key: cord-0855980-xgnmv4h6
authors: Jia, Hongling; Liu, Chaowu; Li, Dantong; Huang, Qingsheng; Liu, Dong; Zhang, Ying; Ye, Chang; Zhou, Di; Wang, Yang; Tan, Yanlian; Li, Kuibiao; Lin, Fangqin; Zhang, Haiqing; Lin, Jingchao; Xu, Yang; Liu, Jingwen; Zeng, Qing; Hong, Jian; Chen, Guobing; Zhang, Hao; Zheng, Lingling; Deng, Xilong; Ke, Changwen; Gao, Yunfei; Fan, Jun; Di, Biao; Liang, Huiying
title: Metabolomic analyses reveals new stage-specific features of the COVID-19
date: 2021-07-22
journal: Eur Respir J
DOI: 10.1183/13993003.00284-2021
sha: c7767630517ee45064e865c0386c54f394b76a6c
doc_id: 855980
cord_uid: xgnmv4h6

The current pandemic of coronavirus disease 19 (COVID-19) has affected more than 160 million of individuals and caused millions of deaths worldwide at least in part due to the unclarified pathophysiology of this disease. Therefore, identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual by acquiring extensive insights into the pathophysiological significance during disease progression. We provide a comprehensive view of metabolic characterization of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both discovery cohort and validation cohort, which suggest that metabolic reprogramming of glucose metabolism and urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and urea cycle may be a viable approach to fight against COVID-19 at various stages along the disease course.

Coronavirus disease 2019 (COVID- 19) , with its devastating consequences for the patients and their families, remains a major public health concern that has been posing an increasing financial and social burden worldwide [1, 2] . Notably, infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, may be asymptomatic or may result in a wide spectrum of clinical symptoms, ranging from mild acute febrile illness to moderate, clinically severe, and critical life-threatening infections [3] [4] [5] . This disease progression from mild to severe of COVID-19 are supposed to be driven by multi organ failure with specific metabolic alterations in patients. It is well known that viruses completely rely on their host's cell energy and metabolic resources for fueling the different stages of viral infection, such as enter, multiply, and exit for a new round of infection [6] . For certain respiratory tract viruses, the consequential development of hypoxia caused by lung function impairment may also alter metabolic profiles [7, 8] . The metabolite profiles of coronaviruses' infection have been previously investigated by several research groups and they showed that levels of a series of small molecule metabolites, such as, free fatty acids, kynurenine, sphingolipids, glucose and amino acids, were altered as a result of virus infection, which were supposed to be involved in the changes of organ functions and immune responses [9, 10] .

However, COVID-19genesis is a multistep process, samples collected from the severe patients or the mixed groups (including both mild or moderate, and severe type) were used in these investigations. Thus, the evidence of distinct stage-specific phenotypes of metabolite profiles caused by SARS-CoV-2 infection is still not fully understood.

In this study, a metabolomic analysis has been applied to screen metabolic variations, and further to figure out the comprehensive view of endogenous metabolites. Metabolomics approaches are generally classified as untargeted analysis or targeted analysis. The untargeted analysis is a relative-quantitative procedure that could maximally extracts the information of metabolites from the complex biological matrix without bias, whereas targeted quantitative metabolomics focuses on the detection of specific metabolites associated with some detailed class or metabolic pathways through the standard curve. We first analyzed the metabolic variations with untargeted approach, and then confirmed with targeted approach. Our further analysis showed that these altered metabolites are mainly involved in TCA cycle and Urea cycle pathway. More importantly, recovery group were included in our investigation. We obtained more valuable information about metabolic variations, through comparing recovery group with other groups.

From January to March, 2020, 63 adult COVID-19 patients who were confirmed at Guangzhou Center for Disease Control and Prevention (CDC) laboratory and then were recruited and referred to the designated treatment center of Guangzhou for treatment. The diagnosis of COVID-19 was made following the Protocol for Novel Coronavirus Pneumonia Diagnosis and Treatment issued by the National Health Commission of the People's Republic of China [11] . According to guidelines of Chinese Clinical Guidance For COVID-19 Pneumonia Diagnosis and Treatment (Trial 6th version, table S1), patients were classified into three subgroups: 18 cases in mild group, 12 cases in severe group, and 20 cases in recovery group.

For the prospective validation study, the other 93 COVID-19 patients who had been diagnosed with COVID-19 were independently enrolled. Of these 93 peripheral blood samples, 3 samples had to be excluded due to hemolysis, leaving us with the data of 90 patients for validation analysis.

During this same period, 13 and 28 healthy volunteers in discovery and validation cohort respectively, including 22 male and 19 female, were recruited at the Medical Examination Center, No.8 People's Hospital of Guangzhou with their ages ranging from 42 to 56 (mean 48).

Blood samples of mild and severe group were collected at the time of diagnosis and when the patient's condition reached the severe standard, respectively. The time of sample collection of recovery group was that when COVID-19 test were negative for twice and isolation was released. Blood samples of healthy control were collected from the individuals who had annual physical examination during that period of time.

Peripheral blood samples were collected in vacuum negative pressure blood collection vessel. All subsequent processes were handled under Biosafety Level 3 containment conditions following risk assessments and code of practice approved by Guangdong CDC. Blood samples in serum separator tubes were centrifuged at 4°C at 1,500g for 10 min and serum aliquoted and stored at -80 °C until analysis.

Microsoft Excel (MS Excel 2013, version 15.0) is used for data collection on demographics and clinical information. Data on demographics, clinical presentations on admission, chest computed tomography (CT) scan and laboratory tests were collected. Laboratory data collected on each patient included complete blood count, urine and stool analysis, blood biochemistry, coagulation function, biomarkers of infection, as well as viral testing through nasopharyngeal.

Clinical data were obtained by reviewing clinical charts, nursing records, and results of laboratory testing and CT scan for all laboratory confirmed patients. The accuracy of the clinical data were confirmed by two pediatricians caring for the patients.

To explore whether SARS-CoV-2 infection could induce an intense infection response, we also tested three cytokines using samples from 4 groups by ELISA assay. The human TNF-a (Cat:AD11069Hu), IL-1β (Cat: AD11042Hu) and IL-6 (Cat: AD10759Hu) ELISA kits were purchased from Beijing Andy Gene Co., Ltd. The concentrations of these three inflammatory biomarkers were determined according to the manufacturer's protocols and absorbance was measured at 450 nm using a microplate reader. All samples were analyzed in triplicate and the average concentration for each patient was calculated.

Untargeted analysis of serum samples was similar to that described previously with minor modifications [12] . 100 µL serum samples were extracted by adding 300 µL of precooled methanol and acetonitrile (2:1, v/v). After vortex for 1min and incubate at -20 °C for 2 hours, samples were centrifuged for 20 min at 4000 rpm. The supernatant was transferred into Eppendorf tubes for vacuum freeze-drying. The metabolites were resuspended in 150 µL of 50% methanol and centrifuged for 30 min at 4000 rpm, and the supernatants were transferred to auto sampler vials for analysis. A quality control (QC) samples were prepared by pooling the same volume of each sample to evaluate the reproducibility of the analysis. The samples were maximize identification of differences in metabolic profiles between groups. In addition to the multivariate statistical method, the Student't test or Wilcoxon's rank-sum test was also applied to estimate the significance of each metabolite. The P values across all metabolites within each comparison were adjusted by a false discovery rate method. Finally, the significantly differential metabolites need to meet the following criteria: ①VIP>1; ②log2FC>0.25 or <-0.25;

③FDR<0.05.

All data were analyzed with R (v3.5.0) and differential metabolomic analysis was performed with MSstats R package which includes log2 transformation, normalization and P-value calculation on the Spectronaut and skyline quantitative data. The clustering algorithm used Hierarchical Cluster (HCA)with hcaMethods R package, and the distance calculation was performed in Euclidean distance with the heatmap-package in R software to reveal the relationship between samples and metabolites. KEGG pathway enrichment analysis of differential metabolites was performed with the KEGG database (v89.1) using Fisher's exact test by comparison with all the identified metabolites. Correlation analysis was performed using pearson correlation coefficient analysis with corrplot R package.

Categorical variables were summarized in percentage and compared between different groups using χ² test or Fisher's exact test. Continuous variables were expressed as the mean and 95% confidential interval (95%CI) or the median and inter quartile range (IQR), whichever deemed appropriate. For the multiple comparison of the four independent groups, the raw values of each metabolite were selected and normalized to the control group respectively. The Tukey's range test was conducted for all 11 metabolites, and the significance level for the test, alpha value, were set to 0.5.

To verify whether the identified metabolites could be used as potential predictors for classification of different stage groups, logistic regression model with 5-fold cross-validation approach was designed and performed to predict the reliability and failure rate. All possible combinations containing N metabolites (N=1, 2, 3,..., 9) were exhaustively tested by the model, and the predictive power of each combination was recorded and ranked according to the Area-Under-Curve (AUC) value by receiver operating characteristic (ROC) analysis. To ensure a large enough sample size, all corresponding AUC were calculated for discovery cohort and validation cohort as a whole, and presented as specific or average value. 

The study protocol for patient and healthy volunteer recruitment and sampling was approved by the Ethical Committee of Guangzhou CDC with reference number (GZCDC-ECHR-2020A0002).

To determine the alterations in circulating metabolites from COVID-19 patients, we applied untargeted metabolomic analysis in the discovery cohort including 13 healthy subjects as normal group, 18 mild patients, 12 severe cases, and 20 individuals recovery from severe scenario of COVID-19. Clinical signs and symptoms, together with the laboratory parameters of patients that infected with COVID-19 were collected and analyzed (table 1) . Compared to mild group, patients in severe group showed significant suppression of lymphocyte, but increase of leucocytes and AST, indicating the disturbance of immune system and liver function. 

To further quantify the changes of circulating metabolites between those four groups, we performed the targeted metabolomics analysis by Q300 Kit (Metabo-Profile, Shanghai, China).

Q300 Kit is the most comprehensive kit for analysis of targeted metabolic profiling. With coverage of up to 306 metabolites and more than 12 biochemical classes, the kit not only includes the differential metabolites detected by untargeted screening like glutamine, arginine, ornithine, glucose, lactate, pyruvate, but also contains other metabolites such as indoles, bile acids, and short-chain fatty acids, etc. These metabolites involved in nitrogen metabolism, TCA cycle, fatty acid metabolism, amino acid metabolism, and bile acid metabolism, etc.

In this study, we first quantified serum metabolites from 59 subjects (discovery cohort, 4 samples were excluded due to less volume). Based on the concentrations presented in different groups, totally 199 metabolites were divided into 16 clusters (figure S3, table S3). Then we classified these clusters into three groups, according to whether the metabolites return to normal figure S3B were found to decrease as the disease developed, but did not return to normal levels.

We compared the metabolomic profiling of various stages of COVID-19 to healthy group to identify and characterize specific metabolites and metabolic pathways involving the progression of COVID-19. We focused on the difference between the various stages of COVID-19 with normal group. A significance threshold of 0.5 was generally adopted for Q 2 and R 2 in OPLS-DA model [19, 20, 21] . Clear differences were found for the following groups: normal versus mild, figure S4 ). Furthermore, we quantified serum metabolites from another 118 subjects as an independent validation (for demographic characteristics, see table S4 ). The same model was adopted, and the results showed high consistency, which were presented in figure S5 . 

The urea cycle and TCA cycle responsible for amino acid metabolism and energy metabolism were ranked as the top 2 affected metabolic pathways according to the analysis (figure 3). 11 metabolites that belong to or are closely related to the two cycles, were observed with significant changes among different groups (figure 3, table S6) in the discovery cohort.

Furthermore, 9 out of the 11 metabolites were testified with significant changes among different groups (figure 3, table S7). These 9 metabolites were creatine, arginine, ornithine, asparate, pyruvate, malate, citrulline, glutamine and 2-oxoglutarate. Focused analysis highlighted significant increases of 2-oxoglutarate, asparate and ornithine levels, compared to normal group. Malate and 2-oxoglutarate presented with the highest level in severe group, indicating that TCA cycle got seriously affected. Ornithine, which plays the key role in urea cycle, increased consistently and reached the highest level in the recovery group. In contrast, the level of arginine, another vital amino acid in urea cycle, decreased significantly in severe group compared to that in normal group, suggesting the dysregulation of urea cycle during the COVID-19 progression.

To study if the nine metabolites could be used as biomarker to risk stratify COVID-19

patients, several classic models were trained with the discovery cohort and validated with the validation cohort including decision trees (figure S7), random forest (figure S8), support vector machine (SVM) (figure S9), and logistic regression (figure S10). We adopt logistic regression for its better performance. The mean AUC for logistic regression using 3, 5, and 7 metabolites in validation cohort were presented in figure S11. Using different combinations of the above 9 metabolites and the whole study participant's data (discovery cohort + validation cohort), we found that the more metabolites involved, the higher classification value observed of all distinguish between any two groups (figure 4A-F). However, the alteration pattern of circulating metabolites was different during the disease progression. For example, when we applied fewer metabolites, the model can distinguish "severe" from "normal" with relatively higher accuracy, but not for "mild" from "normal" (figure 4-G). When the number of metabolites increased, the accuracy of the model distinguishing mild from severe was improved (figure 4G-I). Furthermore, relatively higher AUC value to distinguish "recovery" from "normal" also suggests that subjects who survived the severe scenario might still suffer from long-lasting collateral damage regarding to metabolic conditions. For further exploration, we performed logistic regression using 9 metabolites individually in discovery, validation and the combined cohorts and found that creatine, 2-oxoglutarate and pyruvate maintained passing performance in three cohorts (figure S12), leading to the potential for the metabolites to serve as predictive biomarkers. To better validate our findings, we compared the 9 metabolites with the results of Shen et. al. [9] and found 4 metabolites that overlapped: malate, pyruvate, citrulline and glutamine. We then built logistic regression model using these 4 metabolites as features and trained with both discovery and validation cohort. The model was then validated utilizing the published results from Shen et.

al. paper data and achieved good performance in distinguishing severe and normal (figure S13),

proving the model could hold up across studies. Such results indicated that malate, pyruvate, citrulline and glutamine might serve as potential biomarkers to risk stratify COVID-19 severe patients. We identified circulating metabolites, including malate, 2-oxoglutaratek and asparate, which were increased compared to normal level, suggesting that the dysregulated glucose metabolism and TCA cycle could be crucial in susceptibility, severity and recovery during COVID-19 disease course. Glucose either goes through aerobic metabolism and provides energy for diverse biological processes or oxidized through pentose phosphate pathway to yield nicotinamide adenine dinucleotide phosphate (NADPH) to maintain redox homeostasis, which involves in host immune responses to against pathogenic microorganisms under aerobic condition. While under anaerobic condition, which is most common in COVID-19 patients, glucose goes through glycolysis and is fermented to lactate with limited amount of ATP production, and causes the elevated blood lactate and LDH levels [22, 23] . Our finding indicates that, in addition to anaerobic glycolysis, which usually occurs in most COVID-19 patients, TCA cycle was also enhanced during all stages of the disease. We proposed that SARS-CoV-2 viruses hijack the host machinery, including glucose metabolism, to promote pathogenesis of COVID-19.

Infection with SARS-CoV-2 of Caco-2 cells was reported to upregulate glucose metabolism and block glycolysis with non-toxic concentrations of 2-deoxy-d-glucose (2-DG) to prevented SARS-CoV-2 replication [24] . SARS-CoV-2 viruses in infected cells consumed large amount of cellular ATP to support the viral replication as high ATP concentration promoted the translocation of SARS-CoV in the unwinding of duplex RNA, which is required for viral replication [25] .

Type I IFNs (IFN-I) is major components of the innate immune system and represents critical antiviral molecules [26, 27] . Viral infection causes the host to activate an antiviral response that, in part, is dependent on mitochondrial antiviral signaling protein (MAVS) to produce type I interferons. Cell model and animal model of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response. This response is defined by low levels of type I and type III interferons juxtaposed to elevated chemokines and high expression of IL-6. Reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19 [28] . In order to show the inflammation marker changes in our cohort, we also selected 18 samples with sufficient volume from each group to examined IL-6, TNF-α and IL-1β level, which were showed to be involved in COVID-19 infection. Our results showed that IL-1β level was higher in mild stage, and was the highest in severe stage, compared with control. But it was much lower in recovery stage, although still higher than that in control group (figure S14-A). IL-6 and TNF-α had the same trends (figure S14-B and -C). These results gave us the clue that inflammation level was closely related with COVID-19 disease development and might be involved in the production of metabolites. We therefore further conducted correlation analysis and found that the level of asparate, creatine, malate, and 2oxoglutarate were positively correlated with the expression of cytokines (figure S15). Clinical study showed that patients with no IFN-production presented poorer outcome, and all of them required invasive ventilation and longer intensive care unit stay. The viral load tended to be higher in IFN-negative patients with COVID-19 at disease diagnosis [29] .

In our study, dysregulated urea cycle with decreased arginine and increased ornithine was also identified in the sera of COVID-19 patients, suggesting metabolic reprogramming may be involved in COVID-19 disease progress. Another example showing the metabolic reprogramming in antiviral response came from a mouse model of virus infection. Type I interferon was found to alter the expression and function of key enzymes of the urea cycle in hepatocytes, result in decreased arginine and increased ornithine concentrations in the circulation and suppress virus-specific CD8+ T cell responses [30, 31, 32] . However, whether type I interferon also regulates urea cycle in alveolar epithelial cells during SARS-COV-2 infection is an important question, and remains to be investigated.

In our dataset, the level of arginine significantly decreased in sera of COVID-19 patients compared to that in healthy control group, which was consistent to the findings from other teams [33] . It is well known that arginine can either be converted to ornithine and urea using enzyme arginase or to nitric oxide and citrulline through enzyme nitric oxide synthase [34] . Nitric oxide or its derivatives was reported to affect the fusion between the S protein of SARS-CoV and its cognate receptor, angiotensin converting enzyme 2, and reduce viral RNA production in the early steps of viral replication [35] . observed key discriminant metabolites including elevated α-1-acid glycoprotein and an increased kynurenine/ tryptophan ratio in COVID-19 patients, indicating that liver dysfunction was one of the potential pathological mechanisms [31] . In another study on plasma metabolomic and lipidomic alterations associated with COVID-19, carbamoyl phosphatase, which participates in the urea cycle, was shown to be reduced in patients [36] . This finding is particularly consistent with our finding that the level of citrulline, which is formed from carbamoyl phosphate, was significantly downregulated in mild and severe patients. These data collectively indicated that profiling of sera from COVID-19 patients with that from control individuals. They identified molecular changes, which implicated dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression, and might be useful to screen the potential blood biomarkers for severity evaluation [9] . Seriously ill COVID-19 patients are technically afflicted with sepsis, which involves life-threatening organ dysfunction caused by a person's dysregulated immune response to an infection. Our dysregulated metabolite data also showed considerable overlap with previous studies, suggesting the underlying relationship between dysregulated metabolic pathways and development of sepsis [37] [38] [39] [40] . Generally speaking, elucidation of the full spectrum of pathophysiological mechanisms, how these metabolic reprogramming can be targeted and providing a "proof of principle" for COVID-19 treatment will be important in further investigations.

Although we successfully recruited 63 and 118 patients for the discovery and validation cohort, respectively, the cohorts were still relatively in small size to obtain the best reliability. To confirm our findings, metabolic analysis of serum sample from a larger scale of COVID-19 cohorts maybe warranted. However, high consistency of the results from the validation cohort and the discovery cohort further consolidated our results. In addition, the cohorts had some important parameters that were not matched. For example, severe cases often occurred in the elderly. Although there were no significant differences among control group, mild group, and recovery group, it did create a natural mismatch between mild or recovery group and severe group, which should be taken into account when interpreting results.

Taken together, our metabolomic data presented here provided a comprehensive view of circulating metabolite characterization from COVID-19 patients at all stages and identified metabolic reprogramming of glucose metabolism and urea cycle as potential pathological mechanisms of COVID-19. Targeting host metabolism might be a viable approach to fight against COVID-19 at various stages along the disease course [41] .

We thank all patients and healthy individuals involved in this study, as well as the dedicated medical and research staffs, who fight against SARS-CoV-2 worldwide. We thank Jinling Tang 

The authors declare no competing interests. 

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Abbreviation: HGB, hemoglobin; APTT, activated partial thromboplastin time

BUN, blood urea nitrogen; Scr, serum creatinine; CK, creatine kinase; LDH, lactate dehydrogenase; CRP, C-reactive protein; NR, normal reference. a Including hypertension, type 2 diabetes mellitus, cancer, hyperlipidemia, liver disease and other chronic diseases, b missing for 9 severe group