T-Cell Immune Responses and Immunological Factors Associated with Coronavirus Disease 2019 Progression as Predictors for the Severity of the Disease in Hospitalized Patients.

INTRODUCTION: The prevalence of coronavirus disease 2019 (COVID-19) has rapidly increased worldwide. More investigation is needed to progress toward understanding the exact role of immune responses in the pathology of the disease, leading to improved anticipation and treatment options. METHODS: In the present study, we examined the relative expression of T-bet, GATA3, RORγt, and FoxP3 transcription factors as well as laboratory indicators in 79 hospitalized patients along with 20 healthy subjects as a control group. In order to make an exact comparison between various degrees of severity of disease, patients were divided into critical (n = 12) and severe (n = 67) groups. To evaluate the expression of genes of interest by performing real-time PCR, blood samples were obtained from each participant. RESULTS: We found a significant increase in the expression of T-bet, GATA3, and RORγt and a reduction in the expression of FoxP3 in the critically ill patients compared to the severe and control groups. Also, we noticed that the GATA3 and RORγt expressions were elevated in the severe group in comparison with healthy subjects. Additionally, the GATA3 and RORγt expressions showed a positive correlation with elevation in CRP and hepatic enzyme concentration. Moreover, we observed that the GATA3 and RORγt expressions were the independent risk factors for the severity and outcome of COVID-19. DISCUSSION: The present study showed that the overexpression of T-bet, GATA3, and RORγt, as well as a decrease in the FoxP3 expression was associated with the severity and fatal outcome of COVID-19.

Determining clinically relevant features in cytometry data using persistent homology.

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease.

SARS-CoV-2 infection elicits a robust B cell response, resulting in the generation of long-lived plasma cells and memory B cells. Here, we aimed to determine the effect of COVID-19 severity on the memory B cell response and characterize changes in the memory B cell compartment between recovery and five months post-symptom onset. Using high-parameter spectral flow cytometry, we analyzed the phenotype of memory B cells with reactivity against the SARS-CoV-2 spike protein or the spike receptor binding domain (RBD) in recovered individuals who had been hospitalized with non-severe (n = 8) or severe (n = 5) COVID-19. One month after symptom onset, a substantial proportion of spike-specific IgG+ B cells showed an activated phenotype. In individuals who experienced non-severe disease, spike-specific IgG+ B cells showed increased expression of markers associated with durable B cell memory, including T-bet and FcRL5, as compared to individuals who experienced severe disease. While the frequency of T-bet+ spike-specific IgG+ B cells differed between the two groups, these cells predominantly showed an activated switched memory B cell phenotype in both groups. Five months post-symptom onset, the majority of spike-specific memory B cells had a resting phenotype and the percentage of spike-specific T-bet+ IgG+ memory B cells decreased to baseline levels. Collectively, our results highlight subtle differences in the B cells response after non-severe and severe COVID-19 and suggest that the memory B cell response elicited during non-severe COVID-19 may be of higher quality than the response after severe disease.