Anti-SARS-CoV-2 Antibody Subclass Response and Cytokine Storm in Severe COVID-19 Patients Under Intensive Care Unit

Anti-SARS-CoV-2 Antibody Subclass Response and Cytokine Storm in Severe COVID-19 Patients Under Intensive Care Unit | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Anti-SARS-CoV-2 Antibody Subclass Response and Cytokine Storm in Severe COVID-19 Patients Under Intensive Care Unit Hiochelson Najibe Santos Ibiapina, Fabio Magalhães-Gama, Juliana Costa Ferreira Neves, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7366441/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has diverse clinical presentations and varying degrees of severity, accounting for millions of deaths and the leading cause of intensive care unit (ICU) admissions during the pandemic. In this study, we sought to evaluate potential immunological biomarkers (antibodies, chemokines, cytokines, and growth factors) that predict clinical outcomes in circulating samples from patients with "Severe COVID" admitted to the ICU. Methods This is a prospective longitudinal study using peripheral blood samples from 30 patients admitted to the ICU with severe COVID-19 [with the outcomes discharge (DIS) and death (DEA)] and 30 healthy controls. Clinical and laboratory data were collected during patient evaluation. Furthermore, immunological molecules are being quantified using the Luminex methodology, a multiplex immunoassay. Results Males were the most common sex (70%), and 57% of patients had hypertension and diabetes mellitus. The circulating response profile differed between the study groups, with patients, regardless of outcome, presenting a heterogeneous profile of molecule production throughout the follow-up period. The DIS group showed greater control over the production of immune molecules, particularly IL-13, while the DEA group presented a profile consistent with a cytokine storm. Conclusions We suggest that IgM Anti-N and IgM Anti-RBD antibodies, along with molecules such as IFN-g (D0), TNF-a, and FGF-basic (D7), as well as CXCL8, CCL4, CXCL10, and G-CSF (D14), may be potential biomarkers of worsening clinical outcomes. Furthermore, IL-13 may play a protective role in these ICU patients. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Biological sciences/Microbiology SARS-CoV-2 Viral Infection Immunological response Antibodies subclass cytokine storm Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION COVID-19 was initially described in China, following reports of pneumonia of unknown origin, which rapidly drew the attention of the World Health Organization (WHO) due to its swift transmission capabilities ( 1 ). In 2020, the WHO declared COVID-19 a new pandemic and Brazil became the epicenter of cases in South America, reporting over 330,000 cases ( 1 – 8 ). According to the Ministry of Health (MS) in Brazil, the cumulative number of cases reported is 38,777,842, with 711,650 confirmed deaths ( 9 ). Patients infected with SARS-CoV-2 may present a wide range of symptoms, from localized to systemic manifestations, which vary according to clinical classification. This classification can also vary by age group or conditions such as pregnancy, comorbidities, and immune status ( 10 , 11 ). The primary symptoms are systemic, such as fever or fatigue, but can also affect specific organs or systems, including cardiovascular symptoms. In critically ill patients, these manifestations and associated complications can lead to ICU admission ( 12 , 13 ). Through the process of recognizing and activating the inflammatory response, SARS-CoV-2 is capable of evading immune detection by using mechanisms that block the signaling required for interferon (IFN) production and its regulatory genes ( 14 – 17 ). Moreover, increased IFN-III production and induction of IFN-stimulated genes in the upper airways are associated with reduced disease risk and severity ( 18 – 21 ). Furthermore, prolonged IFN secretion is associated with worse outcomes, likely due to its induction of chemokine production and its impact on cell recruitment to the site of infection ( 22 – 24 ). In contrast to this impaired initial innate defense with disrupted IFN signaling due to SARS-CoV-2 escape mechanisms, pro-inflammatory cytokines and chemokines are strongly elevated ( 22 , 23 , 25 – 28 ). On the other hand, responses to SARS-CoV-2 focus on the production of antibodies (Abs) and cell activation, with the effectiveness of both being associated with the expression of specific structural and non-structural viral proteins and the ability to recognize and modulate effective immune responses by the host ( 29 , 30 ). Studies have shown that most neutralizing Abs bind to distinct epitopes in the receptor-binding domain (RBD) of the spike protein, primarily the S1 subunit. Additionally, a smaller and less potent fraction of neutralizing Abs binds to the N-terminal domain (NTD) and other parts of the S protein ( 25 , 31 – 36 ). In infected, vaccinated, or pre-immunized individuals, the neutralizing antibody activity against SARS-CoV-2 is of greater magnitude, which can be explained by differences in viral load and exposure to the virus ( 12 , 33 ). Conversely, in immunosuppressed patients, the antibody-mediated response tends to be weak, leading to chronic SARS-CoV-2 infection, viral progression, and reduced sensitivity to neutralizing antibodies ( 37 ). Therefore, this study aimed to understand the dynamics and role of antibodies, cytokines, chemokines, and growth factors as biomarkers of clinical outcome in patients with COVID-19 admitted to the ICU. MATERIAL AND METHODS Study Area The COVID-19 patients were enrolled at admission in the ICU from Hospital Risoleta Tolentino Neves (Belo Horizonte, MG) and from Hospital das Clınicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (Ribeirão Preto, SP). Sampling This observational longitudinal investigation was comprised of non-probabilistic convenience sampling of serum specimens from critically COVID-19 patients admitted ICU and healthy controls during the peak circulation of the SARS-CoV-2 B.1 lineage in the hospital coverage areas. No COVID-19 vaccine was available at the time of sample collection; therefore, all patients were unvaccinated. In summary, Fig. 1 details the process of inclusion and selection of study population. The criteria for admission to the ICU were patients who presented: acute respiratory failure, requiring invasive mechanical ventilation or; acute respiratory failure requiring non-invasive ventilation; presenting PaCO 2 ≥ 50mmHg and pH ≤ 7.35; or hemodynamic instability or shock, defined as arterial hypotension (SBP < 90mmHg or MAP < 65mmHg). Ethical Issues The participants enrolled in this study provided written consent by signing the consent form. The study protocol was submitted and approved by the Ethics Committee at Instituto René Rachou-FIOCRUZ-Minas (CAAE: 42560721.7.0000.5091) and Hospital das Clínicas of the Faculty of Medicine of Ribeirão Preto – Universidade de São Paulo/USP-RP (CAAE: 30816620.0.0000.5440). This investigation followed the principles of the Helsinki Declaration and Resolution No. 466/2012 of the Brazilian National Health Council for research involving human subjects. Biological Sample Approximately 6mL of peripheral blood was collected in at ICU admission (D0), 7 days (D7) and 14 days (D14) hospitalization by venous puncture in Gel separator (Gel BD SST® II Advance) tube. The samples were stored in a freezer at -80◦C for measurement of circulating soluble molecules. Furthermore, serum sample aliquots were thawed at 37°C, centrifuged at 24,000 x g to remove lipid layer and large debris and the supernatant filtered through 0.45mm syringe filter to further maximize the debris removal. Assessment of Circulating Soluble Molecule Level All immune molecules were quantified using the Luminex technique at the Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas). Both data acquisition and molecule levels were measured on the Luminex 200 system, and the data were analyzed using the BioPlex Manager software. The concentrations of immunoglobulins (IgM, IgG, and IgA) were quantified using the Bio-Plex Pro™ Human IgM SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64399243), the Bio-Plex Pro™ Human IgG SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64420111), and the Bio-Plex Pro™ Human IgA SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64420108). In addition, chemokines (CXCL8, CXCL10, CCL2, CCL3, CCL4, CCL5, CCL11), cytokines (IL-1beta, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, IFN-γ, TNF-α), and cell growth factors (G-CSF, GM-CSF, FGF basic, and VEGF) were measured using the Bio-Plex Pro™ Human Cytokine 27-plex Assay Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64480554). The dosages of soluble molecules were carried out at three different time intervals: D0, D7 and D14. Data Analysis Statistical analyses of the data and circulating levels of the soluble molecules were performed with the software Graphpad Prism (v8.0.2) and Stata (v13.0). Data normality tests were performed using the Shapiro-Wilk test. The comparison of values ​​between two data groups was performed using the Mann-Whitney test, whereas for comparison of the variables with three or more groups, data analysis was performed using the Kruskal-Wallis test, followed by Dunn's post-test for multiple comparisons between groups. In addition, the signature analysis was performed by converting continuous variable results of each mediator into categorical data. For this purpose, the global median values obtained from the entire dataset, encompassing all participants, served as the cut-off. This allowed for the classification of patients into low (below the cut-off) or high (above the cut-off) immune mediator production groups. In parallel, a signature analysis was performed by grouping the immune mediators according to their class (antibodies, chemokines, cytokines and cell growth factors) to evaluate the profile of mediator production between the groups. Statistical significance of this analysis was determined using Fisher´s exact test. These delta signatures were visualized in lollipop charts grouped according to the class of immune mediators. Furthermore, construction of integrative networks between the antibodies, cytokines, chemokines and growth factors, were performed from the association of these markers in each clinical group. The Spearman correlation test was performed, and the networks were built with Cytoscape 3.10.1 software (Cytoscape Consortium San Diego, CA, USA), following the recommendations and instructions in the software. The levels of statistical significance defined in all cases was p < 0.05. RESULTS Clinical and Sociodemographic Data Table 1 summarizes the epidemiological characteristics of the study patients. The most common sex was male (70%), however 78% of deaths occurred among females. Both groups had arterial hypertension (HTN) and diabetes mellitus (DM), although 57% of the patients who died had both comorbidities. Furthermore, chronic obstructive pulmonary disease (COPD) was not significantly frequent, and the SASP score was similar across outcomes. Table 1 Clinical and Sociodemographic Data of Health Controls (HC), Discharge and Death groups COVID-19 patients Variables HC n = 30 Discharge n = 23 Death n = 7 p value Sex , n (%) Male 18 ( 60 ) 16 ( 70 ) 2 ( 22 ) 0.153 Female 12 ( 40 ) 7 ( 30 ) 5 ( 78 ) Age (years), Median [IQR] 67 [59–70] 61 [51–68] 67 [62–74] 0.111 HTN , n (%) Yes - 13 ( 57 ) 6 ( 86 ) 0.079 No - 10 ( 43 ) 1 ( 14 ) DM , n (%) Yes - 15 ( 65 ) 4 ( 57 ) 0.228 No - 8 ( 35 ) 3 ( 43 ) HTN + DM , n (%) Yes - 10 ( 43 ) 4 ( 57 ) 0.674 No - 13 ( 57 ) 3 ( 43 ) COPD , n (%) Yes - - 1 ( 14 ) 0.537 No - 23 (100) 6 ( 86 ) BMI , Median [IQR] - 30 [26–33] 29 [26–40] 0.224 SAPS3 , Median [IQR] - 66 [58–74] 67 [54–85] 0.799 HTN : Hypertension; BMI : Body Mass Index; DM : Diabetes Mellitus; COPD : Chronic Obstructive Pulmonary Disease; SAPS3 : Simplified Acute Physiology Score III Follow-Up Production of Antibodies in COVID-19 Patients under ICU care To understand the dynamics of antibody production in COVID-19 and their evolution throughout hospitalization in critically patients serum samples were collected at admission (D0), and on days 7 (D7) and 14 (D14) post-admission, and further categorized according to the clinical outcome. Figure 1 shows the dynamics of patient antibody production according to class (IgM, IgG, and IgA) and their respective targets (Anti-S1, Anti-N, and Anti-RBD). Data analysis demonstrated that antibody concentrations were higher in the group of patients with COVID-19 (ALL) when compared to participants in the control group (HC). When analyzing patient antibody concentrations according to clinical outcome (DIS vs. DEA), antibody concentrations were higher in the Discharge (DIS) group. It is important to mention that the concentrations of IgM anti-N and IgM anti-RBD at day 0, as they were statistically significantly higher in the Death (DEA) group. COVID-19 Patients Presented Different Serum Levels of Immunological Soluble Molecules When Compared to HCD Group To further explore the process of immune molecule production, we compared the production of cytokines, chemokines, and growth factors between ICU patients, regardless of clinical outcome (ALL) and the healthy control group (HC). The results are presented in Fig. 3 . Analysis of ALL vs. HC showed that a higher production of CCL3, CXCL10, IL-6, TNF-a, IFN-g and IL-1Ra in the ALL group - which demonstrates greater inflammatory activity with leukocyte recruitment that can lead to damage to the host itself due to an exaggerated response. On the other hand, CCL11, IL-17, IL-4, IL-5 IL-13, FGF-Basic, PDGF, VEGF, GM-CSF and IL-7 showed levitated levels in the HC group, being compatible with a more regulatory pattern - aiming to control the physiological activities of the organism. Signature of Immunological Soluble Molecules Demonstrated in ALL Patients When Compared to HC Group To reinforce the idea of a cytokine storm and exacerbate production of immune molecules in severe COVID-19, we demonstrate in Fig. 4 the signature of immune biomarkers throughout the ICU follow-up. This analysis clearly shows that the host's immune system is unable to assertively mount a targeted response to the virus. Furthermore, the production curves of these mediators are heterogeneous, with distinct peak levels observed at different evaluation time points. It is worth noting that the number of molecules that are overproduced also increases, demonstrating a difficulty in immune regulation, since a Th1 response is required. Comparisons of serum concentrations of each molecule at Day 0 (HC – green circles; ALL – red circles) with their respective intervals, demonstrated a discrepancy between the concentrations observed in inflammatory molecules when comparing HC and ALL (Figura 4A). Complementary analysis are presenting in Fig. 4 B that shows the molecules in ascending order of concentration in COVID-19-positive patients. Those that did not show a statistical difference in the HC vs. ALL comparisons are shown in gray. Data analysis showed that only CCL4 and IgG Anti-N remained without statistical difference throughout ICU follow-up, despite being among the most highly produced molecules on Day 14. Follow-Up of the Production of Immunological Soluble Molecules in Patients Under ICU Care To address the potential biomarkers related to clinical outcomes, the chemokines, cytokines, and growth factors, were intra-group and inter-group (Fig. 5 ). During the follow-up, we can observe heterogeneity in the serum concentration of all molecules throughout the evaluation days, however, some molecules show a progressive increase with statistical difference in the DIS group, such as IFN-g and IL-1Ra, however, their concentrations are still lower than the DEA group. Data regarding D7 showed a change in the dynamics of molecule production, with a more homogeneous pattern. However, TNF-a and FGF-basic remained elevated in the DEA group, while IL-13 was prominent in the DIS group (and remained at D14). Overall, the DEA group showed increased production of CXCL8, CCL4, CXCL10, TNF-α, IL-10, and G-CSF, while the DIS group maintained elevated production of only IL-13 (Fig. 5 ). Signature of Immunological Soluble Molecules Demonstrated in Patients Under ICU care Figure 6 A shows the dynamics of immune molecule production throughout the ICU care period in the ALL and HC groups, serving as a baseline and parameter for comparison. Thus, it was possible to observe that the profile of highly produced molecules is robust and diverse, with antibody production throughout hospitalization exhibiting a heterogeneous sharing pattern, but highlighting IgM and IgA anti-RBD, which exhibit high production at all follow-up periods. In addition, the analysis of the production pattern between the ALL and HC groups, pointed out that there is almost an inversion in the way the molecules are produced, demonstrating a modulation process by viral exposure. In Fig. 6 B, we performed an exploratory analysis within the ALL group to evaluate patients according to the outcome (DIS and DEA) and the pattern of molecule production throughout hospitalization. In this sense, we observed that IgM anti-RBD and IgA anti-S1 antibodies, IgA anti-RBD, and the chemokine CCL5 presented high production in the DIS group, while in the DEA group, IgM anti-N and anti-RBD antibodies, IgA anti-RBD, in addition to the molecules CXCL-8, CXCL-10, CCL3, IL-1β, TNF-a, IFN-g, IL-17, IL-1Ra, and FGF-basic presented high production throughout all days of hospitalization. Thus, there was an exclusive high production at all time points evaluated of the IgA anti-S1 and CCL5 molecules in the DIS group, but in the DEA group, there were IgM anti-N CXCL8, CXCL10, CCL3, IL-1β, TNF-a, IFN-g, IL-17, IL-1Ra and FGF-basic. This suggests a potential relationship with the appropriate outcomes. Biological interaction networks according to days of hospitalization and clinical outcome The biological interaction networks between antibodies, cytokines, chemokines, and growth factors allow us to gain a complex view of the molecules patterns (Fig. 7 ). This analysis was also performed throughout the study ICU follow-up days. Network analysis demonstrated that the ALL group exhibits robust antibody interactions, with a greater number of positive correlations among antibody categories, but many negative correlations with other groups of immune molecules at D0, suggesting an important role for antibodies in these patients. Conversely, these negative relationships diminish over the course of hospitalization and begin to become positive between antibodies and other groups of molecules, while maintaining robust interactions only between antibodies. Furthermore, the interaction networks in the DIS and DEA groups show that patients who progressed to discharge had a much higher number of interactions between molecules than those who died. It is also possible to observe that in the DIS group, there is a slow decrease in interactions over the days of hospitalization, while in the DEA group, this decline is abrupt and disordered. DISCUSSION Severe COVID-19 is characterized by an exacerbated inflammatory response and multisystem dysfunction, often culminating in the need for invasive ventilatory support and admission to an intensive care unit (ICU). Patients admitted in this setting have a high morbidity and mortality rate, associated with factors such as advanced age, pre-existing comorbidities, and cytokine storm. The demographic and clinical data on age, sex, and previous comorbidities collected in this study corroborate those previously described, as most patients had comorbidities such as hypertension, diabetes mellitus (DM), or both, regardless of clinical outcome.( 38 – 43 ). In this line, chronic obstructive pulmonary disease (COPD) are described as a factor that influences pulmonary complications, only one patient had this comorbidity, which reinforces the idea suggested by Fraser et al. (2021). The authors reported that severe COVID-19, due to the complexity, can be a factor that leads to the need for ICU admission, regardless of other complications such as previous comorbidities ( 44 , 45 ). Furthermore, although the Simplified Acute Physiology Score III (SAPS3) score has been defined as a reliable predictor of mortality ( 46 , 47 ), our study observed very similar values in both groups, regardless of the outcome observed. This corroborates with Basiri et al., 2025 ( 48 ) which suggests that although SAPS3 can be used for these patients, other scores, such as Sequential Organ Failure Assessment (SOFA), may be more appropriate for clinical management. From a laboratory perspective, several markers have already been described for at-risk patients ( 38 – 43 , 49 ) but once infected, the body initiates an innate immune response ( 50 , 51 ), followed by a humoral response with the production of antibodies classified into several subclass, generally according to the stage of the disease, such as IgM, IgG, and IgA ( 52 ). The humoral immune response has been extensively investigated, with studies reporting discrepancies in its magnitude depending on factors such as seroconversion, length of hospital stays, target antigen, or isotype investigated ( 44 , 53 – 57 ). It has been reported that the S and N proteins are dominant antigens in coronaviruses and can activate the production of IgM, IgG, and IgA antibodies as a response by the host body. It is noteworthy that the S protein has a region called the RBD (receptor-binding domain), which is important in its pathophysiological process and is an important target of the response ( 58 – 61 ). Our results demonstrate that on Days 7 and 14, IgM, IgG, and IgA antibody concentrations were similar in both groups, corroborating data from Salgado et al., 2023 ( 62 ) suggesting that the magnitude of antibody production is more impaired in patients with severe COVID-19, but that the humoral response is similar in patients regardless of the outcome. Some studies suggest that serum IgA and IgG concentrations are higher in patients with mild disease than in hospitalized patients ( 53 , 63 , 64 ), in this context, our data demonstrates that, on the day of ICU admission (D0), although there is no statistical difference, IgA and IgG concentrations are higher in the DIS group, which may apparently be beneficial for these patients. Furthermore, it should also be considered that higher IgA concentrations are generally found in patients who have presented some type of complication or gastrointestinal reaction resulting from COVID-19 infection. ( 62 ) However, our data demonstrates that IgA concentrations remained stable throughout the days of ICU hospitalization, unlike other studies that suggest a rapid decrease in IgA concentrations when compared to IgG concentrations ( 65 – 68 ). Salgado et. al., 2023 ( 62 ) suggests that altered IgG concentrations may be associated with mortality in hospitalized patients. Nonetheless, our data demonstrates no difference between the concentrations of this antibody in the studied groups. Therefore, we emphasize that factors intrinsic to the patients (such as evolutionary factors) may interfere with this process, since the way the immune system conducts this immunological reaction through cytokines can influence the humoral response. Fraser et al., 2021( 45 ) describes that IgM concentrations peak around day 13 after ICU admission, but our patients showed peaks on D0, despite having symptoms for several days. Regarding the cytokines production during infection, an amplified immune response has already been identified in some patients with severe COVID-19, called a "cytokine storm." In this context, has been shown that the disease severity is associated with an increase in inflammatory cytokines such as interferons, TNF, IL-6, and IL-1β ( 69 – 71 ). When dysregulated, these mediators can lead to physiological changes compatible with sepsis, presenting vascular alterations ( 72 , 73 ), this would justify the suggestion by Basiri et al., 2025 ( 48 ) to use the SOFA score in the evaluation of such patients. In the present study, Figs. 3 and 4 clearly illustrate this immunological dysregulation in severe COVID-19, as evidenced by the cytokine production profiles observed. It is notable that IFN-g plays a central role in the cytokine storm ( 74 ) due to its systemic regulation. In our investigation, elevated concentrations in the DEA group reinforce the idea that these patients are experiencing this type of immunological complication. Ruan et al., ( 75 ) showed that critically ICU patients had higher concentrations of TNF-a, IL-2, IL-7, and IL-10, which corroborates our findings. Although there was no significant difference between the two groups, the levels of these cytokines were higher in the DEA group. Despite the elevated production of these molecules on Day 0, as seen in Fig. 5 , statistical differences were only observed for IFN-g and IL-9 in the DEA group patients. Sadhu et al. (2022) ( 76 ) demonstrated that IL-9 aggravates SARS-CoV-2 infection and is associated with increased airway inflammation. Furthermore, on D14, more significant changes in production dynamics can be observed between groups, possibly associated with the worsening of the clinical condition of these patients. Higher concentrations of IFN-g and TNF-a have been previously described and associated with extensive lung damage in patients with SARS-CoV-1 and MERS-CoV. The analysis of DEA group, both of these cytokines were higher, which would justify the worsening of their condition throughout their hospitalization ( 77 – 82 ). Moreover, elevated IL-10 concentrations are observed as an attempt to control the cytokine storm process, as it plays an important immunomodulatory role, further demonstrating the worsening condition of patients. The increase in chemokines, especially CXCL8, along with IL-9, is associated with bilateral pneumonia in patients with COVID-19 ( 76 , 78 , 83 ). Although IL-13 production remained high in the group that progressed to discharge, its relationship with COVID-19 has been described as controversial, according to our results. It has been associated with worsening conditions, given that its action is linked to the activation of the Th2 profile ( 84 , 85 ). On the other hand, an in vitro study conducted by Donlan et al., 2021 suggests that IL-13 may play a protective role due to its action in mucus production and reduced ACE-2 expression. This activation pattern could hinder viral replication and explain the elevated serum concentrations on Day 7 and Day 14. in the DIS group. The evaluating the interactions between all the molecules studied, we observed in DIS group that presented a robust pattern of correlations between IgM, IgG, and IgA antibodies, which may suggest an attempt atthe immune system to control the inflammatory process - given that antibodies also can neutralize viral particles and also due to the negative correlation between antibodies and inflammatory cytokines such as IL-6, TNF, and IFN-g, already described in other studies as markers of severity in COVID-19 ( 70 , 71 ). Furthermore, IgA anti-S1 antibodies showed positive correlations with VEGF, that may suggest an attempt at tissue reconstitution and activation of this endothelium, considering that IgA is an antibody involved in mucosal defense and VEGF is a vascular endothelial growth factor, this ( 72 , 86 – 89 ). Still regarding the DIS group, at D7, our findings demonstrated that the IgM anti-S1 antibody exhibits many interactions with all groups of immunological molecules studied, suggesting a crucial role in the immunological regulation of these patients, preventing possible complications resulting from an exaggerated immune response. This corroborates the findings of Fraser et al., 2021 ( 45 ) who suggested that patients with higher IgM concentrations survive, with their highest concentration peaking at D14. Therefore, when we evaluate these interactions at D14, it is still noticed that the role of IgM anti-S1 interacting with cytokines and chemokines reinforces this idea. Regarding the DEA group, our data pointed out that a poor number of correlations between antibodies was observed within the absence of positive interactions with growth factors. However, it continues to maintain the pattern of negative correlations between antibodies and the other groups of immunological molecules, that the DIS group also exhibits. On D7 there is a decrease in the number of correlations, including antibodies categories, which corroborates what was suggested by Salgado et al., 2023, that early decreases in IgA and IgG antibody values are observed in patients who present worse outcomes ( 62 ). While on D14, it is possible to observe that there is an onset of response similar to D7 in the DIS group, but now with the focal point being IgM anti-N, IgA anti-N, and IgG anti-S1. Finally, this study has certain limitations that should be acknowledged. First, participant recruitment occurred during the peak of the COVID-19 pandemic, which may have restricted sample size and limited the assessment of variables intrinsically related to SARS-CoV-2, such as viral load. Furthermore, the type of complication analyzed requires cautious interpretation to minimize potential biases associated with the primary cause of hospitalization. Despite these limitations, the complication investigated has significant clinical relevance regarding clinical outcome. CONCLUSION In summary, patients who are present with COVID-19 and require ICU care exhibit immune molecule dynamics consistent with a cytokine storm. We suggest that IgM Anti-N and IgM Anti-RBD antibodies, along with molecules on their respective days of evaluation, such as IFN-g (D0), TNF-a, and FGF-basic (D7), as well as CXCL8, CCL4, CXCL10, and G-CSF (D14), may be potential biomarkers of worsening clinical outcomes. Furthermore, IL-13 may play a protective role in these ICU patients. Finally, we believe that the level of circulating antibodies, chemokines, cytokines, and growth factors, and their response profile against COVID-19, reflect the clinical need for ICU care, and this can be used to assess patient clinical prognosis. Declarations CONFLICTS OF INTEREST The authors declare no conflicts of interest related to the content of this manuscript. FUNDING This study was supported by a research scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Funding Code 001 and PROAP Program #1247/2022); by the Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) (PDPG/CAPES/FAPEAM Program #038/2022 and POSGRAD Program #002/2025); and by Fundacão de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (Grant # APQ-00432-20 and APQ-01499-21). The study was also supported by the Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico– CNPq and Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP). Author Contribution HNSI: Conceptualization, Investigation, Writing – original draft, Writing – review & editing, Investigation, Data analysis, Curation and Visualization; FMG: Data analysis, Curation and Visualization; IACR: Data analysis, Curation and Visualization; JCFN: Curation and Visualization; FSAH: Curation and Visualization; AAL: Conceptualization, Investigation and Methodology; ALR: Conceptualization, Investigation and Methodology; GMF: Conceptualization, Investigation and Methodology; TFCFS: Conceptualization, Investigation and Methodology; AAOP: Conceptualization, Investigation and Methodology; DPAM: Conceptualization, Investigation and Methodology; JPBS: Conceptualization, Investigation and Methodology; ;ACCA: Methodology, Curation and Visualization; DF: Conceptualization, Investigation and Methodology; VPM: Methodology, Curation and Visualization; MSSA: Conceptualization, Investigation and Methodology; ATC Conceptualization, Supervision, Funding acquisition, Resources, Validation; VLDB: Conceptualization, Investigation and Methodology; CB: Conceptualization, Investigation and Methodology; MGM: Conceptualization, Investigation and Methodology; MCS: Conceptualization, Supervision and coordination, Investigation, Writing – review & editing; MAM: Conceptualization, Investigation and Methodology; JGCR: Methodology, Curation and Visualization; OAMF: Conceptualization, Supervision and coordination, Investigation, Funding acquisition, Resources, Writing – review & editing. AGC: Conceptualization, Writing – original draft, Writing – review & editing, Data analysis, curation and visualization. All authors read and approved the final version of the manuscript. Acknowledgement The authors thank the ICU team from Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto– Universidade de São Paulo (USP) and from Hospital Risoleta Tolentino Neves for their support during sample collection and medical records assessment. The authors also thank the Program for Technological Development in Tools for Health (PDTIS-FIOCRUZ) for the use of its facilities and Flow Cytometry Platform. We are grateful to Grupo Integrado de Pesquisas em Biomarcadores at Instituto René Rachou, Fundação Oswaldo Cruz of Minas Gerais state (FIOCRUZ-Minas) for excellent technical assistance and support with the assays. Data Availability The original contributions presented in the study are included in the article/Supplementary Material. 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Int J Infect Dis [Internet]. ;95:332–9. (2020). Available from: https://linkinghub.elsevier.com/retrieve/pii/S1201971220302575 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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(UFMG)","correspondingAuthor":false,"prefix":"","firstName":"Daisymara","middleName":"Priscila Almeida","lastName":"Marques","suffix":""},{"id":511936185,"identity":"709eac9f-35a7-40be-be83-aba28c56ce00","order_by":11,"name":"Joaquim Pedro Brito-de-Sousa","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Joaquim","middleName":"Pedro","lastName":"Brito-de-Sousa","suffix":""},{"id":511936186,"identity":"4d958ddc-0278-4f42-9f98-0d23ebda3a73","order_by":12,"name":"Ana Carolina Campi-Azevedo","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Carolina","lastName":"Campi-Azevedo","suffix":""},{"id":511936187,"identity":"c6e44276-a55d-49f9-ab31-b32c8655cf1a","order_by":13,"name":"Vanessa Peruhype-Magalhães","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Peruhype-Magalhães","suffix":""},{"id":511936188,"identity":"f6783f42-fd46-4cd2-86cc-40b6a6dc6076","order_by":14,"name":"Márcio Sobreira Silva Araújo","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Márcio","middleName":"Sobreira Silva","lastName":"Araújo","suffix":""},{"id":511936189,"identity":"df44f1a7-1937-466a-9556-310a9ec0d7fc","order_by":15,"name":"Andréa Teixeira-Carvalho","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Andréa","middleName":"","lastName":"Teixeira-Carvalho","suffix":""},{"id":511936190,"identity":"0a18aedd-6616-4885-996c-869bffe457f9","order_by":16,"name":"Vânia Luiza Deperon Bonato","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Vânia","middleName":"Luiza Deperon","lastName":"Bonato","suffix":""},{"id":511936191,"identity":"d9cf44b8-4cbd-45a8-af00-2c2aa9012cde","order_by":17,"name":"Christiane Becari","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Christiane","middleName":"","lastName":"Becari","suffix":""},{"id":511936192,"identity":"309e4f51-e269-4cc5-b0d5-279e4b2d3882","order_by":18,"name":"Mayra Gonçalves Manegueti","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Mayra","middleName":"Gonçalves","lastName":"Manegueti","suffix":""},{"id":511936193,"identity":"52d726ce-3ab1-4c99-86ae-d3f77dd3a11d","order_by":19,"name":"Marcelo Cordeiro-Santos","email":"","orcid":"","institution":"Universidade do Estado do Amazonas (UEA)","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Cordeiro-Santos","suffix":""},{"id":511936194,"identity":"619d5199-e601-4812-bd93-c76a6b4a5a83","order_by":20,"name":"Maria Auxiliadora-Martins","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Auxiliadora-Martins","suffix":""},{"id":511936195,"identity":"69839d97-aeb8-4e72-97d3-23afab62c495","order_by":21,"name":"Jordana Grazziela Coelho-dos-Reis","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Jordana","middleName":"Grazziela","lastName":"Coelho-dos-Reis","suffix":""},{"id":511936196,"identity":"2deb1c10-ba33-411b-a4e0-29ea6978c59e","order_by":22,"name":"Olindo Assis Martins-Filho","email":"","orcid":"","institution":"Instituto René Rachou, Fundação Oswaldo Cruz (FIOCRUZ-Minas)","correspondingAuthor":false,"prefix":"","firstName":"Olindo","middleName":"Assis","lastName":"Martins-Filho","suffix":""},{"id":511936197,"identity":"2304d44c-2550-41a7-988b-7408c4d99f46","order_by":23,"name":"Allyson Guimarães Costa","email":"data:image/png;base64,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","orcid":"","institution":"Universidade do Estado do Amazonas (UEA)","correspondingAuthor":true,"prefix":"","firstName":"Allyson","middleName":"Guimarães","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2025-08-13 15:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7366441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7366441/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90982102,"identity":"eaf018a9-3a4e-4c01-9ea7-2e2aaa45f3e0","added_by":"auto","created_at":"2025-09-10 09:27:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":250467,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating selection of the 30 participants\u003c/p\u003e","description":"","filename":"Figura1Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/c9a57060eafc59f765f8193d.png"},{"id":90982104,"identity":"e6b63de6-4fb2-410a-a292-9084078e35c0","added_by":"auto","created_at":"2025-09-10 09:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1127564,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figura2Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/af7c2dea76357104f479197f.png"},{"id":90982105,"identity":"f1a8e204-c0e6-463e-9d6f-086cbb32c0ef","added_by":"auto","created_at":"2025-09-10 09:27:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1131971,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the serum concentration values of each molecule in the HC and ALL groups.\u003cstrong\u003e \u003c/strong\u003eSignificant difference with the HD group \u003cstrong\u003e\u0026nbsp;͆ \u003c/strong\u003ep \u0026lt;0.05. Statistical analyses were performed using the Mann-Whitney test.\u003c/p\u003e","description":"","filename":"Figura3Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/bfa3c3d54e874918307f058c.png"},{"id":90982103,"identity":"f75c532c-8c41-4b46-a81f-4675ee3bf892","added_by":"auto","created_at":"2025-09-10 09:27:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1243180,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figura4Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/c5d2ac483e8095ecf48bab68.png"},{"id":90983115,"identity":"6a00c2fa-3c95-478c-b6f0-0d9e7b87c652","added_by":"auto","created_at":"2025-09-10 09:35:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":393017,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the serum concentration values of each molecule in the DIS and DEA groups; Statistical difference between days in group DIS or DEA (▬); Statistical difference between DIS and DEA groups, considering significance at * p \u0026lt;0.05 (D0), \u003csup\u003e# \u003c/sup\u003ep \u0026lt;0.05 (D7) and \u003csup\u003e○\u003c/sup\u003e p \u0026lt;0.05 (D14); Statistical analyses were performed by the Kruskal-Wallis test, followed by Dunn’s test in order to compare pairs.\u003c/p\u003e","description":"","filename":"Figura5Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/40838009d9d47cbfe8b3ba6e.png"},{"id":90982114,"identity":"96fffe5b-50bd-4736-89b0-dd1cdd8b1868","added_by":"auto","created_at":"2025-09-10 09:27:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2207402,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figura6Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/6022385c0e90c3d2d2a75a0b.png"},{"id":90983120,"identity":"d6c92e29-1ed2-4f19-b2f3-a66d90f94bb0","added_by":"auto","created_at":"2025-09-10 09:35:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":12471433,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figura7Final.png","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/9c7368c3ef7cdef9440cef72.png"},{"id":97248823,"identity":"3b59b069-34ce-43d1-a178-3d83a8998eca","added_by":"auto","created_at":"2025-12-02 13:07:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20118746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7366441/v1/9034dab7-e820-4e88-bd55-b3b7533bc30d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anti-SARS-CoV-2 Antibody Subclass Response and Cytokine Storm in Severe COVID-19 Patients Under Intensive Care Unit","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCOVID-19 was initially described in China, following reports of pneumonia of unknown origin, which rapidly drew the attention of the World Health Organization (WHO) due to its swift transmission capabilities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2020, the WHO declared COVID-19 a new pandemic and Brazil became the epicenter of cases in South America, reporting over 330,000 cases (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). According to the Ministry of Health (MS) in Brazil, the cumulative number of cases reported is 38,777,842, with 711,650 confirmed deaths (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatients infected with SARS-CoV-2 may present a wide range of symptoms, from localized to systemic manifestations, which vary according to clinical classification. This classification can also vary by age group or conditions such as pregnancy, comorbidities, and immune status (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The primary symptoms are systemic, such as fever or fatigue, but can also affect specific organs or systems, including cardiovascular symptoms. In critically ill patients, these manifestations and associated complications can lead to ICU admission (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThrough the process of recognizing and activating the inflammatory response, SARS-CoV-2 is capable of evading immune detection by using mechanisms that block the signaling required for interferon (IFN) production and its regulatory genes (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Moreover, increased IFN-III production and induction of IFN-stimulated genes in the upper airways are associated with reduced disease risk and severity (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, prolonged IFN secretion is associated with worse outcomes, likely due to its induction of chemokine production and its impact on cell recruitment to the site of infection (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In contrast to this impaired initial innate defense with disrupted IFN signaling due to SARS-CoV-2 escape mechanisms, pro-inflammatory cytokines and chemokines are strongly elevated (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, responses to SARS-CoV-2 focus on the production of antibodies (Abs) and cell activation, with the effectiveness of both being associated with the expression of specific structural and non-structural viral proteins and the ability to recognize and modulate effective immune responses by the host (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Studies have shown that most neutralizing Abs bind to distinct epitopes in the receptor-binding domain (RBD) of the spike protein, primarily the S1 subunit. Additionally, a smaller and less potent fraction of neutralizing Abs binds to the N-terminal domain (NTD) and other parts of the S protein (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In infected, vaccinated, or pre-immunized individuals, the neutralizing antibody activity against SARS-CoV-2 is of greater magnitude, which can be explained by differences in viral load and exposure to the virus (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Conversely, in immunosuppressed patients, the antibody-mediated response tends to be weak, leading to chronic SARS-CoV-2 infection, viral progression, and reduced sensitivity to neutralizing antibodies (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to understand the dynamics and role of antibodies, cytokines, chemokines, and growth factors as biomarkers of clinical outcome in patients with COVID-19 admitted to the ICU.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eThe COVID-19 patients were enrolled at admission in the ICU from Hospital Risoleta Tolentino Neves (Belo Horizonte, MG) and from Hospital das Clınicas da Faculdade de Medicina de Ribeir\u0026atilde;o Preto da Universidade de S\u0026atilde;o Paulo (Ribeir\u0026atilde;o Preto, SP).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eThis observational longitudinal investigation was comprised of non-probabilistic convenience sampling of serum specimens from critically COVID-19 patients admitted ICU and healthy controls during the peak circulation of the SARS-CoV-2 B.1 lineage in the hospital coverage areas. No COVID-19 vaccine was available at the time of sample collection; therefore, all patients were unvaccinated. In summary, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the process of inclusion and selection of study population. The criteria for admission to the ICU were patients who presented: acute respiratory failure, requiring invasive mechanical ventilation or; acute respiratory failure requiring non-invasive ventilation; presenting PaCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;50mmHg and pH\u0026thinsp;\u0026le;\u0026thinsp;7.35; or hemodynamic instability or shock, defined as arterial hypotension (SBP\u0026thinsp;\u0026lt;\u0026thinsp;90mmHg or MAP\u0026thinsp;\u0026lt;\u0026thinsp;65mmHg).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eEthical Issues\u003c/h3\u003e\n\u003cp\u003e The participants enrolled in this study provided written consent by signing the consent form. The study protocol was submitted and approved by the Ethics Committee at Instituto Ren\u0026eacute; Rachou-FIOCRUZ-Minas (CAAE: 42560721.7.0000.5091) and Hospital das Cl\u0026iacute;nicas of the Faculty of Medicine of Ribeir\u0026atilde;o Preto \u0026ndash; Universidade de S\u0026atilde;o Paulo/USP-RP (CAAE: 30816620.0.0000.5440). This investigation followed the principles of the Helsinki Declaration and Resolution No. 466/2012 of the Brazilian National Health Council for research involving human subjects.\u003c/p\u003e\n\u003ch3\u003eBiological Sample\u003c/h3\u003e\n\u003cp\u003eApproximately 6mL of peripheral blood was collected in at ICU admission (D0), 7 days (D7) and 14 days (D14) hospitalization by venous puncture in Gel separator (Gel BD SST\u0026reg; II Advance) tube. The samples were stored in a freezer at -80◦C for measurement of circulating soluble molecules. Furthermore, serum sample aliquots were thawed at 37\u0026deg;C, centrifuged at 24,000 x g to remove lipid layer and large debris and the supernatant filtered through 0.45mm syringe filter to further maximize the debris removal.\u003c/p\u003e\n\u003ch3\u003eAssessment of Circulating Soluble Molecule Level\u003c/h3\u003e\n\u003cp\u003eAll immune molecules were quantified using the Luminex technique at the Instituto Ren\u0026eacute; Rachou, Funda\u0026ccedil;\u0026atilde;o Oswaldo Cruz (FIOCRUZ-Minas). Both data acquisition and molecule levels were measured on the Luminex 200 system, and the data were analyzed using the BioPlex Manager software. The concentrations of immunoglobulins (IgM, IgG, and IgA) were quantified using the Bio-Plex Pro\u0026trade; Human IgM SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64399243), the Bio-Plex Pro\u0026trade; Human IgG SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64420111), and the Bio-Plex Pro\u0026trade; Human IgA SARS-CoV-2 / RBD / S1 / N / 4-plex Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64420108). In addition, chemokines (CXCL8, CXCL10, CCL2, CCL3, CCL4, CCL5, CCL11), cytokines (IL-1beta, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, IFN-γ, TNF-α), and cell growth factors (G-CSF, GM-CSF, FGF basic, and VEGF) were measured using the Bio-Plex Pro\u0026trade; Human Cytokine 27-plex Assay Kit, Bio-Rad Laboratories, Hercules, CA, USA (Lot: 64480554). The dosages of soluble molecules were carried out at three different time intervals: D0, D7 and D14.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses of the data and circulating levels of the soluble molecules were performed with the software Graphpad Prism (v8.0.2) and Stata (v13.0). Data normality tests were performed using the Shapiro-Wilk test. The comparison of values ​​between two data groups was performed using the Mann-Whitney test, whereas for comparison of the variables with three or more groups, data analysis was performed using the Kruskal-Wallis test, followed by Dunn's post-test for multiple comparisons between groups. In addition, the signature analysis was performed by converting continuous variable results of each mediator into categorical data. For this purpose, the global median values obtained from the entire dataset, encompassing all participants, served as the cut-off. This allowed for the classification of patients into low (below the cut-off) or high (above the cut-off) immune mediator production groups. In parallel, a signature analysis was performed by grouping the immune mediators according to their class (antibodies, chemokines, cytokines and cell growth factors) to evaluate the profile of mediator production between the groups. Statistical significance of this analysis was determined using Fisher\u0026acute;s exact test. These delta signatures were visualized in lollipop charts grouped according to the class of immune mediators. Furthermore, construction of integrative networks between the antibodies, cytokines, chemokines and growth factors, were performed from the association of these markers in each clinical group. The Spearman correlation test was performed, and the networks were built with Cytoscape 3.10.1 software (Cytoscape Consortium San Diego, CA, USA), following the recommendations and instructions in the software. The levels of statistical significance defined in all cases was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eClinical and Sociodemographic Data\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the epidemiological characteristics of the study patients. The most common sex was male (70%), however 78% of deaths occurred among females. Both groups had arterial hypertension (HTN) and diabetes mellitus (DM), although 57% of the patients who died had both comorbidities. Furthermore, chronic obstructive pulmonary disease (COPD) was not significantly frequent, and the SASP score was similar across outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical and Sociodemographic Data of Health Controls (HC), Discharge and Death groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCOVID-19 patients\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDischarge\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;23\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeath\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years), Median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 [59\u0026ndash;70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 [51\u0026ndash;68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 [62\u0026ndash;74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDM\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHTN\u0026thinsp;+\u0026thinsp;DM\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e, Median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 [26\u0026ndash;33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 [26\u0026ndash;40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSAPS3\u003c/b\u003e, Median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 [58\u0026ndash;74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 [54\u0026ndash;85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e: Hypertension; \u003cb\u003eBMI\u003c/b\u003e: Body Mass Index; \u003cb\u003eDM\u003c/b\u003e: Diabetes Mellitus; \u003cb\u003eCOPD\u003c/b\u003e: Chronic Obstructive Pulmonary Disease; \u003cb\u003eSAPS3\u003c/b\u003e: \u003cem\u003eSimplified Acute Physiology Score III\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFollow-Up Production of Antibodies in COVID-19 Patients under ICU care\u003c/h2\u003e\u003cp\u003eTo understand the dynamics of antibody production in COVID-19 and their evolution throughout hospitalization in critically patients serum samples were collected at admission (D0), and on days 7 (D7) and 14 (D14) post-admission, and further categorized according to the clinical outcome. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the dynamics of patient antibody production according to class (IgM, IgG, and IgA) and their respective targets (Anti-S1, Anti-N, and Anti-RBD).\u003c/p\u003e\u003cp\u003eData analysis demonstrated that antibody concentrations were higher in the group of patients with COVID-19 (ALL) when compared to participants in the control group (HC). When analyzing patient antibody concentrations according to clinical outcome (DIS vs. DEA), antibody concentrations were higher in the Discharge (DIS) group. It is important to mention that the concentrations of IgM anti-N and IgM anti-RBD at day 0, as they were statistically significantly higher in the Death (DEA) group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCOVID-19 Patients Presented Different Serum Levels of Immunological Soluble Molecules When Compared to HCD Group\u003c/h2\u003e\u003cp\u003eTo further explore the process of immune molecule production, we compared the production of cytokines, chemokines, and growth factors between ICU patients, regardless of clinical outcome (ALL) and the healthy control group (HC). The results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Analysis of ALL vs. HC showed that a higher production of CCL3, CXCL10, IL-6, TNF-a, IFN-g and IL-1Ra in the ALL group - which demonstrates greater inflammatory activity with leukocyte recruitment that can lead to damage to the host itself due to an exaggerated response. On the other hand, CCL11, IL-17, IL-4, IL-5 IL-13, FGF-Basic, PDGF, VEGF, GM-CSF and IL-7 showed levitated levels in the HC group, being compatible with a more regulatory pattern - aiming to control the physiological activities of the organism.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSignature of Immunological Soluble Molecules Demonstrated in ALL Patients When Compared to HC Group\u003c/h2\u003e\u003cp\u003eTo reinforce the idea of a cytokine storm and exacerbate production of immune molecules in severe COVID-19, we demonstrate in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e the signature of immune biomarkers throughout the ICU follow-up. This analysis clearly shows that the host's immune system is unable to assertively mount a targeted response to the virus. Furthermore, the production curves of these mediators are heterogeneous, with distinct peak levels observed at different evaluation time points. It is worth noting that the number of molecules that are overproduced also increases, demonstrating a difficulty in immune regulation, since a Th1 response is required.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparisons of serum concentrations of each molecule at Day 0 (HC \u0026ndash; green circles; ALL \u0026ndash; red circles) with their respective intervals, demonstrated a discrepancy between the concentrations observed in inflammatory molecules when comparing HC and ALL (Figura 4A). Complementary analysis are presenting in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB that shows the molecules in ascending order of concentration in COVID-19-positive patients. Those that did not show a statistical difference in the HC vs. ALL comparisons are shown in gray. Data analysis showed that only CCL4 and IgG Anti-N remained without statistical difference throughout ICU follow-up, despite being among the most highly produced molecules on Day 14.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFollow-Up of the Production of Immunological Soluble Molecules in Patients Under ICU Care\u003c/h2\u003e\u003cp\u003eTo address the potential biomarkers related to clinical outcomes, the chemokines, cytokines, and growth factors, were intra-group and inter-group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). During the follow-up, we can observe heterogeneity in the serum concentration of all molecules throughout the evaluation days, however, some molecules show a progressive increase with statistical difference in the DIS group, such as IFN-g and IL-1Ra, however, their concentrations are still lower than the DEA group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eData regarding D7 showed a change in the dynamics of molecule production, with a more homogeneous pattern. However, TNF-a and FGF-basic remained elevated in the DEA group, while IL-13 was prominent in the DIS group (and remained at D14). Overall, the DEA group showed increased production of CXCL8, CCL4, CXCL10, TNF-α, IL-10, and G-CSF, while the DIS group maintained elevated production of only IL-13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSignature of Immunological Soluble Molecules Demonstrated in Patients Under ICU care\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows the dynamics of immune molecule production throughout the ICU care period in the ALL and HC groups, serving as a baseline and parameter for comparison. Thus, it was possible to observe that the profile of highly produced molecules is robust and diverse, with antibody production throughout hospitalization exhibiting a heterogeneous sharing pattern, but highlighting IgM and IgA anti-RBD, which exhibit high production at all follow-up periods. In addition, the analysis of the production pattern between the ALL and HC groups, pointed out that there is almost an inversion in the way the molecules are produced, demonstrating a modulation process by viral exposure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, we performed an exploratory analysis within the ALL group to evaluate patients according to the outcome (DIS and DEA) and the pattern of molecule production throughout hospitalization. In this sense, we observed that IgM anti-RBD and IgA anti-S1 antibodies, IgA anti-RBD, and the chemokine CCL5 presented high production in the DIS group, while in the DEA group, IgM anti-N and anti-RBD antibodies, IgA anti-RBD, in addition to the molecules CXCL-8, CXCL-10, CCL3, IL-1β, TNF-a, IFN-g, IL-17, IL-1Ra, and FGF-basic presented high production throughout all days of hospitalization. Thus, there was an exclusive high production at all time points evaluated of the IgA anti-S1 and CCL5 molecules in the DIS group, but in the DEA group, there were IgM anti-N CXCL8, CXCL10, CCL3, IL-1β, TNF-a, IFN-g, IL-17, IL-1Ra and FGF-basic. This suggests a potential relationship with the appropriate outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eBiological interaction networks according to days of hospitalization and clinical outcome\u003c/h2\u003e\u003cp\u003eThe biological interaction networks between antibodies, cytokines, chemokines, and growth factors allow us to gain a complex view of the molecules patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This analysis was also performed throughout the study ICU follow-up days.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNetwork analysis demonstrated that the ALL group exhibits robust antibody interactions, with a greater number of positive correlations among antibody categories, but many negative correlations with other groups of immune molecules at D0, suggesting an important role for antibodies in these patients. Conversely, these negative relationships diminish over the course of hospitalization and begin to become positive between antibodies and other groups of molecules, while maintaining robust interactions only between antibodies. Furthermore, the interaction networks in the DIS and DEA groups show that patients who progressed to discharge had a much higher number of interactions between molecules than those who died. It is also possible to observe that in the DIS group, there is a slow decrease in interactions over the days of hospitalization, while in the DEA group, this decline is abrupt and disordered.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSevere COVID-19 is characterized by an exacerbated inflammatory response and multisystem dysfunction, often culminating in the need for invasive ventilatory support and admission to an intensive care unit (ICU). Patients admitted in this setting have a high morbidity and mortality rate, associated with factors such as advanced age, pre-existing comorbidities, and cytokine storm. The demographic and clinical data on age, sex, and previous comorbidities collected in this study corroborate those previously described, as most patients had comorbidities such as hypertension, diabetes mellitus (DM), or both, regardless of clinical outcome.(\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this line, chronic obstructive pulmonary disease (COPD) are described as a factor that influences pulmonary complications, only one patient had this comorbidity, which reinforces the idea suggested by Fraser et al. (2021). The authors reported that severe COVID-19, due to the complexity, can be a factor that leads to the need for ICU admission, regardless of other complications such as previous comorbidities (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Furthermore, although the Simplified Acute Physiology Score III (SAPS3) score has been defined as a reliable predictor of mortality (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), our study observed very similar values in both groups, regardless of the outcome observed. This corroborates with Basiri et al., 2025 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) which suggests that although SAPS3 can be used for these patients, other scores, such as Sequential Organ Failure Assessment (SOFA), may be more appropriate for clinical management.\u003c/p\u003e\u003cp\u003eFrom a laboratory perspective, several markers have already been described for at-risk patients (\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) but once infected, the body initiates an innate immune response (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), followed by a humoral response with the production of antibodies classified into several subclass, generally according to the stage of the disease, such as IgM, IgG, and IgA (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe humoral immune response has been extensively investigated, with studies reporting discrepancies in its magnitude depending on factors such as seroconversion, length of hospital stays, target antigen, or isotype investigated (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). It has been reported that the S and N proteins are dominant antigens in coronaviruses and can activate the production of IgM, IgG, and IgA antibodies as a response by the host body. It is noteworthy that the S protein has a region called the RBD (receptor-binding domain), which is important in its pathophysiological process and is an important target of the response (\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results demonstrate that on Days 7 and 14, IgM, IgG, and IgA antibody concentrations were similar in both groups, corroborating data from Salgado et al., 2023 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) suggesting that the magnitude of antibody production is more impaired in patients with severe COVID-19, but that the humoral response is similar in patients regardless of the outcome. Some studies suggest that serum IgA and IgG concentrations are higher in patients with mild disease than in hospitalized patients (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), in this context, our data demonstrates that, on the day of ICU admission (D0), although there is no statistical difference, IgA and IgG concentrations are higher in the DIS group, which may apparently be beneficial for these patients. Furthermore, it should also be considered that higher IgA concentrations are generally found in patients who have presented some type of complication or gastrointestinal reaction resulting from COVID-19 infection. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eHowever, our data demonstrates that IgA concentrations remained stable throughout the days of ICU hospitalization, unlike other studies that suggest a rapid decrease in IgA concentrations when compared to IgG concentrations (\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Salgado et. al., 2023 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) suggests that altered IgG concentrations may be associated with mortality in hospitalized patients. Nonetheless, our data demonstrates no difference between the concentrations of this antibody in the studied groups. Therefore, we emphasize that factors intrinsic to the patients (such as evolutionary factors) may interfere with this process, since the way the immune system conducts this immunological reaction through cytokines can influence the humoral response. Fraser et al., 2021(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) describes that IgM concentrations peak around day 13 after ICU admission, but our patients showed peaks on D0, despite having symptoms for several days.\u003c/p\u003e\u003cp\u003eRegarding the cytokines production during infection, an amplified immune response has already been identified in some patients with severe COVID-19, called a \"cytokine storm.\" In this context, has been shown that the disease severity is associated with an increase in inflammatory cytokines such as interferons, TNF, IL-6, and IL-1β (\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). When dysregulated, these mediators can lead to physiological changes compatible with sepsis, presenting vascular alterations (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), this would justify the suggestion by Basiri et al., 2025 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) to use the SOFA score in the evaluation of such patients. In the present study, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e clearly illustrate this immunological dysregulation in severe COVID-19, as evidenced by the cytokine production profiles observed.\u003c/p\u003e\u003cp\u003eIt is notable that IFN-g plays a central role in the cytokine storm (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) due to its systemic regulation. In our investigation, elevated concentrations in the DEA group reinforce the idea that these patients are experiencing this type of immunological complication. Ruan et al., (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e) showed that critically ICU patients had higher concentrations of TNF-a, IL-2, IL-7, and IL-10, which corroborates our findings. Although there was no significant difference between the two groups, the levels of these cytokines were higher in the DEA group. Despite the elevated production of these molecules on Day 0, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, statistical differences were only observed for IFN-g and IL-9 in the DEA group patients. Sadhu et al. (2022) (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) demonstrated that IL-9 aggravates SARS-CoV-2 infection and is associated with increased airway inflammation. Furthermore, on D14, more significant changes in production dynamics can be observed between groups, possibly associated with the worsening of the clinical condition of these patients.\u003c/p\u003e\u003cp\u003eHigher concentrations of IFN-g and TNF-a have been previously described and associated with extensive lung damage in patients with SARS-CoV-1 and MERS-CoV. The analysis of DEA group, both of these cytokines were higher, which would justify the worsening of their condition throughout their hospitalization (\u003cspan additionalcitationids=\"CR78 CR79 CR80 CR81\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Moreover, elevated IL-10 concentrations are observed as an attempt to control the cytokine storm process, as it plays an important immunomodulatory role, further demonstrating the worsening condition of patients. The increase in chemokines, especially CXCL8, along with IL-9, is associated with bilateral pneumonia in patients with COVID-19 (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough IL-13 production remained high in the group that progressed to discharge, its relationship with COVID-19 has been described as controversial, according to our results. It has been associated with worsening conditions, given that its action is linked to the activation of the Th2 profile (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). On the other hand, an \u003cem\u003ein vitro\u003c/em\u003e study conducted by Donlan et al., 2021 suggests that IL-13 may play a protective role due to its action in mucus production and reduced ACE-2 expression. This activation pattern could hinder viral replication and explain the elevated serum concentrations on Day 7 and Day 14. in the DIS group.\u003c/p\u003e\u003cp\u003eThe evaluating the interactions between all the molecules studied, we observed in DIS group that presented a robust pattern of correlations between IgM, IgG, and IgA antibodies, which may suggest an attempt atthe immune system to control the inflammatory process - given that antibodies also can neutralize viral particles and also due to the negative correlation between antibodies and inflammatory cytokines such as IL-6, TNF, and IFN-g, already described in other studies as markers of severity in COVID-19 (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Furthermore, IgA anti-S1 antibodies showed positive correlations with VEGF, that may suggest an attempt at tissue reconstitution and activation of this endothelium, considering that IgA is an antibody involved in mucosal defense and VEGF is a vascular endothelial growth factor, this (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan additionalcitationids=\"CR87 CR88\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStill regarding the DIS group, at D7, our findings demonstrated that the IgM anti-S1 antibody exhibits many interactions with all groups of immunological molecules studied, suggesting a crucial role in the immunological regulation of these patients, preventing possible complications resulting from an exaggerated immune response. This corroborates the findings of Fraser et al., 2021 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) who suggested that patients with higher IgM concentrations survive, with their highest concentration peaking at D14. Therefore, when we evaluate these interactions at D14, it is still noticed that the role of IgM anti-S1 interacting with cytokines and chemokines reinforces this idea.\u003c/p\u003e\u003cp\u003eRegarding the DEA group, our data pointed out that a poor number of correlations between antibodies was observed within the absence of positive interactions with growth factors. However, it continues to maintain the pattern of negative correlations between antibodies and the other groups of immunological molecules, that the DIS group also exhibits. On D7 there is a decrease in the number of correlations, including antibodies categories, which corroborates what was suggested by Salgado et al., 2023, that early decreases in IgA and IgG antibody values are observed in patients who present worse outcomes (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). While on D14, it is possible to observe that there is an onset of response similar to D7 in the DIS group, but now with the focal point being IgM anti-N, IgA anti-N, and IgG anti-S1.\u003c/p\u003e\u003cp\u003eFinally, this study has certain limitations that should be acknowledged. First, participant recruitment occurred during the peak of the COVID-19 pandemic, which may have restricted sample size and limited the assessment of variables intrinsically related to SARS-CoV-2, such as viral load. Furthermore, the type of complication analyzed requires cautious interpretation to minimize potential biases associated with the primary cause of hospitalization. Despite these limitations, the complication investigated has significant clinical relevance regarding clinical outcome.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, patients who are present with COVID-19 and require ICU care exhibit immune molecule dynamics consistent with a cytokine storm. We suggest that IgM Anti-N and IgM Anti-RBD antibodies, along with molecules on their respective days of evaluation, such as IFN-g (D0), TNF-a, and FGF-basic (D7), as well as CXCL8, CCL4, CXCL10, and G-CSF (D14), may be potential biomarkers of worsening clinical outcomes. Furthermore, IL-13 may play a protective role in these ICU patients. Finally, we believe that the level of circulating antibodies, chemokines, cytokines, and growth factors, and their response profile against COVID-19, reflect the clinical need for ICU care, and this can be used to assess patient clinical prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICTS OF INTEREST\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest related to the content of this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis study was supported by a research scholarship from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) (Funding Code 001 and PROAP Program #1247/2022); by the Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado do Amazonas (FAPEAM) (PDPG/CAPES/FAPEAM Program #038/2022 and POSGRAD Program #002/2025); and by Fundac\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de Minas Gerais (FAPEMIG) (Grant # APQ-00432-20 and APQ-01499-21). The study was also supported by the Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico\u0026ndash; CNPq and Fundac\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo (FAPESP).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHNSI: Conceptualization, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Investigation, Data analysis, Curation and Visualization; FMG: Data analysis, Curation and Visualization; IACR: Data analysis, Curation and Visualization; JCFN: Curation and Visualization; FSAH: Curation and Visualization; AAL: Conceptualization, Investigation and Methodology; ALR: Conceptualization, Investigation and Methodology; GMF: Conceptualization, Investigation and Methodology; TFCFS: Conceptualization, Investigation and Methodology; AAOP: Conceptualization, Investigation and Methodology; DPAM: Conceptualization, Investigation and Methodology; JPBS: Conceptualization, Investigation and Methodology; ;ACCA: Methodology, Curation and Visualization; DF: Conceptualization, Investigation and Methodology; VPM: Methodology, Curation and Visualization; MSSA: Conceptualization, Investigation and Methodology; ATC Conceptualization, Supervision, Funding acquisition, Resources, Validation; VLDB: Conceptualization, Investigation and Methodology; CB: Conceptualization, Investigation and Methodology; MGM: Conceptualization, Investigation and Methodology; MCS: Conceptualization, Supervision and coordination, Investigation, Writing \u0026ndash; review \u0026amp; editing; MAM: Conceptualization, Investigation and Methodology; JGCR: Methodology, Curation and Visualization; OAMF: Conceptualization, Supervision and coordination, Investigation, Funding acquisition, Resources, Writing \u0026ndash; review \u0026amp; editing. AGC: Conceptualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Data analysis, curation and visualization. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the ICU team from Hospital das Cl\u0026iacute;nicas da Faculdade de Medicina de Ribeir\u0026atilde;o Preto\u0026ndash; Universidade de S\u0026atilde;o Paulo (USP) and from Hospital Risoleta Tolentino Neves for their support during sample collection and medical records assessment. The authors also thank the Program for Technological Development in Tools for Health (PDTIS-FIOCRUZ) for the use of its facilities and Flow Cytometry Platform. We are grateful to Grupo Integrado de Pesquisas em Biomarcadores at Instituto Ren\u0026eacute; Rachou, Funda\u0026ccedil;\u0026atilde;o Oswaldo Cruz of Minas Gerais state (FIOCRUZ-Minas) for excellent technical assistance and support with the assays.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Furthermore, the datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWiersinga, W. J. et al. Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. \u003cem\u003eJAMA - J. Am. Med. Assoc.\u003c/em\u003e \u003cstrong\u003e324\u003c/strong\u003e (8), 782\u0026ndash;793 (2020).\u003c/li\u003e\n\u003cli\u003eMuralidar, S., Visaga, S. \u0026amp; Sekaran, S. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S1201971220302575\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, Viral Infection, Immunological response, Antibodies subclass, cytokine storm","lastPublishedDoi":"10.21203/rs.3.rs-7366441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7366441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSARS-CoV-2, the virus responsible for the COVID-19 pandemic, has diverse clinical presentations and varying degrees of severity, accounting for millions of deaths and the leading cause of intensive care unit (ICU) admissions during the pandemic. In this study, we sought to evaluate potential immunological biomarkers (antibodies, chemokines, cytokines, and growth factors) that predict clinical outcomes in circulating samples from patients with \"Severe COVID\" admitted to the ICU.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis is a prospective longitudinal study using peripheral blood samples from 30 patients admitted to the ICU with severe COVID-19 [with the outcomes discharge (DIS) and death (DEA)] and 30 healthy controls. Clinical and laboratory data were collected during patient evaluation. Furthermore, immunological molecules are being quantified using the Luminex methodology, a multiplex immunoassay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMales were the most common sex (70%), and 57% of patients had hypertension and diabetes mellitus. The circulating response profile differed between the study groups, with patients, regardless of outcome, presenting a heterogeneous profile of molecule production throughout the follow-up period. The DIS group showed greater control over the production of immune molecules, particularly IL-13, while the DEA group presented a profile consistent with a cytokine storm.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe suggest that IgM Anti-N and IgM Anti-RBD antibodies, along with molecules such as IFN-g (D0), TNF-a, and FGF-basic (D7), as well as CXCL8, CCL4, CXCL10, and G-CSF (D14), may be potential biomarkers of worsening clinical outcomes. Furthermore, IL-13 may play a protective role in these ICU patients.\u003c/p\u003e","manuscriptTitle":"Anti-SARS-CoV-2 Antibody Subclass Response and Cytokine Storm in Severe COVID-19 Patients Under Intensive Care Unit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 09:27:45","doi":"10.21203/rs.3.rs-7366441/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17556cac-f930-468b-9ad9-b8b5ed706d40","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54371874,"name":"Health sciences/Biomarkers"},{"id":54371875,"name":"Health sciences/Diseases"},{"id":54371876,"name":"Biological sciences/Immunology"},{"id":54371877,"name":"Health sciences/Medical research"},{"id":54371878,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-11-24T12:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 09:27:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7366441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7366441","identity":"rs-7366441","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}