Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission

Immunologic mediators profile in COVID-19 convalescence

SARS-CoV-2 caused the pandemic situation experienced since the beginning of 2020, and many countries faced the rapid spread and severe form of the disease. Mechanisms of interaction between the virus and the host were observed during acute phase, but few data are available when related to immunity dynamics in convalescents. We conducted a longitudinal study, with 51 healthy donors and 62 COVID-19 convalescent patients, which these had a 2-month follow-up after symptoms recovery. Venous blood sample was obtained from all participants to measure blood count, subpopulations of monocytes, lymphocytes, natural killer cells and dendritic cells. Serum was used to measure cytokines, chemokines, growth factors, anti-N IgG and anti-S IgG/IgM antibodies. Statistic was performed by Kruskal-Wallis test, and linear regression with days post symptoms and antibody titers. All analysis had confidence interval of 95%. Less than 35% of convalescents were anti-S IgM+, while more than 80% were IgG+ in D30. Anti-N IgG decreased along time, with loss of seroreactivity of 13%. Eosinophil count played a distinct role on both antibodies during all study, and the convalescence was orchestrated by higher neutrophil-to-lymphocyte ratio and IL-15, but initial stages were marked by increase in myeloid DCs, B1 lymphocytes, inflammatory and patrolling monocytes, G-CSF and IL-2. Later convalescence seemed to change to cytotoxicity mediated by T lymphocytes, plasmacytoid DCs, VEGF, IL-9 and CXCL10. Anti-S IgG antibodies showed the longest perseverance and may be a better option for diagnosis. The inflammatory pattern is yet present on initial stage of convalescence, but quickly shifts to a reparative dynamic. Meanwhile eosinophils seem to play a role on anti-N levels in convalescence, although may not be the major causative agent. We must highlight the importance of immunological markers on acute clinical outcomes, but their comprehension to potentialize adaptive system must be explored to improve immunizations and further preventive policies.

Fragile Treg cells: Traitors in immune homeostasis?

Regulatory T (Treg) cells play a key role in maintaining immune tolerance and tissue homeostasis. However, in some disease microenvironments, Treg cells exhibit fragility, which manifests as preserved FoxP3 expression accompanied by inflammation and loss of immunosuppression. Fragile Treg cells are formatively, phenotypically and functionally diverse in various diseases, further complicating the role of Treg cells in the immunotherapeutic response and offering novel targets for disease treatment by modulating specific Treg subsets. In this review, we summarize findings on fragile Treg cells to provide a framework for characterizing the formation and role of fragile Treg cells in different diseases, and we discuss how this information may guide the development of more specific Treg-targeted immunotherapies.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Excess of body weight is associated with accelerated T-cell senescence in hospitalized COVID-19 patients

BACKGROUND

Several risk factors have been involved in the poor clinical progression of coronavirus disease-19 (COVID-19), including ageing, and obesity. SARS-CoV-2 may compromise lung function through cell damage and paracrine inflammation; and obesity has been associated with premature immunosenescence, microbial translocation, and dysfunctional innate immune responses leading to poor immune response against a range of viruses and bacterial infections. Here, we have comprehensively characterized the immunosenescence, microbial translocation, and immune dysregulation established in hospitalized COVID-19 patients with different degrees of body weight.

RESULTS

Hospitalised COVID-19 patients with overweight and obesity had similarly higher plasma LPS and sCD14 levels than controls (all p < 0.01). Patients with obesity had higher leptin levels than controls. Obesity and overweight patients had similarly higher expansions of classical monocytes and immature natural killer (NK) cells (CD56+CD16-) than controls. In contrast, reduced proportions of intermediate monocytes, mature NK cells (CD56+CD16+), and NKT were found in both groups of patients than controls. As expected, COVID-19 patients had a robust expansion of plasmablasts, contrasting to lower proportions of major T-cell subsets (CD4 + and CD8+) than controls. Concerning T-cell activation, overweight and obese patients had lower proportions of CD4+CD38+ cells than controls. Contrasting changes were reported in CD25+CD127low/neg regulatory T cells, with increased and decreased proportions found in CD4+ and CD8+ T cells, respectively. There were similar proportions of T cells expressing checkpoint inhibitors across all groups. We also investigated distinct stages of T-cell differentiation (early, intermediate, and late-differentiated - TEMRA). The intermediate-differentiated CD4 + T cells and TEMRA cells (CD4+ and CD8+) were expanded in patients compared to controls. Senescent T cells can also express NK receptors (NKG2A/D), and patients had a robust expansion of CD8+CD57+NKG2A+ cells than controls. Unbiased immune profiling further confirmed the expansions of senescent T cells in COVID-19.

CONCLUSIONS

These findings suggest that dysregulated immune cells, microbial translocation, and T-cell senescence may partially explain the increased vulnerability to COVID-19 in subjects with excess of body weight.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.

Increased blood immune regulatory cells in severe COVID-19 with autoantibodies to type I interferons

The hallmark of severe COVID-19 is an uncontrolled inflammatory response, resulting from poorly understood immunological dysfunction. While regulatory T (Treg) and B (Breg) cells, as the main elements of immune homeostasis, contribute to the control of hyperinflammation during COVID-19 infection, we hypothesized change in their levels in relation to disease severity and the presence of autoantibodies (auto-Abs) to type I IFNs. Cytometric analysis of blood of 62 COVID-19 patients with different severities revealed an increased proportion of conventional (cTreg; CD25+FoxP3+) and unconventional (uTreg; CD25-FoxP3+) Tregs, as well as the LAG3+ immune suppressive form of cTreg/uTreg, in the blood of severe COVID-19 cases compared to the milder, non-hospitalized cases. The increase in blood levels of cTreg/uTreg, but not LAG3+ cTreg/uTreg subtypes, was even higher among patients with severe COVID-19 and auto-Abs to type I IFNs. Regarding Bregs, compared to the milder, non-hospitalized cases, the proportion of IL-35+ and IL-10+ Bregs was elevated in the blood of severe COVID-19 patients, and to a higher extent in those with auto-Abs to type I IFNs. Moreover, blood levels of cTreg, LAG3+ cTreg/uTreg, and IL-35+ and IL-10+ Breg subtypes were associated with lower blood levels of proinflammatory cytokines such as IL-6, IL-17, TNFα, and IL-1β. Interestingly, patients who were treated with either tocilizumab and/or a high dose of Vitamin D had higher blood levels of these regulatory cells and better control of the proinflammatory cytokines. These observations suggest that perturbations in the levels of immunomodulatory Tregs and Bregs occur in COVID-19, especially in the presence of auto-Abs to type I IFNs.