Akusjärvi S, et al. Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication. Mol Cell Proteomics. 2021;20: 100159. pmid:34619366
  52. 52. Lawler NG, Gray N, Kimhofer T, Boughton B, Gay M, Yang R, et al. Systemic Perturbations in Amine and Kynurenine Metabolism Associated with Acute SARS-CoV-2 Infection and Inflammatory Cytokine Responses. J Proteome Res. 2021;20: 2796–2811. pmid:33724837
  53. 53. Ansone L, Briviba M, Silamikelis I, Terentjeva A, Perkons I, Birzniece L, et al. Amino Acid Metabolism is Significantly Altered at the Time of Admission in Hospital for Severe COVID-19 Patients: Findings from Longitudinal Targeted Metabolomics Analysis. Microbiol Spectr. 2021;9. pmid:34878333
  54. 54. Sarubbo F, El Haji K, Vidal-Balle A, Bargay Lleonart J. Neurological consequences of COVID-19 and brain related pathogenic mechanisms: A new challenge for neuroscience. Brain, Behav Immun—Heal. 2022;19: 100399. pmid:34870247
  55. 55. Silverstein FS, Buchanan K, Johnston MV. Perinatal Hypoxia‐Ischemia Disrupts Striatal High‐Affinity [3H]Glutamate Uptake into Synaptosomes. J Neurochem. 1986;47: 1614–1619. pmid:2876058
  56. 56. McLean C, Ferriero D. Mechanisms of hypoxic-ischemic injury in the term infant. Semin Perinatol. 2004;28: 425–432. pmid:15693399
  57. 57. de Erausquin GA, Snyder H, Carrillo M, Hosseini AA, Brugha TS, Seshadri S. The chronic neuropsychiatric sequelae of COVID-19: The need for a prospective study of viral impact on brain functioning. Alzheimer’s Dement. 2021;17: 1056–1065. pmid:33399270
  58. 58. Nalbandian A, Sehgal K, Gupta A, Madhavan MV., McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27: 601–615. pmid:33753937
  59. 59. Doğan HO, Şenol O, Bolat S, Yıldız ŞN, Büyüktuna SA, Sarıismailoğlu R, et al. Understanding the pathophysiological changes via untargeted metabolomics in COVID-19 patients. J Med Virol. 2021;93: 2340–2349. pmid:33300133
  60. 60. Jia H, Liu C, Li D, Huang Q, Liu D, Zhang Y, et al. Metabolomic analyses reveals new stage-specific features of the COVID-19. Eur Respir J. 2021; 2100284.
  61. 61. Cengiz M, Borku Uysal B, Ikitimur H, Ozcan E, Islamoğlu MS, Aktepe E, et al. Effect of oral l-Glutamine supplementation on Covid-19 treatment. Clin Nutr Exp. 2020;33: 24–31. pmid:32835086
  62. 62. Baranovicova E, Bobcakova A, Vysehradsky R, Dankova Z, Halasova E, Nosal V, et al. The ability to normalise energy metabolism in advanced covid-19 disease seems to be one of the key factors determining the disease progression—a metabolomic nmr study on blood plasma. Appl Sci. 2021;11: 4–6.
  63. 63. Hložek T, Křížek T, Tůma P, Bursová M, Coufal P, Čabala R. Quantification of paracetamol and 5-oxoproline in serum by capillary electrophoresis: Implication for clinical toxicology. J Pharm Biomed Anal. 2017;145: 616–620. pmid:28797958
  64. 64. Al-Jishi E, Meyer BF, Rashed MS, Al-Essa M, Al-Hamed MH, Sakati N, et al. Clinical, biochemical, and molecular characterization of patients with glutathione synthetase deficiency. Clin Genet. 1999;55: 444–449. pmid:10450861
  65. 65. Barberis E, Timo S, Amede E, Vanella VV., Puricelli C, Cappellano G, et al. Large-scale plasma analysis revealed new mechanisms and molecules associated with the host response to sars-cov-2. Int J Mol Sci. 2020;21: 1–25. pmid:33207699
  66. 66. Binayke A, Zaheer A, Dandotiya J, Gupta SK, Mani S, Tripathi M, et al. Innate immune response and distinct metabolomic signatures together drive and shape the SARS-CoV-2-specific T cell response during COVID-19. bioRxiv. 2022; 2022.03.11.483930. Available:
  67. 67. Polonikov A. Endogenous Deficiency of Glutathione as the Most Likely Cause of Serious Manifestations and Death in COVID-19 Patients. ACS Infect Dis. 2020;6: 1558–1562. pmid:32463221
  68. 68. Li Y, Hou G, Zhou H, Wang Y, Tun HM, Zhu A, et al. Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery. Signal Transduct Target Ther. 2021;6. pmid:33859163
  69. 69. Chen X, Gu M, Li T, Sun Y. Metabolite reanalysis revealed potential biomarkers for COVID-19: A potential link with immune response. Future Microbiol. 2021;16: 577–588. pmid:33973485
  70. 70. Wu J, Zhao M, Li C, Zhang Y, Wang DW. The SARS-CoV-2 induced targeted amino acid profiling in patients at hospitalized and convalescent stage. Biosci Rep. 2021;41: 1–11. pmid:33625490
  71. 71. Lu SC. Regulation of glutathione synthesis. Mol Aspects Med. 2009;30: 42–59. pmid:18601945
  72. 72. Stipanuk MH. Role of the Liver in Regulation of Body Cysteine and Taurine Levels: A Brief Review. Neurochem Res. 2004;29: 105–110. pmid:14992268
  73. 73. Dai L, Noverr MC, Parsons C, Kaleeba JAR, Qin Z. xCT, not just an amino-acid transporter: A multi-functional regulator of microbial infection and associated diseases. Front Microbiol. 2015;6: 1–5. pmid:25745420
  74. 74. Guo W, Li K, Sun B, Xu D, Tong L, Yin H, et al. Dysregulated Glutamate Transporter SLC1A1 Propels Cystine Uptake via Xc- For Glutathione Synthesis in Lung Cancer. Cancer Res. 2021;81: 552–566. pmid:33229341
  75. 75. Lee JW, Su Y, Baloni P, Chen D, Pavlovitch-Bedzyk AJ, Yuan D, et al. Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19. Nat Biotechnol. 2021. pmid:34489601
  76. 76. Muir A, Danai LV., Gui DY, Waingarten CY, Heiden MGV. Environmental cystine drives glutamine anaplerosis and sensitizes cells to glutaminase inhibition. bioRxiv. 2017; 1–27.
  77. 77. Rumsey SC, Levine M. Absorption, transport, and disposition of ascorbic acid in humans. J Nutr Biochem. 1998;9: 116–130.
  78. 78. Hemilä H. Vitamin C and infections. Nutrients. 2017;9: 2020–2022. pmid:28353648
  79. 79. Atila A, Alay H, Yaman ME, Akman TC, Cadirci E, Bayrak B, et al. The serum amino acid profile in COVID-19. Amino Acids. 2021;53: 1569–1588. pmid:34605988
  80. 80. Danlos FX, Grajeda-Iglesias C, Durand S, Sauvat A, Roumier M, Cantin D, et al. Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers. Cell Death Dis. 2021;12. pmid:33707411
  81. 81. Ergin Tuncay M, Neselioglu S, Asfuroglu Kalkan E, Inan O, Sena Akkus M, Ates I, et al. Modified Proline Metabolism and Prolidase Enzyme in COVID-19. Lab Med. 2022; 1–6. pmid:35394547
  82. 82. Clayton PT. Is susceptibility to severe COVID-19 disease an inborn error of metabolism? J Inherit Metab Dis. 2020;43: 906–907. pmid:32608016
  83. 83. Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, Invernizzi P, et al. Genomewide Association Study of Severe Covid-19 with Respiratory Failure. N Engl J Med. 2020;383: 1522–1534. pmid:32558485
  84. 84. Wang J, Xue Z, Hua C, Lin J, Shen Z, Song Y, et al. Metabolomic analysis of the ameliorative effect of enhanced proline metabolism on hypoxia-induced injury in cardiomyocytes. Oxid Med Cell Longev. 2020;2020. pmid:33294127
  85. 85. Natarajan SK, Zhu W, Liang X, Zhang L, Demers AJ, Zimmerman MC, et al. Proline dehydrogenase is essential for proline protection against hydrogen peroxide-induced cell death. Free Radic Biol Med. 2012;53: 1181–1191. pmid:22796327
  86. 86. Shi D, Yan R, Lv L, Jiang H, Lu Y, Sheng J, et al. The serum metabolome of COVID-19 patients is distinctive and predictive. Metabolism. 2021;118: 154739. pmid:33662365
  87. 87. Perła-kaj J. COVID-19 and One-Carbon Metabolism. 2022.
  88. 88. Zhou X, He L, Wu C, Zhang Y, Wu X, Yin Y. Serine alleviates oxidative stress via supporting glutathione synthesis and methionine cycle in mice. Mol Nutr Food Res. 2017;61: 1–13. pmid:28759161
  89. 89. Ye J, Fan J, Venneti S, Wan YW, Pawel BR, Zhang J, et al. Serine catabolism regulates mitochondrial redox control during hypoxia. Cancer Discov. 2014;4: 1406–1417. pmid:25186948
  90. 90. Ghini V, Meoni G, Pelagatti L, Celli T, Veneziani F, Petrucci F, et al. Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients. PLoS Pathog. 2022;18: 1–16. pmid:35446921
  91. 91. Masuda R, Lodge S, Nitschke P, Spraul M, Schaefer H, Bong SH, et al. Integrative Modeling of Plasma Metabolic and Lipoprotein Biomarkers of SARS-CoV-2 Infection in Spanish and Australian COVID-19 Patient Cohorts. J Proteome Res. 2021;20: 4139–4152. pmid:34251833
  92. 92. Bruzzone C, Bizkarguenaga M, Gil-Redondo R, Diercks T, Arana E, García de Vicuña A, et al. SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum. iScience. 2020;23. pmid:33043283
  93. 93. Bizkarguenaga M, Bruzzone C, Gil-Redondo R, SanJuan I, Martin-Ruiz I, Barriales D, et al. Uneven metabolic and lipidomic profiles in recovered COVID-19 patients as investigated by plasma NMR metabolomics. NMR Biomed. 2022;35: 1–10. pmid:34708437
  94. 94. Huang H, Li H, Chen S, Zhou X, Dai X, Wu J, et al. Prevalence and Characteristics of Hypoxic Hepatitis in COVID-19 Patients in the Intensive Care Unit: A First Retrospective Study. Front Med. 2021;7: 1–8. pmid:33681238
  95. 95. Dean DA, Klechka L, Hossain E, Parab AR, Eaton K, Hinsdale M, et al. Spatial metabolomics reveals localized impact of influenza virus infection on the lung tissue metabolome. bioRxiv. 2021; 2021.11.22.469643.
  96. 96. Cai F, Shou X, Ye Q. Epidemiological Study on Mycoplasma pneumoniae and Chlamydia pneumoniae Infection of Hospitalized Children in a Single Center During the COVID-19 Pandemic. Front Cell Infect Microbiol. 2022;12: 1–8. pmid:35386684
  97. 97. Knudsen PK, Lind A, Klundby I, Dudman S. The incidence of infectious diseases and viruses other than SARS-CoV-2 amongst hospitalised children in Oslo, Norway during the Covid-19 pandemic 2020–2021. J Clin Virol Plus. 2022;2: 100060. pmid:35262033
  98. 98. Quéromès G, Frobert E, Burtseva E, Drăgănescu A, Koul PA, Komissarov A, et al. Clinical and phylogenetic influenza dynamics for the 2019–20 season in the global influenza hospital surveillance network (GIHSN)–Pilot study. J Clin Virol. 2022;152: 105184. pmid:35594785
  99. 99. Cruz-Lopez F, Garza-González E, Morfin-Otero R, Villarreal-Treviño L, Rodriguez-Noriega E, Martínez-Meléndez A. Analysis of influenza and dengue cases in Mexico before and during the COVID-19 pandemic. Infectious diseases (London, England). England; 2022. pp. 232–234. pmid:34751092