key: cord-0868080-1vws4qam authors: Lee, Jee‐Soo; Han, Dohyun; Kim, So Yeon; Hong, Ki Ho; Jang, Myoung‐jin; Kim, Man Jin; Kim, Young‐gon; Park, Jae Hyeon; Cho, Sung Im; Park, Wan Beom; Lee, Kyung Bok; Shin, Ho Seob; Oh, Hyeon Sae; Kim, Taek Soo; Park, Sung Sup; Seong, Moon‐Woo title: Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID‐19 progression date: 2021-05-14 journal: Proteomics DOI: 10.1002/pmic.202000278 sha: 60a7ac8a74f82f15b532fdaeead421f09053a9f7 doc_id: 868080 cord_uid: 1vws4qam In managing patients with coronavirus disease 2019 (COVID‐19), early identification of those at high risk and real‐time monitoring of disease progression to severe COVID‐19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in‐depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID‐19 (non‐severe patients, n = 13; patients who progressed to severe COVID‐19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3‐19 and BNC2, as early potential prognostic indicators of severe COVID‐19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID‐19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS‐CoV‐2 infection, which are valuable for understanding of COVID‐19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID‐19. onset [1, 2] . Therefore, precise prognostication and early identification of disease progression are needed to effectively manage patients who are at high risk of clinical progression to severe COVID- 19 . Several previous studies have reported clinical laboratory findings including lactate dehydrogenase (LDH), C-reactive protein (CRP), albumin, D-dimer, lymphocytes, neutrophils, and platelets at admission as parameters for progressive risk estimation [3] [4] [5] [6] . Plasma levels of proteins associated with cytokine storms and immune response including interleukin (IL)-2, IL-6, IL-7, G-CSF, interferon (IFN)-γ inducible protein 10, monocyte chemoattractant protein-1, macrophage inflammatory protein-1α, and tumour necrosis factor (TNF)-α have been reported to be elevated in severe COVID-19 patients [2, 7, 8] . In addition, mass spectrometry (MS)-based quantitative proteome profiling has been performed in several recent studies, which reveal characteristic molecular changes and biological processes in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and at high risk of clinical progression [9, 10] . These studies have reported that severe COVID-19 patients have altered abundance of proteins involved in immune/inflammatory regulation (e.g., IL-6 and type 1 IFN), coagulation (e.g., fibrinogen and SERPINA10), lipid metabolism, and acute phase response (e.g., CRP) [11, 12] . Further proteome profiling may enable us to expand the understanding of prognostic molecular signatures altered at initial assessment or during the disease course in patients with a high risk of disease progression. In this study, we addressed two questions about prognostication in COVID-19: (1) Which protein markers are differentially expressed preceding clinical deterioration and are able to significantly predict the high risk in patients who progressed to severe COVID-19 compared to patients who had a non-severe disease course. (2) Which differentially expressed protein (DEP) markers exhibit longitudinal changes that effectively reflect clinical progression of the disease. We performed longitudinal proteome profiling in serum samples of COVID-19 patients at admission, and serially collected samples during their hospital stay. We then applied pathway analysis to gain insight into the relevant biological processes of altered protein expression. The overall strategy in this study is shown in Figure 1 . A total of 137 serum samples (sampled at admission, n = 25; longitudinally sampled during hospitalization, n = 112) were collected from 25 patients with laboratory-confirmed SARS-CoV-2 infection ( Figure S1 ). Each sampling time point is described in Table S1 . The protein digestion process was optimized to 2 μL of serum as previously described, with some modifications [14] . peptides were loaded onto a custom styrenedivinylbenzene reversedphase sulfonate (SDB-RPS)-StageTips, as described previously [14] . Following washing with 0.2% TFA, the peptides were eluted with 80% acetonitrile containing 1% ammonia. The eluate was dehydrated in a vacuum-centrifuge and stored at -80 • C. All LC-MS/MS analyses were performed using Quadrupole Orbitrap mass spectrometers, Q-Exactive Plus (Thermo Fisher Scientific, F I G U R E 1 Study workflow. In-depth proteome profiling was performed on 137 serum samples collected from 25 patients with COVID-19 (non-severe, n = 13; severe, n = 12). First, differentially expressed proteins (DEPs) between the two groups were identified. Then, the abundance of each candidate protein at admission and the risk probability of progression to severe COVID-19 were evaluated. Also, dynamics of DEPs in patients with disease progression to severe COVID-19 was assessed. Waltham, MA, USA) coupled to an Ultimate 3000 RSLC system (Dionex, Sunnyvale, CA, USA) via a nano electrospray source, as described with some modifications [15] . Peptide samples were separated on a 2column setup, with a trap column (75 μm I.D. × 2 cm, C18 3 μm, 100 Å) and an analytical column (50 μm I.D. × 15 cm, C18 1.9 μm, 100 Å). Prior to sample injection, the dried peptide samples were redissolved in solvent A (2% acetonitrile and 0.1% formic acid). After the samples were loaded onto the nano LC, a 90-min gradient from 8% to 26% solvent B (100% acetonitrile and 0.1% formic acid) was applied to all samples. The spray voltage was 2.0 kV in the positive ion mode, and the temperature of the heated capillary was set to 320 • C. The data-independent acquisition (DIA) method consisted of a survey scan at 35,000 resolution from 400 to 1220 m/z (AGC target of 3 × 10 6 or 60 ms injection time). Then, 19 DIA windows were acquired at 35,000 resolutions with an automatic gain control target of 3 × 10 6 and auto for injection time [16] . The stepped collision energy was 10% at 27%. To generate the spectral libraries for DIA, 24 data-dependent acqui- The proteins were inferred by the software, and the quantification information was acquired at the protein level by using the q-value < 0.01 criteria, which was used for subsequent analyses. The relative abundance of all proteins in all longitudinal samples were presented in We evaluated the association between the level of each candidate protein marker (i.e., DEPs) at admission and estimated the risk of disease progression using the Cox proportional hazard model. Hazard ratios with 95% confidence intervals (CIs) were calculated using Cox proportional hazard regression. The best cut-off value (BCV) of each candidate protein was determined using maximally selected log-rank statistics, as previously described [19] . The time to clinical progression to severe COVID-19 was plotted using Kaplan-Meier curves, and compared with a log-rank test; p value < 0.05 was considered statistically significant. We further explored the effect of longitudinal changes in each candidate protein level on the risk of clinical progression using a linear mixed-effects model; the level of statistical significance was set as adjusted p value < 0.05, FDR corrected [20] . Analysis was performed using commercially available software (SPSS 19.0, Stata SE12.0, and SAS version 9.4). The demographics and clinical characteristics of the study patients are summarized in Table 1 and Figure S2 . The median age was 70 years (interquartile range [IQR], 65 to 76 years), and 48.0% of the patients were male. The median time from symptom onset to admission was 5 days (IQR, 4-9 days). All patients were followed up for >10 days after admission. Among the total population, 13 (52.0%) patients who maintained an ordinal scale ≤ 4 (category 3, n = 4; category 4, n = 9) were classified into the non-severe group, and 12 (48.0%) patients with clinical deterioration to ordinal scale >4 (category 5, n = 9; category 6, n = 2; category 8, n = 1) were classified into the severe group. In the non-severe group, 9 (69.2%) patients required supplemental oxygen using either nasal prongs or mask; all the patients in the non-severe group improved and were discharged. In the severe group, the median time from admission to clinical deterioration requiring high-flow oxygen was 5 days (IQR, 3.5-7.5 days). Invasive mechanical ventilation and renal replacement therapy were administered in 25.0% (n = 3) and 8.3% (n = 1) of patients in the severe group, respectively. One patient (8.3%) died, and 11 (91.7%) patients were discharged with clinical improvement. Overall, 608 proteins were identified and quantified in the crosssectional samples. An average of 484 proteins were quantified in the individual samples ( Figure S3A ). To identify the differences within and between groups, protein profiles were generated by drawing multi-scatter plots and calculating Pearson's correlation coefficients (PCCs; Figure S3B ). Non-severe and severe groups showed mean PCCs of 0.88 and 0.89, respectively. The average PCC between the non-severe and severe groups was 0.87. Based on serum protein expression levels of patients in the non-severe and severe groups, two groups were separated by principal component analysis ( Figure S3C ). After proteins were determined to be quantified by at least 50% in either the non-severe or severe group subjects, 513 proteins were subjected to statistical analysis. We found that 46 proteins were differentially expressed in the serum, sampled immediately after patients had progressed to severe COVID-19, in severe patients compared with that in non-severe patients (Table 2, Figure 2A ) Volcano plot analysis of differentially expressed proteins (DEPs) between two groups. The expression difference in protein between severe groups and non-severe groups is plotted on the x-axis, and false discovery rate -adjusted p value is plotted on the y-axis (-log10 scale). Upregulated and downregulated proteins in the severe group are represented red and blue, respectively. (B) Gene ontology enrichment analysis. Heatmap of 46 proteins that are differentially abundant in severe COVID-19. Protein levels are presented on a low to high scale (bluewhite -red). We revealed that several DEPs (IGLV3-19, BNC2, CRP, LBP, RBP4, TTR, APOC1, APOA2, KNG1, PF4, and SERPINA4) were candidate predictors of progression to severe COVID-19. Notably, IGLV3-19, a variable domain of immunoglobulin light chains, and BNC2, an activator of genes in the IFN signalling pathway [21] , are novel candidate markers that have not yet been reported in SARS-CoV-2 infection. Table S4 ). Next, the BCV of each candidate protein was determined, and cumulative progression rates to severe COVID-19 were compared between the patients with high and low serum protein levels ( Figure 4) . Patients in the high-IGLV3-19, high-CRP, and high-LBP groups showed significantly higher progression rates, respectively, while patients in the low-BNC2, low-RBP4, low-TTR, low-APOC1, low-APOA2, low-KNG1, low-PF4, and low-SERPINA4 groups showed significantly higher progression rates (log-rank p < 0.05). To further investigate the dynamics of DEPs in patients with disease progression to severe COVID-19, we examined longitudinal changes in serum DEPs sampled at sequential time points (median, 7 time points; F I G U R E 3 Estimated probability of disease progression to severe COVID-19, according to serum level of each protein. The risk probability was estimated using a Cox proportional hazard regression model. Data sampled at the time of admission for both severe and non-severe groups were assessed. HR, hazard ratio; CI, confidence interval. Kaplan-Meier estimates of cumulative clinical progression to severe COVID-19. Cumulative progression estimates to severe COVID-19 are present in patients with high serum levels (coloured in blue) and in patients with low serum levels (coloured in red). The best cut-off value of each protein was determined using maximally selected log-rank statistics. Data sampled at the time of admission for both severe and non-severe groups were assessed. HR, hazard ratio; CI, confidence interval. TA B L E 3 Linear mixed effect model to identify which longitudinal changes of candidate protein markers were predictive of disease progression IQR, 6-11 time points) from each patient during hospitalization (Figure S1 , Table S1 ). The longitudinal changes in 40 DEPs were significantly different between non-severe patients and patients who were progressing to severe COVID-19 (adjusted p < 0.05; Table 3 , Figure S5 ) Here, we performed proteome profiling using serum samples from patients with COVID-19 at admission and serially collected more during hospitalization. We aimed to identify potential early prognostic protein markers that could stratify high-risk patients prior to disease progression. We then further explored DEPs, which exhibit longitudinal changes that effectively reflect the clinical progression of the disease. We found that IGLV3-19 and BNC2 could be potentially important prognostic factor for severe COVID-19. We observed increased abun- Of interest, which has not been reported yet, MAN1A1 and MAN1C1 persistently increased in the severe group subjects, and was significantly associated with the risk of disease progression. These are Golgi alpha-1,2-mannosidases, which are primarily involved in Nglycosylation that covalently adds glycan residues to the asparagine of proteins. This post-translational modification is increased in certain pathological conditions [25] . We speculate that MAN1A1 and ity. In this study, we verified that a number of proteins belonging to the acute phase response, lipid metabolism, and platelet degranulation were differently expressed between the groups of non-severe and severe patients [11, 12] . In contrast, we could not verify differential abundance of proteins belonging to the complement system. We further identified potential biomarkers differentially expressed preceding clinical deterioration in severe group and described longitudinal dynamics of the potential biomarkers during COVID-19 disease progression. Acute phase proteins such as CRP and LBP initiate the host innate immune response to viral infection. LBP transfers lipopolysaccharides (LPS) to CD14, which subsequently bind to the toll-like receptor 4 (TLR4)/myeloid differentiation factor 2 (MD-2) complex. The CD14-LPS-MD2-TLR4 complex initiates the signalling cascade of proinflammatory cytokines such as IL-6, IL-8, and TNF-a [28] . The interactions of LBP and pro-inflammatory cytokines in viral infections have been previously documented [12, 29] . The hepatic synthesis of RBP4 and TTR generally decreases during acute inflammatory states, and although the underlying pathomechanisms still remain to be clarified, a reduction in serum RBP4 and TTR has been reported as a predictor of poor clinical outcome in other diseases such as sepsis and critical ill patients [30, 31] . Dysregulation of multiple apolipoproteins (e.g., APOA1, APOA2, APOC1, and APOM) and suppressed platelet degranulation in the serum of patients with severe COVID-19 have been previously reported [11, 12, 32] , which is in accordance with the findings of the present study. Currently, it is unclear whether SARS-CoV-2 infection and the pathogenesis of severe COVID-19 directly alter platelet activity [33] . However, considering that platelets directly recognize pathogens and release antimicrobial mediators by degranulation in viral infections, dysregulation of platelet degranulation in SARS-CoV-2 infection implies a possible impaired immune response in severe COVID-19 patients [34] . Our proteomic analysis further elucidates the predictive role of initial and serial measurement of these protein abundances over clinical outcomes in patients with COVID-19. One of the limitations of our study is that our findings have not yet been externally validated. However, we performed proteomic analysis of sequential time points from each patient, which enabled us to identify reliable DEPs that were consistently differentially expressed in severe disease status, at initial assessment, and during hospitalization. Our results should be validated in an independent patient cohort. In addition, the number of included patients was small (n = 25). Future larger prospective studies are warranted to validate these potential biomarkers. In summary, this study investigated the proteomics dynamics through longitudinal proteomic profiling on serum samples from patients with COVID-19, and demonstrates proteins significantly changed in severe versus non-severe disease. Our findings may give some new insights into host response to SARS-CoV-2 infection, which are valuable for understanding of disease progression to severe COVID-19. Our data could be integrated with additional data in the near future to develop protein biomarkers, which might enable better predicting and monitoring progression to severe COVID-19. Clinical course and mortality risk of severe COVID-19 Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. 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How to cite this article: Lee Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID-19 progression The authors declare no conflict of interest. The data that support the findings of this study are openly available in ProteomeXchange Consortium via the PRIDE at https://www.ebi.ac. uk/pride/archive, reference number PXD023686.