key: cord-0968681-n7ir9sls
authors: Xu, Juanjuan; Zhou, Mei; Luo, Ping; Yin, Zhengrong; Wang, Sufei; Liao, Tingting; Yang, Fan; Wang, Zhen; Yang, Dan; Peng, Yi; Geng, Wei; Li, Yunyun; Zhang, Hui; Yang, Jin
title: Plasma metabolomic profiling of patients recovered from COVID-19 with pulmonary sequelae 3 months after discharge
date: 2021-02-17
journal: Clin Infect Dis
DOI: 10.1093/cid/ciab147
sha: 4c8a8b8d42857bd01e0ab08b007771fd38f27a31
doc_id: 968681
cord_uid: n7ir9sls

BACKGROUND: Elucidation of the molecular mechanisms involved in the pathogenesis of coronavirus disease (COVID-19) may help to discover therapeutic targets. METHODS: To determine the metabolomic profile of circulating plasma from COVID-19 survivors with pulmonary sequelae 3 months after discharge, a random, outcome-stratified case-control sample was analyzed. We enrolled 103 recovered COVID-19 patients as well as 27 healthy donors, and performed pulmonary function tests, computerized tomography (CT) scans, laboratory examinations, and liquid chromatography-mass spectrometry. RESULTS: Plasma metabolite profiles of COVID-19 survivors with abnormal pulmonary function were evidently different from those of healthy donors or subjects with normal pulmonary function. These alterations were associated with disease severity and mainly involved amino acid, and glycerophospholipid metabolic pathways. Furthermore, increased levels of triacylglycerols, phosphatidylcholines, prostaglandin E2, arginine, and decreased levels of betain and adenosine were associated with pulmonary CO diffusing capacity and total lung capacity. The global plasma metabolomic profile differed between subjects with abnormal and normal pulmonary function. CONCLUSIONS: Further metabolite-based analysis may help to identify the mechanisms underlying pulmonary dysfunction in COVID-19 survivors, and provide potential therapeutic targets in the future.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic respiratory virus with high infection and fatality rates. Previous studies have shown that despite recovery from severe acute respiratory syndrome (SARS), survivors had unresolved health issues, such as persistence of active alveolitis and impairment of gas diffusion [1] [2] .

Early analysis of coronavirus disease (COVID- 19) survivors suggests a high rate of lung function abnormalities [3] [4] [5] [6] .

Treatment options for pulmonary fibrosis are limited [7] . Therefore, there is a critical need to identify the molecular pathways involved in the development of pulmonary fibrosis and to develop novel treatment strategies. Metabolomics, a rapidly emerging field of -omics‖ research, can provide pathobiological molecular profiles that encompass both microbial and host interactions; this makes it a valuable tool for identification of biomarkers associated with disease development pathways, and for understanding the biological mechanisms driving the pathogenetic pathways.

Metabolomics approaches were useful to identify novel biomarkers and new pathobiological pathways associated with viral infections. SARS-CoV-2 infection has been demonstrated to cause multiple organ failure, suggesting systemic pathological effects [8] . Such systemic alterations may be reflected by a change in the levels of plasma metabolites. Therefore, we used plasma samples from COVID-19 survivors to profile their plasma metabolome.

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A total of 130 participants were ultimately included in this prospective study, including 34 mild/moderate patients (RMs), 69 severe/critical patients (RCs) who had been discharged from Wuhan Union Hospital for three months, and 27 uninfected healthy donors (HDs) who were matched for sex and body mass index (BMI) as controls. We excluded participants with the underlying lung diseases. All participants were negative for the SARS-CoV-2 nucleic Table   S1 ).

We collect case information and contact information of COVID-19 recovered patients (RPs) who were discharged from March 1 to March 30, 2020 in Wuhan Union hospital, against mandatory discharge criteria (normal body temperature lasting longer than three days; respiratory symptoms improved significantly; negative results of two consecutive SARS-CoV-2 RNA tests at least 24 hours apart). RPs who were meeting the inclusion criteria and being willing to participate were interviewed face-to-face in the outpatient clinic of Wuhan Union hospital at the point of 3 months of discharge. At the visit, all the participants received the nucleic acid test and antibody detection for SARS-COV-2, pulmonary-function test and chest CT scan. Routine blood test, biochemical and coagulation tests were completed for them at the same time. Then, we collected their peripheral blood samples and stored at -80℃ for subsequent metabolites detection.

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The standard protocol used here is in accordance with previously published method [9] [10] [11] [12] , and details have been listed in Supplementary methods.

An LC-ESI-MS/MS system (Shim-pack UFLC SHIMADZU CBM A UPLC system, coupled with QTRAP® 6500+ System MS) was used to analyze metabolites. To detect metabolites as much as possible, the hydrophilic and hydrophobic metabolites were respectively extracted and analyzed as per previously reported methods [13] , and details have been listed in Supplementary methods. The list of multiple reaction monitoring (MRM) transitions of detected metabolite is shown in Table S2 . Peak areas of metabolites and lipids were obtained using the Analyst software (version 1.6.3).

Orthogonal partial least square-discriminate analysis (OPLS-DA) was conducted using SIMCA-P software (version 11.0; Umetrics). For clinical characteristics and laboratory tests, and artificial intelligence of chest CT data analyses, Kruskal-Wallis (K-W) test for multiple groups and Mann-Whitney U test for two groups were used for continuous variables, and chisquare test or fisher's exact test for category variables. For lung function comparison between the three groups, analysis of covariance was used for continuous variables by setting the age and comorbidities as the covariates, chi-square test or fisher's exact test for all category variables. For metabolite profile comparison between every two groups, the metabolite profiles were firstly log transformed, then linear regression models were fitted for each metabolite profile by setting the age and comorbidities as covariates. In addition, for metabolite profile multiple tests, we used the false discovery rate (FDR) to control the false A c c e p t e d M a n u s c r i p t 7 positive (FDR <0.1 and p value < 0.05). The spearman correlations among the differential metabolites and clinical indices were calculated for correlation analyses. The statistical analyses were conducted by SPSS software (version 18.0.0) and R software (version 3.6.3).

Heatmaps of differential metabolites and relationships were displayed using the Multi Experiment Viewer software (MeV, version 4.7.4). Analyses of metabolite enrichment were conducted using the Metaboanalyst online software (http://www.metaboanalyst.ca/).

All the 103 recovered COVID-19 patients were enrolled at three months after their discharge.

And 27 HDs were included at the same time. Compared with RPs, HDs have significantly less co-morbidity and that the only co-morbidity in any of the HDs was hypertension.

Moreover, all the included HDs were confirmed as having almost normal CT scans and normal PFTs. More than 80% RPs tested IgG positive for SARS-Cov2 (Table 1) , suggesting the importance of humoral immunity in their recovery. In RMs or RCs, factors indicative of poor prognosis, namely lymphopenia and increased aspartate transaminase (AST) levels had returned to normal levels compared with those of HDs. However, laboratory parameters related to liver function (total bilirubin [TBIL], direct bilirubin [DBIL], albumin, A/G) and renal function (Cys-C) remained aberrant in RMs or RCs, compared with those in HDs. (Table 2) . Artificial intelligence (AI)-derived CT features for quantifying pneumonia lesions were studied to assess lung rehabilitation. All the findings indicated that the impact of COVID-19 on lungs A c c e p t e d M a n u s c r i p t 8 persisted in RMs and RCs. It seems like there is more lesion involvement in the right lung lower lobe of RCs compared to the RMs. Moreover, ground-glass opacities (GGO), the most common radiological abnormality identifiable at admission, was of significantly higher ratio in RCs than in RMs. Additional radiological features, such as solid components, appeared more frequently in RCs than in RMs. Overall, it seems like there is more R lung involvement in the RCs compared to the RMs.

Anomalies were mainly noted in lung volume and diffusion capacity (Table 3) 

Totally, 1124 metabolites (Table S2) were detected from 127 plasma samples (excluded 3 hemolysis samples). In QC analysis, CV values of more 90% of the metabolites were less than 20%, respectively ( Figure S1 ). 52 metabolites were differentially expressed in RMs and RCs, when compared with HDs ( Figure 1A ). Furthermore, plasma metabolic alterations in RCs were more significant than that in RMs ( Figure 1B , C).

In OPLS-DA analysis, the samples of COVID-19 RPs with normal and abnormal DLCO (ND&RM, ND&RC, AD&RM, and AD&RC) were separated from those of HDs, illustrating their evident differential plasma metabolite profiles (Figure 2A ). Compared with HDs, 51, 37, A c c e p t e d M a n u s c r i p t 9 95, and 169 metabolites were marked differentials in these four groups, respectively ( Figure   2B ). 21 metabolites, such as betaine, purine, stearidonic acid, vitamin D3, guanosine, few species of phosphatidylcholines (PCs), were the common differentials in those with abnormal DLCO (Table S3) .

Additionally, each group exhibited unique metabolite characteristics, such as elevated levels of glycerolipids and decreased levels of some acylcarnitine (AC) and organic acid (OA) in the AD&RC group. Compared with the alterations in the AD&RC group, the difference in the AD&RM group was mild, as evidenced by increased some sphingomyelin (SM), reduced some OAs ( Figure 2CD ).

RPs was clustered according to COVID-19 severity ( Figure 2A ); this separation was considerably more significant compared to that for DLCO. AD&RM and AD&RC samples presented many unique alterations, such as increased levels of AC, OA, SM in the AD&RM; while increased levels of amino acid (AA), fatty acid (FA), and triacylglycerol (TG) in the AD&RC group ( Figure 3A , B). Compared with the AD&RM group, decreased short-chain AC, FA, and inversely increased some AA, and OA were in the AD&RC group ( Figure 3C , Table S3 ). Pathway enrichment of differential metabolites revealed that lysine degradation, taurine and hypotaurine metabolism, alpha-linolenic acid metabolism, glycerophospholipid metabolism, were mainly disturbed in the subjects with abnormal pulmonary diffusion capacity ( Figure   3D , E). were decreased, and acetyltyrosine, acetylleucine, methylhistidine, some species of OA, PC, PE, and AC were increased ( Figure 4B ). Pathway enrichment analyses of differential metabolites showed that alpha-linolenic acid metabolism, arginine and proline metabolism, and Vitamin B6 metabolism were mainly disturbed in the AT subjects ( Figure 4C ).

Thirty and twenty-seven RPs with normal and abnormal DLCO presented abnormal CT findings (ACT&ND and ACT&AD), respectively. Compared with HDs, 44, 73, 63, and 57 metabolites were significantly altered in these four groups, respectively ( Figure 5A ).

Compared with abnormal CT groups, levels of OA, methylhisitidine, carnitine C5:1, and some TGs were increased in the ACT&AD group, while levels of some TGs and bile acids, including glycocholic acid, glycochenodeoxycholate, and glycinedeoxycholate were increased in the ACT&ND group ( Figure 5B , C).

During correlation analysis, many differential metabolites displayed significant relationships with the index of pulmonary diffusion capacity. For example, levels of DLCO%pred and DLCO/VA%pred were negatively associated with levels of arginine, and some SM in the RM A c c e p t e d M a n u s c r i p t 11 samples, and levels of Prostaglandin E2 (PGE2) and Prostaglandin E3 (PGE3), some species of TG in the RC samples ( Figure 6A , B).

In the association of TLC-related index, many metabolites such as kynurenine, acetyltyrosine, acetyl-leucine and methylhistidine, some of TGs, PCs were negatively correlated with the levels of TLC%pred or RV%pred; conversely, vitamin D3, guanosine, stearidonic acid were positively associated with these index ( Figure 6C ).

The levels of total GGO ratio, total solid ratio, or total lesion ratio were negatively correlated with levels of taurocholic acid, guanosine, trihydroxythrombadienoate, and hydroxymethylacetophenone; conversely, they were positively correlated with levels of citrulline, some TG and so on ( Figure 6D ).

Our results demonstrated that the COVID19 survivors who had more severe/critical infection also had more abnormal PFTs. Pathway analysis revealed that these alterations related to abnormal pulmonary function mainly involved the metabolic pathways of arginine biosynthesis, arginine and proline metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, glycerolipid metabolism, as well as sphingolipid metabolism. This may suggest that the metabolic alterations appear to be a marker of more severe clinical presentations as well as more abnormal PFTs. Impaired diffusion capacity is the most common lung function abnormality. Among plasma metabolic alterations, we found that lipid alterations in RPs with abnormal diffusion capacity were significant (Figures 2 and 3) . Furthermore, these alterations were associated with COVID-19 severity ( Figure 3C ). Among these lipids, levels of TG and PC were remarkably A c c e p t e d M a n u s c r i p t 12 associated with the levels of DLCO%pred, or DLCO/VA%pred ( Figure 6A-B) . Previous studies revealed that the levels of TG and PC were significantly altered in COVID-19 patients [13] [14] [15] , while the high levels of TG (18:2/18:3/20:4) and low levels of PC (18:0/20:3) can be used as potential biomarkers of COVID-19 [13] . Even at 3 months after discharge, levels of many individual TGs remained significantly high in COVID-19 RPs, especially in the RCs.

TGs were negatively associated with DLCO% pred. TG is a major energy storage molecule in cells. Excessive accumulation of TG in humans is associated with metabolic diseases and diabetes [16] . Similarly, there is a negative correlation between TG levels and DLCO among hyperlipemic patients, which may be related to alterations in surface-active lipoproteins in the lungs, caused by hyperlipoproteinemia or fat microembolism [17] . Since COVID-19 particularly affects the lungs, we hypothesize that SARS-CoV-2 may reduce DLCO by modulating pulmonary surface-active lipoproteins, thereby causing more TGs to be released into the circulation. This effect may be long-lasting among COVID-19 survivors, even at 3 months after discharge. Therefore, improvement of TG metabolism may provide a novel strategy for identification of therapeutic targets.

Prostaglandin E2 (PGE2), an eicosanoid, is a major immune mediator, and is used as a therapeutic target for treating various diseases [18] . Additionally, PGE2 is upregulated in cases of influenza A virus (IAV) and Helicobacter infections, which may inhibit the production of type I interferon and cause apoptosis in macrophages to further accelerate viral replication [19, 20] . Additionally, PGE2 inhibition can suppress antigen presentation and Tcell-mediated immunity. Targeted suppression of PGE2 has been shown to improve survival against IAV infection [19] . In our study, PGE2 levels were higher in the AD&RC group than those in ND&RC group. Furthermore, PGE2 levels were negatively associated with DLCO%pred and DLCO/VA%pred values. These trends have also been reported in patients with interstitial pneumonia and chronic obstructive pulmonary disease (COPD) [21, 22] . This study had several limitations. First, this was a single-center prospective study with a relatively small sample size. Second, patients with asymptomatic infection were not included in this study. Third, blood routine tests, liver and kidney function tests, and chest CT findings were not sensitive indicators of the organ injury presented by metabolomics. Therefore, future large-sized cohort studies using more sensitive measures are warranted.

In conclusion, our results demonstrated that plasma metabolite profiles of COVID-19 survivors with abnormal pulmonary function remarkably differed from those of HDs.

Pathway analysis revealed that these alterations related to abnormal pulmonary function mainly involved the metabolic pathways of lysine degradation, taurine and hypotaurine metabolism, alpha-linolenic acid metabolism, glycerophospholipid metabolism, arginine biosynthesis, as well as arginine and proline metabolism. M a n u s c r i p t 17 Central Universities, HUST: 2020kfyXGYJ011. The research sponsors did not participate in the study design; data collection, analysis, and interpretation; they were not involved in the writing of the manuscript and in the decision to submit the manuscript for publication.

The authors declared that no conflict of interest exists. A c c e p t e d M a n u s c r i p t 24 A c c e p t e d M a n u s c r i p t 26 showing the number of differential metabolites between the comparisons of HD and ND&NCT, ND&ACT, AD&NCT, and AD&ACT,AT. Heat map of differential features discovered in the ACT groups when compared with NCT groups with abnormal (B) and normal (C) pulmonary diffusion capacity. Red, green, and black denote relatively higher, lower, and mean levels, respectively. 

Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors

Thin-section CT in patients with severe acute respiratory syndrome following hospital discharge: preliminary experience

Anormal pulmonary function and residual CT abnormalities in rehabilitating COVID-19 patients after discharge

Prediction of the Development of Pulmonary Fibrosis Using Serial Thin-Section CT and Clinical Features in Patients Discharged after Treatment for COVID-19 Pneumonia

Temporal Changes of CT Findings in 90 Patients with COVID-19

Pneumonia: A Longitudinal Study

Abnormal pulmonary function in COVID-19 patients at time of hospital discharge

Type III procollagen is a reliable marker of ARDS-associated lung fibroproliferation

Extrapulmonary manifestations of COVID-19

Radiological findings from 81 patients with COVID-19 pneumonia in

China: a descriptive study

Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

Standardization of Spirometry

Standardization of Spirometry

Plasma metabolomic and lipidomic alterations associated with COVID-19

Omics-Driven Systems Interrogation of Metabolic Dysregulation

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Structure and catalytic mechanism of a human triacylglycerol-synthesis enzyme

Disturbances in pulmonary gaseous exchange in primary hyperlipoproteinemias

Prostaglandin E2 synthesis and secretion: the role of PGE2 synthases

Targeted prostaglandin E2 inhibition enhances antiviral immunity through induction of type I interferon and apoptosis in macrophages

Prostaglandin E2 prevents Helicobacter-induced gastric preneoplasia and facilitates persistent infection in a mouse model

Increased levels of prostaglandin E-major urinary metabolite (PGE-MUM) in chronic fibrosing interstitial pneumonia

The relationship between inflammatory markers and spirometric parameters in ACOS, Asthma, and COPD

Lung volume, median (IQR)

FRC (L) % predicted 111

Diffusion capacity, median (IQR)

Fractional exhaled nitric oxide, median (IQR)

Analysis of covariance was used for continuous variables by setting the age and comorbidities as the predictor variables. Chisquare test or Fisher's exact test for all category variables. DLCO was measured through single-breath method. Abbreviations: RMs: recovered mild/moderate patients; RCs: recovered severe/critical patients; IQR: interquartile range; BMI: body mass index; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; TLC: total lung capacity; RV: residual volume; FRC: functional residual capacity; DLCO: diffusing capacity of the lung for carbon monoxide; VA: alveolar ventilation

The authors are sincerely grateful to all the patients, individuals, and investigators who participated in this study. We are grateful for the assistance provided by Wuhan Metware Biotechnology Co., Ltd for metabolomics analysis, and Wuhan YITU Company for support on artificial intelligence. In particular, we want to express our sincere thanks to the Professor Hao Xingjie, School of Public Health, Huazhong University of Science and Technology, for his guidance and help on metabolomics statistical analysis.

This study was supported in part by the National Natural Science Special Foundation of 

M a n u s c r i p t 28