Cytokine Profiling in Different SARS-CoV-2 Genetic Variants.

This study is a successor of our previous work concerning changes in the chemokine profile in infection that are associated with different SARS-CoV-2 genetic variants. The goal of our study was to take into account both the virus and the host immune system by assessing concentrations of cytokines in patients infected with different SARS-CoV-2 variants (ancestral Wuhan strain, Alpha, Delta and Omicron). Our study was performed on 340 biological samples taken from COVID-19 patients and healthy donors in the timespan between May 2020 and April 2022. We performed genotyping of the virus in nasopharyngeal swabs, which was followed by assessment of cytokines' concentration in blood plasma. We noted that out of nearly 30 cytokines, only four showed stable elevation independently of the variant (IL-6, IL-10, IL-18 and IL-27), and we believe them to be 'constant' markers for COVID-19 infection. Cytokines that were studied as potential biomarkers lose their diagnostic value as the virus evolves, and the specter of potential targets for predictive models is narrowing. So far, only four cytokines (IL-6, IL-10, IL-18, and IL-27) showed a consistent rise in concentrations independently of the genetic variant of the virus. Although we believe our findings to be of scientific interest, we still consider them inconclusive; further investigation and comparison of immune responses to different variants of SARS-CoV-2 is required.

SARS-CoV-2 escape from cytotoxic T cells during long-term COVID-19.

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties. While escape from humoral immunity certainly contributes to intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin's lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented the binding of known immunogenic SARS-CoV-2 HLA class I antigens. By experimentally assessing the effect of a subset of the escape mutations, we show that they resulted in a loss of as much as ~1% of effector CD8 T cell response. Our results indicate that CD8 T cell escape represents a major underappreciated contributor to SARS-CoV-2 evolution in humans.

Heterogenous CD8+ T Cell Maturation and 'Polarization' in Acute and Convalescent COVID-19 Patients.

BACKGROUND: The adaptive antiviral immune response requires interaction between CD8+ T cells, dendritic cells, and Th1 cells for controlling SARS-CoV-2 infection, but the data regarding the role of CD8+ T cells in the acute phase of COVID-19 and post-COVID-19 syndrome are still limited. METHODS: . Peripheral blood samples collected from patients with acute COVID-19 (n = 71), convalescent subjects bearing serum SARS-CoV-2 N-protein-specific IgG antibodies (n = 51), and healthy volunteers with no detectable antibodies to any SARS-CoV-2 proteins (HC, n = 46) were analyzed using 10-color flow cytometry. RESULTS: Patients with acute COVID-19 vs. HC and COVID-19 convalescents showed decreased absolute numbers of CD8+ T cells, whereas the frequency of CM and TEMRA CD8+ T cells in acute COVID-19 vs. HC was elevated. COVID-19 convalescents vs. HC had increased naïve and CM cells, whereas TEMRA cells were decreased compared to HC. Cell-surface CD57 was highly expressed by the majority of CD8+ T cells subsets during acute COVID-19, but convalescents had increased CD57 on 'naïve', CM, EM4, and pE1 2-3 months post-symptom onset. CXCR5 expression was altered in acute and convalescent COVID-19 subjects, whereas the frequencies of CXCR3+ and CCR4+ cells were decreased in both patient groups vs. HC. COVID-19 convalescents had increased CCR6-expressing CD8+ T cells. Moreover, CXCR3+CCR6- Tc1 cells were decreased in patients with acute COVID-19 and COVID-19 convalescents, whereas Tc2 and Tc17 levels were increased compared to HC. Finally, IL-27 negatively correlated with the CCR6+ cells in acute COVID-19 patients. CONCLUSIONS: We described an abnormal CD8+ T cell profile in COVID-19 convalescents, which resulted in lower frequencies of effector subsets (TEMRA and Tc1), higher senescent state (upregulated CD57 on 'naïve' and memory cells), and higher frequencies of CD8+ T cell subsets expressing lung tissue and mucosal tissue homing molecules (Tc2, Tc17, and Tc17.1). Thus, our data indicate that COVID-19 can impact the long-term CD8+ T cell immune response.

A Comparative Study of the Plasma Chemokine Profile in COVID-19 Patients Infected with Different SARS-CoV-2 Variants.

BACKGROUND: Infection caused by SARS-CoV-2 mostly affects the upper and lower respiratory tracts and causes symptoms ranging from the common cold to pneumonia with acute respiratory distress syndrome. Chemokines are deeply involved in the chemoattraction, proliferation, and activation of immune cells within inflammation. It is crucial to consider that mutations within the virion can potentially affect the clinical course of SARS-CoV-2 infection because disease severity and manifestation vary depending on the genetic variant. Our objective was to measure and assess the different concentrations of chemokines involved in COVID-19 caused by different variants of the virus. METHODS: We used the blood plasma of patients infected with different variants of SARS-CoV-2, i.e., the ancestral Wuhan strain and the Alpha, Delta, and Omicron variants. We measured the concentrations of 11 chemokines in the samples: CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1ß, CCL7/MCP-3, CCL11/Eotaxin, CCL22/MDC, CXCL1/GROα, CXCL8/IL-8, CXCL9/MIG, CXCL10/IP-10, and CX3CL1/Fractalkine. RESULTS: We noted a statistically significant elevation in the concentrations of CCL2/MCP-1, CXCL8/IL-8, and CXCL1/IP-10 independently of the variant, and a drop in the CCL22/MDC concentrations. CONCLUSIONS: The chemokine concentrations varied significantly depending on the viral variant, leading us to infer that mutations in viral proteins play a role in the cellular and molecular mechanisms of immune responses.

Alterations in B Cell and Follicular T-Helper Cell Subsets in Patients with Acute COVID-19 and COVID-19 Convalescents

Background. Humoral immunity requires interaction between B cell and T follicular helper cells (Tfh) to produce effective immune response, but the data regarding a role of B cells and Tfh in SARS-CoV-2 defense are still sparse. Methods. Blood samples from patients with acute COVID-19 (n = 64), convalescents patients who had specific IgG to SARS-CoV-2 N-protein (n = 55), and healthy donors with no detectable antibodies to any SARS-CoV-2 proteins (HC, n = 44) were analyses by multicolor flow cytometry. Results. Patients with acute COVID-19 showed decreased levels of memory B cells subsets and increased proportion plasma cell precursors compared to HC and COVID-19 convalescent patients, whereas for the latter the elevated numbers of virgin naïve, Bm2′ and “Bm3+Bm4” was found if compared with HC. During acute COVID-19 CXCR3+CCR6− Tfh1-like cells were decreased and the levels of CXCR3−CCR6+ Tfh17-like were increased then in HC and convalescent patients. Finally, COVID-19 convalescent patients had increased levels of Tfh2-, Tfh17- and DP Tfh-like cells while comparing their amount with HC. Conclusions. Our data indicate that COVID-19 can impact the humoral immunity in the long-term.

Changes in Anti-SARS-CoV-2 IgG Subclasses over Time and in Association with Disease Severity.

IgG is the most prominent marker of post-COVID-19 immunity. Not only does this subtype mark the late stages of infection, but it also stays in the body for a timespan of at least 6 months. However, different IgG subclasses have different properties, and their roles in specific anti-COVID-19 responses have yet to be determined. We assessed the concentrations of IgG1, IgG2, IgG3, and IgG4 against different SARS-CoV-2 antigens (N protein, S protein RBD) using a specifically designed method and samples from 348 COVID-19 patients. We noted a statistically significant association between severity of COVID-19 infection and IgG concentrations (both total and subclasses). When assessing anti-N protein and anti-RBD IgG subclasses, we noted the importance of IgG3 as a subclass. Since it is often associated with early antiviral response, we presumed that the IgG3 subclass is the first high-affinity IgG antibody to be produced during COVID-19 infection.

A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring.

Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition. Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020-03.08.2020 and 01.11.2020-19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness. Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic. Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19.

Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic.

PURPOSE: The COVID-19 pandemic showed an urgent need for decision support systems to help doctors at a time of stress and uncertainty. However, significant differences in hospital conditions, as well as skepticism of doctors about machine learning algorithms, limit their introduction into clinical practice. Our goal was to test and apply the principle of "patient-like-mine" decision support in rapidly changing conditions of a pandemic. METHODS: In the developed system we implemented a fuzzy search that allows a doctor to compare their medical case with similar cases recorded in their medical center since the beginning of the pandemic. Various distance metrics were tried for obtaining clinically relevant search results. With the use of R programming language, we designed the first version of the system in approximately a week. A set of features for the comparison of the cases was selected with the use of random forest algorithm implemented in Caret. Shiny package was chosen for the design of GUI. RESULTS: The deployed tool allowed doctors to quickly estimate the current conditions of their patients by means of studying the most similar previous cases stored in the local health information system. The extensive testing of the system during the first wave of COVID-19 showed that this approach helps not only to draw a conclusion about the optimal treatment tactics and to train medical staff in real-time but also to optimize patients' individual testing plans. CONCLUSIONS: This project points to the possibility of rapid prototyping and effective usage of "patient-like-mine" search systems at the time of a pandemic caused by a poorly known pathogen.