key: cord-0933076-ccfkqert authors: Bauer, A.; Pachl, E.; Hellmuth, J. C.; Kneidinger, N.; Frankenberger, M.; Stubbe, H. C.; Ryffel, B.; Petrera, A.; Hauck, S. M.; Behr, J.; Kaiser, R.; Scherer, C.; Deng, L.; Teupser, D.; Ahmidi, N.; Muenchhoff, M.; Schubert, B.; Hilgendorff, A. title: Proteome reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients date: 2022-03-06 journal: nan DOI: 10.1101/2022.03.02.22271106 sha: 68478e345125ac59d69b3e673dd8697192b71f4b doc_id: 933076 cord_uid: ccfkqert SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Many studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. To this end, we biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of this specific disease phase assignment, proteome analysis revealed a severity dependent general type-2 centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response together with the regulation of proteins related to SARS-CoV-2-specific symptoms by unbiased proteome screening both confirms results from targeted approaches and provides novel information for biomarker and therapy development. Graphical Abstract. Sars-CoV-2 remains a challenging threat to our health care system with many pathophysiological mechanisms not fully understood, especially in high-risk patients. Therefore, we characterized a cohort of hospitalized COVID-19 patients with multiple comorbidities by quantitative plasma proteomics and deep clinical phenotyping. The individual patient's disease progression was determined and the subsequently assigned proteome profiles compared with a healthy and a chronically inflamed control cohort. The identified disease phase and severity specific protein profiles revealed an antiviral immune response together with coagulation activation indicating the formation of NETosis side-by-side with tissue remodeling related to the inflammatory signature . The SARS-CoV-2 pandemic continues to pose an immediate threat to global health. As of January 2022, worldwide COVID-19 cases exceed 250 million and deaths have surpassed 5,4 million (WHO Coronavirus (COVID-19) Dashboard). Clinical manifestations vary from asymptomatic carrier to severe illness, organ dysfunction, chronic health impairment including long-COVID, and death (Williamson et al, 2020) . To gain deeper insight and inform patient care, epidemiological approaches addressed clinical characteristics of different SARS-CoV-2 infection phases in the overall population and identified risk factors for adverse outcomes such as diabetes or hyperlipidemia Huang et al, 2020; Guan et al, 2020) . Studies focused on the identification of clinical signs and early markers that reliably enable monitoring and treatment strategies Zhou et al, 2020) including nationwide approaches stemming from the United Kingdom, Germany, France, Israel, and the USA (Williamson et al, 2020; Nachtigall et al, 2020; Piroth et al, 2021) . While these attempts are already challenging in the general population (Booth et al, 2021; Hodges et al, 2020; Williamson et al, 2020) , the aim has yet to be reached in cohorts of high-risk patients characterized by a complex picture of preexisting comorbidities. Care for these patients results in resource-intensive monitoring and treatment and thus remains a critical hurdle even for maximum care hospitals. Poor vaccination response rates in a large number of such complex cases and vaccine breakthroughs further complicate the picture (Boyarsky et al, 2021; Malinis et al, 2021; Juthani et al, 2021) . Characterization of immune phenomena such as the 'cytokine storm' Buszko et al, 2021) helped to guide treatment initiation and aided first therapeutic approaches Huang et al, 2020; Zhang et al, 2020a) . Changes in human plasma protein levels have been suggested as disease indicators (Shu et al, 2020; Messner et al, 2020; Park et al, 2020) , in line with the implementation of protein markers for other viral diseases (Oxford et al, 2016) . The analyses were furthermore used to gain pathophysiological insight in order to develop new therapeutic strategies (Demichev et al, 2021; Haljasmägi et al, 2020; Filbin et al, 2021) . To increase pathophysiologic insight and enable the identification of disease indicators in high-risk COVID-19 patients with significant preexisting conditions to inform monitoring and treatment decisions in the most important disease phases, we profiled host responses to SARS-CoV-2 infection by the use of quantitative plasma proteomics. Tracing disease trajectories by individual expression of inflammation markers enabled us to improve general time-of-infection-based approaches (Schulte-Schrepping et al, 2020) . We thereby successfully identified disease grade and disease phase-specific proteome profiles side-by-side with the regulation of characteristic routine laboratory variables in a high-risk, multimorbid patient cohort. Our study included survivors and non-survivors from COVID-19 and a range from mild to severe disease symptoms compared to patients with acute non-COVID-19 related inflammation, as well as non-inflammatory control cases. We thereby delineated both COVID-19-specific and general immune responses together with the phase-specific involvement of coagulation and remodeling processes as well as the differential regulation of proteins related to SARS-CoV-2-specific symptoms with the potential to significantly inform monitoring and treatment approaches. The study prospectively enrolled 64 patients with PCR confirmed SARS-CoV-2 infection during the first phase of the COVID-19 pandemic in Germany (03/2020 to 08/2020), before steroid treatment for SARS-CoV-2 was routinely prescribed. Patients were enrolled shortly before or at the onset of the acute infection phase when laboratory signs of infection and disease-specific symptoms develop. Twenty-five patients with acute (inflammatory control group; Ctrl-infl) or no/low non-COVID-19 related inflammation (healthy control group; Ctrl-noninfl) were additionally included in the study as control groups (see Materials and Methods -Clinical Data Collection, patient grouping, and disease phase assignment, and Table 1) . A maximum WHO score ≥4 (Blueprint, 2020) during the hospital stay was observed in 27 patients (C19-ox group) and ≤3 in 37 patients (C19-nonox group) ( Figure 1A) . The disease severity groups showed differences in age and gender (age: C19-ox: 70 (IQR 59-79); C19-nonox: 57 (IQR 48-70); females: C19-ox: 37.0%; C19-nonox group: 32.4%), whereas no difference was observed for the time between symptom onset and hospitalization (C19-ox: 5 (IQR 1-8); C19-nonox: 5 (IQR 2-9) (days)) in contrast to a greater length of hospital stay in C19-ox patients (C19-ox: 12 (IQR 12-56); C19-nonox: 10 (IQR 7-17)). Most prevalent symptoms for C19-ox and C19-nonox patients were dyspnea (59.3%; 37.8%), fever (51.9%; 54.1%), fatigue (51.9%; 29.7%), and dry cough (48.2%; 48.7%). Both C19-ox and -nonox patients presented with different comorbidities including cardiovascular disease (66.7%; 64.9%), pre-existing lung disease (33.3%; 13.5%), immune compromise (37.0%; 24.3%), diabetes (33.3%; 18.9%), and hyperlipidemia (22.2%; 18.9%). In the course of the disease, some patients suffered from acute kidney failure (C19-ox: 11.1%; C19-nonox: 2.7%), whereas secondary bacterial, fungal, and/or viral infections ('superinfection') occurred more frequently in C19-ox patients (C19-ox: 44.4%; C19-nonox: 27.0%). 22.2% and 44.4% of patients in the C19-ox group underwent non-invasive or invasive ventilation. Therapeutic interventions including antibiotic (C19-ox: 81.5%; C19-nonox: 64.9%) and antithrombotic therapy (C19-ox: 59.3%; C19-nonox: 62.2%), parenteral nutrition (C19-ox: 37.0%; C19-nonox: 50.0%), non-opioid analgesics (C19-ox: 33.3%; C19-nonox: 40.5%) were administered in the majority of C19 patients irrespective of disease severity. Asthma therapy (C19-ox: 55.6%; C19-nonox: 8.1%) and antiviral treatment (C19-ox: 44.4%; C19-nonox: 16.2%) were used more frequently in higher disease grades. Non-C19 patients were assigned to the two control groups based on levels of the inflammatory markers IL-6 and CRP, Ctrl-infl (n=14) and Ctrl-noninfl (n=11) ( Table 1) . Median age in years did not differ between both control groups (Ctrl-infl: 74 (IQR 61-67); Ctrl-noninfl: 69 (IQR 51-75)), whereas female patients were more frequent in the Ctrl-noninfl group (Ctrl-infl: 14.3%; Ctrl-noninfl: 36.7%). The median length of hospital stay (in days) was 9 (IQR 7-12) in Ctrl-infl and 1 (IQR 0-2) in Ctrl-noninfl. Ctrl-infl and Ctrl-noninfl patients were characterized by a high prevalence of comorbidities including cardiovascular disease (85.7%; 63.6%), pre-existing lung disease (14.3%; 36.4%), immune deficiency (50.5%; 9.1%), hyperlipidemia (28.6%; 18.2%), and diabetes (21.4%; 18.2%). Patient characteristics at hospital admission for all study groups are presented in Table S1 & S2, details about the results of performed statistical tests are shown in Table S3 . Next to disease-severity assignment according to the WHO criteria (Blueprint, 2020), we grouped samples of the 64 C19 patients in three distinct disease phases -analysis were obtained 61 (IQR 38-75) days after the patient surpassed the inflammation peak (Figure 2D & 2E) . C19 patients from all groups entered the hospital five (IQR 3-16) days before and were discharged seven (IQR 5-15) days after the inflammation peak. All patients in need of intensive care during their hospitalization were admitted to the ICU 12 (IQR 3-25) days before and discharged seven (IQR 0-20) days after the inflammation peak. In the majority of C19 patients, mechanical ventilation was initiated four days before the inflammation peak (IQR 2-10) and was terminated in the acute-late phase eight (IQR 4-21) days after the inflammation peak. Twenty-two C19 patients developed a secondary (super)infection (Figure 2A (left)). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 At the time of admission, C19 patients showed significant changes in routine biochemical indices: Patients assigned to severe disease (C19-ox) showed comparable elevation of neutrophils when compared to the Ctrl-infl group, whereas patients with lower disease grades (C19-nonox) were characterized by blood cell counts within the physiologic range apart from monocytosis (Table S1 & S2) . Whereas CRP levels were found to be significantly different in all other group comparisons, CRP levels were comparable in patients from the C19-ox and Ctrl-infl group together with elevated levels of procalcitonin (PCT) and fibrinogen, thereby indicating the pathologic but non-discriminatory elevation of these parameters in C19-ox patients when compared to patients suffering from inflammation of different origin (Table S1 & S2) . Fibrinogen levels in C19-ox patients showed log2(FC)=0.42 higher abundance when compared to the C19-nonox patients. However, ferritin levels were pathologically log2(FC)=1.7 (C19-ox) to log2(FC)=2.5 (C19-nonox) increased in both C19 severity groups when compared to Ctrl-infl in contrast to lower partial thromboplastin time (PTT, sec) in both C19 disease grades with most pronounced changes in C19-ox (log2(FC)=-0.27, Table S1 & S2) . Reference values for routine laboratory indices are given in Table S4 . 10 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 6, 2022. ; Figure 3 . Dot plot of routine biochemical indices revealed significant (P-value ≤ 0.05) differences, describing a general C19 and a severity-based C19 signature when compared to the Ctrl-infl (A) and Ctrl-noninfl group (B) as well as the trajectory (C). Points represent significant level differences in the respective group comparison, where the color of the dots describes the effect size (log2 fold change) and the size of the dots the significance niveau (adjusted P-value). D: Distribution of selected biochemical indices over the course of the disease. When investigating the course of the disease, blood cell counts in C19 patients in the acute-early phase of disease were characterized by decreased lymphocyte counts Table S4 ). Whereas C19-ox patients in their acute-early and acute-late phase were comparable to the Ctrl-infl group with respect to elevated monocyte proportions, C19-nonox patients showed lower levels in the acute-early phase (log2(FC)=-3.05) that increase in the acute-late phase (log2(FC)=0.58). Likewise, neutrophil proportions were significantly elevated in C19 patients in the acute-early phase compared to acute-late phase (log2(FC)=0.13), with a trend to higher neutrophil levels in more diseased patients in the acute disease phase (acute-early: log2(FC)=0.12; acute-late: log2(FC) = 0.14). In the acute-late (C19-ox: log2(FC)=-3.54, C19-nonox: log2(FC)=-1.16) and recovery phase (C19-ox: log2(FC)=-4.27, C19-nonox: log2(FC)=-4.86) CRP levels were lower in both C19 severity groups when compared to the Ctrl-infl group (Figure 3) . The overall decline in CRP values in the course of the disease in C19 patients is most pronounced in more diseased individuals to a level of the Ctr-noninfl cohort, whereas in C19-nonox patients moderately elevated CRP levels remain in the acute disease phase and normalize in the recovery phase together with the C19-ox CRP levels ( Figure 3D , Table S5&6 ). Likewise, IL-6 values were differentially regulated through the course of the disease ( Figure 3D , Table S5&6 ) with C19-ox patients in their acute-early phase showing significantly higher IL-6 levels (log2(FC)=1.83, Figure 3C ) when compared to C19-nonox patients, in line with recent data (Herold et al, 2020) . Likewise, other inflammation parameters normalized in C19 patients in a disease-severity characteristic manner: While C19-ox patients in their acute-early phase showed elevated PCT levels that were indistinguishable from the Ctrl-infl 11 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Table S5&6 ). Accompanying the inflammatory response, platelet counts showed a physiologic niveau in more severely diseased patients in the acute-early phase to elevated levels exceeding Ctrl-infl levels in the acute-late phase (log2(FC)=0.75). In C19-nonox patients, the analysis revealed persistently high and even further increasing platelet counts in the course of the disease when compared to the Ctrl-infl and Ctrl-noninfl groups (Ctrl-infl -acute-early: log2(FC)=0.27, Ctrl-infl -acute-late: log2(FC)=0.43). Elevated fibrinogen levels in the acute-early and -late phase were observed in both C19 groups compared to Ctrl-noninfl, declining in more diseased patients in the acute-late phase with C19-nonox patients reaching and C19-ox patients reaching close to physiologic levels in the recovery phase ( Figure 3C & D, Figure S2 , Table S5 ). Likewise, elevated PTT levels in C19-ox patients were comparable to Ctrl-infl in acute-early and normalized to physiological levels in the acute-late phase. In C19-nonox patients, moderately higher PTT levels compared to Ctrl-noninfl in acute-early normalized during the acute-late phase (Figure 3 , Table S5&6 ). Levels for kidney function, i.e., creatinine levels, and estimated glomerular filtration rate (eGFR) or cardiac injury, i.e., hsTroponinT did only show significant differences in the acute-late phase when compared to Ctrl-infl with creatinine and hsTroponinT 12 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. By comprehensively dissecting systemic immune responses at the protein level in C19 patients we significantly added to pathophysiologic insight and enabled the identification of future disease indicators using plasma proteomics obtained on the Olink® Explore platform (see Materials and Methods -Olink plasma proteomics). Disease characterization by these means is outlined in the following chapters. The comparison to Ctrl-noninfl patients identified the differential regulation of 356 Protein regulation in both acute phases was characterized by strong activation of innate and adaptive immune responses including a type-2 immune response, i.e., and TNFR2 non-canonical NF-kB pathway signaling (i.e., TNFs bind their physiological receptors; TNF receptor superfamily (TNFSF) members mediating non-canonical NF-kB pathway), Neutrophil degranulation, DAP12 interactions, Immunoregulatory interactions between a lymphoid and a non-lymphoid cell and GPCR signaling (i.e., compared to Ctrl-noninfl ( Figure 4E , Table S8 ). The inflammation markers LGALS9 (Signaling by Interleukins, Interleukin-2 family signaling) and SIRPB1 (Neutrophil were significantly upregulated in both acute phases compared to Ctrl-noninfl, next to LRIG1, not represented in any regulated pathway. Interestingly, regulated proteins 14 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint such as LGALS9 or SIRPB1 hold matrix remodeling capacities next to immune functions (Hsu et al, 2020; Chen et al, 2019b) . Protein regulation in the acute-early phase was uniquely characterized by the involvement of inflammatory processes including interleukin signaling (i.e., (Regulation of TLR by endogenous ligand) were predominantly regulated in the acute-early phase in all C19 patients, whereas no differential regulation was observed in the acute-late phase when compared to Ctrl-noninfl. While TNFRSF8 (TNFs bind their physiological receptors, TNFR2 non-canonical NF-kB pathway) and HAVCR2 (Signaling by Interleukins, Interleukin-2 family signaling) were predominantly upregulated in the acute-late phase, their regulation was less pronounced in the acute-early phase when compared to Ctrl-noninfl. Regulation of coagulation processes could be observed during both acute disease phases when compared to Ctrl-noninfl (i.e., Cell surface interactions at the vascular wall, Platelet activation, signaling and aggregation; Response to elevated platelet 15 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; protein signature in the acute-late phase was dominated by processes such as the Non-integrin membrane-ECM interactions, and regulation of the apoptosis pathways (i.e., CASP8 activity is inhibited, Regulation by c-FLIP, Dimerization of procaspase-8). These processes were accompanied by a decrease in the expression of ITGA11 (Extracellular matrix organization, Integrin cell surface interactions, top-ranked protein in the acute-late phase) and the increased expression of SDC1 (Signaling by Interleukins, Extracellular matrix organization, Other interleukin signaling, Cell surface interactions at the vascular wall). Not represented in the enriched pathways, the metabolic marker CA6 was found to be regulated in the acute-late phase. The downregulation of CA6 is strongly linked to low salivary zinc concentrations, associated with decreased taste acuity (hypogeusia) (Shatzman & Henkin, 1981) , and has been used in the diagnosis of Early Sjögren's Syndrome (Jin et al, 2019) . In summary, the proteomic response of C19 patients during the acute disease phase was characterized by the activation of classical inflammatory pathways, including neutrophil degranulation, NETosis, as well as pro-inflammatory / HLA-DRlo monocyte expansion (Bardoel et al, 2014; Middleton et al, 2020; Nicolai et al, 2020; Zhou et al, 2021; Smet et al, 2021) (Figure 5 ). In addition, we detected a strong activation of interleukin signaling including activation of TNF signaling and a type-2 inflammation with the potential to counteract TNF-related signaling, especially in monocyte-related functions. These changes occurred together with the activation of both cytotoxic and humoral related immune defense mechanisms related to Interleukin-2 family signaling and DAP-12 in the acute-late phase, indicating the development of an adaptive immune response (Spolski et al, 2018 ). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint Whereas angiotensinogen, surfactant and SIRP metabolism, ROS regulation, and IL-6 signaling dominated protein regulation in the acute-early disease phase, the acute-later course was characterized by the differential expression of proteins indicating matrix degradation and apoptosis ( Figure 4E) . Pathway enrichment analysis reflected vascular activation and organ damage that persisted into the acute-late phase together with markers of both coagulation and thrombolysis along with platelet degranulation in both acute-early and acute-late disease ( Figure 4E ). However, protein regulation associated with coagulation processes was more pronounced in the acute-late phase. The comparison of C19 patients in the acute disease phases with Ctrl-infl patients demonstrated the differential regulation of eight proteins, all regulated in the acute-early phase irrespective of disease severity, whereas the comparison of the 18 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; acute-late phase demonstrated no differentially expressed proteins ( Figure 4A & B, Figure S3 , Table S7 ). The C19-specific profile in the acute-early phase demonstrated the upregulation of antiviral signaling (DDX58, IFNL1, SAMD9L, GRN), lysosomal protein degradation (LGMN, TPP1), and Toll-like receptor signaling (Pišlar et al, 2020) , as well as epithelial cell injury through upregulation of AGRN and downregulation of MUC16, as previously described in COVID-19 (Smet et al, 2021) of which a significant proportion was also regulated in the comparison to the Ctrl-noninfl group, i.e., DDX58, SAMD9L, LGMN, TPP1, and AGRN regulated in both acute phases; IFNL1 and GRN regulated in the acute-early phase. However, no coagulation markers or interleukin signaling-associated proteins were found differentially abundant. The differential regulation of DDX58 (RIG-I) controls the recognition of infected cells while IFNL1 leads to the activation of the JAK/STAT signaling pathway resulting in the expression of IFN-stimulated genes (ISGs). Interestingly, these ISGs mediate the antiviral state essential for containment of SARS-CoV-2 in the upper respiratory tract , while loss of ISG function is associated with severe COVID-19 (Bastard et al, 2020; Zhang et al, 2020b) . The host response is further characterized by the regulation of apoptosis, cell cycle arrest, and DNA damage through SAMD9L, the activation of defense mechanisms involving monocyte differentiation and MHC class II presentation through LGMN, lysosomal protease functions controlled by TPP1 and activated through acidification such as GRN that holds a role in inflammation previously associated with COVID-19. Epithelial cell damage was indicated by the regulation of AGRN, as part of the lung basal membrane and MUC16, controlling mucus secretion and engaged in epithelial cell replication and apoptosis. In summary, specific protein regulation in C19 likely related to an activation of the immune system not reflected by routine laboratory variables (i.e., CRP, IL-6, PCT, ferritin, and neutrophil proportions) that did not distinguish C19-ox patients from Ctrl-infl patients. A strong antiviral immune response side-by-side with markers indicating apoptosis and DNA damage both confirm previous findings as well as delineates the C19 immune response in the early course of the disease. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint When analyzing the general inflammatory response based on the comparison to Similarly, PDGFRA was only differentially regulated in C19-nonox but not in C19-ox, whereas coagulation activation was not observed by pathway enrichment analysis. Remodeling processes were indicated by the unique enrichment of pathways in the C19-nonox patients that related to Glycosaminoglycan metabolism (i.e., A 21 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. The protein profile in lower disease severity grades showed no differentially regulated proteins when comparing the acute-early and the acute-late phase to the Ctrl-infl group (Figure 4D) . Similarly, no differentially regulated proteins could be identified in the recovery phase for both oxygen-dependent and independent patients ( Figure 4B ). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10. 1101 /2022 In summary, processes identified in C19 patients in general as outlined above were found to be more prominently or solely regulated in severely diseased C19 patients. When comparing severity groups, these changes were accompanied by a strong induction of an innate immune response in the early-acute phase indicated by In contrast, protein regulation in less severely diseased C19 patients was dominated by a general inflammatory response exemplified by the group-specific regulation of interleukin-2 and IFNG signaling in the acute-early phase, as well as a shared pattern, i.e., regulated in both severity groups that included remodeling processes indicated by regulation of Extracellular matrix organization, Post-translational protein phosphorylation, and Regulation of Insulin-like Growth Factor transport and uptake by Insulin-like Growth Factor Binding Proteins in the acute-early and -late phase ( Figure 4E) . Interestingly, no pathways or proteins directly related to coagulation were found to be significantly regulated in less diseased patients, confirming previous clinical observations in these patients (Nicolai et al, 2020) . Changes observed in routine laboratory variables were reflected in protein expression patterns, i.e., elevated neutrophil numbers in more diseased patients correlated with the increased presence of markers for neutrophil degranulation and coagulation. Next, we investigated the disease phase-dependent regulation of plasma proteins in C19 patients. Changes over the entire disease trajectory revealed 45 inflammation markers, four coagulation markers, 20 markers indicating remodeling processes, and 19 metabolic markers to be differentially regulated ( Figure 6A, Figure S3 , Table S9 & S10). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint Specifically, 33 proteins were found to be differentially abundant between the acute-early and acute-late phase, yielding significant differences in interleukin signaling ( Figure 6A & B & E & F, Figure S3 ). As expected, 17 of those 33 proteins showed strong differences between the acute-early and acute-late phase when compared to Ctrl-noninfl. Here, CD14, IFNG, TNFSF13B, CTSO, ANGPTL1, GRN, C1QA, AGER, IFNL1, LAG3, HLA-E, CCL8, GAS6, IL4R, CTSZ, and PLA2G15 were upregulated in the acute-early phase when compared to Ctrl-noninfl but were not significantly regulated in later phases, indicating an innate immune host response that involves vascular and matrix remodeling specific proteins and prominent interferon-related signaling in the acute-early disease phase. In contrast, PADI4 was only upregulated in the acute-late phase when compared to Ctrl-noninfl, holding a critical role in granulocyte and macrophage-dependent immune responses and a critical role in NET formation . Upregulation of LGALS9, LRIG1, CXCL10, SIGLEC1, CD300C, and TCN2 in both acute phases compared to Ctrl-noninfl with significantly higher expression levels in the acute-early phase than in the acute-late phase pointed towards an innate immune response while involving factors that contribute to NET formation as well as stroke risk. GLB1, OXT (log2(FC)>2), FGF21, IL34, BAG3, SEMA4C, TLR3, GPR37, ANXA5 were found to be significantly upregulated in the C19 intragroup comparison of disease phases, i.e., during the acute-early disease course, whereas NCF2 was upregulated in the acute-late phase. When comparing the acute-early to the recovery phase, we identified 59 differentially regulated proteins involved in innate and adaptive immunity, as well as remodeling processes with a predominant upregulation in the acute-early phase. The top 10 regulated proteins reflected this by the inclusion of inflammatory (CD300E, IL15, IFNG, LGALS9, CD14, CXCL10, IFNGR2, LILRB4), metabolic (CASC4), and remodeling processes (CDON9). Downregulated proteins were engaged in immune response mechanisms including cell adhesion, adaptive immune processes, cell-cell matrix interaction, and related metabolic activity (CD1C, TNFSF11, SELPLG, SKAP1 (inflammation), CDON, THBS4, MFAP5 (remodeling), PLTP (metabolism)) ( Figure 6A & B, Figure S3 ). Between the acute-late and the recovery phase, 17 proteins were found to be differentially regulated including inflammatory (BST2 (log2(FC)>2), SDC1, DDX58, 24 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint LGALS9, LILRB4), and coagulation specific (C4BPB, ITIH3) processes together with Remodeling of cardiac muscle and blood vessels (CDH2, SMOC1) and other tissues (GFRA1, MDK, BAIAP2, SMOC1), and metabolic activity (CASC4, MME, FKBP5). Whereas these proteins were upregulated in the acute-late phase, the proteins ITGA11 and ROBO2 with a role in tissue remodeling were upregulated in the recovery phase (Figure 6A & B, bottom left circle) . It has to be noted that important proteins such as BAIAP2 and GFRA1 that were identified by the phase comparison hold critical functions in the central nervous system. While the majority of proteins, especially those associated with coagulation, showed a constant decrease in abundance over the disease trajectory, some proteins increased over time, e.g., CD1C, SELPLG (inflammation), SKAP1, TNFSF11 (inflammation), PLTP (metabolism), CDON, and MFAP5 (remodeling). Other proteins displayed more complex regulation patterns such as delayed changes, e.g., ITGA11, ROBO2, and THBS4 remained unchanged in acute disease and increased in recovery phase, while ANGPTL1 and FGF21 decreased in the acute-late and FKBP5 in the recovery phase; or alternating patterns (e.g., PADI4, SDC1, and BAIAP2) (Figure S4&S5) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint acute-late phase when compared to the recovery phase. (Figure 6A & D, Figure S3 ). Disease trajectory regulated inflammation associated pathways were primarily observed between acute-early and recovery phases, including interleukin signaling (i.e., Interleukin-10 signaling), Neutrophil degranulation, Cargo concentration in the ER, and Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell, capturing the innate and adaptive immune system, as well as ROS regulation. Remodeling processes further characterize the comparison between acute-early and recovery phase and comprised the Regulation of Insulin-like Growth Factor transport and uptake by Insulin-like Growth Factor Binding Proteins, as well as modification. Transport to the Golgi and subsequent modification was uniquely regulated in this comparison ( Figure 6F ). For proteins contributing to NET formation, a gradual decrease was detected over time for CCL8, LGALS9, ANXA5, GRN, CTSC, and MME when comparing the acute-early phase to later stages, whereas PADI4 and NCF2 showed an increase in the acute-late phase (Figure S4) , implicating neutrophil hyperactivation following the inflammatory peak. In summary, the acute-early disease phase is specifically characterized by an innate immune, virus-related host response that involves vascular and matrix remodeling, while matrix remodeling proteins were also found to be upregulated in the recovery phase when compared to the acute-late phase ( Figure 6A & B & E & F, Figure S3 ). In contrast, the critical regulator of NET formation PADI4 was differentially regulated in the acute-late phase. Protein expression pattern in both acute phases indicated regulation of innate immune defense mechanisms such as activation and recruitment of leukocytes, autophagy and indicated by the regulation of CXCL10, SIGLEC1, CD300C, NCF2, ANAX5, and BAG3 and matrix remodeling as identified through the differential expression of SEMA4, LGALS9, and GLB1, promoting mesenchymal activation and matrix formation. Interestingly, TCN2, engaged in vitamin B12 uptake, has been described to modify stroke risk (Hsu et al, 2011) , whereas GPR37 signaling has been shown to modulate the migration of olfactory ensheathing cells (Saadi et al, 2019) . This expression pattern was most prominent in C19-ox patients who 26 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 showed a strong time-dependent regulation of innate, adaptive immune, and stress response, as well as activation of the coagulation and complement system, e.g., upregulation of ITIH3 (coagulation) and C4BPB (coagulation), in the acute-late phase when compared against the recovery phase ( Figure 6A & C & D, Figure S3 ). CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 comparisons (C19-ox/C19-nonox) considering different disease phases; purple: inflammation; blue: coagulation; brown: remodeling; green: metabolism C: Intersections of differentially abundant plasma proteins for different disease phases in C19-nonox. D: Log2 fold-change of phase-regulated proteins. Protein symbols in bold are associated with NETosis, IL-1, or TNF signaling. E: Overlapping pathways of all disease phase comparisons. Although to date, numerous studies have described the wide range of symptoms of severe SARS-CoV-2 infection, e.g., acute respiratory distress syndrome (ARDS), lymphopenia, coagulopathy, and multi-organ damage (Bernardes et al, 2020; Faust et al, 2020; Wiersinga et al, 2020) , a detailed analysis of the underlying sequence of events is still missing. Studies that targeted protein regulation in COVID-19 patients aimed for a better understanding of disease-related processes while trying to identify potential biomarkers at the same time and have reported different immune response-related phenomena. The so-called "cytokine storm" comprised regulation of CXCL8, CXCL10, IL-6, TNFalpha, and IFNG, indicating that the synergism between TNF-α and IFNG, known to trigger inflammatory cell death and tissue damage, may account for SARS-CoV-2 mortality due to cytokine shock (Karki et al, 2021; Buszko et al, 2021; Yang et al, 2021) and potentially addressed by existing therapies (Tang et al, 2020) . Our study addressed the gap of existing knowledge with regard to a differentiated understanding of disease dynamics while specifically considering disease stage and severity, thereby significantly adding to existing knowledge in the field (Figure 7) . Rooting the protein markers detected by an unbiased approach in disease pathophysiology, we achieved the identification of critical disease-stage and -phase-specific indicators in high-risk COVID-19 patients. We both confirmed as well as newly discovered urgently needed markers in a COVID-19 patient population that is omnipresent in university hospitals due to diverse preexisting conditions. 28 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; Lam et al, 2021) . In general, cutoffs of inflammation markers have been used with good success in predicting COVID-19 severity at admission (Herold et al, 2020) but did not consider individual trajectories and threshold of laboratory variables that are likely of importance when studying a cohort with significant preexisting conditions and related medications. The significant variation in the course of the disease when comparing different patient groups and treatment settings (Tolossa et al, 2021) , including the average time for symptom resolution (2 to 71 days (Abrahim et al, 2020) or 10-14 (mild disease) to 21-42 (severe disease) days (Bhapkar et al, 2020; Jin et al, 2020) ), likely renders solely 'time-after-infection' based disease phase assignment inaccurate, 29 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. This response could be largely attributed to severe disease, whereas protein regulation in mild-to-moderately affected C19 patients was dominated by the disease-stage specific regulation of interleukin-2 and INFG signaling, as well as the shared regulation of remodeling processes as indicated by the regulation of Factor Binding Proteins in the acute-early and late phase. However, the differential regulation indicated no activation of coagulation processes in less diseased patients in comparison to the Ctr-noninfl group although anticoagulation treatments were equally administered in both groups (C19-nonox: 62.16%, C19-ox: 59.26%). Interpretation of these findings, however, needs to take into account that deaths in 30 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; the C19-ox group resulted in the overrepresentation of survival-related changes to protein expression in later disease phases. The strong upregulation of proteins related to NET formation Brinkmann et al, 2004) was observed during both acute COVID phases (acute-early: 58/78, acute-late: 54/78) with 36 proteins similarly regulated. Activation of NET formation in context with other indicators of inflammasome activation (Bardoel et al, 2014; Middleton et al, 2020; Nicolai et al, 2020; Zheng et al, 2020; Zhou et al, 2021; Smet et al, 2021) specifically characterized patients with severe disease (WHO >= 4). The regulated proteins included CD177, a prominent activation marker present on the surface of circulating neutrophils (Nicolai et al, 2020; Bai et al, 2017) , MME (CD10) as an immaturity marker of neutrophils and previously associated with severe COVID-19 (Schulte-Schrepping et al, 2020; Kaiser et al, 2021) , PDGFRA as a marker of platelet degranulation, and PADI4 as a key regulator of NETosis, whereas the classical NETosis/degranulation marker MPO was not found to be regulated in any of the comparisons. Similarly, strong activation of inflammasome related processes was indicated by the regulation of AGER as an important regulator of CASP-11 inflammasome activation (Chen et al, 2019a) side-by-side with an upregulation of IL-1 and IL-18 (Zheng et al, 2020) , as well as IL-6 and TNF expression together with an overenrichement of TNF receptor superfamily (TNFSF) members mediating non-canonical NF-kB pathway (Zheng et al, 2020) . Regulation of PLAUR points towards thromboembolic phenomena in these patients (Nicolai et al, 2020; Schulte-Schrepping et al, 2020; Wilk et al, 2020) , which were controversially discussed for their dependence on disease severity (Nicolai et al, 2020) . When tracking the disease course, we observed the differential regulation of protein expression related to angiotensinogen, surfactant and SIRP metabolism, ROS regulation, and IL-6 signaling during early disease in the overall comparison and especially in the C19-ox patients in comparison to the non-inflammatory control group, whereas the later phase is characterized by the predominant regulation of proteins associated with matrix degradation and apoptosis. On the one hand, we hereby show regulation of significant players in the immune host response confirming the role of inflammatory cell death and tissue damage (Karki et al, 2021) . On the other hand, we were able to add to previous studies by showing the dynamic of NETosis and inflammasome regulation (Bardoel et al, 2014; 31 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint Middleton et al, 2020; Nicolai et al, 2020; Zhou et al, 2021; Smet et al, 2021) in severely affected patients in contrast to a type-2 centered immune response involving interleukin 4, 10, 13, and TNF signaling in both disease groups or in less severe disease only (e.g., Interleukin-2 family and IN signaling) . These changes were found to be accompanied by remodeling processes. Activation of the coagulation system was primarily detected in severely diseased patients in our cohort, although clinical reports also detected thromboembolic events in less severe disease (Chen et al, 2021; Clavijo et al, 2021) , potentially due to the lack of detection regarding local, organ-specific events. Activation of the coagulation system in more severely diseased patients, as well as activation of the complement system likely drives thrombo-inflammation in COVID-19 (Afzali et al. 2021) . Regulation of proteins such as CA6 (associated with hypogeusia) or TCN2 (associated with stroke) identify disease characteristics, thereby supporting the significant potential of our unbiased approach to inform both pathophysiologic understanding and biomarker development. Previous studies that employed proteome analysis mirrored our findings such as activation of the complement system, monocyte signaling (CD14, proteins of the LGAL family) and inflammation (CD48, SIRPB1) (Park et al, 2020) , as well as the regulation of different plasma protease inhibitors such as ITIH3 (Geyer et al, 2021; Messner et al, 2020; Park et al, 2020; Shen et al, 2020) in COVID-19. Further in line with our findings, vascular markers such as vWF and proteins indicating coagulation activation were found to be regulated in previous studies, but in contrast to our studies described an early decrease (Messner et al, 2020; Shen et al, 2020) . Likewise, proteins involved in metabolic processes such as lipoprotein homeostasis (PLTP) were differentially regulated in COVID-19 patients. Enabling us to put the proteomic signatures into perspective and validate disease phase assignment, we comprehensively tracked biochemical indices. Here, comparable changes were observed in C19-ox and non-C19 related inflammation (Ctrl-infl) patients including, despite its common use in SARS-CoV2 (Liu et al, 2020a) , nondiscriminatory CRP levels when comparing C19-ox patients with subjects suffering from non-COVID related inflammation. Proteomic analysis, 32 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; however, significantly broadened the picture by demonstrating significant differences in protein expression between these groups. In contrast, PTT levels differed between C19-ox and Ctrl-infl patients, in line with the observed proteomic pattern indicating coagulation activation in C19-ox patients. Although shortened PTT times were discussed to predict poor outcome in patients with varying diseases (Reddy et al, 1999) , its role in COVID-19 is still controversially discussed (Devreese, 2021) . Changes in proteome pattern during the course of disease in each severity group were mirrored by cell numbers as well as coagulation and inflammation markers. Regarding differential blood counts, the analysis in C19-ox patients indicated lymphopenia, low monocyte levels, and neutrophilia when compared to C19-nonox patients, in line with previous studies Williamson et al, 2020; Lombardi et al, 2020 ) but again did not differentiate well severely affected patients from subjects with inflammation of different origin (Ctrl-infl) supporting the controversial discussion of their predictive value (Woodruff et al, 2020) . In context with the changes in differentiated blood cell counts, the proteome changes likely reflected the activation of the immune system and again indicated the added value in delineating COVID-19 immune responses in relation to disease severity and -phase (Figure 3 & 4) . Lower monocyte levels in more severely affected patients, however, could relate to the extravasation of these cells and suggest the subsequent activation of macrophages as indicated by the observed proteomic signature (Arango Duque & Descoteaux, 2014) . Association of INFG and TNF-associated monocyte polarization and the pro-fibrotic potential of monocyte-derived alveolar macrophages underline the potential of the observed signature to induce long-term remodeling (Castro et al, 2018; Misharin et al, 2017) . With regard to the impact of (co)morbidities, previous studies identified risk factors that were in part reflected in our cohort. Whereas C19-ox patients were characterized by increased age when compared to C19-nonox patients in line with previous studies Williamson et al, 2020) , we could not observe a higher rate of male patients in more severe disease Williamson et al, 2020) . Similarly, we did not observe a significantly reduced time between symptom onset and hospitalization for more diseased patients 33 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 Zhou et al, 2020), but confirmed a longer hospital stay Zhou et al, 2020) , higher rates for ICU admission Zhou et al, 2020; Grasselli et al, 2020) , invasive ventilation Zhou et al, 2020) , and adverse outcome. Further, we demonstrated a higher incidence of comorbidities, e.g., pre-existing lung disease, e.g., COPD or asthma Williamson et al, 2020) , hypertension Williamson et al, 2020) , diabetes Williamson et al, 2020) , kidney diseases (Williamson et al, 2020) , or impaired immune function Williamson et al, 2020) in more diseases patients (C19-ox). In contrast, we did not find disease symptoms more prevalent in C19-ox patients compared to C19-nonox patients with the exception of fatigue, dyspnea, and an increased incidence in secondary infections, in line with previous studies Zhou et al, 2020) and in part explaining the increased duration in hospital stay. Limitations of the present study include its observational design and the retrospective analysis resulting in missing data in a small number of patients. Partially counteracting these imitations, the study benefits from homogenous and comprehensive clinical monitoring in a high-risk patient collective that continuously dominates patient admission in university hospitals during the COVID pandemic. While providing a very good basis for biomarker identification in different disease phases and severity grades, results have to be confirmed in targeted approaches in different clinical centers. These prospective studies need to include -amongst others -environmental or social factors not investigated in the current study while considering the impact of emerging SARS-CoV-2 variants and the effect of the potentially gender-dependent vaccination status, not present in the first pandemic wave addressed in our approach (Ovies et al, 2021) . In summary, we identified a COVID-related protein signature that indicates an antiviral response together with NET / inflammasome activation predominantly driven by their regulation in severely affected patients. In contrast, regulation in less severely diseased patients was found to be characterized by a type-2 centered immune response. The findings were enabled by the newly identified disease trajectories based on the individual course of important routine laboratory variables. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 This approach both confirms findings from previous studies and also facilitates the identification of new proteins with significant potential to serve as COVID-19 disease indicators at the same time. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint Diabetes, high cholesterol, cardiovascular disease, lung disease, kidney disease, immuno-compromised status, steroid intake during or before proteomics sampling, superinfection during proteomics sampling 36 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint The study prospectively enrolled 65 patients with PCR confirmed SARS-CoV-2 infection during the first phase of the COVID-19 pandemic in Germany (03/2020 to 08/2020), before steroid treatment for SARS-CoV-2 was routinely prescribed. Patients were enrolled shortly before or at the onset of the acute infection phase when laboratory signs of infection and disease-specific symptoms develop. Twenty-five patients with acute (inflammatory control group; Ctrl-infl) Comprehensive electronic health records of all 90 patients were provided including baseline information like age, gender, medical background about comorbidities, and medication before hospital admission. Furthermore, information about the clinical course was provided including different routine biomedical indices (e.g., blood cell measurements), different inflammation markers like CRP, IL-6, or Ferritin, coagulation markers such as platelet count or PTT, and other body function values (e.g., creatinine or hsTroponinT, measured at admission as well as repeatedly over the hospital stay as needed), received treatments (e.g., ventilation or medication), and adverse events (e.g., acute kidney failure, thrombosis/embolism, or death). COVID-19 patients were classified according to ordinal scale for clinical improvement of COVID-19 infection reported by the WHO (Blueprint, 2020) and grouped into two sub-cohorts based on the need for oxygen supply, i.e., disease severity (WHO≥4 -C19-ox; WHO≤3 -C19-nonox) (Figure 1A) . To specify the host immune response to COVID-19 infection while considering the underlying disease phase, we developed a novel approach for a high-risk, 37 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted March 6, 2022. multimorbid patient cohort to improve upon general time-of-infection based approaches (e.g., (Schulte-Schrepping et al, 2020)) or approaches using thresholds for levels of inflammation markers (e.g., (Herold et al, 2020; Chen et al, 2020a) ). As IL-6 (> 80 pg/mL) and CRP levels (> 97 mg/L) correctly classified 80% of patients regarding their risk of respiratory failure (Herold et al, 2020) , we used these markers and extended this approach by defining disease phases based on individual trajectories while considering important clinical hallmarks. Using inflammation markers, we individually identified an inflammation peak for each patient, defined as the time point of the highest measured CRP or IL-6 value (whichever occurred later) broadened by a window of 24h after this peak to account for individual differences in inflammation marker decline. Accordingly, a total of three disease phases were distinguished: COVID-19-acute-early, phase. The COVID-19-acute-early phase was defined as the time between disease onset, i.e., the onset of clinical symptoms and/or first positive PCR test and the end of the inflammation peak. The COVID-19-acute-late phase was defined as the time after the inflammation peak until hospital discharge. The COVID-19-recovery phase was defined as the time after hospital discharge. In cases with significant discrepancy of disease severity, i.e., maximum WHO score at admission and the individual trajectory of IL-6 and CRP, the samples were assigned to the acute-late phase, assuming a surpassed inflammation peak at admission ( Figure 1B, Figure S1 ). Three samples (Patient 12 and 18, Figure S1 ) without any IL-6 and CRP peaks were assigned based on the proteomic data, by applying a k-nearest neighbors clustering algorithm to assign the samples to their most likely disease phase while validating clinical symptoms for group assignment. Non-C19 patients were assigned to two control groups based on the presence of inflammation: We included 14 patients with acute non-COVID related inflammation (Ctrl-infl) characterized by a maximum CRP>0.5 mg/dl or IL-6>5.9 pg/ml and 11 subjects without elevated inflammation markers (Ctrl-noninfl), i.e., CRP≤0.5 mg/dl and IL-6≤5.9 pg/ml ( Figure 1A) . Phase and group definitions can be found in Table 1. 38 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint A total of 129 samples were collected in all study subjects with one to five serial samples over the different phases and subjected to proteomic analysis. Plasma was separated from heparinized whole blood by centrifugation at 2,000 g for 15 minutes at room temperature and immediately stored at -80°C until preparation for proteome analysis. The Olink® Explore 1536 platform was used to measure protein abundance in plasma samples. The full library consisting of four 384-plex panels (Inflammation, Oncology, Cardiometabolic, and Neurology) was used to screen 1,472 proteins. Relative protein abundance was calculated from the number of matched counts on the Illumina NovaSeq 6000 run using two S1 flow cells with 2 × 50 base read lengths. The counts of protein specific-barcode sequences were transformed into Normalized Protein eXpression (NPX) units and an intensity normalization algorithm was applied to reduce the technical variation. The final data were provided in the arbitrary unit (NPX) on a log2 scale. Quality control (QC) was performed at both protein and sample levels. Three internal controls are spiked into each sample in order to monitor the quality of assay performance, as well as the quality of individual samples. Following criteria are applied to pass the sample QC: the average matched counts for each sample must exceed 500 counts; the deviation from the median value of the incubation-and amplification controls for each sample should not exceed +/-0.3 NPX for either of the internal controls. We, therefore, excluded 8 samples, whose mean of the failing proteins deviated more than 0.5 standard deviations from the overall mean of all samples which passed QC. As a further QC instance for comparability, the three proteins TNF, CXCL8, and IL-6 were measured in each of the four Olink® Explore panels. Since all four measurements were highly correlated for each of the three proteins (TNF: r=0.952-0.965; CXCL8: r=0.989-0.998; IL-6: r=0.979-0.997), we kept only one representative for each protein based on the minimal number of QC warnings, conformity in scatter plots, and population variances. Furthermore, Olink® recommends that proteins with a large proportion of samples below the limit of detection (LOD) should be excluded from the 39 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.02.22271106 doi: medRxiv preprint analysis. We, therefore, excluded 77 proteins that were under the LOD in more than 25% of samples in all study groups. We compared clinical covariates and routine biochemical indices within the first 24 hours after hospital admission separately for C19-ox and C19-nonox with both control groups (Ctrl-infl and Ctrl-noninfl) . In addition, we analyzed routine biochemical indices and plasma proteomics by (1) comparing each phase (acute-early, acute-late, recovery) separately with both control groups (Ctrl-infl and Ctrl-noninfl) -overall (i.e., severity-independent), and within C19-ox and C19-nonox; (2) comparing the phases (acute-early, acute-late, recovery) with each other -overall (severity-independent) and within C19-ox and C19-nonox; and (3) comparing the two severity groups (C19-ox and C19-nonox) with each other in each phase (acute-early, Continuous and categorical variables were presented as median (interquartile range (IQR) with 25% and 75% percentiles) and n (%) respectively. We used the Mann-Whitney-Wilcoxon test, Welch test, Tukey's range test, test, and Dirichlet 2 regression to compare differences between the C19 groups and the Ctrl-infl / Ctrl-noninfl control groups where appropriate. All tests were two-sided, and a P-value less than 0.05 was considered statistically significant. We used Python's SciPy package (Inglett et al, 2015) to perform the statistical analysis. The effect sizes are described as log2 fold change (FC). For the proteomics analysis, we used the R package limma ("Linear models for microarray data") (Ritchie et al, 2015) adjusted for the following confounders: age, gender, cardiovascular diseases, diabetes, high cholesterol, lung disease, kidney disease, immuno-compromised status, superinfection during proteomics sampling, steroid treatment during hospital stay during or before proteomics sampling (Table S11 ). Volcano Plots were created using the R package EnhancedVolcano (Blighe et al, 2019) . We conducted overrepresentation tests (based on hypergeometric models with a minimum count of three proteins) for biological processes and pathways using ClusterProfiler (Yu et al, 2012) and ReactomePA (Yu & He, 2016) , while the Enrichplot (Yu, 2018) package was used for visualization of the overrepresentation results. All tests for the proteomics analysis were corrected for multiple testing using 40 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 Benjamini-Hochberg correction, where a P-value less than 0.05 was considered statistically significant. For the significance tests, one sample per person was used in order to avoid autocorrelation. For the acute-early and acute-late phases, we used the proteomics sample, which was collected closest in time to the median difference to the inflammation peak in that phase. For comparisons with the recovery phase, we always used the very last sample, no matter which group it was compared to, in order to keep the sample size as large as possible. In the non-C19 group, only one person had serial samples. We used the sample which was closest to the median CRP value of the non-C19 group. We chose the clinical sampling closest to the proteomics sampling and accepted a range of four days ( Figure S6 ). Not all biochemical indices were available at any given time point ( Figure S7) . Furthermore, no D-Dimer and IL-6 values were available in the Ctrl-noninfl group. Time to recovery and its predictors among adults hospitalized with COVID-19: A prospective cohort study in Ethiopia Thrombosis risk associated with COVID-19 infection. 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Signal Transduct Target Ther 6: 255 pathway analysis and visualization clusterProfiler: an R package for comparing biological themes among gene clusters Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 Inflammasome activation and regulation: toward a better understanding of complex mechanisms Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study The Emerging Role of Neutrophil Extracellular Traps in Arterial, Venous and Cancer-Associated Thrombosis We would like to thank all CORKUM investigators and staff. The authors thank the patients and their families for their participation in the CORKUM registry.