https://www.nature.com/articles/s41598-022-05667-0
Introduction
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.
Results
....
The full list of identified metabolites for each ionization mode
together with the statistical values for the different analyses (ANOVA, U
test and PLS-DA) is shown in Supplementary Tables
S1 and
S2.
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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