Abstract
Cytokine release syndrome (CRS) is a major cause of the multi-organ
injury and fatal outcome induced by SARS-CoV-2 infection in severe
COVID-19 patients. Metabolism can modulate the immune responses against
infectious diseases, yet our understanding remains limited on how host
metabolism correlates with inflammatory responses and affects cytokine
release in COVID-19 patients. Here we perform both metabolomics and
cytokine/chemokine profiling on serum samples from healthy controls,
mild and severe COVID-19 patients, and delineate their global metabolic
and immune response landscape. Correlation analyses show tight
associations between metabolites and proinflammatory
cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a
potential regulatory crosstalk between arginine, tryptophan, purine
metabolism and hyperinflammation. Importantly, we also demonstrate that
targeting metabolism markedly modulates the proinflammatory cytokines
release by peripheral blood mononuclear cells isolated from
SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting
metabolic alterations may be a potential strategy for treating fatal
CRS in COVID-19.
Subject terms: Metabolomics, Cytokines, SARS-CoV-2, Prognostic markers
__________________________________________________________________
Metabolism changes can modulate immune responses in many contexts, and
vice versa. Here the authors associate metabolomic, as well as cytokine
and chemokine, data from stratified COVID-19 patients to find that
arginine, tryptophan and purine metabolic pathways correlate with
hyperproliferation, thus hinting at potential therapeutic targets for
severe COVID-19 patients.
Introduction
Coronavirus disease 2019 (COVID-19), caused by highly infectious severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently
become global pandemic^[52]1, highlighting an urgent need for effective
therapeutic strategies. SARS-CoV-2 infection triggers immune response
contributing to both virus clearance and acute respiratory distress
syndrome (ARDS) development^[53]2,[54]3. The severe COVID-19 patients
often experience cytokine-release syndrome (CRS) referred to as
“cytokine storm”, which is characterized by excessive proinflammatory
cytokine release and leads to widespread damage, multiple organ
failure, and fatal clinical outcomes^[55]4–[56]8. Emerging clinical
trials reveal that immunomodulatory drugs, such as the IL-6
receptor-blocking antibody tocilizumab and JAK1/2 inhibitor
ruxolitinib^[57]9–[58]11, can dampen the hyperactive immune response,
suggesting cytokine-release blockade as a promising treatment option.
Hence, identifying the key factors driving CRS induced by SARS-CoV-2
infection is of utmost importance to provide fresh insights for novel
immunomodulatory therapies.
The association between metabolism and immunity has been reported since
1960s^[59]12. In recent years, many immunometabolism studies have
further illustrated the interplay between host metabolism and immune
responses^[60]13. For example, the immunoregulatory metabolite
succinate, which is linked to the mitochondrial metabolism, has been
recognized as the innate immune signal that enhances the IL-1β
production during inflammation^[61]14. Reciprocally, infection-induced
proinflammatory cytokines, such as IL-6, can modulate glucose and lipid
metabolism, indicating the guiding role of cytokines in host metabolism
reprogramming^[62]15,[63]16. Interestingly, the metabolite itaconate,
identified as an anti-inflammatory regulator of a set of cytokines
(e.g., IL-6 and IL-12), is shown to be induced by type I interferons
and conversely to limit the type I interferon responses, indicating a
negative-feedback loop that involves itaconate and
cytokines^[64]17–[65]19. Notably, metabolic pathways have been reported
to regulate the innate and adaptive host responses to the infection of
various viruses, such as human immunodeficiency virus (HIV)^[66]20,
yellow fever virus^[67]21, and severe fever with thrombocytopenia
syndrome virus (SFTSV)^[68]22. In particular, glucose metabolism plays
an important role in regulating influenza A virus-induced cytokine
storm^[69]23. Since the COVID-19 pandemic, a few studies exploring the
immunological and metabolic signatures in the patients have been
reported^[70]5,[71]7,[72]24–[73]26. The circulating serum metabolites
and inflammatory cytokines have been shown to be tightly correlated in
COVID-19 patients^[74]26–[75]28. A recent study also reveals that
elevated glucose levels in COVID-19 patients promote SARS-CoV-2
replication and cytokine production in monocytes^[76]29. However, our
understanding on the host metabolism–immune response correlation
landscape and, particularly, the potential modulatory role of the
perturbed metabolism in inflammatory responses upon SARS-CoV-2
infection is still limited.
In this study, we characterize the globally dysregulated metabolic
pathways and cytokine/chemokine levels in COVID-19 patients compared to
healthy controls. We identify the escalated correlations between
circulating metabolites and cytokines/chemokines from mild to severe
patients, and further reveal the disturbed metabolic pathways linked to
hyperinflammation in severe COVID-19. We also demonstrate that
targeting arginine, tryptophan, or purine metabolism by metabolite
supplementation or pharmacological inhibition modulates the ex vivo
inflammatory cytokine release by isolated peripheral blood mononuclear
cells (PBMCs) derived from SARS-CoV-2-infected rhesus macaques.
Overall, this study provides novel insights into the immunometabolic
interplay in COVID-19 patients and suggests that metabolic
interventions may be potentially exploited as rational strategies to
suppress SARS-CoV-2-induced CRS.
Results
Study design and patient cohort
To understand how the host metabolism correlates with CRS in COVID-19
patients, we performed both metabolomics and cytokine profiling on
serum samples from the same symptomatic COVID-19 patient cohort. The
cohort comprises 17 healthy controls, 14 mild, and 23 severe COVID-19
patients. The serum samples were taken from the patients that were
about 1–18 days post admission to the hospital. An additional
independent cohort of 7 mild patients with longitudinal follow-up
time-points that occurred 4–36 days post symptom onset was also
included. We collected the clinical information and conducted
immunological and biochemical laboratory tests as well as metabolomics
and cytokine profiling on the serum samples from these patients. To
ascertain the association between metabolism and cytokine release, we
performed correlation analysis between the levels of metabolites and
cytokines, and further validated the functional effects of
the metabolic intervention on cytokine release ex vivo by PBMCs derived
from SARS-CoV-2-infected rhesus macaques (Fig. [77]1a). The basic
clinical features of the cohort were detailed in Table [78]S1.
Significant reduction in lymphocyte count, and marked increase in
C-reactive protein (CRP), alanine amino-transferase (ALT), aspartate
aminotransferase (AST), direct bilirubin (DBIL), and glucose were
exhibited in severe COVID-19 patients, which were consistent with
previous findings^[79]1,[80]30 (Supplementary Fig. [81]1 and
Supplementary Data [82]1).
Fig. 1. Study design and metabolic profiling in serum samples from mild and
severe COVID-19 patients.
[83]Fig. 1
[84]Open in a new tab
a Overview of cohort (including 21 mild patients, 23 severe COVID-19
patients, and 17 healthy controls) and the study design. b t-SNE plot
distributed healthy controls (n = 17), mild patients (n = 14), and
severe patients (n = 23) according to serum metabolites detected from
targeted and untargeted metabolomics. c, d Volcano plots comparing
serum metabolites of mild (c) or severe (d) patients with healthy
controls. Significantly altered metabolites are highlighted in red
(increased) and blue (decreased). The top 5 metabolites that
significantly increased or decreased are marked with text. Two-sided
Mann-Whitney U test followed by Benjamini-Hochberg (BH) multiple
comparison test with FDR < 0.05 and fold change >1.25 or <0.8. e KEGG
metabolic pathways enriched by significantly changed serum metabolites
in mild (c) and severe (d) patients. One-sided Fisher’s exact test
followed by BH multiple comparison test with FDR < 0.1. f Schematic
depicting the key disturbed metabolic pathways in response to
SARS-CoV-2 infection. Gray nodes represent metabolites that were not
tested. Metabolite alterations are represented by color intensity, and
borders are color-coded by statistical significance.
Metabolic profiling of serum samples from COVID-19 patients
To determine the metabolic perturbations associated with SARS-CoV-2
infection, we profiled the serum samples from 17 healthy controls and
44 COVID-19 patients by using both targeted and untargeted metabolomics
analyses (Supplementary Fig. [85]2a and Supplementary Data [86]2 and
[87]3). Targeted metabolomics analysis was performed on an
ultra-high-performance liquid chromatography–triple quadruple mass
spectrometry system (UHPLC-MS/MS). A total of 258 metabolites were
monitored and 134 metabolites were reliably detected by the targeted
metabolomics method. Untargeted metabolomics analysis was performed on
an UHPLC quadruple TOF high-resolution MS/MS system. After data
preprocessing and metabolite identification, 155 metabolites were
identified from 6072 metabolite features extracted from the raw data
acquired in positive- and negative-ionization modes. A total of 36
metabolites were identified from both targeted and untargeted
metabolomics and showed consistent alterations along with the
increasing disease severity. By integrating the targeted and untargeted
metabolomics datasets, we identified a total of 253 metabolites,
including 134 metabolites (with the 36 overlap metabolites included)
from targeted metabolic profiling and 119 metabolites (with the 36
overlap metabolites excluded) from untargeted metabolic profiling, and
observed distinct metabolic profiles among healthy controls (n = 17),
mild (n = 14), and severe (n = 23) patients (Fig. [88]1b and
Supplementary Fig. [89]2b–d). To determine whether SARS-CoV-2 infection
shares the same metabolic disturbances with other respiratory diseases,
we collected serum samples from 20 non-COVID-19 acute upper respiratory
tract infection patients followed by a targeted metabolomics analysis
(Supplementary Data [90]2 and [91]3). A clear segregation among the
three groups (i.e., healthy controls, COVID-19 patients, non-COVID-19
acute upper respiratory tract infection patients) was observed,
suggesting the uniquely disturbed metabolic profiles in COVID-19
patients (Supplementary Fig. [92]2e). Volcano plots highlighted 89
differential metabolites (FDR < 0.05, fold change >1.25 or <0.8, 50 up
and 39 down) between healthy controls (n = 17) and mild patients
(n = 14), and 88 differential metabolites (FDR < 0.05, fold change
>1.25 or <0.8, 37 up and 51 down) between healthy controls (n = 17) and
severe patients (n = 23), reflecting markedly dysregulated metabolic
status of COVID-19 (Fig. [93]1c, d).
To characterize the dysregulated metabolic pathways in COVID-19
patients compared to healthy controls, we performed pathway enrichment
analyses and observed that primary bile acid biosynthesis, amino acid
metabolism, and nucleic acid metabolism were significantly perturbated
in both mild and severe patients (Fig. [94]1e). In contrast, several
metabolic pathways, such as nicotinate and nicotinamide metabolism,
tryptophan metabolism, and citrate cycle (TCA cycle) were altered only
in severe patients (Fig. [95]1e). Interestingly, metabolites that
displayed constant upward or downward trend along disease severity were
mostly associated with purine metabolism, nicotinate and nicotinamide
metabolism, tryptophan metabolism, TCA cycle, and arginine metabolism
(Supplementary Fig. [96]2f). Intermediates in arginine metabolism have
been regarded as regulators of lymphocyte suppression during immune
response^[97]31. Our previous work has demonstrated that arginine
deficiency is associated with T cell dysregulation in SFTSV
infection^[98]22. Here we observed that glutamate and aspartic acid
were upregulated, while glutamine and citrulline were downregulated
along disease severity (Supplementary Fig. [99]2g). Succinate, an
intermediate of TCA cycle that has been proved to be an innate immune
signaling molecule during inflammation in the macrophage^[100]14,
displayed a successive increase along disease severity (Supplementary
Fig. [101]2h). Moreover, the increase in kynurenine and decline in
tryptophan and serotonin suggested the enhanced activity of
the rate-limiting enzyme indole 2,3-dioxygenase 1 (IDO1) (Supplementary
Fig. [102]2i), which is reported to be a modulator of
inflammation^[103]32,[104]33. In addition, NAD^+ metabolism is found to
be altered by host–pathogen interactions during innate and adaptive
immune responses^[105]34, including SARS-COV-2 infection^[106]35. We
observed that the level of nicotinamide mononucleotide (NMN), a key
metabolite in the NAD^+ metabolism, decreased as the severity of
COVID-19 increases (Supplementary Fig. [107]2i). Of note, these core
disturbed metabolites in COVID-19 patients showed no significant
changes in non-COVID-19 acute upper respiratory tract infection
patients when compared to healthy controls (Supplementary
Fig. [108]2f–i). We also observed that several lysophosphatidylcholines
(LPCs) were increased in COVID-19 patients (Supplementary
Fig. [109]2j). Taken together, our data delineate the global metabolic
alterations along the increase in the severity of COVID-19
(Fig. [110]1f).
Cytokines release correlates with altered metabolism in COVID-19 patients
Extensive studies proved that metabolism and immune response are
inextricably linked^[111]13,[112]20,[113]36. To clarify the correlation
between metabolism and inflammatory responses after SARS-CoV-2
infection, we assessed the cytokine and chemokine levels in COVID-19
patients and healthy controls (Supplementary Data [114]4). Consistent
with previous study reports^[115]5–[116]8,[117]37, COVID-19 patients
presented marked elevation (FDR < 0.05) in 28 cytokines and chemokines
compared to healthy controls (Supplementary Fig. [118]3a).
Particularly, the CRS-related cytokines, including IL-6, IL-1β, IL-10,
IL-18, and IFN-γ, displayed progressive increase along disease severity
from healthy controls to mild and severe patients (Supplementary
Fig. [119]3b), highlighting the broad and strong inflammatory responses
in severe COVID-19 patients.
Next, using linear regression after adjusting for age and gender (Eq.
(1), Methods), we performed independent correlation analysis between
cytokines and metabolites in mild and severe patients, respectively.
Cytokine–metabolite correlations with FDR < 0.1 were considered
significant. Systematic pathway analysis revealed that the dysregulated
metabolic pathways highly correlated with several cytokines (e.g.,
IL-15, IL-10, and IL-2RA) in mild patients (Supplementary
Fig. [120]4a). However, a tight correlation between dysregulated
metabolic pathways and important inflammatory cytokines (e.g., IL-6,
IP-10, IL-8, M-CSF, and IL-1α) in severe patients was observed
(Fig. [121]2a). Of note, the inflammatory cytokines, including IL-6,
IP-10, and M-CSF, showed increasing correlations with metabolites from
mild to severe patients (Supplementary Fig. [122]4b–d). Specifically, 8
and 20 correlations were observed between IL-6 and metabolites in mild
and severe patients, respectively; 10 and 33 correlations were observed
between IP-10 and metabolites in mild and severe patients,
respectively; 6 and 29 correlations were observed between M-CSF and
metabolites in mild and severe patients, respectively (Supplementary
Fig. [123]4b–d). In addition, the general correlations between
metabolites and cytokines were intensified in severe patients compared
to those in mild patients (Supplementary Fig. [124]4e, f). Next, we
focused on the correlations between metabolites and inflammatory
cytokines linked to CRS in severe patients. Interestingly, 14
inflammatory cytokines, such as IL-6, M-CSF, IP-10, GM-CSF, IL-18,
IL-1α, and IL-1β, strongly correlated with metabolites involved in
arginine metabolism, tryptophan and NAD^+ metabolism, purine and
pyrimidine metabolism, cysteine and methionine metabolism, TCA cycle,
and primary bile acid metabolism in severe patients (Fig. [125]2b–e).
These observations provide evidence that host metabolic reprogramming
broadly and highly correlates with inflammatory cytokines linked to
CRS. Notably, several key metabolites involved in arginine metabolism
(e.g., arginine, glutamine, and aspartic acid, citrulline, urea, and
proline) displayed strong correlation with CRS-related cytokines (e.g.,
IL-6, IL-1β, M-CSF, IL-12 p70, IFN-α2) in severe patients
(Fig. [126]2f). In addition, purine metabolism (e.g., xanthosine,
xanthine, guanosine, adenine, GMP, adenosine, and guanine) exhibited
strong correlation with CRS-related cytokines (e.g., IL-6, M-CSF,
MCP-3, GM-CSF, IL-1α, and IL-1β) (Fig. [127]2g). Metabolites involved
in tryptophan and NAD^+ metabolism, such as kynurenine and NMN, showed
positive correlation with MCP-3, M-CSF, and IL-6. In contrast,
nicotinic acid had negative correlation with IL-6 and IP-10
(Fig. [128]2h). Collectively, the correlations between metabolites and
cytokines in severe patients suggest that the disturbances in these
metabolic pathways are inextricably linked to the hyperinflammation in
COVID-19.
Fig. 2. Metabolite–cytokine correlation in serum samples from COVID-19
patients.
[129]Fig. 2
[130]Open in a new tab
a Pathway enrichment analysis of metabolites significantly associated
with the indicated cytokines in severe patients (n = 23). Two-sided t
test followed by Benjamini-Hochberg (BH) multiple comparison test with
FDR < 0.1. “abs. T statistics” is the mean absolute T statistics of
significant metabolites in the pathway and is represented by color
intensity. The dot size represents pathway significance (one-sided
Fisher’s exact test followed by BH multiple comparison test). b–e
Correlation networks of key CRS-related cytokines and metabolites in
severe patients. Nodes and edges are color-coded by molecule types and
metabolic pathways, and association directions, respectively. Networks
were clustered by fast greedy modularity optimization algorithm. f–h
Chord diagrams depicting the significant correlations of cytokines with
metabolites involved in arginine metabolism (f), purine metabolism (g),
tryptophan and NAD^+ metabolism (h), respectively, in severe patients.
Chords are color-coded by association directions consistent with (b–e).
Longitudinal metabolite–cytokine correlation in follow-up mild COVID-19
patients
To further identify the dynamic correlations between metabolites and
cytokines at longitudinal stages after SARS-CoV-2 infection, we
performed c-means clustering analysis on both metabolite and cytokine
data from the hospitalized mild patients at 4–36 days after symptom
onset (Supplementary Data [131]2–[132]4). We identified four main
clusters of longitudinal trajectories that characterized distinct
metabolic and immune signatures in these patients with acute antibody
responses to SARS-CoV-2 infection^[133]38 (Supplementary Fig. [134]5a,
b). Molecules enriched in cluster 1 increased at symptoms onset but
gradually deceased during hospitalization; molecules in cluster 2
exhibited a sharp decrease at symptoms onset and sustained stable
levels in later time-points; however, molecules in cluster 3 sustained
steady levels but presented a delayed elevation in the very late
events; cluster 4 contained molecules that elevated gradually and
declined in late phases (Fig. [135]3a).
Fig. 3. Longitudinal trajectories and metabolite–cytokine correlation in mild
COVID-19 patients.
[136]Fig. 3
[137]Open in a new tab
a Longitudinal trajectory clustering of significantly changed serum
metabolites, cytokines in follow-up patients (n = 7) with mild
COVID-19. Metabolite and cytokine abundance in healthy controls were
used as base line. Black lines represent the average trajectory for
each cluster. b Heatmap comparison of cytokines at distinct time-points
in follow-up patients (n = 7). Color intensity represents the Log2 fold
change of mean cytokine abundance in each interval relative to healthy
controls. c Pathway enrichment analysis of metabolites in each cluster.
One-sided Fisher’s exact test followed by Benjamini-Hochberg (BH)
multiple comparison test with FDR < 0.1. d Relative abundance
trajectories of metabolites in follow-up patients (n = 7). Blue solid
lines pass through the mean of each measurement at the specific time
interval, and dotted lines represent the mean of measurements in
healthy controls (n = 17). Generalized additive model (GAM) regression
lines are represented by the black solid lines, with 95% confidence
intervals for the regression line donated by gray filled areas. P value
was assessed by one-way ANOVA. Data are presented as mean ± SEM. with
individual data points shown. e Chord diagrams of significant
associations between metabolites and core CRS-related cytokines in
cluster 1 (left), cluster 2 (middle), and clusters 3 and 4 (right),
respectively. Two-sided t test followed by BH multiple comparison test.
Three CRS-related cytokines including IL-6, IP-10, and M-CSF belonged
to cluster 1 (Fig. [138]3b). Notably, the levels of IL-6, which is
highly correlative to CRS^[139]39, slightly decreased during the first
2 weeks of symptom onset and remained at low level in later phases
(Fig. [140]3b and Supplementary Fig. [141]5c). The IFN-γ-inducible
protein, IP-10/CXCL-10, is considered as a member of CXC chemokine
family with proinflammatory and severity-related properties in
COVID-19^[142]8. The levels of IP-10 showed a sharp decline in the
initial phase of treatment and sustained at relatively low levels
during hospitalization (Fig. [143]3b and Supplementary Fig. [144]5c).
Consistently, the myeloid cytokine M-CSF also decreased rapidly during
the initial treatment phase and sustained a stable level afterwards
(Fig. [145]3b and Supplementary Fig. [146]5c). In addition,
proinflammatory cytokines in cluster 2, including G-CSF, IL-8, MIP-1α,
and MCP-3, also showed decreased levels in mild patients compared to
healthy controls and remained at steady levels during hospitalization
(Fig. [147]3b and Supplementary Fig. [148]5c). However, proinflammatory
cytokines in cluster 3 (e.g., IL-17A and TNF-β) and cluster 4 (e.g.,
IL-1α, IL-1β, IL-18, and MCP-1) showed the upward trend along the
follow-up time-points (Fig. [149]3b and Supplementary Fig. [150]5c).
These observations indicate that alleviation of inflammatory immune
responses may be accompanied with clinical recovery in hospitalized
mild patients.
Interestingly, several cytokines enriched in cluster 4 were associated
with suppression of the inflammatory responses and viral replication,
such as IL-10 and IFN-α2 (Fig. [151]3b and Supplementary Fig. [152]5c).
IL-10, which reportedly plays a role in antagonizing inflammatory cell
populations and suppressing immune hyperactivity^[153]40, continued to
elevate over time and was maintained at high levels (Fig. [154]3b and
Supplementary Fig. [155]5c). Also, IFN-α2 is reported to play a crucial
role in combating infection through inhibiting viral replication and
preventing viral entry into neighboring cells, thus used for treating
several viral infections, including hepatitis B and C^[156]41. We
observed the steadily elevated and sustained levels of IFN-α2 over
hospitalization (Fig. [157]3b and Supplementary Fig. [158]5c). The
increased levels of IL-10 and IFN-α2 may reflect the presence of a
negative-feedback loop to control the inflammatory responses and virus
infection. These data suggest that a protective immune response may
occur along with downward trend of proinflammatory cytokines and
clinical recovery in hospitalized mild patients^[159]42.
We next characterized metabolites that were enriched in four clusters
and specific metabolite–cytokine correlations (Eq. (1), Methods).
Interestingly, metabolites associated with arginine metabolism were
enriched in clusters 2 and 3 (Fig. [160]3c). We observed an upward
trend of arginine, ornithine, glutamate, and proline, whereas a
decrease in citrulline (Fig. [161]3d and Supplementary Fig. [162]6a,
b). Arginine metabolism reportedly played a crucial role in the
regulation of immune responses^[163]22,[164]31. Correlation analysis
showed that intermediates in arginine metabolism highly correlated with
proinflammatory cytokines including IL-6, M-CSF, and MIP-1β
(Fig. [165]3e). Dysregulated tryptophan metabolism and NAD^+ metabolism
were evident by a marked alteration in associated metabolites, which
exhibited four trajectories (Fig. [166]3c). The increased metabolites
(i.e., tryptophan, indole) and decreased metabolites (i.e., kynurenine,
kynurenic acid) reflected the attenuation of the hyperactivation of
tryptophan-kynurenine pathway (Fig. [167]3d and Supplementary
Fig. [168]6a, b), which plays an important role in modulating the
inflammation^[169]32. Moreover, kynurenine positively correlated with
proinflammatory cytokines including IP-10, MCP-3, and M-CSF, whereas
tryptophan negatively correlated with MIP-1α, suggesting the regulatory
role of tryptophan metabolism in inflammatory responses (Fig. [170]3e).
Additionally, a large proportion of intermediates in purine metabolism
displayed the upward trend, such as inosine and adenine (Fig. [171]3d
and Supplementary Fig. [172]6a, b), and showed the negative correlation
with most proinflammatory cytokines (Fig. [173]3e). Overall, our data
identify the longitudinal metabolite–cytokine correlation dynamics
along with clinical recovery of hospitalized mild patients.
Effects of modulating metabolism on cytokine release by PBMCs ex vivo
Our data presented above delineated the strong correlation between
cytokines and metabolites, we therefore asked whether intervening
arginine metabolism, tryptophan metabolism, and purine metabolism could
regulate cytokine induction. To this end, we first experimentally
infected a rhesus macaque (female, 5-years old) with
SARS-CoV-2^[174]43. Although no obvious clinical signs were observed
during the infection course, the SARS-CoV-2-infected rhesus macaque
indeed showed a slight decrease in body weight from 1 to 8 days post
infection (d.p.i.) and 8% weight loss at 8 d.p.i. (Supplementary
Fig. [175]7a, b). The viral RNA load in nasal and throat swab was
detectable at 7 d.p.i. (Supplementary Fig. [176]7c). We also found the
local inflammatory infiltration with thickening of the alveolar in lung
tissues of the SARS-CoV-2-infected rhesus macaque (Supplementary
Fig. [177]7d). In addition, some proinflammatory cytokines, including
IL-6, MCP-1, IL-10, G-CSF, IL-12 (p40), MIP-1β, TGF-α, and IL-1α,
showed obvious increase at 7 d.p.i. (Supplementary Fig. [178]7e). These
results were consistent with previous studies showing rhesus macaques
can be infected with SARS-CoV-2 and exhibit rapid virus replication and
inflammatory response^[179]44–[180]47. We then isolated PBMCs from the
SARS-CoV-2-infected and mock-infected rhesus macaques at 7 d.p.i., and
measured the cytokine level after treatment with metabolites or
compounds interfering with these key metabolic pathways, by optimized
concentrations (Fig. [181]4a and Supplementary Fig. [182]8).
Fig. 4. Targeting metabolism modulates cytokine release in PBMCs ex vivo
model.
[183]Fig. 4
[184]Open in a new tab
a Schematic representation of the experimental workflow. PBMCs,
isolated from peripheral blood of the mock-infected and
SARS-CoV-2-infected rhesus macaques, were seeded in 96-well plates with
vehicle or different drugs dissolved in medium; 24 h post-seeding,
cytokine abundance in cell culture was quantified. b–d Metabolism
diagrams and level of indicated cytokines and chemokines measured 24 h
after supplementation of 1.25 mM arginine (b), 0.1 mM IDO1 inhibitor
Epacadostat (c), and 0.1 mM inosine monophosphate dehydrogenase (IMPDH)
inhibitor mycophenolic acid (MPA, d) in PBMCs (n = 3). Data are
presented as mean ± SEM. with individual data points shown. One-way
ANOVA followed by Benjamini-Hochberg (BH) multiple comparison test.
Interestingly, we observed that supplementation of arginine markedly
inhibited the SARS-CoV-2-induced proinflammatory cytokine release by
PBMCs, most of which are linked to CRS including IL-1α, IL-1β, IL-2,
IL-6, TNF-α, GM-CSF, G-CSF, and MIP-1α (Fig. [185]4b, Supplementary
Fig. [186]9, and Supplementary Data [187]4). Notably, the elevated
level of IL-10, which is responsible for the inhibition of
proinflammatory cytokine release from macrophage and dendritic cell
(DC) populations^[188]40, was also suppressed (Fig. [189]4b). However,
arginine supplementation did not alter the cytokine release
(Supplementary Fig. [190]10 and Supplementary Data [191]4) or metabolic
profiles including arginine-related metabolites (Supplementary
Fig. [192]11 and Supplementary Data [193]3) in PBMCs isolated from
healthy donors. These observations suggest that serum arginine
metabolism may play an ameliorative role in the SARS-CoV-2-induced
hyperinflammation, which, thus, could be exploited as a potential
therapeutic target for CRS in COVID-19.
The conversion of tryptophan into kynurenine in immune cells is finely
regulated by the enzyme IDO1, which is reportedly involved in
regulating hyperinflammatory responses^[194]33. The elevated ratio
between circulating kynurenine and tryptophan (Kyn/Trp) in patient’s
serum described above suggested an increased activity of IDO1. Addition
of Epacadostat, an IDO1 inhibitor, suppressed the SARS-CoV-2-induced
proinflammatory cytokine release including IL-1α, IL-1β, IL-6, TNF-α,
GM-CSF, G-CSF, IL-17A, and MIP-1α, which confirmed an essential role of
tryptophan metabolism in exaggerated cytokine release upon SARS-CoV-2
infection (Fig. [195]4c, Supplementary Fig. [196]12, and Supplementary
Data [197]4). Notably, treatment with Epacadostat also significantly
suppressed the baseline cytokine release, such as IL-6, IL-1α, and
IL-1β, in PBMCs isolated from healthy donors (Supplementary
Fig. [198]13 and Supplementary Data [199]4). In addition, direct
inhibition of purine metabolism with mycophenolic acid (MPA), which
blocks the rate-limiting enzyme inosine monophosphate dehydrogenase
(IMPDH) in de novo synthesis of guanosine nucleotides, significantly
reduced the levels of IL-10, IFN-γ, IL-15, IL-12 p40, IL-17A, and TNF-α
induced by SARS-CoV-2 infection. However, a profound increase in
proinflammatory cytokines of IL-6, GM-CSF, IL-1α, and IL-1β was also
observed, which suggests the exacerbated hyperinflammation upon
interfering with purine metabolism (Fig. [200]4d, Supplementary
Fig. [201]14, and Supplementary Data [202]4). Additionally, treatment
with MPA also promoted the baseline cytokine release, such as IL-6,
IL-1α, and IL-1β, in PBMCs isolated from healthy donors (Supplementary
Fig. [203]15 and Supplementary Data [204]4).
Given the effects of metabolic manipulation on cytokine release, we
next wondered whether administration of exogenous cytokines could
reciprocally cause the metabolic alterations. To this end, we treated
the PBMCs from healthy donors with a cytokine cocktail (i.e., IL-6,
IL-1α, IL-1β, IFN-γ, and TNF-α) for 24 h and collected the cell pellets
and culture media for subsequent metabolomics analyses. Our data showed
that treatment with exogenous cytokine mixture did not significantly
alter the metabolic profile of PBMCs (Supplementary Fig. [205]16 and
Supplementary Data [206]3). However, cytokine mixture indeed induced a
markedly increased release of kynurenine and decreased release of
tryptophan to the PBMCs culture media. Several metabolites linked to
arginine metabolism, including arginine and glutamate, also showed
significantly elevated release from PBMCs (Supplementary Fig. [207]17
and Supplementary Data [208]3). These alterations in metabolite levels
were consistent with metabolic changes in the serum of COVID-19
patients, suggesting that these metabolic reprogramming may arise, at
least in part, from SARS-CoV-2 infection-induced excessive inflammatory
cytokines.
Taken together, these data indicate a potential regulatory crosstalk
between metabolites and cytokines targeting dysregulated host
metabolism may serve as a viable approach to suppress
SARS-CoV-2-induced inflammatory cytokine release.
Discussion
CRS reportedly contributes to vascular damage, immunopathology, and
adverse clinical outcomes in COVID-19 patients^[209]2,[210]4,[211]7.
Hence, strategies to constrain the proinflammatory cytokine release are
emerging as potential therapies for COVID-19^[212]11,[213]48.
Increasing studies have trialed strategies, including monoclonal
antibodies targeting inflammatory cytokines or small-molecule
inhibiting the upstream or downstream regulatory pathways, to dampen
the inflammatory responses^[214]11,[215]48. However, better
understanding of the driving causes of cytokine storm and identifying
potential multi-cytokine blockers are still of urgent need. Considering
the previously reported high correlation between metabolism and immune
response^[216]20,[217]36,[218]49,[219]50, and the key role of
metabolism in regulating cytokine release upon viral
infection^[220]14,[221]29, we speculated that the metabolism and CRS
may correlate well and intervening the dysregulated host metabolism may
modulate the SARS-CoV-2-induced CRS. We thus characterized the
metabolic and immune profiling in the same COVID-19 patient cohort, and
our network correlation analysis between circulating metabolite and
cytokine levels and the functional validation experiments by PBMCs ex
vivo revealed the potential regulatory role of arginine metabolism,
tryptophan metabolism, and purine metabolism in proinflammatory
responses.
Our metabolomics data revealed alterations in circulating metabolite
levels in patient’s serum samples and identified dysregulated metabolic
pathways upon SARS-CoV-2 infection. Consistent with recent
studies^[222]24–[223]26, the metabolites that are associated with
arginine metabolism, tryptophan metabolism, TCA cycle, as well as
purine and pyrimidine metabolism changed markedly. In our study, we
found that metabolites involved in TCA cycle, including citrate,
isocitrate, oxalosuccinate, and malate were decreased in COVID-19
patients, which was in line with a recent study demonstrating that
metabolites constituting the TCA cycle are generally reduced^[224]25,
potentially suggesting the reduced energy production upon SARS-CoV-2
infection. Importantly, we found succinate was increased, which may
enhance the cytokine production through its immunoregulation role as an
inflammatory signal^[225]14. It has also been shown that succinate
oxidation is crucial for SARS-CoV-2 replication^[226]29. The
significant reduction in several amino acids, such as tryptophan,
glutamine, citrulline, and urea observed in our study, was highly
consistent with other studies^[227]24–[228]26,[229]51. Particularly,
the decrease in tryptophan and increase in kynurenine reflecting the
hyperactivation of rate-limiting enzyme IDO1 showed the consistency
with the independent cohorts from other studies^[230]26,[231]27.
Notably, while we demonstrated that the metabolic reprogramming induced
by SARS-CoV-2 infection was radically different from the non-COVID-19
acute upper respiratory tract infection patients, we could not exclude
the possibility that other types of pneumonia and/or coronaviruses may
result in similar metabolic disturbances as with COVID-19.
The cytokine profiling of COVID-19 patients in our study provided
further evidence that the CRS-associated cytokines, such as IL-6,
IL-1β, IL-10, IL-18, and IFN-γ, were dramatically elevated in severe
patients. Conversely, the increased inflammatory responses experienced
progressive reduction accompanied with clinical recovery in mild
COVID-19 patients during hospitalization. These findings highlight the
need for novel therapies to block multiple cytokines linked to CRS.
Emerging evidence suggests the key role of metabolic regulation in
cytokine release^[232]20,[233]22,[234]31. For example, choline uptake
and metabolism modulate the IL-1β and IL-18 production in stimulated
macrophages^[235]52. α-ketoglutarate-supplemented diet reportedly
induces IL-10 production, thus leading to suppression of chronic
inflammation and extension of life span^[236]53. Particularly, elevated
glucose levels in COVID-19 patients promote SARS-CoV-2 replication and
cytokine production in monocytes^[237]29. A very recent study suggests
that the high kynurenic acid-to-kynurenine ratio is linked to immune
responses and clinical outcomes in male COVID-19 patients^[238]27. In
addition, Vitamin D deficiency associates with the uncontrolled
cytokine production and disease severity of COVID-19^[239]54,[240]55,
emphasizing the need of Vitamin D supplementation for COVID-19
treatment. These findings indicate the possibility that modulating
dysregulated metabolism may serve as a potential therapeutic approach
for controlling cytokine release.
Interestingly, the correlation network analysis in our study revealed
that metabolite–cytokine correlations in severe patients were
intensified when compared to those in mild patients. In particular,
circulating inflammatory cytokine levels, such as IL-6, IP-10, and
M-CSF, were highly correlated with metabolites constituting arginine
metabolism, tryptophan metabolism, and nucleic acid metabolism in
severe patients. Moreover, our time-series clustering analysis in mild
patients identified four distinct clusters of longitudinal trajectories
delineating the crosstalk between metabolism and inflammatory response.
These results suggest that perturbation of metabolic pathways may
partially contribute to the consequential CRS in COVID-19. It has been
shown that the serum metabolites involved in tryptophan and kynurenine
metabolism correlate with IL-6 in COVID-19 patients^[241]26. Recent
studies further identify the important role of
IDO1-kynurenine/kynurenic acid-arylhydrocarbon receptors (AhRs)
signaling in inflammation and multiple organ injuries in SARS-CoV-2
infection^[242]27,[243]56, which is consistent with our findings that
the increased ratio between circulating kynurenine and tryptophan was
positively correlated with proinflammatory cytokines. In addition, our
functional validation study also showed that manipulation of tryptophan
metabolism by IDO1 inhibitor led to marked decline in proinflammatory
cytokines, indicating its therapeutic potential in controlling CRS in
the SARS-CoV-2 infection. Arginine is a conditionally essential amino
acid for adult mammals and is involved in immune dysfunctions during
viral infection^[244]22,[245]31. Intriguingly, we observed that
circulating levels of arginine had a significant positive correlation
with CRS-related proinflammatory cytokines. Given that arginine and its
downstream metabolites (e.g., ornithine and citrulline) are known to be
essential for T cell activation, and thus regulate innate and adaptive
immunity^[246]31,[247]57,[248]58, we speculated that arginine
metabolism upon SARS-CoV-2 infection may involve a negative-feedback
loop to restrict inflammation. As expected, supplementation of arginine
markedly inhibited the elevation in proinflammatory cytokines from
PBMCs upon SARS-CoV-2 infection, confirming the anti-inflammatory
effect of arginine. We also showed that inhibition of purine metabolism
exacerbated inflammatory response. However, the inhibition of
pyrimidine biosynthesis pathway reportedly arrests SARS-CoV-2
replication and suppresses inflammatory cytokine release^[249]59. These
results suggest the importance of the balance between purine and
pyrimidine metabolism in viral replication and immune response.
Notably, treatment with exogenous cytokine cocktail contributed to
enhanced release of arginine and kynurenine, and reduced release of
tryptophan to culture medium by PBMCs derived from healthy donors,
which could be an explanation for the metabolic alterations in the
serum of COVID-19 patients. These findings provided clues for the
reciprocal regulatory circuits between metabolic alterations and
cytokine release, and supported the metabolic intervention as a
potential strategy to suppress excessive inflammation. Indeed, combined
agents targeting multiple pathogenic factors involved in the
hyperinflammation are emerging as the way forward for supportive care
for COVID-19^[250]48. It is therefore possible that cocktails of drugs
targeting multiple metabolic pathways for global cytokine blockade
might constitute a new class of therapeutic strategy.
Due to the ethical consideration and restricted accessibility to
COVID-19 patient samples, our study validated the effects of
supplemented metabolites or pharmacological inhibitors in regulating
the CRS induced by SARS-CoV-2 infection using the ex vivo model of
PBMCs isolated from infected rhesus macaques or healthy donors.
Although the isolated PBMCs ex vivo models have been extensively used
for the evaluation of cytokine release^[251]23,[252]60 and rhesus
macaque has been reported to be a great animal model recapitulating
rapid virus replication and inflammatory responses observed in human
upon SARS-CoV-2 infection^[253]45,[254]46, it would have been ideal to
perform such analyses in rhesus macaque in vivo models or PBMCs
collected from COVID-19 patients. We tested the impacts of metabolism
intervening on the immunological responses in the heterogenous PBMCs.
Yet, given that SARS-CoV-2 infection reduces innate antiviral defenses
while activates inflammatory cytokine release^[255]61–[256]63, analyses
on sorted subpopulations of immune cells would help to more precisely
understand the roles of metabolism in regulating the release of
specific cytokines with discriminating functions, proinflammatory or
suppressive, from different immune cell types.
In summary, our study performed the metabolic and immune profiling in
COVID-19 patients and showed that reprogrammed host metabolism was
tightly linked to the burst of proinflammatory cytokines. Beyond
providing a resource of metabolism and immunology data to support
further investigation of COVID-19, our study also uncovered new
insights related to tight correlation between metabolism and cytokine
release, and thereby provided a potential therapeutic strategy for the
treatment of fatal CRS induced by SARS-CoV-2 infection.
Methods
Patients and samples
A total of 44 COVID-19 patients, 20 non-COVD-19 acute upper respiratory
tract infection patients, and 17 healthy controls were enrolled in this
study. Cross-sectional serum samples from 37 COVID-19 patients were
collected from Chongqing Three Gorges Central Hospital, Chongqing
Public Health Medical Center and Yongchuan Hospital Affiliated to
Chongqing Medical University. Sequential serum samples from 7 mild
patients were collected from Yongchuan Hospital Affiliated to Chongqing
Medical University with 3-day intervals. Non-COVID-19 acute upper
respiratory tract infection patients with respiratory symptoms of
common cold, such as fever, cough, nasal congestion, and sore throat,
were enrolled from the First and the Second Affiliated Hospitals of
Chongqing Medical University. Serum samples from 17 healthy controls
were collected from the Second and the Third Affiliated Hospitals of
Chongqing Medical University. Patients were confirmed to be infected
with SARS-CoV-2 by RT-PCR assays (DAAN Gene) on nasal and pharyngeal
swab specimens. Briefly, two target genes, including open reading
frame1ab (ORF1ab) and nucleocapsid protein (N), were simultaneously
amplified and tested during RT-PCR (primers can be found in
Supplementary Table [257]2). Primers of RT-PCR testing for SARS-CoV-2
were designed according to the recommendation by the Chinese CDC. PCR
cycling: 50 °C for 15 min, 95 °C for 15 min, 45 cycles at 94 °C for
15 s, 55 °C for 45 s (fluorescence collection). Ct values <37 and >40
were defined as positive and negative, respectively, for both genes.
Clinical data collection
Epidemiologic, demographic, clinical presentations, laboratory tests,
treatment, and outcome data were collected from inpatient medical
records without allowing for identification. Laboratory data collected
for each patient included complete blood count, coagulation profile,
serum biochemical tests (including renal and liver function,
electrolytes, lactate dehydrogenase, and creatine kinase), serum
ferritin, and biomarkers of infection.
Clinical definitions
Clinical classification was defined based on the COVID-19 Treatment
Guidelines (National Health Commission of the People’s Republic of
China). A confirmed case of SARS-CoV-2 infection was defined as an
individual with nasopharyngeal swabs positive for SARS-CoV-2 nucleic
acid by RT-PCR as described above. Severe COVID-19 cases were those
meeting any of the following criteria: (1) respiratory distress (≥30
times/min), (2) oxygen saturation ≤ 93% at rest, (3) the arterial
partial pressure of oxygen (PaO[2])/the fraction of inspired oxygen
(FiO[2]) ≤300 mmHg. Mild patients were defined as COVID-19 patients
with symptoms but could not be classified as severe. Symptoms onset
date was defined as the date on which symptoms were first observed.
Symptoms included fever, fatigue, dry cough, inappetence, myalgia,
dyspnea, expectoration, sore throat, diarrhea, nausea, dizziness,
headache, abdominal pain, chill, rhinorrhea, chest stuffiness, or nasal
congestion.
Detection of IgG and IgM against SARS-CoV-2
All serum samples of COVID-19 patients were inactivated at 56 °C for
30 min and stored at −20 °C before testing. IgG and IgM against
SARS-CoV-2 in plasma samples were tested using magnetic
chemiluminescence enzyme immunoassay kits supplied by Bioscience Co.
(approved by the China National Medical Products Administration;
approval numbers 20203400183 (IgG) and 20203400182 (IgM)), according to
the manufacturer’s instructions. Antibody levels are presented as the
measured chemiluminescence values divided by the cutoff (S/CO).
Alkaline phosphatase-conjugated Affinipure Goat Anti-Human IgG
(Proteintech) was used.
Cytokine measurement in serum samples from patients and healthy controls
Concentrations of 48 cytokines and chemokines in serum samples were
measured using the Bio-Plex Human Cytokine Screening Panel (48-Plex no.
12007283, Bio-Rad) on a Luminex 200 (Luminex Multiplexing Instrument,
Merck Millipore) following the manufacturer’s instructions.
Virus preparation
Viral stocks of SARS-CoV-2 were obtained from the Center of Diseases
Control, Guangdong Province, China. Virus samples were amplified on
Vero E6 cells and concentrated by ultrafilter system via 300 kDa module
(Millipore). Vero E6 cells were cultured in Roswell Park Memorial
Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum
(FBS).
Animals and experimental procedures
For SARS-CoV-2 virus infections, we inoculated a rhesus macaque with
total 5 mL of 10^6 pfu/ mL SARS-CoV-2 intratracheally (2.5 mL) and
intranasally (2.5 mL). Body weight, body temperature, and clinical
signs were monitored daily. We collected nasal, throat, rectal swabs,
and whole blood for viral genome quantification. Real-time RT-PCR was
used to quantify viral genome in samples using TaqMan Fast Virus
One-step Master Mix (ThermoFisher, USA) and purified viral RNA of
SARS-CoV-2 as a standard curve, which was performed on CFX384 Touch
Real-Time PCR Detection System (Biorad, USA). Primers and probes of the
genome were synthesized according to sequences reported by China CDC
(primers can be found in Supplementary Table [258]2). PCR cycling:
25 °C for 2 min, 50 °C for 15 min, 95 °C for 2 min, then 40 cycles at
95 °C for 5 s and 60 °C for 31 s. For paraffin-embedded sections,
tissues were collected and fixed in 10% neutral-buffered formalin,
embedded in paraffin, and 5 μM sections were prepared for hematoxylin
and eosin (H&E) staining.
Cytokine measurement in serum samples from rhesus macaque
MILLIPLEX MAP Non-Human Primate Cytokine Magnetic Bead Panel-Immunology
Multiplex Assay (PRCYTOMAG-40K, Millipore, USA) was used according to
the manufacturer’s protocol, which was performed on Bio-Plex machine.
Inflammatory cytokines in this panel included IL-1β, IL-4, IL-5, IL-6,
IL-8/CXCL8, G-CSF, GM-CSF, IFN-γ, IL-1RA, IL-2, IL-10, IL-12 p40,
IL-13, IL-15, IL-17A/CTLA8, MCP-1/CCL2, MIP-1β/CCL4, MIP-1α/CCL3,
sCD40L, TGF-α, TNF-α, VEGF, and IL-18.
Isolation of PBMCs from peripheral blood
PBMCs were isolated from peripheral blood of mock-infected and
SARS-CoV-2-infected rhesus macaques or healthy donors using
Ficoll-PaqueTM (Sigma-Aldrich). Peripheral blood sample (4 mL) was
drawn into vacutainer tubes. The Ficoll density gradient centrifugation
method was used to separate the PBMCs. We diluted the blood with 1×
phosphate-buffered saline (PBS) 1:1, and then transferred it to the
Ficoll tube. After centrifugation at 1000g for 20 min, the buffy coat
of PBMCs was pooled and transferred into a 15 mL falcon. PBMCs were
then washed twice with 10 mL PBS and centrifuged at 250g for 10 min.
The cell pellets were resuspended in RPMI 1640 medium.
Measurement of cytokine release by PBMCs
PBMCs (1 × 10^5 cells/well) isolated from mock-infected and
SARS-CoV-2-infected rhesus macaque or healthy donors were seeded in
96-well plates and treated with different compounds. The compounds
including L-arginine (S5634), Epacadostat (S7910), Mycophenolic acid
(S2487) were purchased from Selleck. For cytokine measurement of PBMCs
derived from rhesus macaques, we used MILLIPLEX MAP Non-Human Primate
Cytokine Magnetic Bead Panel-Immunology Multiplex Assay (PRCYTOMAG-40K,
Millipore, USA) as mentioned in the section “Cytokine measurement in
serum samples from rhesus macaque”. For cytokine measurement of PBMCs
derived from healthy donors, we used Bio-Plex Human Cytokine Screening
Panel (48-Plex no. 12007283, Bio-Rad) as mentioned in the section
“Cytokine measurement in serum samples from patients and healthy
controls”.
Ethical approval
The study was approved by the Ethics Commission of Chongqing Medical
University (ref. no. 2020003). Written informed consent was waived by
the Ethics Commission of the designated hospitals for emerging
infectious diseases. All animal experiments were performed according to
protocols approved by the Institutional Animal Care and Use Committee
of Institute of Medical Biology, Chinese Academy of Medical Science
(Ethics number: DWSP202002 001), and performed in the ABSL-4 facility
of Kunming National High-level Biosafety Primate Research Center,
Yunnan, China.
Serum sample preparation for metabolomics
Human serum samples, 20 μL each, were heated at 56 °C for 30 min
followed by adding 60 μL ethanol to inactive SARS-CoV-2 virus. The
suspension was evaporated to dryness using a SpeedVac concentrator
(Thermo Scientific). Metabolites from the serum pellet were extracted
with 540 μL 80% methanol in water, followed by vigorous vortex and
cooled centrifugation at 4 °C. Then, equal aliquots of the culture
medium from each sample (20 μL) were pooled together to make the
quality control (QC) samples. The remaining culture medium was divided
into two fractions, one for targeted metabolomics and the other for
untargeted metabolomics analysis. All the samples were evaporated to
dryness.
PBMCs and cell culture medium sample preparation for metabolomics
The PBMCs isolated from healthy donors were seeded
(1 × 10^5 cells/well) in 24-well plates and treated with different
concentrations of cytokines mixture (IL-6, IL-1α, IL-1β, IFN-γ, TNF-α,
SinoBiological). After 24 h, 100 μL cell culture medium from each well
was pipetted and exacted by adding 400 μL methanol, followed by
vigorous vortex and centrifugation. The supernatant was evaporated to
dryness. After removing the culture medium and washing with 0.9% NaCl,
cell metabolites were extracted with 80% MeOH (methanol:water = 80:20,
500 μL/10^5 cells), followed by vigorous vortex and centrifugation. The
supernatant was evaporated to dryness.
Targeted metabolomics
For targeted metabolomics, dried metabolites were reconstituted in
LC-MS grade water with 0.03% formic acid, vortex-mixed, and centrifuged
at 4 °C for 15 min to remove debris. Samples were randomized and
blinded before analyzing by LC-MS/MS.
Chromatographic separation was performed on a Nexera UHPLC system
(Shimadzu), with a RP-UPLC column (HSS T3, 2.1 mm × 150 mm, 1.8 μm,
Waters) and the following gradient: 0–3 min 99% A; 3–15 min 99–1% A;
15–17 min 1% A; 17–17.1 min 1–99% A; 17.1–20 min 99% A. Mobile phase A
was 0.03% formic acid in water. Mobile phase B was 0.03% formic acid in
acetonitrile. The flow rate was 0.25 mL/min, the column was at 35 °C
and the autosampler was at 4 °C. Mass data acquisition was performed
using an AB QTRAP 6500+ triple quadrupole mass spectrometer (SCIEX,
Framingham, MA) in multiple reaction monitoring (MRM) mode for the
detection of 258 unique endogenous water-soluble metabolites as
previously described, with some modifications^[259]22,[260]64.
Chromatogram review and peak area integration were performed using
MultiQuant 3.0.2 (SCIEX, Framingham, MA).
Untargeted metabolomics
For untargeted metabolomics, dried samples were reconstituted in
acetonitrile/water mixture (v/v, 1:1). After vortex, samples were
centrifuged at 4 °C for 15 min to remove debris, samples were
randomized and blinded before LC-MS/MS analysis.
Chromatographic separation was performed on an Agilent 1290 infinity II
LC system, with an Agilent Eclipse Plus C18 column (2.1 mm × 100 mm,
1.8 μm). The gradient was set as follows: 0–2 min 95% A; 2–20 min 95-0%
A; 20–25 min 0% A; post-run time for equilibration, 5 min in 95% A.
Water containing 0.1% formic acid and acetonitrile containing 0.1%
formic acid acted as mobile phase A and B in the positive-ion mode of
mass spectrometry analysis. While in the negative-ion mode of mass
spectrometry analysis, the 0.1% formic acid was replaced with 1 mM
ammonium fluoride. The flow rate was set as 0.3 mL/min and the
temperatures of the column and autosampler were set as 40 and 4 °C,
respectively. The data acquisition was performed on a 6546 Q-TOF mass
spectrometry equipped with a dual electrospray (ESI) ion source
(Agilent Technologies, Santa Clara, CA). The optimized ESI Q-TOF
parameters were set as follows: the temperature and flow rate of sheath
gas were 350 °C and 11 L/min; the voltages of capillary, fragmentor,
and skimmer were set as 4000, 140, and 65 V, respectively. The spectra
were internally mass calibrated in real time by continuous infusion of
a reference mass solution using an isocratic pump connected to a dual
sprayer feeding into an ESI source. MassHunter Acquisition software
(Agilent Technologies, Santa Clara, CA, v10.1) was employed to perform
data acquisition.
Raw data were obtained from the Agilent Masshunter Workstation
(Profinder software, v10.0) and the metabolite features were extracted
by excluding missing values based on 80% rule^[261]65. The metabolites
were identified based on their retention times, accurate masses, MS/MS
spectra, and isotopic patterns. Further, to expand the qualitative
coverage of specific metabolic pathways, the Agilent Pathway to PCDL
software (vB.08.00) was employed to perform targeted extraction of
metabolites from the raw data. According to the above criteria, a total
of 155 metabolites were eventually identified.
Normalization and integration of targeted and untargeted metabolomics data
For both targeted and untargeted metabolomics, QC samples composed of
an equal aliquot of all test samples were prepared and inserted in an
interval of ten test samples to monitor the stability of instrument and
normalize the variations during the run. This served as an additional
QC measure of analytical performance and a reference for normalizing
raw metabolomics data across samples.
To remove potential inter-batch variations, the mean peak area of each
metabolite from all the QC samples in all given batches (QC[all]), as
well as the mean peak area of each metabolite from the QC samples that
are the most adjacent to a given group of test samples (QC[adj]) were
first calculated. The ratio between these two mean peak areas for each
metabolite was computed by dividing the same QC[all] by each QC[adj]
and used as the normalization factor for each given group of test
samples. The peak area of each metabolite from each test sample was
normalized by multiplying their corresponding normalization ratio to
obtain the normalized peak areas. In addition, to effectively correct
the sample-to-sample variation in biomass that may contribute to
systematic differences in metabolite abundance detected by LC-MS, we
generated the scaled data by comparing the normalized peak area of each
metabolite to the sum of the normalized peak area from all the detected
(for targeted metabolomics) or identified metabolites (for untargeted
metabolomics) in that given sample.
Our validation analyses suggested that these normalization and scaling
steps could effectively correct both the inter-sample artificial
differences in sample biomass and inter-batch systematic variations in
detected metabolite abundance.
A total of 36 metabolites overlapped among the 134 metabolites detected
by the targeted metabolomics analysis and 155 metabolites detected by
the untargeted metabolomics analysis. For the overlap metabolites
identified from targeted and untargeted metabolomics, data from
targeted method were used for the subsequent analysis. Finally, 253
metabolites were integrated into the final metabolomics data matrix
including 134 metabolites from targeted metabolic profiling and 119
metabolites from untargeted metabolic profiling. The final metabolomics
data matrix (102 rows of patients/controls and 253 columns of
metabolites) used for the downstream analyses or modeling is included
in Supplementary Data [262]3.
Partial least squares discrimination analysis (PLS-DA)
PLS-DA was performed on normalized metabolomics data using SIMCA-P
software (v14.1, Umetrics, Umea, Sweden) and unit variance (UV) scaling
was utilized before multivariate analysis.
t-Distributed stochastic neighbor embedding (t-SNE) dimensionality reduction
t-SNE scatterplots were generated of log10-transformed metabolomics
data using R Rtsne (v0.15) package with perplexity of 5 and theta of
0.01.
Identification of significantly altered metabolites and cytokines in mild and
severe COVID-19 patients
Mann-Whitney U test followed by Benjamini-Hochberg (BH) multiple
comparisons test was performed by R stat (v3.6.0) package. Serum
cytokines with FDR < 0.05 and metabolites with FDR < 0.05, fold change
>1.25 or <0.8 were considered significant and used for subsequent
analysis.
Longitudinal trajectory analysis of serum metabolites and cytokines
To estimate serum cytokine and metabolite longitudinal trajectories of
follow-up mild COVID-19 patients, generalized additive model (GAM)
adjusted for age and gender was fitted for each cytokine and
metabolite. GAM was performed by R mgcv (v1.8-31) package with default
parameters.
Mann-Whitney U test and BH multiple comparison test were carried out at
each time-point to identify significant altered cytokines and
metabolites compared with healthy controls (base lines). Cytokines and
metabolites with FDR < 0.05 were considered significant. Longitudinal
clustering was performed using the z-scaled abundance of significant
cytokines and metabolites by R Mfuzz (v2.44.0) package, and parameter m
was set to 1.5.
Correlation analysis of serum metabolites and cytokines
Correlations between cytokines and metabolites were analyzed by linear
regressions models after adjusting for gender and age. Linear
regression models conducted by R lm base function were calculated using
Log10-transformed abundance of metabolites and cytokines in mild
patients, severe patients, and follow-up patients.
[MATH: Log10Cytokineabundance=Gender+Age+Log10Metaboliteabundance :MATH]
1
P values were corrected using BH multiple comparisons test.
Correlations with FDR < 0.1 were considered significant and used for
subsequent analysis.
Then, based on the significant cytokine–metabolite correlations in
severe patients, weighted, undirected correlation networks were built
by R igraph (v1.2.5) package. Clusters were determined based on fast
greedy modularity optimization algorithm.
KEGG pathway analysis
KEGG metabolic pathways and involved metabolites were downloaded from
KEGG API ([263]https://www.kegg.jp/kegg/rest/keggapi.html). Significant
enriched KEGG pathways were determined by R clusterProfiler (v3.12.0)
package with BH multiple comparison test as FDR < 0.1 and enriched for
at least 3 metabolites (for significantly altered metabolites),
FDR < 0.1 and enriched for at least 3 cytokines (for
cytokine-associated metabolites), or FDR < 0.05 (for metabolites in
each trajectory cluster in follow-up patients).
For metabolites with progressive change in mild and severe patients,
metabolites with abundance in severe patients > mild patients > healthy
controls or severe patients < mild patients < healthy controls, and
showed significance in severe patients compared with mild patients were
considered consistently altered. Based on these metabolites, metabolic
pathways with FDR < 0.1 were considered significantly enriched.
Statistical analysis
Quantification methods and statistical analysis methods for metabolite
and cytokine analyses were mainly described and referenced in the
respective subsections in Methods.
Additionally, statistical significance of cytokines in cell culture
media and relative abundance of metabolites in PBMC pellets or culture
media between different conditions were considered using one-way ANOVA
followed by BH multiple comparison test, which were performed by
GraphPad prism (v8.2.1).
Reporting summary
Further information on research design is available in the [264]Nature
Research Reporting Summary linked to this article.
Supplementary information
[265]Supplementary Information^ (43.4MB, pdf)
[266]Reporting Summary^ (330.6KB, pdf)
[267]41467_2021_21907_MOESM3_ESM.pdf^ (106.9KB, pdf)
Description of Additional Supplementary Files
[268]Supplementary Data 1^ (33.4KB, xlsx)
[269]Supplementary Data 2^ (667.8KB, xlsx)
[270]Supplementary Data 3^ (5.5MB, xlsx)
[271]Supplementary Data 4^ (69.6KB, xlsx)
Acknowledgements