key: cord-0689763-7j9nn9ff
authors: Chen, Yang; Zhang, Nan; Zhang, Jie; Guo, Jiangtao; Dong, Shaobo; Sun, Heqiang; Gao, Shuaixin; Zhou, Tingting; Li, Min; Liu, Xueyuan; Guo, Yaxin; Ye, Beiwei; Zhao, Yingze; Yu, Tongqi; Zhan, Jianbo; Jiang, Yongzhong; Wong, Catherine C.L.; Gao, George F.; Liu, William J.
title: Immune response pattern across the asymptomatic, symptomatic and convalescent periods of COVID-19
date: 2021-11-11
journal: Biochim Biophys Acta Proteins Proteom
DOI: 10.1016/j.bbapap.2021.140736
sha: 73386b83c92976d7a7484dc1ff462b11b9ef6da8
doc_id: 689763
cord_uid: 7j9nn9ff

We present an integrated analysis of urine and serum proteomics and clinical measurements in asymptomatic, mild/moderate, severe and convalescent cases of COVID-19. We identify the pattern of immune response during COVID-19 infection. The immune response is activated in asymptomatic infection, but is dysregulated in mild and severe COVID-19 patients. Our data suggest that the turning point depends on the function of myeloid cells and neutrophils. In addition, immune defects persist into the recovery stage, until 12 months after diagnosis. Moreover, disorders of cholesterol metabolism span the entire progression of the disease, starting from asymptomatic infection and lasting to recovery. Our data suggest that prolonged dysregulation of the immune response and cholesterol metabolism might be the pivotal causative agent of other potential sequelae. Our study provides a comprehensive understanding of COVID-19 immunopathogenesis, which is instructive for the development of early intervention strategies to ameliorate complex disease sequelae.

Since the outbreak of COVID-19, research has moved rapidly to develop diagnostic kits and test candidate vaccines 3 . Unfortunately, key questions about the pathogenic mechanisms underlying COVID- 19 have not been satisfactorily answered. This information is crucial for the design of rational therapeutic strategies. Inflammation and immune activation are expected to accompany a viral infection, and these processes have been extensively studied in SARS-CoV-2 infection. It has been suggested that COVID-19 is characterized by lymphopenia and increased numbers of neutrophils [4] [5] [6] . A growing body of evidence indicates that most patients with severe COVID-19 have high levels of proinflammatory cytokines including interleukin-6 (IL-6) [7] [8] [9] . More detailed analysis using single-cell transcriptomics of peripheral-blood mononuclear cells (PBMCs) accompanied by targeted serum proteomics showed that severe COVID-19 is marked by dysregulation of blood myeloid cells, including accumulation of immature neutrophils with an immunosuppressive profile, and repressed monocyte activation, accompanied by release of massive amounts of calprotectin (S100A8/S100A9) [10] [11] [12] [13] [14] . Multiple untargeted quantitative proteomic studies of serum or urine samples showed that COVID-19 patients have significant immune dysregulation 15-20 . However, current studies are mostly focused on the shift between moderate and severe COVID-19, and therefore lack a panoramic view of the pathogenicity of the whole disease process.

An important feature of COVID-19 is the existence of asymptomatic carriers who are able to transmit the virus to others. These asymptomatic carriers make the diagnosis, control and prevention of COVID-19 even more complicated. Asymptomatic infections are reported to be more common in populations of young and middle-aged individuals with functional performance status and no underlying diseases 21 . However, the pathogenesis of asymptomatic infection is not clear. In addition, it was not until recently that COVID-19 survivors were reported to be troubled with fatigue or muscle weakness, sleep difficulties, and increased neurological and psychiatric morbidity in the 6 months after COVID-19 infection. More strikingly, patients discharged from hospital after COVID-19 had increased rates of multiorgan dysfunction [22] [23] [24] . How these sequelae are related to disease pathogenesis is completely unknown, which makes it impossible to design early and rational interventions to prevent long-term symptoms in survivors. Thus, urgent research is needed to establish the risk factors for sequelae.

It remains unclear to what extent immune responses are causative or exacerbated factors to the disease and could be used for accurate patient stratification, as in asymptomatic, mild/moderate, severe and convalescent cases. Alterations of proteins in human body fluids are well recognized as direct indicators of pathophysiological changes caused by diseases. In this study we profiled the protein level of urine samples from asymptomatic carriers and of serum sample from recovered patients for 6 or 12 months through the data-independent acquisition (DIA) proteomics approach. We integrated the previously acquired proteomics data from urine of mild and severe COVID-19 patients and from serum of recovered patients for less than 1 month to get a whole picture of the disease progress on molecular level. We found that distinct changes in the levels of proteins related to immune function marked the different clinical trajectories of the disease. We also identified prolonged changes in proteins related to cholesterol transport, myocardial and coagulation disorders, which provides hints to explain the fatigue or neurological outcomes in convalescing patients. More importantly, the differentially expressed proteins identified and validated among multiple groups could contribute to diagnosis or prognosis and might be potential therapeutic targets.

We aimed to obtain comprehensive understanding of the entire COVID-19 disease process from acute to convalescent phase including asymptomatic, mild/moderate, severe cases. We collected urine samples from 70 asymptomatic carriers and 36 healthy controls (cohort 1) (Table S1, Figure 1a ). Asymptomatic carriers refer to people with positive detection of SARS-CoV-2 nucleic acid by reverse transcriptase-polymerase chain reaction (RT-PCR), but with no typical clinical symptoms or signs, and no apparent abnormalities. We also collected serum samples from 30 healthy controls, 60 patients (7 severe) recovered from COVID-19 for 6 months and 58 (7 severe) patients recovered from COVID-19 for 12 months (cohort 2) (Table S1, Figure 1a ). For the recovered patients, serum samples were taken at 6 or 12 months after diagnosis. Serum samples were first subjected to depletion of high-abundance serum proteins. Total proteins from the low-abundance fraction of serum samples or from urine samples were extracted, denatured, and digested into peptides by trypsin for dataindependent acquisition (DIA) mass spectrometry. The resulting quantitative proteomic data were subsequently analyzed for differentially expressed proteins (DEPs) and enriched pathways and further validated by ELISA (Figure 1a ).

The raw data were firstly processed using a double boundary Bayes (DBB) imputation J o u r n a l P r e -p r o o f a. Experimental design for the quantitative proteomic analysis in this study. In cohort 1, a total of 106 urine samples were analyzed from 2 groups: healthy controls, n=36; asymptomatic carriers, n=70. In cohort 2, a total of 148 serum samples were taken from 3 groups: healthy controls, n=30; convalescent patients (7 severe cases) recovered for 6 months, n=60; convalescent patients (7 severe cases) recovered for 12 months, n=58. The cohort 3 and 4 are data from other studies. All differentially expressed proteins (DEPs) selected for further analysis meet the criteria that fold change >2 or <0.5, two-tailed t-test; p < 0.05).

b. Principal components analysis (PCA) showing the inter-group differences in cohort 1.

Individuals in the healthy control group and the asymptomatic carrier group are indicated by coloured symbols in the figure.

c. Volcano plot of identified urine DEPs comparing asymptomatic carriers with the healthy control group in cohort 1 (two-tailed t-test; p < 0.05, fold change >2 or <0.5). f. Hierarchical clustering shows 2 distinct groups differentiated according to similarity.

g. KEGG-based enrichment analysis of DEPs in the subclusters shown in f. KEGG terms were sorted by P value.

We found that many proteins involved in viral latency, viral genome replication, viral entry into host cells and lysosome functions are increased in urine samples from asymptomatic J o u r n a l P r e -p r o o f carriers ( Figure S1a , b). This suggests an infection stress response as expected. In asymptomatic carriers, many proteins involved in tight junction organization and tight junction assembly are increased except for FZD5 ( Figure S1c ). We previous observed that tight junction proteins are significantly decreased in urine samples of symptomatic COVID-19 patients 20 . This difference in tight junction protein levels between asymptomatic carriers and symptomatic COVID-19 patients might indicate a defense process at the very early stage of infection.

Most interestingly, we found that DEPs in asymptomatic carriers are highly enriched in immune response functions. Many proteins involved in the innate immune response, leukocyte-mediated immunity, neutrophil-mediated immunity and cytokine production were 

Reports of long-term consequences of COVID-19 in adult patients after discharge from hospital have raised serious concerns. In our study, more than 50% of discharged patients reported feeling unwell at 12 months (Supplementary Table 2 ). There is no molecular clue to how these sequelae occur, which makes diagnosis and intervention very difficult. Altered In another study by our group, we carried out quantitative proteomic analysis of serum samples from patients diagnosed with mild/moderate or severe COVID-19 at 2 distinct time points, shortly after diagnosis with COVID-19 and before discharge from hospital (cohort 4 in Figure 1a ) 32 . The immune response of mild/moderate patients is largely repressed at both time points (Figure 3e, g) . However, severe patients showed signs of an activated immune response (Figure 3f, h) . Up-regulated proteins include subunits of the proteasome system for protein degradation and antigen presentation. These results are consistent with previous proteomic studies on urine samples from moderate and severe patients 20 . The increased level of α-Synuclein (SNCA) might also be informative. SNCA has been well studied for its J o u r n a l P r e -p r o o f neuropathological roles, and extracellular α-synuclein also triggers immune cell activation, proliferation, secretion of cytokines and other immune mediators, and phagocytosis [33] [34] [35] [36] .

Interestingly, there is difference between the upregulated immune response proteins in severe patients and in asymptomatic carriers. It is still not clear, however, how these molecules determine the switch from asymptomatic infection to mild/moderate COVID-19 or from mild/moderate to severe status.

We and others have shown that cholesterol metabolism is directly related to the pathogenesis and severity of COVID-19, and an increased serum total cholesterol level seems to be specific for COVID-19 32, 37 . Interestingly, we found that cholesterol metabolism pathways all showed some degree of alteration with Apolipoprotein A-I (ApoA1) down regulated in asymptomatic patients, COVID-19 patients, and convalescent patients (Figure 4a -e) 20,32 .

ApoA1 is a major component of high-density lipoproteins (HDL), which are important for reverse cholesterol transport. The disturbance of serum HDL may suggest a defect in cholesterol reverse transport and cholesterol functions. We found that HDL-C levels were reduced at both 6 and 12 months of recovery (Figure 4f ), which further supports the notion that cholesterol transport is disrupted along the entire progression of COVID-19, including recovery. HDL also has major pleiotropic functions that could play a pivotal role in acute inflammatory conditions. Evidence suggests that HDLs are globally protective for the endothelial layer. One study reported that HDLs from COVID-19 patients were less protective for endothelial cells stimulated with TNFα than HDLs from healthy subjects 37 .

Thus, we hypothesize that disrupted HDL involved cholesterol transport, which occur at the J o u r n a l P r e -p r o o f earliest stage of infection and persist throughout the entire period, might be an early inducing factor as well as a long-lasting adverse factor which determines disease outcome.

It has been reported by multiple groups that blood coagulation is disturbed in COVID-19 patients 38 . We found that levels of the coagulation factors IX and FBLN1 were significantly increased at both 6 and 12 months of recovery (Figure 4g, h) . Blood coagulation and inflammation are universal responses to infection and there is close crosstalk between them.

For instance, inflammatory cytokines and leukocyte elastase can down-regulate natural anticoagulant proteins that help to maintain endothelial-cell integrity and control clotting 39 .

Thus, immune suppression might contribute to the tendency for clot formation during recovery.

Several studies have reported major cardiac complications in COVID-19 patients including COVID-19-related myocarditis [40] [41] [42] . Moreover, recovered patients also present impaired cardiac function independent of preexisting conditions, which indicates that COVID-19 results in long-term heart sequelae. We found DEPs enriched in cardiovascular system development at 6 and 12 months of recovery (Figure 4i, j) . Specifically, the level of junction plakoglobin (JUP) is significantly reduced. JUP is a binding partner for major proteins in desmosomes, which are junction structures important in cardiomyopathy. Evidence shows that impaired desmosome assembly reduces incorporation of JUP into the cell membrane in arrhythmogenic right ventricular cardiomyopathy (ARVC) 43 c. Interaction diagrams of the indicated pathways when serum proteins from COVID-19 patients after recovery for 6 months are compared to healthy controls (cohort 2). d, e. Scatter plot graphs showing DEPs from a-c. One-way ANOVA was used to analyze the data. Data are presented as mean ± SEM. * P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001, **** P-value < 0.0001, t-test.

f. HDL-C levels in convalescent COVID-19 patients after recovery for 6 or 12 months (cohort 2). Data are presented as mean ± SEM. * P-value < 0.05, ** P-value < 0.01, *** Pvalue < 0.001, **** P-value < 0.0001, t-test. g, i. Interaction diagrams of blood coagulation, fibrin clot formation (g) or cardiovascular system development (i) when serum proteins from convalescent COVID-19 patients after recovery for 6 months are compared to healthy controls (cohort 2). h, j. Scatter plot graphs showing DEPs from g and i. One-way ANOVA was used to analyze the data. Data are presented as mean ± SEM. * P-value < 0.05, ** P-value < 0.01, *** Pvalue < 0.001, **** P-value < 0.0001, t-test. Table 2 ). Thus, the molecular alteration was more sensitive than blood tests for prediction of dysfunction. From the timeline of each observed disorders and the known inter-relationships of these processes, we speculate that immune responses and cholesterol metabolism might be the risk factors and pivotal causative agents of other molecular disorders, especially in the recovery stage, which are manifested as complex clinical symptoms. It remains to be explored why disorders of the immune system and cholesterol metabolism persist even when the virus is not detectable in recovering patients. The limitations of this study lie in the possibility that changes in serum or urine protein levels partially but not completely represents the activation or suppression of the pathways that the proteins function in. More rigorous mechanistic studies in cellular or mouse models are required to trace the origin of these proteins, how they are released, and how they function in disease conditions. Single cell multi-omics studies for cell lineage tracing of samples from convalescent patients should be integrated with unbiased proteomics to dissect the relationship between disease pathogenicity and host cell response. Isolating neutrophils or monocytes from convalescent patients and testing their functions in vitro may also provide insight into specific immune mechanism. In addition, more personalized study requires molecular mapping of longitudinal samples from one patient across the disease progression.

In conclusion, our work gives a whole view of disease progression at the proteomic level and finds that distinct immune response patterns and altered cholesterol metabolism constitute the central hub for clinical trajectories of the disease and risk factors for long-term sequelae. Our study provides a valuable proteomics resource at multiple disease stages to comprehensively understand COVID-19-associated host responses. Our results shed light on the pathogenesis of COVID-19 and, more importantly, provide insight into the urgently needed mechanism of sequelae and early intervention strategies.

Urine samples were collected based on the protocol for routine mid-stream urine in the morning. Serum samples were collected in pro-coagulation vacuum tubes using standard venepuncture protocols. Serum was extracted by centrifugation for 10 min at 3000 rpm. The urine and serum samples were inactivated at 65 °C for 1 hour and subsequently stored at J o u r n a l P r e -p r o o f −80 °C before use. The study was approved by the Ethics Committee of the National

No.IVDC2020-021). Written informed consent was obtained from all participants.

Abundant protein depletion was performed on the Agilent 1290 Infinity II liquid chromatography system coupled with the Multi Affinity Removal Column, Human-14.

Protein concentration was measured using a BCA Protein Assay Kit (Thermo Scientific).

Urine samples were processed as described previously (Tian et al., 2020) . Firstly, urine proteins were precipitated by trichloroacetic (TCA) acid solution at 4 °C for 4 h, reduced by 20 mM (2-carboxyethyl) phosphine hydrochloride (TCEP) and alkylated with 40 mM iodoacetamide. The mixture was digested with 3 μg trypsin protease overnight. After desalting, 1/3 lysate was used to measure peptide concentration while the remaining 2/3 was dissolved in Milli-Q water with 0.1% formic acid (FA) for mass spectrometry analysis.

Serum proteins were also precipitated by TCA solution, reduced by 20 mM TCEP and alkylated with 40 mM iodoacetamide. The mixture was digested with trypsin protease at a was generated by determination of OD values from serially dilutions of the standard samples with known protein concentrations provided by the manufacturers.

Omicsbean software was used for data analysis including data imputation, normalization, and principal component analysis (PCA). Fold-change of 2 and fold-change<0.5 and P-value (ttest) of 0.05 were used to filter differentially expression proteins. Mfuzz was used to detect different sub-clustering models of gene expression among groups. Network visualization was performed using the ggplot228 packages and Cytoscape v.3.5.129 implemented in the Omicsbean workbench. Ingenuity Pathway Analysis (IPA) was performed to explore the downstream effects in the dataset of significantly up-or down-regulated proteins.

The experimental data that support the findings of this study have been deposited in iProX (integrated proteome resources) of ProteomeXchange with the accession code PXD026442.

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The authors thank Bruker Daltonics Inc. for their support in proteomic analysis. The authors are grateful to OmicsBean (Gene For Health Inc.) for their assistance in data analysis. The authors thank Xuezhao Feng (Xinjiang Medical University, Urumqi, China) for helping sample collection. This work is supported by the Big Science Strategy Plan

The authors declare no competing interests.