1 Introduction

The global outbreak coronavirus disease 2019 (COVID-19) viral pneumonia caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), which originated in Wuhan, China, in December 2019 has become a worldwide pandemic claiming several thousands of lives worldwide. According to last update 4th of October 2021 of John Hopkins- Coronavirus Resource Center (https://coronavirus.jhu.edu/map.html), this virus has infected more than 234,987,662 people worldwide and 4,803,300 confirmed deaths. Despite the fact that more than 80% of COVID-19 patients have mild symptoms, such as anosmia (loss of smell) and ageusia (loss of taste) and in some cases accompanied with other more moderate symptoms including fever, exhaustion, cough, and shortness of breath (Hui et al., 2020; Yang et al., 2.3 Statistical analysis

Metabolite levels were normalized using the Bioconductor package limma-voom and presented as log2 (Ritchie et al., 2015a). Metabolites normality was confirmed using Shapiro–Wilk test and Kolmogorov–Smirnov test. Log-transformed normalized intensities were used in LIMMA analyses to identify differential changes in metabolites between diseased and control groups. We used the default Benjamini–Horchberg correction for multiple testing (Dudoit et al., 2002; Smyth Gordon, 2004). One-way analysis of variance (ANOVA) and post hoc Tukey multiple comparison analyses were applied to compare between more than two independent variables. Correlation of sphingosine, 5-Aminolevulinic acid, l-Tryptophan, and l-Kynurenine saliva concentrations of COVID-19 patients and serum markers of COVID-19 severity such as D-dimer and C-reactive protein was evaluated using Pearson’s coefficient test with two-tailed p-value < 0.05 considered significant.

Principal component analysis (PCA) was implemented using PCAGO R Shiny framework which is available at github.com/hoelzer-lab/pcagom. jHeatmap was used to create the heatmaps which is available at http://jheatmap.github.io/jheatmap/ (Deu-Pons et al., 2014). Metabolite Set Enrichment Analysis (MSEA) were carried using MetaboAnalystR3 package which is available at github.com/xia-lab/MetaboAnalystR (Pang et al., 2020; Liao et al., 2020; Vardhana & Wolchok, 2020).Thereby it is expected that metabolite reprogramming observed in severe COVID-19 disease to correlate to this immune dysregulation. Following this assumption, **ao and colleague studied the plasma metabolite programming during COVID-19 infection (**ao et al., 2021).They revealed strong correlation between metabolite dysregulation and hyperinflammation found in severe COVID-19 (**ao et al., 2021). Further, they confirmed this association by modulating viral induced inflammation using metabolite supplements (**ao et al., 2021). In another study, kynurenic acid to Kynurenine ratio, specifically in males was positively associated with age, inflammatory marker levels and negatively associated with T cell immune response during COVID-19 (Thomas et al., 2020).

Kynurenine has a central role in modulating inflammation and immune response (Chen & Guillemin, 2009). Kynurenine shunt that moves the tryptophan degradation toward kynurenine and away from serotonin has been associated independently with depression, fatigue, muscle weakness, and other neurologic diseases (Mangoni, 1974; Németh et al., 2005). COVID-19 disease causes long-lasting symptoms months after recovery from acute infection that resembles kynurenine shunt presentations (Eroğlu et al., 2021; Huang et al., 2021). It is therefore possible for the metabolic programming to play a role in manifestation of both acute and long-term symptoms observed in COVID-19 (Huang et al., 2021; Ramakrishnan et al., 2021; Saheb Sharif-Askari et al., 2021). More studies are needed to profile the long-term metabolic change in those suffering from lingering long COVID symptoms.

In addition to severe metabolic pathways, we have also profiled those that were specific to non-severe group which included sphingosine. Our differential metabolite analyses in both saliva and plasma samples have shown higher sphingosine levels in non-severe than severe COVID-19 (Fig. 3A, B and Fig. 6A). Interestingly, salivary sphingosine pattern revealed an inverse relationship between sphingosine levels and disease severity with significant increase of sphingosine levels in asymptomatic and its complete absence in severe disease. We detected sphingosine in salivary controls but not in plasma controls. Similar to our result, Janneh et al. (Janneh et al., 2021) and Shen et al. (Shen et al., 2020) associated the reduced plasma sphingosine levels to COVID-19 severity. In their findings plasma sphingosine levels differentiated symptomatic from non-symptomatic infection but it could not differentiate severe from non-severe disease (Janneh et al., 2021; Shen et al., 2020).

Sphingosine present abundantly in healthy nasal, tracheal, and bronchial epithelial cells protecting the respiratory tracts from both bacterial and viral respiratory infection (Verhaegh et al., 2020). It prevent SARS-CoV-2 viral entry into host cells; sphingosine binds to ACE2 receptor and block the interaction between SARS-CoV-2 viral spike protein and host ACE2 receptor (Edwards et al., 2020). It is involved in synthesis and regulation of multivesicular bodies which modulate diverse cellular processes (Lang et al., 2020). Additional antiviral effect of sphingosine was shown against herpes simplex virus in macrophages where sphingosine binds, traps and degrades the virus within the multivesicular bodies (Lang et al., 2020). The bactericidal effect of this sphingolipid have been shown against different bacterial strains (Grassmé et al., 2017; Seitz et al., 2019). Verhegh and colleagues demonstrated that protonated amino group (NH3+) group of sphingosine binds to the—negatively charged lipid cardiolipin in Pseudomonas aeruginosa and Staphylococcus aureus plasma membranes which then induce rapid permeabilization and loss of bacterial metabolic activity (Verhaegh et al., 2020).

Sphingosine is phosphorylated by sphingosine kinases to generate Sphingosine-1-phosphate (Liu et al., 2002). In our study we did not detect sphingosine-1-phosphate, however recently Marfia group linked the decreased serum level of sphingosine-1-phosphate with severe COVID-19 disease (Marfia et al., 2021). Sphingosine-1-phosphate is a pleotropic sphingolipid that acts as a major modulator of vascular and immune system (Törnquist et al., 2021). It displays dose-dependent effect on inflammation; at normal nanomolar physiologic concentration it acts as anti-inflammatory while at higher micromolar concentration it shows pro-inflammatory activity (Fettel et al., 2019; Whetzel et al., 2006). Airway smooth muscles treated with micromolar concentration of sphingosine-1-phosphate induced their proinflammatory effect which was inhibited upon treatment with corticosteroid, dexamethasone (Che et al., 2013). In small dose, Sphingosine-1-phosphate analogue, Fingolimod, showed reduction of inflammatory response and entered a clinical trial for treatment of cytokine storm during COVID-19; however the trial was stopped due to occurrence of lymphopenia adverse reaction (www.clinicaltrial.gov NCT04280588)(Marfia et al., 2021). Further studies are required to assess the potential of sphingolipid modulation towards control of SARS-CoV-2 viral infection as well as cytokine storm.

Resembling sphingosine findings, 5-Aminolevulinic acid metabolite levels were detected in non-severe COVID-19, while this metabolite was not present in control and severe COVID-19 in both saliva and plasma results. This metabolite has antiviral effect and thereby their detection in the less severe infection suggest that it could have played a role in the control of infection severity. Ben-Hur and colleague showed that 5-Aminolevulinic Acid supplementation followed by photodynamic therapy was effective both in-vivo and in-vitro to inactivate intracellular herpes simplex virus (HSV) and human immunodeficiency virus (HIV) (Smetana et al., 1997). More recently, Japanese researchers have shown that pretreatment with this amino acid blocked the SARS-CoV-2 infection in both VeroE6 cells and human colon-derived Caco-2 cells in a time-dependent manner (Sakurai et al., 2021). Mechanistically, 5-ALA supplementation induced accumulation of protoporphyrin IX and heme inside host cells, that interfered with viral replication through targeting the G-quadruplex (G4) structure within the virus nonstructural protein 3 (Nsp3) (Sakurai et al., 2021). 5-Aminolevulinic Acid is bioavailable orally and could potentially be used for SARS-CoV-2 infection prevention (Sakurai et al., 2021).

Beside sphingosine, other lipid mediators such as eicosanoids were previously shown to be upregulated during COVID-19 (Regidor et al., 2021). Eicosanoids were associated with lipid mediator storm during critical SARS-CoV-2 infection as well as post COVID-19 syndrome following mild disease (Archambault et al., 2021; Bohnacker et al., 2022). SARS-CoV-2 infection induced a long-term immune abnormalities in macrophage effector functions and eicosanoid metabolism which persisted up to 3–5 month following mild COVID-19 (Bohnacker et al., 2022). Future investigations are needed to study the role of other bioactive lipids during and after severe viral infections. LC–MS/MS analysis could be used for simultaneous detection of bioactive lipids such as eicosanoids in different patients’ samples of serum, bronchial alveolar lavage fluid (BALF), and sputum (Thakare et al., 2018).

The current study identified the metabolites that are specific to severe and non-severe groups. Like sphingosine, metabolites presented in Fig. 3A, B such as N-Acetylputrescine could play a key role in development or resistance to severe viral infection and thereby further research is needed to understand the mechanism that regulate their abundance. Moreover, many of the dysregulated metabolites are expected to be directly or indirectly associated with clinical manifestation observed in acute phase and long-term sequel of COVID-19 and deserve further investigation. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid (Fig. 7A, B and E, F), and positively correlated with L-Tryptophan and l-Kynurenine (Fig. 7C, D and G, H).

This study was conducted to profile the global metabolic dysregulation during acute COVID-19 disease, and to focus on those metabolites that are specific to non-severe and severe groups. Our study only provides a snapshot of SARS-CoV-2 induced metabolic reprogramming. Future longitudinal studies are required to better characterize the early, acute, and long-term change in COVID-19. Our study was limited by the sample size and larger cohort could be required to confirm the current findings. Although we have used age and gender matched cohort (Supplementary Table 1), metabolite change observed could be cofounded or not by other factors such as diet, disease, and medication. To control for the potential cofounder’s larger sample size will be needed. Another limitation was that deuterium-labeled internal standards were not used prior to sample extraction.

5 Conclusion

In conclusion, SARS-CoV-2 infection induce distinctive metabolic derangement in severe and non-severe COVID-19. Metabolic change could be detected in both salivary and plasma samples, however saliva collected from active side of infection presented with higher intensity of metabolic dysregulation. General COVID-19 metabolites (such as Linoleic acid and Alpha-ketoisovaleric in Fig. 3A, B) could be used for infection detection while metabolites distinguishing severe from non-severe stages (such as sphingosine and kynurenine) could be used as a prognostic marker. Further, metabolites such as l-Kynurenine, sphingosine and 5-aminolevulinic could play active role in modulating immune system inflammatory and antiviral response and could represent an effective therapeutic approach for the treatment of COVID-19 early and late immune dysregulations.