SARS-CoV-2 infection risks developing into life-threatening COVID-19 disease. Whereas age, hypertension, and chronic inflammatory conditions are risk factors, underlying host factors and markers for disease severity, e.g. requiring intensive care unit (ICU) treatment, remain poorly defined. To this end, we longitudinally profiled blood inflammation markers, antibodies, and 101 plasma proteins of hospitalized COVID-19 patients who did or did not require (ICU admission. Whereas essentially all patients displayed SARS-CoV-2-specific antibodies and virus-neutralization capacity within 12-15 days, a rapid, mostly transient upregulation of selective inflammatory markers including IL-6, CXCL10, CXCL11, IFNg, IL-10, and monocyte-attracting CCL2, CCL7 and CCL8, was particularly evident in ICU patients. In addition, there was consistent and sustained upregulation of apoptosis-associated proteins CASP8, TNFSF14, HGF, and TGFB1, with HGF discriminating between ICU and non-ICU cohorts. Thus, COVID-19 is associated with a selective inflammatory milieu within which the apoptotic pathway is a cardinal feature with potential to aid risk-based patient stratification.
Research Article
Longitudinal proteomic profiling reveals increased early inflammation and sustained apoptosis proteins in severe COVID-19
https://doi.org/10.21203/rs.3.rs-64299/v1
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The coronavirus, SARS-CoV-2, has rapidly evolved into a global pandemic crisis. Although most infected individuals are either asymptomatic or develop mild symptoms, the infection poses the risk of potentially life-threatening COVID-19 disease to ~5-20% of symptomatic individuals, depending on their age group 1 2.
A relatively rapid deterioration of lung function observed in many admitted to intensive care unit (ICU), has been commonly attributed to an overreacting immune response. However, other studies have questioned the generality of increases in a broad spectrum of cytokines or the association of virus-load with disease-severity 3. Hence, there is a pressing need for greater granularity in characterizing the COVID-19 immune response, in which regard data currently available are highly informative but yet incomplete.
Thus, most patients across a range of disease severities were reported to display a core peripheral blood immune signature, in essence comprising hyperactivation and severe depletion of selective CD4+ and CD8+ ab T cell subsets, Vg9Vd2 T cells, and NK cells; adaptive B cell responses; and profound alterations in the composition of the blood monocyte and dendritic cell compartments 4, 5, 6. Within this signature, discrete components were associated with disease-severity5. Moreover, many but not all patients developed highly increased neutrophil-to-lymphocyte ratios, particularly as clinical manifestations diversify 7.
Additionally, aberrant leukocyte counts and composition were associated with increases in discrete inflammatory markers, specifically C-reactive protein (CRP) and procalcitonin (PCT), while interleukin-6 (IL-6) has emerged as a biomarker for COVID-19 disease-course, with gradual but stark elevations associated with increased risk of death 8, 9, 10, 11. Likewise, plasma IL-6, IL-10, and IL-8 levels are high in many COVID-19 patients 10, 12, 13.
Third, most patients reportedly develop antibodies to nucleocapsid (N) and Spike (S) proteins14, 15, 16, including to RBD, a receptor-binding domain of the S protein, that is essential for virus entry into host cells via the ACE2 receptor17. Antibodies to the N protein, which protects viral RNA, are apparently prevalent in patients with SARS-CoV and SARS-CoV-2 infections 18, 19, and can comprise a relatively sensitive early biomarker for infection 20, 21.
With these findings as a backdrop, our study has undertaken a deep analysis of plasma in COVID-19 patients requiring ICU admission versus those who did not. We have mapped out longitudinal trajectories of antibodies and over 100 discrete plasma proteins, identifying traits particularly penetrant in the ICU cohort. Besides antibody formation and transient elevations of discrete cytokines, those traits conspicuously included a sustained, apoptosis-associated signature that composed a cardinal feature of disease, possibly related to the presentation of stark lymphopenia particularly in severely affected individuals.
We studied longitudinal plasma samples from 40 hospitalized COVID-19 patients, relative to declared onset of symptoms (Table 1). Because the patients were not treated with immunomodulatory drugs, the results reflect the natural course of their disease. Based on oxygen levels and ICU requirements, the disease-status of 25 patients was classified as moderate and they were treated in the hospital ward (hereafter non-ICU). Conversely, 15 patients were classified as severe and were treated in the ICU (Supplementary Fig 1A). Blood plasma samples were collected from most patients on almost every day of hospitalization. As the mean age of COVID-19 patients was 66 years (range 21-92 years), we compared their results to negative controls mostly comprising individuals over 60 years old. In some instances, we additionally studied a small group of SARS-CoV-2+ mild COVID-19 patients who attended the emergency medical department, but who were not hospitalized.
Blood inflammation markers and cell proportions
Compared to healthy control levels (<5 mg/L), the CRP levels of ICU patients were highly elevated upon hospital admission (mean ~100 mg/L), being already significantly greater than the non-ICU cohort, and this difference continued to grow during the disease course (peak mean value of ~225 mg/L, versus ~95 mg/L for non-ICU) (Supplementary Fig 1B). The PCT levels were more comparable across the ICU/non-ICU cohorts at admission, but they too reached higher levels among ICU patients during their hospitalization (Supplementary Fig 1C). Consistent with other reports, most patients had markedly decreased lymphocyte representation, and increased neutrophil representation, with peak lymphocytopenia and neutrophil frequencies being more extreme in the ICU cohort relative to the non-ICU cohort (Supplementary Fig 1D-G). Both the ICU and non-ICU cohorts displayed comparable and decreased blood basophil (Supplementary Fig 1H) and eosinophil (Supplementary Fig 1I) frequencies upon hospitalization, and those low levels were sustained over the disease course.
Antibody trajectories to S1, S2, RBD and N proteins.
We used the LIPS method 22 to analyze antibody responses to S1, S2 (two subunits of Spike protein), RBD, and N protein, which we previously showed to correlate well with the ELISA method as reported earlier 21. All ICU and non-ICU patients developed IgG antibodies to S1, S2, N and RBD proteins during their hospitalization (Fig 1A-D). The seroconversion of the COVID-19 patients occurred 12 to 15 days after symptom onset, with an average of 13 days from the start of the disease (medians 15, 13, 12 and 13 days for S1, S2, RBD IgG and N, respectively; Fig 1E), although overall there was considerable variation in seroconversion kinetics, ranging from 5 to 25 days. Anti-RBD antibodies tended to appear 1-2 days earlier than the antibodies to the three other proteins. We found no significant difference in seroconversion times between non-ICU and ICU cohorts but noted several early antibody responders among non-ICU patients, but none among the ICU patients (Fig 1F). Following seroconversion, the antibody levels to the four proteins analyzed peaked at around 18-22 days. Although individual antibody levels varied considerably, the non-ICU patients tended to reach peak levels earlier, whereas the ICU patients peaked later but at higher levels, an observation that needs to be viewed in the context of the greater time-period over which plasma from the ICU cohort could be sampled (Fig 1G).
We found high anti-RBD levels among the ICU patients, suggesting strong virus-neutralizing immune responses; tested 15 ICU plasma for their capacity to neutralize SARS-CoV-2 virus; and correlated the titration results to LIPS values measuring antibodies to S1, S2, RBD and N proteins (Fig 1E). Thirteen of those ICU patients developed a neutralization titer of 1:640, 4 of whom had titers of >1280. Microneutralization titers correlated well with IgG RBD (r=0.7) and S1 (r=0.73) (Fig 1H) 23, and increased over time in 9 of 11 (82%) patients analyzed (Fig 1I).
All patients, for whom we had plasma samples for a longer period than the first 11 days, developed systemic anti-RBD IgA antibodies, albeit to varying levels, and those antibodies also correlated well with IgG anti-RBD antibodies (Fig 1J), and with virus neutralization (Fig 1H). There was no significant difference between ICU and non-ICU groups (Fig 1K), although the ICU group tended to have more patients with higher levels of RBD IgA antibodies (6 out of 11 ICU versus 3 out of 18 non-ICU patients for which IgA antibody data were available), and stronger correlations with RBD IgG antibodies (Fig 1J and 1H). The seroconversion of IgA RBD antibodies occurred at the same time of anti-IgG RBD (median 12 days) (Fig 1N), and tended to be earlier in the non-ICU cohort (median 10 vs 13 days) (Fig 1M), albeit that the ICU cohort developed higher anti-RBD IgA levels (Fig 1M).
We also studied the activation of the classical complement pathway in COVID-19 patients by measuring two complement components, C1q and terminal complement complex (TCC). Although COVID-19 patients in general tended to have higher levels of C1q, this difference was not significant (Supplementary Fig 2A), and there was no difference in TCC levels (Supplementary Fig 2B).
Early stage inflammatory mediators
We applied Olink and Legendplex technologies to undertake a targeted proteomic analysis of individual soluble inflammation-associated proteins and their corresponding correlations in COVID-19 patients. The Olink proximity extension assay (PEA) revealed a significant change of inflammation-associated plasma markers when comparing ICU and non-ICU groups with SARS-CoV-2 negative controls. In ICU and non-ICU patients, we observed increased levels of a triad of IL-6, IL-10, and CXCL10 (also known as IP10). Levels peaked within 24-72 hours after hospitalization (i.e. ~10 days after initial symptom onset), but declined thereafter before patient discharge, with the possible exception of IL-6 which remained high in several ICU patients after an initial decrease (Fig 2A-C). As was recently noted 4, the IL-6 – IL-10 – CXCL10 triad showed striking correlations with disease severity and the levels of these analytes were elevated in the great majority of hospitalized COVID19 patients, attesting to a highly inflammatory response to the virus.
Other prominent inflammation-associated markers, upregulated at the early stage of disease in ICU and non-ICU patients, were CXCL11, CCL2, CCL7, CCL8, PD-L1, and IL-18R1 (Fig 2D-J). IFNg, which is an activator of CXCL10, CXCL11, CXCL9, and other interferon-stimulated genes, was also elevated in the early stages of disease and declined over time, but the analysis of IFNg was complicated by considerable inter-individual variation among healthy controls (Fig 2E and 2K). Nonetheless, while hospital admission levels of the cited cytokines and chemokines were highly variable, there was a constancy to their decline in both patient cohorts, particularly for IFNg, CCL7, IL-6 and CXCL10 (Fig 2K). Although the PCA plot with the 10 markers measured at the early stage of the disease did not segregate ICU and non-ICU groups, there was some separation from the mild cohort (Fig 2L). Of note, this group of cytokines and chemokines collectively composes a set of well-established inflammation markers associated with activated myeloid and T cells, that may be highly germane to reports of myeloid and T cell dysregulation in COVID-19.
Sustained markers related to apoptosis and inflammation
Possibly in relation to the overt subset-selective T apoptosis reported for COVID-19 24, we found significantly increased and sustained levels of plasma markers associated with apoptosis. Among these, we observed consistent increases in the levels of CASP8, TNFSF14 and TGFB1, three biomarkers established to be related to inflammation combined with apoptosis. The fourth top upregulated marker, HGF, is best characterized in relation to anti-apoptotic function, possibly reflecting a negative feedback mechanism (Fig 3A-C). Their levels were already elevated at hospitalization, were in almost every case significantly greater in the ICU cohort compared to severe and mild patients, and were sustained over the disease course (Fig 3A-C). Indeed, the capacity of HGF to largely discriminate ICU from non-ICU patients was observed over the complete time-course of sampling (Fig 3C).
Additional plasma proteins with similar patterns included markers such as Oncostatin M (OSM), S100A12, IL-7, CCL23, VEGFA and CSF-1 (Fig 3D-J). Notwithstanding some very considerable overlap, a PCA of the average values of the 10 cited markers seemed more efficient at segregating the ICU and non-ICU cohorts examined in this study than did the collection of cytokines and chemokines activated at early disease (Fig 3K). This may reflect their different patterns of regulation in COVID-19, and argues for the measurement of apoptosis-related proteins in other, independent cohorts.
To validate the PEA (Olink) profiling, we compared ICU, non-ICU and mild-disease patients by Legendplex assay. This confirmed severity-associated increases levels of the triad of IL-6, CXCL10, and IL-10, and provided some evidence of TNF upregulation (Supplementary Fig 3A-M). Moreover, the analysis also confirmed the decline of IL-6, CXCL10, IL-10 and IFNg cytokine levels in both the ICU and non-ICU cohorts over time, and the lack of convincing segregation in PCA plot (Supplementary Fig 3N-O). In short, the results of the two plasma protein screening platforms cross-validated (Supplementary Fig 4). We further tested 2 soluble proteins, sCD25 and sCD14, that reflect activated immune cells and are commonly upregulated in in sepsis. Indeed, seemingly reflective of dynamic immune responses, we found increased peak levels of sCD25, shed by T cells, and sCD14, shed by macrophages and monocytes in ICU and non-ICU patients (Supplementary Fig 5A-B), which clustered together but not with early-stage inflammatory markers (Supplementary Fig 5C).
Discrete immune marker clusters segregate COVID-19 patients.
Since the proteomic analysis of secreted proteins suggested distinct patterns of regulation, we performed a cluster analysis of all PEA (Olink) profiling markers that were altered in ICU and non-ICU patients (Fig 4A). We found 7 clusters of plasma proteins that segregated based on Spearman correlations. As anticipated, the clustering analysis confirmed a strong correlation between early-stage inflammatory markers, forming a discrete profile of IL-6, IL-10, CXCL10, CXCL11, IFNg, CCL2, CCL7, CCL8, IL-18, IL-18R1 and PD-L1. We also observed a cluster containing HGF, TNFSF14, S100A12 and OSM. In contrast, CASP8 clustered separately, together with EIF4EBP1, SIRT2 and STAMBP and also with TGFB1 and IL-7. Strikingly, TRANCE showed a strong negative correlation with most of the early-stage inflammatory markers indicating an opposite trend of this marker in COVID-19 pathogenesis.
The correlations were disease-associated as they were different in healthy controls. Moreover, the samples collected at the beginning of hospitalization manifested a substantially greater degree of systemic inflammation, exemplified by the strong correlation between CXCL10, CXCL11, IFNg and PD-L1 when compared to samples collected during the disease course or immediately prior to discharge (Fig 4B-M). This suggests the potential of admission levels of certain analytes to be prognostic of subsequent disease course, as has been recently proposed 4 5. Such risk-based patient stratification might be further improved by assessments of markers with strong disease-associated correlations, e.g. between HGF and TNFSF14, and between IL-6 with CASP8 and HGF (Fig 4H, J, M). Indeed, some of the strong correlations reflect associations between related family members mediating immune-stromal crosstalk such as that between IL-6 and LIF (Fig 4E), and that between IL-6 and OSM.
Distinct immunotype in ICU patients
The patients with severe disease displayed stronger upregulation of discrete inflammation and apoptotic markers suggesting distinct immunotypes that might relate to disease characteristics and outcomes. To determine whether patients with severe COVID-19 could be categorized based on their expression of secreted proteins, we conducted clustering analysis using patients’ marker measurements at their peak levels. At least 4 disease subgroup clusters emerged (Fig 5). The two top patient clusters, including most of the ICU patients were represented by the strongest activation of multiple cytokines and chemokines, and were most segregated by HGF, OSM, S100A12 and CCL7. Strikingly, the most prominent distinct immunotype was present among three patients diagnosed with ARDS (COVID4, COVID7, and COVID38) and one patient who died (COVID49). The presence of ICU-specific immunotypes may usefully reflect accelerated inflammatory responses and pathophysiological mechanisms in subgroups of severe patients, which may require tailored immunomodulatory treatment strategies.
We have undertaken a longitudinal comparison of antibodies and plasma proteins, altogether comprising 101 different markers, in COVID-19 ICU and non-ICU patients and have contextualized them with patients with mild disease and age-matched negative controls. This has revealed a signature of hyperinflammation apparent early in the disease-course, long before seroconversion, for both ICU and non-ICU patients. However, rather than being chaotic, the signature comprised elevated levels of discrete cytokines and chemokines, some of which correlated with disease severity. Moreover, those cytokines and chemokines target myelomonocytic cells and T cells that have been reported to display the most overt immunophenotypes in COVID-19. Those phenotypes include severe, subset-selective T cytopenia, possibly in relation to which, a set of proteins associated with apoptosis form a cardinal feature of COVID-19.
As previously reported, we found high levels of acute-phase inflammatory cytokine IL-6, targeted by tocilizumab in COVID19 patient treatments. CD4+ T cells and monocytes are considered as the primary sources of high IL-6 levels in COVID-19, which in turn correlate with decreased monocytic HLA-DR expression, one of the hallmarks of the disease 9, 25. Furthermore, increased levels of other pro-inflammatory cytokines and chemokines, including CXCL10, CXCL11, and IL-10, were already characteristic patients upon admission to hospital26. Accordingly, increased albeit variable levels of IFNg were present during the early stages of hospitalization.
In parallel, the monocyte-attracting chemokines CCL2, CCL8, and CCL7, associated with septic shock and tuberculosis, were elevated supporting the role of monocytes in disease pathogenesis. Indeed, the analysis of bronchoalveolar lavage fluid from the patients detected a substantial increase in lung macrophages with enrichment of monocyte-derived FCN1+ macrophages 27 28. Interestingly, transgenic mice expressing the human ACE2 gene and infected with SARS-CoV-2 also demonstrated an accumulation of monocytes and macrophages in the lungs after infection, providing evidence that SARS-CoV-2 precipitates monocyte influx and macrophage accumulation during infection 29. Moreover, we detected increased levels of soluble sCD14 produced by myeloid cells and has hitherto been correlated with the severity of bacterial sepsis or septic shock.
However, the most conspicuous, sustained differences observed over the disease course were in CASP8, HGF, TNFSF14, and in TGFB1, that are each associated with apoptotic mechanisms and inflammation. They share several mechanistic features and have multiple implications in cardiovascular and other chronic inflammatory diseases. CASP8 is released to the extracellular medium after the apoptotic stimulation of myeloid and T cells. TNFSF14 /LIGHT is primarily produced by activated T cells and myeloid cells, is increased in plasma from patients with acute ischemic atherosclerotic stroke 30, is involved in the progression of chronic heart failure 31, and was recently reported to be upregulated in COVID-19 patients together with OSM and S100A12 6. HGF, whose upregulation was specific for ICU group, has anti-apoptotic effects and inflammation with elevated concentrations in chronic inflammatory diseases. HGF frequently counteracts TGFB1, another cytokine involved in apoptosis, and expressed at elevated levels in COVID19 patients. It might therefore be considered that the apoptotic events that drive increased HGF levels, underpin T cell apoptosis as a fundamental trait that contributes to SARS-CoV-2-specific immune pathology; hence, the association of HGF with disease severity and ICU requirement.
In sum the findings of our study can inform our understanding and management of COVID-19 as we collectively seek to limit the threat of SARS-CoV-2 transmission. While many major unknowns remain to be tackled, this study has highlighted the potential utility of tracking, and potentially targeting, a discrete spectrum of inflammatory mediators and apoptosis regulators.
Samples. The study was conducted in accordance with the Helsinki Declaration, with the approval from the Ethics Committee of the University of Tartu (312/M-1). Plasma samples were obtained from 40 COVID-19 patients (Table 1) hospitalized at the Tartu University Hospital, Estonia. 15 patients were treated in ICU and 25 in ordinary COVID-19 ward. Most of the ICU patients required mechanical ventilation within the first 10 days after the onset of symptoms with a median duration of the intensive care unit (ICU) stay of 10 days (range 1-54 days). Moderate COVID-19 patients were hospitalized for a median duration of 11 days (range 1-44 days). The diagnoses for all patients were confirmed by PCR analysis for SARS-CoV-2 virus. The samples from a group of 6 patients with mild disease were collected from the individuals who came to emergency medical unit, were tested positive for SARS-CoV-2 but sent home for quarantine and not admitted to the hospital. We studied altogether 119 healthy controls (age range 23-87 years) without recent infection or COVID-19 symptoms (fever or cough) for last month. Blood samples of two groups of patients (mostly over 60 years of age) without Covid-19 disease were collected from Internal Medicine unit and CRP under 5 mg/L: one group of 18 individuals was studied for clinical blood analysis and another group of 18 controls for the expression of multiple plasma proteins. In addition, 70 control individuals, including healthy blood donors, were studied for antibody reactivities.
LIPS. SARS-CoV-2 S (S1 aa 1-680 and S2 aa 820-1211), S1 RBD (aa 329-538), and N (aa 2-419) fragments were cloned into pNanoLuc vector, and LIPS was performed as reported 21. The HEK293 cell lysates containing NanoLuc‐fusion proteins (0.5 - 1 × 106 luminescence units; LU) were incubated with plasma samples and Protein G Sepharose beads (Creative BioMart) to capture antibodies (in 1:40 dilution). After washing, substrate was added (Nano-Glo™ Luciferase Substrate, Promega), and luminescence was measured in VICTOR X Reader (PerkinElmer Life Sciences). Results are expressed as fold changes (FC) of LU-s (FC=LU sample/average LU of 5 healthy control samples, the discrimination level is mean plus 2 standard deviations of control samples).
Microneutralization. The assay was performed in a BSL-3 laboratory as described previously 16. Briefly, fifty plaque-forming units (PFU) of the SARS-CoV-2/Finland/1/2020 strain, passaged in Vero E6 cells, were added to the plasma dilutions and incubated for 1 h at 37°C. Neutralizing antibody levels were assessed by cytopathic effect (CPE) stained with crystal violet. The neutralization endpoint titer was determined as the endpoint of the serum that inhibited the SARS-CoV-2 infection in parallel wells. The titer ≥20 was considered as positive.
ELISA. Quantification of human C1q, terminal complement complex (TCC), human Interleukin 2 receptor alpha (CD25/IL-2 Rα) and soluble Presepsin (sCD14) were performed by commercial ELISA kits: C1q (catalogue number HK356-02, Hycult Biotech, Netherlands), TCC (product code COMPL TCC RUO, SVAR, Sweden), CD25/IL-2 Rα (catalogue number DY223, R&D Systems, USA) and sCD14 (catalogue number abx576557, Abbexa, UK). All ELISAs were performed as indicated by the manufacturer and optical density was measured with Multiscan MCC/340 ELISA reader (Labsystems, USA).
LEGENDplex profiling. Bead-based LEGENDplexTM Human Anti-Virus Response Panel 13-plex kit (catalogue number 740349, BioLegend, USA) was used to quantify 13 human proteins, including interferons (α, β, γ, λ1 and λ2/3), interleukins (1β, 6, 8, 10, 12), TNF-α, IP-10 and GM-CSF. Assay was performed according to manufacturer’s protocol. Data were acquired by BD LSRFortessaTM flow cytometer (BD Biosciences, USA) and LEGENDplexTM Data Analysis Software was used for data analysis (BioLegend).
Olink proximity extension profiling. In total, 92 inflammation-related protein biomarkers were measured in plasma by the Proximity Extension Assay technique by using the Proseek Multiplex Inflammation panel by Olink® Proteomics. The assay uses two oligonucleotide-conjugated antibodies that bind to protein targets. Upon binding to the protein epitope, the paired oligonucleotide sequences are amplified through a quantitative real-time PCR (qRT-PCR) reaction. Data is then generated using normalized protein expression (NPX) values on a log2 scale whereby a higher NPX correlates with higher protein expression. Proteins containing NPX values >50% below the assay’s limit of detection (LOD) were excluded from the analysis. The data were pre-processed by Olink® using NPX Manager software.
Acknowledgements
The authors would like to thank the patients and control individuals included to the study. We would also like to thank Ruut Uusitalo and Anja Osola for help in performing the microneutralization assays. The study was supported by the European Regional Development Fund (Project No 2014‐2020.4.01.15‐0012 and the Centre of Excellence in Genomics (EXCEGEN) framework), the Estonian Research Council grants PUT1367 and PRG377.
Conflict of interests
The authors declare no conflict of interests.
Author Contribution Statement
LH, APR, MJ, EK, AR, HS, LK, OV, TS designed, analyzed and performed the experiments; AS and HP analyzed data, KK, LM and AT were responsible for the collection and documentation of blood donor samples, LH, AH, KK and PP designed and analyzed, data and wrote the manuscript.
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Table 1. Main characteristics of the patients and their comorbidities
