- Research
- Open access
- Published:
Plasma markers of COVID-19 severity: a pilot study
Respiratory Research volume 23, Article number: 343 (2022)
Abstract
Background
SARS-CoV-2 infected patients show heterogeneous clinical presentations ranging from mild symptoms to severe respiratory failure and death. Consequently, various markers reflect this wide spectrum of disease presentations.
Methods
Our pilot cohort included moderate (n = 10) and severe (n = 10) COVID-19 patients, and 10 healthy controls. We determined plasma levels of nine acute phase proteins (APPs) by nephelometry, and full-length (M65), caspase-cleaved (M30) cytokeratin 18, and ADAMTS13 (a disintegrin-like and metalloprotease with thrombospondin type-1 motif 13) by ELISA. In addition, we examined whole plasma N-glycosylation by capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF).
Results
When compared to controls, COVID-19 patients had significantly lower concentrations of ADAMTS13 and albumin (ALB) but higher M30, M65, α1-acid glycoprotein (AGP), α1-antitrypsin (AAT), ceruloplasmin (CP), haptoglobin (HP), and high-sensitivity C-reactive protein (hs-CRP). The concentrations of α1-antichymotrypsin (ACT), α2-macroglobulin (A2MG) and serum amyloid A (SAA) proteins did not differ. We found significantly higher levels of AAT and M65 but lower ALB in severe compared to moderate COVID-19 patients. N-glycan analysis of the serum proteome revealed increased levels of oligomannose- and sialylated di-antennary glycans and decreased non-sialylated di-antennary glycan A2G2 in COVID-19 patients compared to controls.
Conclusions
COVID-19-associated changes in levels and N-glycosylation of specific plasma proteins highlight complexity of inflammatory process and grant further investigations.
Introduction
The coronavirus disease of 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most infected people experience mild to moderate respiratory symptoms and recover without requiring special care. However, some people get seriously ill or die. In general, older people and people with other medical conditions like obesity, cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. The wide spectrum of clinical presentations of SARS-CoV-2 infected patients implies the involvement of many biological pathways like immunological, inflammatory, and coagulative, and to those pathways related biomarkers. Accumulated data shows that in response to the SARS-CoV-2 infection global changes occur in protein synthesis, processing and posttranslational modifications, like phosphorylation and glycosylation [1]. Some of these changes might reflect the severity of COVID-19. Hitherto, changes in concentrations of D-dimer, cardiac troponin I, renal biomarkers, such as serum urea, creatinine and markers of glomerular filtration rate, C-reactive protein (CRP), ferritin, and certain cytokines/chemokines may have a diagnostic value [2,3,4]. Several studies reported that circulating levels of damage associated molecular patterns, such as cytoskeletal keratin-18 (CK18) fragment M30/M65 ratio (an indicator of cell apoptosis in relation to the total cell death), HMGB-1 or mitochondrial DNA may also reflect the severity of COVID-19 patients [5,6,7]. The activity of ADAMTS13 (a disintegrin and metalloprotease with thrombospondin type 1 motif 13), a regulator of von Willebrand factor (VWF) multimer disruption, typically decline with the increasing extent of inflammatory responses [8]. Recent data show that VWF antigen (VWF:Ag) to ADAMTS13 activity ratio is strongly associated with SARS-CoV-2 severity [9].
High levels of pro-inflammatory cytokines also linked to severity and poor outcomes in SARS-CoV-2 infection [10, 11]. For example, the levels of IL-6 rise sharply in severe manifestations of COVID-19. One meta-analysis reviewing six studies reported that mean IL-6 concentrations are 2.9-fold higher in patients with severe compared to those with non-severe COVID-19 disease [12]. As a matter of fact, IL-6 is the only cytokine that affects liver synthesis and secretion of the full spectrum of acute phase proteins (APPs) [13]. Other inflammatory cytokines, like IL-1β and TNFα, modulate synthesis of specific APPs and can have additive, or inhibitory effects on IL-6-induced APPs expressions [14]. In turn, the synthesis of APPs may be influenced by insulin, dexamethasone, and glucagon [15].
As mentioned above, C-reactive protein (CRP) can be used as a diagnostic marker reflecting the severity and prognosis of COVID-19 [16, 17], and the levels of CRP are helpful to differentiate between viral and bacterial infections [18, 19]. Decrease in serum albumin, which is a negative APP, also suggested as a marker of the severity of SARS-CoV-2 infection [20].
Some studies reveal that the host’s protein glycosylation is altered upon infection with SARS-CoV-2 as well. For example, critically sick patients develop high concentrations of IgG antibodies where the conserved N-glycan in the Fc domain is not fucosylated thereby causing an amplified cytokine storm [21]. The glycosylation pattern of AAT is also modified in severe cases of COVID-19, which affects anti-inflammatory properties of AAT protein [22].
Alterations in human plasma protein levels and/or their molecular forms indicate pathophysiological changes caused by various diseases, including viral infections. Therefore, host proteins might not only provide valuable insights for the pathogenesis of diseases, including COVID-19, but also might be useful as clinical biomarkers, either individually or in combinations, to monitor and evaluate the disease development. To explore the clinical value of plasma inflammatory proteins, we enrolled twenty COVID-19 patients, with moderate or severe symptoms and ten non-COVID-19 controls. We performed whole plasma protein N-glycosylation analysis, and measured plasma levels of M30 and M65, as cell death markers, ADAMTS13, as a marker limiting platelet-rich thrombus formation, and various APPs, including albumin (ALB), α1-acid glycoprotein (AGP), α1-antitrypsin (AAT), ceruloplasmin (CP), haptoglobin (HP), high-sensitivity C-reactive protein (hs-CRP), α2-macroglobulin (A2MG), α1-antichymotrypsin (ACT) and serum amyloid A (SAA). We hoped that in addition to previous reports, results from this pilot study could confirm and/or uncover plasma marker candidates associated with COVID-19 symptom severity.
Material and methods
Patients and biomaterial
Plasma samples prepared from ethylenediaminetetraacetic acid (EDTA) blood were obtained from the Hannover Unified Biobank (HUB). The ethics committee of the Hannover Medical School (MHH) (ethics vote 9001_BO_K) approved the sample collection. Sample processing and storage was performed following the standard procedures of HUB as described by Kopfnagel et al. [23]. For analysis, plasma samples from 20 patients were available: 10 from patients with severe COVID-19; and 10 from patients with moderately severe COVID-19 illness. Plasma samples from 10 age matched healthy donors were obtained from HUB biobank, collected in the frame of a healthy cohort set up by different northern German biobanks [24]. For this cohort, decentralized SOP guided recruitment of healthy volunteers without apparent diseases was carried out. Samples were collected between February 2018 and October 2018. According to the inclusion criteria, adult (≥ 18 years) and healthy Caucasian volunteers were included. Healthy controls had no systemic steroid or antibiotic therapy and respiratory tract infection in the last month, no asthma, autoimmune diseases, severe and very severe chronic obstructive pulmonary disease, GOLD (Global initiative for chronic Obstructive Lung Disease, http://www.goldcopd.org, version 2010), diabetes mellitus, pregnancy or lactating, active tuberculosis (current or in the past) and, immunosuppression (acquired, iatrogenic, or congenital).
Human M30 and M65 ELISA
CK18-Asp396 neo-epitope (M30-Apoptosense) and total soluble CK18 levels in plasma samples were measured by using commercially available ELISA kits, M30-Apoptosense assay kit (Cat# 10011, PEVIVA AB, Bromma, Sweden) and M65-ELISA assay kit (Cat# 10020, PEVIVA AB, Bromma, Sweden) according to the manufacturer’s instructions. Briefly, 25 µl of the sample (standard, blank or serum samples) was added to the assay plate and incubated for 4 h for M30 and 2 h for M65, respectively on a plate shaker at 300 rpm. The unbound conjugate was removed by washing five times, and afterwards 200 µl of TMB (3,3’, 5,5’ tetramethyl-benzidine) solution was added to each well for 20 min. The reaction was terminated by stop solution and absorbance was measured at 450 nm (Tecan Infinite M200, Männedorf, Switzerland).
Human ADAMTS13 ELISA
Human plasma ADAMTS13 concentrations were determined by using a commercially available ADAMTS13 ELISA kit (Cat# ab2345599, Abcam, Cambridge, UK) according to the manufacturer’s instructions. In brief, 50 µl of sample (standard, blank or diluted serum samples), plus 50 µl of antibody cocktail mixture was added to each well and incubated for 1 h on a plate shaker at 400 rpm. The unbound conjugate was removed by washing three times, and afterwards 100 µl of TMB solution was added for 20 min in the dark. The reaction was terminated by adding stop solution and analyzed at 450 nm by microplate reader (Tecan Infinite M200, Männedorf, Switzerland). Assay sensitivity 16 pg/ml, and detection range 0.125–10 ng/ml.
Analysis of plasma APPs
Plasma concentrations of APPs were measured blindly using the nephelometric method (IMMAGE 800 Protein Chemistry Analyzer, Beckman Coulter Inc., CA, USA) at the Department of Genetics and Clinical Immunology at the National Institute of Tuberculosis and Lung Diseases, Warsaw. Analysis sensitivity for measured APPs was for ALB: 22.2 mg/dl, AAT: 10 mg/dl, AGP: 35 mg/dl, hs-CRP: 0.02 mg/dl, CP: 2 mg/dl, HP: 5.83 mg/dl, and A2MG: 40 mg/dl. Plasma levels of ACT and SAA were measured by ELISA sandwich kit from BT Lab Bioassay Technology Laboratory (Shanghai, China) at 450 nm in a spectrophotometric reader Infinite M200 (Tecan, Austria). Assay sensitivity for ACT was 5.17 µg/ml and for SAA was 0.024 µg/ml. All plasma samples were analyzed at the same time, to control for testing variability.
N-glycan analysis by multiplexed capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF)
Sample preparation for CGE-LIF N-glycan analysis was performed as described previously [25, 26] with slight modifications. Briefly, N-glycans from whole blood serum were dissolved in SDS (2%, w/v) in phosphate buffer, the remaining SDS was neutralized with IGEPAL CA-630 (8%, v/v, Sigma-Aldrich), and glycoproteins were digested in solution with peptide-N-glycosidase F (PNGaseF from Elizabethkingia meningoseptica; BioReagent grade, Sigma-Aldrich) to release attached N-glycans. Released N glycans were fluorescently labeled with 8-aminopyrene-1,3,6-trisulfonic acid (APTS; Sigma-Aldrich). Briefly, the glycans were solved in 2 μl APTS (20 mM in 3.5 M citric acid), 2 μl 2-picoline borane complex (PB, 2 M in DMSO, Merck) as a reducing agent, and 2 μl water. After incubation for 16.5 h at 37 °C in darkness, the excess of APTS and reducing agent were removed by HILIC-SPE. Analysis of labeled N-glycans was conducted by xCGE-LIF using an ABI PRISM 3100-Avant Genetic Analyzer (advanced biolab service GmbH, Munich, Germany) and Run 3100-Avant Data Collection Software v.2.0. N-glycans were injected for 15 s at 1.6 kV and after a data delay of 50 s, data was collected for 45 min at 15 kV and 60 °C. Resulting data was analyzed using the GeneMapperTM Software v.3.7. To enable comparison of N-glycan levels between different samples, relative signal intensities of peaks from xCGE-LIF electropherograms were calculated as a percentage referring to the sum of all peak heights within each sample. N-glycan annotation was performed using an in-house established N-glycan database.
Statistical analysis
The IBM SPSS Statistics (version 27.0, IBM Corp., Armonk, New York) and STATA 13.0 (StataCorp LP, College Station, Texas, USA) statistical software packages were used to analyze the data. Graphs were prepared with Graphpad Prism V9 (Insight Partners, New York City, New York, USA). Categorical variables are shown as numbers (n) and percentages (%). Continuous variables are shown as median (interquartile range, IQR), unless indicated otherwise. Continuous variables were tested using the Kolmogorov–Smirnov test for normal distribution. For comparisons of patient groups Fisher exact test, chi-square test, Mann–Whitney U-test or paired t-test were used as appropriate. Pearson correlation coefficient was calculated to show correlation between continuous variables. All tests were two-sided, p-values < 0.05 were considered statistically significant. Statistical differences of relative N-glycan levels were assessed by individual comparisons between the control group and moderate or severe COVID-19 groups.
Results
Patients and controls
We analyzed plasma APP concentrations in retrospectively collected (between April and September 2020) samples from 20 COVID-19 patients: 10 with moderate and 10 with severe COVID-19 symptoms. According to treatment guidelines, moderate COVID-19 disease includes lower respiratory symptoms with a peripheral oxygen saturation (SpO2) equal or higher than 94% on room air, severe disease is defined by SpO2 < 94%, more than 30 breaths/min, or lung infiltrates > 50%, and critical illness include respiratory failure, septic shock and/or multiple organ dysfunction [27].
From the patients of the moderate COVID-19 group, 3 patients were admitted to an intensive care unit (ICU) or got temporary ventilation. All patients of the severe COVID-19 group needed intensive care and mechanical ventilation, 3 of them were supported with extracorporeal membrane oxygenation (ECMO). Detailed characteristics of the cohort are presented in Table 1. For a reference, plasma protein analysis was performed in age matched healthy controls.
Plasma levels of M30 and M65
Compared with healthy controls, severe COVID-19 patients had significantly higher levels of the apoptosis indicator M30 (Fig. 1A). However, independently on disease severity COVID-19 patients had higher levels of the total cell death indicator M65 than controls (Fig. 1B).
We analysed CK18-M30/M65 ratio, as an indicator of the fraction of cells undergoing apoptosis versus all types of cell death. The M30/M65 ratio did not differ significantly between severe and moderate patients [mean (SD), n = 10, severe: 0.19 (0.07) and moderate: 0.203 (0.04), p = 0.64] but was significantly lower as compared to healthy controls [mean (SD), n = 10, controls: 0.27 (0.06) vs. severe (p = 0.01) and moderate (p = 0.029)].
Plasma levels of ADAMTS13
As illustrated in Fig. 2, patients with mild and severe COVID-19 symptoms had significantly lower plasma levels of ADAMTS13 than healthy controls. Decreased levels of ADAMTS13 were linked with thrombotic events observed in patients with COVID-19 [28].
Plasma levels of APPs
As presented in Table 2, when compared to controls, COVID-19 patients independently on their disease severity had lower plasma levels of ALB (by 42%) but significantly higher levels of AGP (by 2.4-fold), AAT (by 1.9-fold), CP (by 1.4-fold), HP (by 3.8-fold), and hs-CRP (by 52-fold). Plasma levels of A2MG, ACT and SAA did not differ significantly between COVID-19 patients and controls.
Next, we investigated whether plasma APP concentrations differ between COVID-19 patients with different disease severity. As shown in Table 3, except for AAT and ALB, plasma concentrations of other APPs did not differ significantly between patients with moderate and severe COVID-19. Plasma levels of AAT were significantly higher (by 1.4-fold) while ALB lower (by 1.6-fold) in severe patients as compared to those with moderate disease. Albeit without statistical significance, levels of AGP, HP, and CP also increased but A2MG decreased in COVID-19 severity-dependent manner.
Correlations between measured plasma APPs
In patients with COVID-19 strong positive correlations were found between plasma levels of hs-CRP and AAT (r = 0.7, p < 0.001), and between HP and AGP (r = 0.61, p < 0.01) (Table 4). When COVID-19 patients were segregated into subgroups, amongst the patients with a moderate disease, strong correlations were found between AAT and AGP (r = 0.91, p < 0.001), HP (r = 0.79, p < 0.01), hs-CRP (r = 0.72, p < 0.01), and CP (r = 0.89, p < 0.01). However, in severe COVID-19 patients the strongest positive correlations were found only between plasma levels of ACT and SAA, hs-CRP and AAT, and between CP and AAT, and negative correlations between HP and ACT as well as between HP and ALB.
Whole plasma N-glycan analysis
N-glycosylation of whole plasma proteins of COVID-19 patients (moderate and severe) and healthy controls were comparatively analyzed by capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF). Thereby, 25 structures, exceeding a threshold of 2% relative signal intensity, were identified (Fig. 3). Of these, for 17 N-glycans relative levels were significantly different between COVID-19 patients and healthy controls. Of note, relative levels of oligomannose (M5, M9) and di-antennary sialylated glycans (A2G2S2 (6,6), A2G2S1(6) were higher in SARS-CoV-2 infected patients relative to controls. On the other hand, the corresponding di-antennary non-sialylated glycan (A2G2) was significantly lower in COVID-19 patients. Relative levels of two core fucosylated N-glycans were comparable between all groups (FA2G2S2(6,6), FA2). Further peaks also displayed significantly different intensities between study groups; however, in our database these picks were not assigned to specific glycan structures. The differences between moderate and severe cases were less pronounced. Here we found only two significantly different structures, which unfortunately could not be annotated.
Discussion
For unknown reasons some individuals infected with SARS-CoV-2 develop moderate symptoms and others become severely ill. Furthermore, patients differ in their response to therapies [30, 31]. These variable clinical manifestations reflect the host`s genetics, immunity, and comorbidities [32, 33]. Therefore, the discovery of biomarker signatures differentiating COVID-19 patients with different severities remains of importance. Since plasma/serum can be collected noninvasively, analyses of circulating protein levels and posttranslational modifications can give much information about the host`s proteome response to COVID-19 [34]. We conducted a pilot study in a small cohort of COVID-19 patients exhibiting severe or moderate symptoms to determine plasma levels of M30/M65, ADAMST13, various APPs and whole protein N-glycosylation profile. Although other studies have already investigated many proteins in COVID-19 patients, our findings may still contribute to the better understanding of the proteome responses to the infection. We provide results from a parallel measurement of nine plasma APPs concentrations, six of which were significantly higher in COVID-19 patients than in healthy subjects. Among these latter, median levels above reference values were found for AGP: 167.0 mg/dl (ref. 51–117 mg/dl), HP: 271.5 mg/dl (ref. 36–195 mg/dl), AAT: 235.5 mg/dl (ref. 88–174 mg/dl) and hs-CRP: 9.68 mg/dl (ref. less than 0.744 mg/dl). Albeit within the reference range, the levels of ALB were significantly lower, but CP were significantly higher in COVID-19 patients as compared to healthy controls.
Though changes in APP levels reflect general inflammatory response, altered magnitudes and profiles of these proteins might mirror distinct processes involved in the SARS-CoV-2 infection and disease progression. For instance, repeated studies show that levels of CRP are useful as a biomarker to follow COVID-19 progression [35], and patient management [36,37,38]. AAT is also acknowledged as one of the key immunomodulatory proteins during the SARS-CoV-2 infection [22, 39] beneficial for COVID-19 patients [40, 41]. AAT is a broad inhibitor of proteases and interacts with different pro-inflammatory substances [42]. Moreover, AAT inhibits the metalloproteinase domain 17 [43] and the type II transmembrane serine protease [39], two host proteases facilitating SARS-CoV-2 viral entry, replication and the pathogenesis of viral infections. AGP, which also was found to be higher in severe versus moderate COVID-19 patients, belongs to the lipocalin protein family. Functions of AGP remain incompletely understood, although typically associated with anti-inflammatory, immune modulatory, and sphingolipid metabolism [44, 45]. On the contrary, levels of A2MG and, especially ALB, decreased in association with COVID-19 severity. A2MG is one of the major blood proteins binding a wide range of substances, such as TGF-β1, TNF-α, IL-1β, and hormones, and inhibiting proteases, like trypsin, chymotrypsin, elastase, metalloproteinases as well as parasite-derived proteinases, and it is involved in blood coagulation and fibrinolysis [46]. Moreover, A2MG binds blood iron, zinc, and copper ions stronger than ALB, and acts as a serum copper transporter. The sequestering of metal ions by A2MG or ALB is one of the host defense strategies against infections [47]. Previous experimental studies have shown that the administration of A2MG prolongs graft survival and protects against sepsis [48, 49]. The impaired fibrinolysis together with lower levels of A2MG suggested as a risk of asthma exacerbations [50]. COVID-19 disease has not yet been associated with low plasma levels of A2MG, which inspires further investigations. However, low serum levels of ALB have already been associated with poor COVID-19 patient prognosis [51, 52]. Researchers also demonstrated a negative relationship between serum ALB levels and a risk of developing thromboembolism [53]. In some patients, however, increased hemolysis and altered levels of hemoglobin and heme-scavenging proteins (i.e., hemopexin, ALB or HP) were described [54]. Thus, triggered induction of AAT, AGP, CRP or other APPs as well as the reduction of A2MG and ALB during the SARS-CoV-2 infection may mirror specific pathological processes. However, it remains unknown which concentrations of these proteins are required to be protective and/or become detrimental.
N-glycosylation is a highly dynamic posttranslational modification strongly affected by viral infections and inflammation [55]. APPs represent a fraction of mostly glycosylated plasma proteins, and therefore, we anticipated changes in N-glycan levels upon SARS-CoV-2 infection. Our N-glycan analysis of whole plasma proteins revealed an increase in the di-antennary di-sialylated glycan A2G2S2(6,6) in COVID-19 patients, which is the most abundant N-glycan on AAT [56]. Increased relative levels of A2G2S2(6,6) could be explained by the observed higher plasma levels of AAT in COVID-19 patients. Also, the di-antennary mono-sialylated glycan A2G2S1(6) is highly elevated in the serum of COVID-19 patients whereas the corresponding non-sialylated di-antennary N-glycan A2G2 was even significantly decreased in COVID-19 patients. These findings hint towards an elevated sialylation of serum protein N-glycans upon SARS-CoV-2 infection. Accordingly, scientists reported that SARS-CoV-2 not only induces a production but also increases the sialylation of AAT [22]. This latter was linked to enhance anti-inflammatory properties of AAT [57]. Furthermore, we detected increased levels of oligomannose N-glycans (M5, M9) from serum proteins of COVID-19 patients. It is well known that glycoproteins with terminal mannoses are more rapidly cleared from the circulation than their complex glycosylated counterparts [58] which might therefore exert broad effects on serum levels of diverse glycoproteins including immunoglobulins [59].
Finally, there are increasing reports regarding cell damage and thromboembolism in COVID-19 cases. As COVID-19 is associated with hypercytokinaemia, cytokines, like IL-1, IL-6 and TNFα, may induce cell death. Therefore, we sought to quantify circulating levels of full length (M65) and caspase-cleaved (M30) cytokeratin 18 (CK-18), as markers of apoptotic and necrotic cell death. Indeed, M65 (total cell death) and M30 (apoptosis) were significantly elevated in COVID-19 patients compared with healthy controls. It is noteworthy that M30/M65 ratio did not differ between COVID-19 patients with different severities but was significantly lower than in controls. This latter suggests that cytotoxic or ischemic necrosis might be the predominant pathway of cell death in these patients. In line, other studies suggested that increased cell death in COVID-19 patients relates to defective organ perfusion, particularly in those who develop microvascular thrombosis [60]. We also found significantly lower levels of ADAMTS13 in COVID-19 patients relative to controls. The reciprocal relationship between VWF and ADAMTS13 in thrombosis is widely studied [61], and several groups reported that COVID-19 causes a significant increase in formation of large VWF multimers, and decrease in activity and/or levels of ADAMTS13 [62,63,64].
Conclusions
The number of patients and controls analyzed in this study is too small to allow for controlling potential confounding factors such as age, sex, smoking status, comorbidities, and therapies. The correlations between identified protein levels and changes in glycosylation need further investigations in larger cohorts. Nevertheless, our findings support a notion that inflammatory markers of different pathways when monitored simultaneously and combined with clinical data may help to improve prognosis and outcomes for COVID-19 patients.
Availability of data and materials
The data that support the findings of this study are available from the corresponding author, SJ, upon reasonable request.
Abbreviations
- A2MG:
-
Alpha2-macroglobulin
- AAT:
-
Alpha1-antitrypsin
- ACT:
-
Alpha 1-antichymo-trypsin
- AGP:
-
Alpha-1 acid glycoprotein
- ADAMTS13:
-
A disintegrin-like and metalloprotease with thrombospondin type-1 motif 13
- ALB:
-
Albumin
- APP:
-
Acute phase protein
- CP:
-
Ceruloplasmin
- ECMO:
-
Extracorporeal membrane oxygenation
- GOLD:
-
Global initiative for chronic Obstructive Lung Disease
- hs-CRP:
-
High sensitivity C-reactive protein
- HP:
-
Haptoglobin
- ICU:
-
Intensive care unit
- SAA:
-
Serum amyloid A
- SpO2 :
-
Peripheral oxygen saturation
References
Vedula P, Tang H-Y, Speicher DW, Kashina A. Protein posttranslational signatures identified in COVID-19 patient plasma. Front Cell Dev Biol. 2022;10:807149.
Chen LYC, Hoiland RL, Stukas S, Wellington CL, Sekhon MS. Confronting the controversy: interleukin-6 and the COVID-19 cytokine storm syndrome. Eur Respir J. 2020;56:2003006.
Kermali M, Khalsa RK, Pillai K, Ismail Z, Harky A. The role of biomarkers in diagnosis of COVID-19—a systematic review. Life Sci. 2020;254:117788.
Laing AG, Lorenc A, Del Molino Del Barrio I, Das A, Fish M, Monin L, Munoz-Ruiz M, McKenzie DR, Hayday TS, Francos-Quijorna I, et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020;26:1623–35.
Gong T, Liu L, Jiang W, Zhou R. DAMP-sensing receptors in sterile inflammation and inflammatory diseases. Nat Rev Immunol. 2020;20:95–112.
Scozzi D, Cano M, Ma L, Zhou D, Zhu JH, O'Halloran JA, Goss C, Rauseo AM, Liu Z, Sahu SK, et al. Circulating mitochondrial DNA is an early indicator of severe illness and mortality from COVID-19. JCI Insight. 2021;6:e143299.
Tojo K, Yamamoto N, Tamada N, Mihara T, Abe M, Goto T. Early alveolar epithelial cell necrosis is a potential driver of ARDS with COVID-19. Medrxiv. 2022;2022.01.23.22269723.
Bockmeyer CL, Claus RA, Budde U, Kentouche K, Schneppenheim R, Losche W, Reinhart K, Brunkhorst FM. Inflammation-associated ADAMTS13 deficiency promotes formation of ultra-large von Willebrand factor. Haematologica. 2008;93:137–40.
Mancini I, Baronciani L, Artoni A, Colpani P, Biganzoli M, Cozzi G, Novembrino C, Boscolo Anzoletti M, De Zan V, Pagliari MT, et al. The ADAMTS13-von Willebrand factor axis in COVID-19 patients. J Thromb Haemost. 2021;19:513–21.
Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ. Hlh Across Speciality Collaboration UK: COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020;395:1033–4.
Henderson LA, Canna SW, Schulert GS, Volpi S, Lee PY, Kernan KF, Caricchio R, Mahmud S, Hazen MM, Halyabar O, et al. On the alert for cytokine storm: immunopathology in COVID-19. Arthritis Rheumatol. 2020;72:1059–63.
Coomes EA, Haghbayan H. Interleukin-6 in Covid-19: a systematic review and meta-analysis. Rev Med Virol. 2020;30:1–9.
Castell JV, Gomez-Lechon MJ, David M, Andus T, Geiger T, Trullenque R, Fabra R, Heinrich PC. Interleukin-6 is the major regulator of acute phase protein synthesis in adult human hepatocytes. FEBS Lett. 1989;242:237–9.
Bode JG, Albrecht U, Haussinger D, Heinrich PC, Schaper F. Hepatic acute phase proteins–regulation by IL-6- and IL-1-type cytokines involving STAT3 and its crosstalk with NF-kappaB-dependent signaling. Eur J Cell Biol. 2012;91:496–505.
Baumann H, Gauldie J. The acute phase response. Immunol Today. 1994;15:74–80.
Luan YY, Yin CH, Yao YM. Update advances on C-reactive protein in COVID-19 and other viral infections. Front Immunol. 2021;12: 720363.
Stringer D, Braude P, Myint PK, Evans L, Collins JT, Verduri A, Quinn TJ, Vilches-Moraga A, Stechman MJ, Pearce L, et al. The role of C-reactive protein as a prognostic marker in COVID-19. Int J Epidemiol. 2021;50:420–9.
Haran JP, Beaudoin FL, Suner S, Lu S. C-reactive protein as predictor of bacterial infection among patients with an influenza-like illness. Am J Emerg Med. 2013;31:137–44.
Yamada T, Wakabayashi M, Yamaji T, Chopra N, Mikami T, Miyashita H, Miyashita S. Value of leukocytosis and elevated C-reactive protein in predicting severe coronavirus 2019 (COVID-19): a systematic review and meta-analysis. Clin Chim Acta. 2020;509:235–43.
Turcato G, Zaboli A, Kostic I, Melchioretto B, Ciccariello L, Zaccaria E, Olivato A, Maccagnani A, Pfeifer N, Bonora A. Severity of SARS-CoV-2 infection and albumin levels recorded at the first emergency department evaluation: a multicentre retrospective observational study. Emerg Med J. 2022;39:63–9.
Larsen MD, de Graaf EL, Sonneveld ME, Plomp HR, Nouta J, Hoepel W, Chen HJ, Linty F, Visser R, Brinkhaus M, et al. Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity. Science. 2021;371:eabc8378.
McElvaney OJ, McEvoy NL, McElvaney OF, Carroll TP, Murphy MP, Dunlea DM, Ni Choileain O, Clarke J, O’Connor E, Hogan G, et al. Characterization of the inflammatory response to severe COVID-19 illness. Am J Respir Crit Care Med. 2020;202:812–21.
Kopfnagel V, Bernemann I, Klopp N, Kersting M, Nizhegorodtseva N, Prokein J, Lehmann U, Stark H, Illig T. The Hannover Unified Biobank (HUB)—Centralized Standardised Biobanking at Hannover Medical School. Open J. 2021;8:1–12.
Bernemann I, Geithner K, Günther K, Habermann J, Herzmann C, Illig T, et al. Untersuchungen zum Einfluss dezentraler Strukturen im Bereich von Biomateriabanken auf die Qualität von Biomaterialproben - Influence of a decentralized biobanking concept on the quality of stored biomaterial samples. In: Hummel M, Illig T, Jahns R, Kiehntopf M, Lieb W, Nauck M, Prokosch HU, Schirmacher P, Claudius Semler S, editors. Moderne Biobanken—fit for purpose!: Tagungsband des 7 Nationalen Biobanken-Symposiums vom 11–12 Dezember 2018 in Berlin. Berlin: Akademische Verlagsgesellschaft; 2018. pp 145–146.
Hennig R, Rapp E, Kottler R, Cajic S, Borowiak M, Reichl U. N-Glycosylation fingerprinting of viral glycoproteins by xCGE-LIF. Methods Mol Biol. 2015;1331:123–43.
Beimdiek J, Hennig R, Burock R, Puk O, Biskup S, Rapp E, Lesinski-Schiedat A, Buettner FFR, Das AM. Serum N-glycomics of a novel CDG-IIb patient reveals aberrant IgG glycosylation. Glycobiology. 2022;32:380–90.
Malin JJ, Spinner CD, Janssens U, Welte T, Weber-Carstens S, Schalte G, Gastmeier P, Langer F, Wepler M, Westhoff M, et al. Key summary of German national treatment guidance for hospitalized COVID-19 patients: key pharmacologic recommendations from a national German living guideline using an Evidence to Decision Framework. Infection. 2022;50:93–106.
Seth R, McKinnon TAJ, Zhang XF. Contribution of the von Willebrand factor/ADAMTS13 imbalance to COVID-19 coagulopathy. Am J Physiol Heart Circ Physiol. 2022;322:H87–93.
Neelamegham S, Aoki-Kinoshita K, Bolton E, Frank M, Lisacek F, Lutteke T, O’Boyle N, Packer NH, Stanley P, Toukach P, et al. Updates to the symbol nomenclature for glycans guidelines. Glycobiology. 2019;29:620–4.
Rodriguez-Morales AJ, Cardona-Ospina JA, Gutierrez-Ocampo E, Villamizar-Pena R, Holguin-Rivera Y, Escalera-Antezana JP, Alvarado-Arnez LE, Bonilla-Aldana DK, Franco-Paredes C, Henao-Martinez AF, et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 2020;34: 101623.
Oldoni E, van Gool A, Garcia Bermejo L, Scherer A, Mayrhofer MT, Florindi F, Demotes J, Kubiak C, Fauvel AC, Bietrix F, et al. Biomarker Research and Development for Coronavirus Disease 2019 (COVID-19): European Medical Research Infrastructures Call for Global Coordination. Clin Infect Dis. 2021;72:1838–42.
Malik P, Patel U, Mehta D, Patel N, Kelkar R, Akrmah M, Gabrilove JL, Sacks H. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. 2021;26:107–8.
Hajjar LA, Costa I, Rizk SI, Biselli B, Gomes BR, Bittar CS, de Oliveira GQ, de Almeida JP, de Oliveira Bello MV, Garzillo C, et al. Intensive care management of patients with COVID-19: a practical approach. Ann Intensive Care. 2021;11:36.
McArdle A, Washington KE, Chazarin Orgel B, Binek A, Manalo DM, Rivas A, Ayres M, Pandey R, Phebus C, Raedschelders K, et al. Discovery proteomics for COVID-19: where we are now. J Proteome Res. 2021;20:4627–39.
Wang L. C-reactive protein levels in the early stage of COVID-19. Med Mal Infect. 2020;50:332–4.
Pepys MB. C-reactive protein predicts outcome in COVID-19: is it also a therapeutic target? Eur Heart J. 2021;42:2280–3.
Ali N. Elevated level of C-reactive protein may be an early marker to predict risk for severity of COVID-19. J Med Virol. 2020;92:2409–11.
Chen W, Zheng KI, Liu S, Yan Z, Xu C, Qiao Z. Plasma CRP level is positively associated with the severity of COVID-19. Ann Clin Microbiol Antimicrob. 2020;19:18.
Wettstein L, Weil T, Conzelmann C, Muller JA, Gross R, Hirschenberger M, Seidel A, Klute S, Zech F, Prelli Bozzo C, et al. Alpha-1 antitrypsin inhibits TMPRSS2 protease activity and SARS-CoV-2 infection. Nat Commun. 2021;12:1726.
Yang C, Keshavjee S, Liu M. Alpha-1 antitrypsin for COVID-19 treatment: dual role in antiviral infection and anti-inflammation. Front Pharmacol. 2020;11: 615398.
Ritzmann F, Chitirala P, Kruger N, Hoffmann M, Zuo W, Lammert F, Smola S, Tov N, Alagem N, Lepper PM, et al. Therapeutic application of alpha-1 antitrypsin in COVID-19. Am J Respir Crit Care Med. 2021;204:224–7.
Janciauskiene S, Wrenger S, Immenschuh S, Olejnicka B, Greulich T, Welte T, Chorostowska-Wynimko J. The multifaceted effects of alpha1-antitrypsin on neutrophil functions. Front Pharmacol. 2018;9:341.
Bergin DA, Reeves EP, Meleady P, Henry M, McElvaney OJ, Carroll TP, Condron C, Chotirmall SH, Clynes M, O’Neill SJ, McElvaney NG. Alpha-1 Antitrypsin regulates human neutrophil chemotaxis induced by soluble immune complexes and IL-8. J Clin Invest. 2010;120:4236–50.
Luo Z, Lei H, Sun Y, Liu X, Su DF. Orosomucoid, an acute response protein with multiple modulating activities. J Physiol Biochem. 2015;71:329–40.
Ruiz M. Into the labyrinth of the lipocalin alpha1-acid glycoprotein. Front Physiol. 2021;12: 686251.
Yoshino S, Fujimoto K, Takada T, Kawamura S, Ogawa J, Kamata Y, Kodera Y, Shichiri M. Molecular form and concentration of serum alpha2-macroglobulin in diabetes. Sci Rep. 2019;9:12927.
Porcheron G, Garenaux A, Proulx J, Sabri M, Dozois CM. Iron, copper, zinc, and manganese transport and regulation in pathogenic Enterobacteria: correlations between strains, site of infection and the relative importance of the different metal transport systems for virulence. Front Cell Infect Microbiol. 2013;3:90.
Dalli J, Norling LV, Montero-Melendez T, Federici Canova D, Lashin H, Pavlov AM, Sukhorukov GB, Hinds CJ, Perretti M. Microparticle alpha-2-macroglobulin enhances pro-resolving responses and promotes survival in sepsis. EMBO Mol Med. 2014;6:27–42.
de Boer JP, Creasey AA, Chang A, Abbink JJ, Roem D, Eerenberg AJ, Hack CE, Taylor FB Jr. Alpha-2-macroglobulin functions as an inhibitor of fibrinolytic, clotting, and neutrophilic proteinases in sepsis: studies using a baboon model. Infect Immun. 1993;61:5035–43.
Bazan-Socha S, Mastalerz L, Cybulska A, Zareba L, Kremers R, Zabczyk M, Pulka G, Iwaniec T, Bazan JG, Hemker C, Undas A. Impaired fibrinolysis and lower levels of plasma alpha2-macroglobulin are associated with an increased risk of severe asthma exacerbations. Sci Rep. 2017;7:11014.
de la Rica R, Borges M, Aranda M, Del Castillo A, Socias A, Payeras A, Rialp G, Socias L, Masmiquel L, Gonzalez-Freire M. Low albumin levels are associated with poorer outcomes in a case series of COVID-19 Patients in Spain: a retrospective cohort study. Microorganisms. 2020;8:1106.
Xu Y, Yang H, Wang J, Li X, Xue C, Niu C, Liao P. Serum albumin levels are a predictor of COVID-19 patient prognosis: evidence from a single cohort in Chongqing, China. Int J Gen Med. 2021;14:2785–97.
Chi G, Gibson CM, Liu Y, Hernandez AF, Hull RD, Cohen AT, Harrington RA, Goldhaber SZ. Inverse relationship of serum albumin to the risk of venous thromboembolism among acutely ill hospitalized patients: analysis from the APEX trial. Am J Hematol. 2019;94:21–8.
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8:475–81.
Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health and disease. Nat Rev Nephrol. 2019;15:346–66.
Clerc F, Reiding KR, Jansen BC, Kammeijer GS, Bondt A, Wuhrer M. Human plasma protein N-glycosylation. Glycoconj J. 2016;33:309–43.
McCarthy C, Dunlea DM, Saldova R, Henry M, Meleady P, McElvaney OJ, Marsh B, Rudd PM, Reeves EP, McElvaney NG. Glycosylation repurposes alpha-1 antitrypsin for resolution of community-acquired pneumonia. Am J Respir Crit Care Med. 2018;197:1346–9.
Schlesinger PH, Doebber TW, Mandell BF, White R, DeSchryver C, Rodman JS, Miller MJ, Stahl P. Plasma clearance of glycoproteins with terminal mannose and N-acetylglucosamine by liver non-parenchymal cells. Studies with beta-glucuronidase, N-acetyl-beta-D-glucosaminidase, ribonuclease B and agalacto-orosomucoid. Biochem J. 1978;176:103–9.
Falck D, Thomann M, Lechmann M, Koeleman CAM, Malik S, Jany C, Wuhrer M, Reusch D. Glycoform-resolved pharmacokinetic studies in a rat model employing glycoengineered variants of a therapeutic monoclonal antibody. MAbs. 2021;13:1865596.
Allegra A, Innao V, Allegra AG, Musolino C. Coagulopathy and thromboembolic events in patients with SARS-CoV-2 infection: pathogenesis and management strategies. Ann Hematol. 2020;99:1953–65.
Levi M, Scully M, Singer M. The role of ADAMTS-13 in the coagulopathy of sepsis. J Thromb Haemost. 2018;16:646–51.
Doevelaar AAN, Bachmann M, Holzer B, Seibert FS, Rohn BJ, Bauer F, Witzke O, Dittmer U, Bachmann M, Yilmaz S, et al. von Willebrand factor multimer formation contributes to immunothrombosis in Coronavirus Disease 2019. Crit Care Med. 2021;49:e512–20.
Escher R, Breakey N, Lammle B. ADAMTS13 activity, von Willebrand factor, factor VIII and D-dimers in COVID-19 inpatients. Thromb Res. 2020;192:174–5.
Hafez W, Ziade MA, Arya A, Saleh H, Ali S, Rao SR, Alla OF, Ali M, Zouhbi MA, Abdelrahman A. Reduced ADAMTS13 activity in correlation with pathophysiology, severity, and outcome of COVID-19; a retrospective observational study. Int J Infect Dis. 2022;117:334–44.
Funding
Open Access funding enabled and organized by Projekt DEAL. This work was supported by Polish National Science Centre Grants 2015/17/B/NZ5/01370 and 2018/29/B/NZ5/02346, by the European Union and the Ministry of Science and Culture (MWK) of Lower-Saxony, (COVAAT EFRE Project ZW 7-85152684), by the Bo Hjelt, Stockholm, Sweden, and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for Forschungsgruppe FOR2953 (Projektnummer: 432218849).
Author information
Authors and Affiliations
Contributions
SJ: concept, manuscript preparation; SW: data presentation and manuscript preparation; JF: data analysis; AR, acute phase protein assay; JB, FFRB: N-glycosylation analysis; SV, IP, TI, AP: providing patient cohort and clinical data; BO, JChW and TW: concept; all authors added comments to the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
The ethics committee of the Hannover Medical School (MHH) (ethics vote 9001_BO_K) approved the sample collection. All individuals included in the study signed informed consent and the study was performed according to the principles set out in the WMA Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors have no conflicts of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Beimdiek, J., Janciauskiene, S., Wrenger, S. et al. Plasma markers of COVID-19 severity: a pilot study. Respir Res 23, 343 (2022). https://doi.org/10.1186/s12931-022-02272-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12931-022-02272-7