https://www.nature.com/articles/s41598-022-05667-0
SARS-COV-2 (severe acute respiratory syndrome coronavirus 2) is extremely infectious and has triggered a global pandemic. Infection of the lungs and human respiratory tract by this coronavirus leads to fever, myalgia and cough, and in some patients to acute respiratory distress syndrome (ARDS). While most patients experience very mild-to-moderate symptoms, around one in five patients develop pneumonia coupled with severe respiratory distress. These patients require treatment in hospital intensive care units (ICU), where infection can lead to multi-organ dysfunction, failure, and sometimes death. The COVID-19 (coronavirus disease 2019) pandemic has led to urgent and intense investigations of this disease, its causative agent, and its interaction with the human host. However, there are still many difficulties for an accurate SARS-CoV-2 patient’s risk categorization, which are consequences of COVID-19 complexity since coronavirus infection reflects a broad spectrum of patient symptoms, and as a result, diverse pathophysiological pathways are perturbed during the disease course. This complexity has taken to many groups to investigate this exciting topic using metabolomics, given that the circulating metabolome provides a snapshot of the physiological state of the organism1,2.
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Many topics have been addressed regarding COVID-19 disease using metabolomics, for instance, metabolomics has displayed sex-specific metabolic shifts in non-severe COVID-19 patients during recovery process, showing that the major plasma metabolic changes were fatty acids in men and glycerophosphocholines and carbohydrates in women5. Metabolomics has also shown that it is possible to differentiate plasma metabolite profiles of COVID-19 survivors with abnormal pulmonary function from those of healthy donors or subjects with normal pulmonary function. These alterations mainly involved amino acid and glycerophospholipid metabolic pathways, increased levels of triacylglycerols (TG), phosphatidylcholines (PC), prostaglandin E2, arginine, and decreased levels of betain and adenosine6. Since many issues regarding the immune, both innate and adaptive, response remains unclear, they are subject to ongoing multi-omic investigations1, as well as comprehensive meta-analysis of global metabolomics datasets of COVID-192. Metabolomics also showed that more than 100 lipids including glycerophospholipid, sphingolipids, and fatty acids (FA) were downregulated in COVID-19 patient sera, probably because of damage to the liver, which is also reflected in aberrancy in bilirubin and bile acids7. Significant differences were also determined between COVID-19 patients and healthy controls in terms of purine, glutamine, leukotriene D4 (LTD4), and glutathione metabolisms. Decrease levels were determined in R‐S lactoglutathione and glutamine (Q, gln) , and increase levels were detected for hypoxanthine, inosine, and LTD48).
As mentioned above, there are still many difficulties for an adequate categorization of SARS-COV-2 patients through the use of potential metabolic markers of clinical severity identified at the beginning of the COVID-19 disease, reason why many different works have addressed this challenging topic. Thus, using high-throughput omics, the dynamic changes in the metabolome (and proteome) profile of non/severe to severe disease cohorts were studied, and they could be used to predict the disease development: for example, the simultaneous decline in the levels of malic acid and glycerol 3-phosphate in healthy to mild to fatal groups9. On the other hand, the level of guanosine monophosphate was found to be modulated along with carbamoyl phosphate in mild to severe patients, suggesting the role of immune dysfunction and nucleotide metabolism in the progression of non/severe COVID-19 to severe condition9.
Danlos et al. also reported alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care)10; and altered tryptophan (w, trp) metabolism into the kynurenine pathway has been related to inflammation and immunity in critical COVID-19 patients in comparison to mild disease patients10. Increased levels of kynurenine and decreased levels of arginine (R, arg) , sarcosine and LPC were also observed as the top-performing metabolites for identifying COVID-19 positive patients from healthy control subjects11. The role of the tryptophan-nicotinamide pathway, linked to inflammatory signals and microbiota, and the involvement of cytosine were also described as possible markers to discriminate and predict the disease evolution12.
Xiao et al. characterized the globally dysregulated metabolic pathways and cytokine/chemokine levels in COVID-19 patients compared to healthy controls. They identified the escalated correlations between circulating metabolites and cytokines/chemokines from mild to severe patients, and revealed the disturbed metabolic pathways linked to hyper-inflammation in severe COVID-19, demonstrating that arginine (R), tryptophan (W), or purine metabolism modulates the inflammatory cytokine release13.
As can be deduced from above and other works14,16,16, the biological mechanisms involved in SARS-CoV-2 infection are only partially understood. Thus in the current work we have explored the plasma metabolome of non-COVID controls as well as 145 COVID patients at diagnose through reverse phase liquid chromatography coupled to quadrupole-time of flight mass spectrometry (RP/HPLC-qTOF MS/MS) analysis. Moreover, patients were stratified based on their clinical evolution in asymptomatic (not requiring hospitalization), patients with mild disease (defined by a total time in hospital lower than 10 days), patients with severe disease (defined by a total time in hospital over 20 days and/or admission at the ICU) and patients with fatal outcome or deceased. In addition, follow up samples between 2 and 3 months after hospital discharge were also obtained from the hospitalized patients with mild prognosis to investigate the disease sequels in the metabolome and how the recovery is reflected in the altered biological pathways. The final goal of the currents work is to find biomarkers that will increase our understanding about how the COVID-19 illness evolves and will improve our prediction about how a patient could progress based on the metabolites profile of plasma obtained at an early stage of the infection.
The correlation analysis showed different sets of metabolites with similar abundancy among the analyzed groups. In ESI (+), several acylcarnitines (3-hydroxybutyrylcarnitine, hexanoyl-l-carnitine, decanoyl-l-carnitine, octanoyl-l-carnitine, arachidonoyl-l-carnitine, linoleoylcarnitine, acetyl-l-carnitine, lauroylcarnitine, oleoyl-l-carnitine and palmitoyl-l-carnitine), PC/LPC compounds (2-lysophosphatidylcholine, PC (p-16:0/0:0), LPC (o-16:0), LPC (20:2), PC (18:1/16:0), LPC (16:0), LPC (p-18:0), LPC (17:0), PC (18:2e) PC (18:1e) and PC (20:4e)), and amino acids (tryptophan, L-valine, L-isoleucine, L-methionine and L-tyrosine), were grouped together; whereas in ESI (−), the most relevant sets were composed by LPC, FA derivatives or bile acids (glycodeoxycholic acid, taurodeoxycholic acid, glycocholic acid, glycoursodeoxycholic acid and taurocholic acid). The Pearson correlation (r) values and the respective p-values are presented in Supplementary Tables S3 and S4 for ESI (+), and Supplementary Tables S5 and S6 for ESI (−).
The Mann–Whitney U test between the different COVID-19 positive groups and the non-COVID control group showed 8 metabolites altered in the asymptomatic group (5 of them with increased values and 3 with decreased values). The number of significantly altered metabolites increased to 26 in the mild disease group, 14 and 12 with increased and decreased values, respectively. Some of them were already observed as altered in the asymptomatic group, such as S-methyl-3-thioacetaminophen and nicotinamide riboside cation, which values increased more in the mild disease group; and N-methyl-2-pyrrolidone, trimethoprim and L-methionine, which values continued to decrease in the mild disease group. In the severe disease group, the number of significantly altered metabolites rose to 45 (32 with increased and 13 with decreased values); and for the deceased group, the total number of altered metabolites was 35 (23 with increased values and 12 with decreased values). Many of these metabolites were already observed as significant in the previous PLS-DA and ANOVA analyses.
The further analysis of the fold change ratio patterns obtained in the previous comparisons suggested 8 main clusters to be formed (Fig. 2, the MFuzz membership value and cluster composition are shown in Supplementary Tables S7 and S8).
This analysis provides
a general overview of groups of metabolites with similar alteration patterns between the different groups of samples (after normalization by the non-COVID control group) and allows the combination of ESI (+) and ESI (−) data together since the normalization using the non-COVID control group eliminates the bias derived from the use of different ESI ionization modes. It has to be noted that this analysis does not take into account the statistical differences after a non-parametric Mann–Whitney U test between the COVID-19 positive samples and the control group, as it only clusters the metabolites according to their fold change ratio similarity.
Among the identified clusters, cluster 1 represented those metabolites which abundance continuously increased from the asymptomatic to the deceased group, and included 3-hydroxybutyrylcarnitine, glycocholic acid, LPE (22:6), nervonic acid and palmitic acid, among others.
On the other hand, cluster 7 represented those metabolites which abundance continuously decreased from the asymptomatic to the deceased group, and was composed by three PC (PC (16:0/20:4), PC (20:4e) and PC (20:5e)) and tryptophan (W).
Based on the previous PLS-DA and ANOVA results, metabolites of cluster 6 were of special interest because their abundance was highest in the severe disease and the deceased groups. This cluster included 2-lysophosphatidylcholine, alpha-linolenic acid, linoleic acid or L-isoleucine (I, ile) , methyl ester.
Finally, cluster 8 was the most crowded (24 metabolites) and was composed by metabolites which abundance mainly increased in the deceased group. Some of these metabolites were adipoyl-l-carnitine, glycodeoxycholic acid and taurodeoxycholic acid.
In order to provide the chemical classes significantly altered in the different group comparisons, a chemical enrichment analysis using ChemRICH was performed. No chemical classes were significantly altered in the comparison between the asymptomatic and the non-COVID control group;
but the chemical class “carnitines” was increased, and the “unsaturated lysophosphatidylcholines” was altered (some species increased, others decreased) in the mild disease group.
In the case of the severe disease group, “androstenols” were significantly decreased (considering 3 metabolites); xanthines were significantly altered with some species increased, others decreased; and 2-pyridinylmethylsulfinylbenzimidazoles, pyridines and unsaturated fatty acids were significantly increased.
This last chemical class was also increased in the deceased group, based on the abundance of nervonic acid, linoleic acid, alpha-linolenic acid, trans-vaccenic acid and palmitoleic acid.
The whole list of chemical classes and the respective p-values obtained for each comparison is presented in Supplementary Tables S9–S11, and the representations of all the annotated metabolites onto biochemical networks, constructed using chemical and biochemical similarities from MetaMapp, is shown in Supplementary Figs. S4–S7.
Finally, metabolite set enrichment analysis using the significantly altered metabolites in each comparison was performed using MBROLE 2.0 (Table 1). It has to be noted that only 5, 17, 28 and 16 altered metabolites in the asymptomatic, mild disease, severe disease and deceased groups, respectively, could be mapped with valid KEGG IDs (many carnitines and omeprazole derivatives could not be mapped).
Table 1 Significantly enriched KEGG human metabolic pathways (in dark shade) from the analysis of significantly altered metabolites (after Mann–Whitney U test with p-value < 0.05) in asymptomatic, mild disease, severe disease and deceased COVID-19 positive groups as compared to non-COVID control patients.
From: Metabolomics study of COVID-19 patients in four different clinical stages
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