Follicular Helper T Cells in the Immunopathogenesis of SARS-CoV-2 Infection.

Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a serious infectious disease that has led to a global pandemic with high morbidity and mortality. High-affinity neutralizing antibody is important for controlling infection, which is closely regulated by follicular helper T (Tfh) cells. Tfh cells play a central role in promoting germinal center reactions and driving cognate B cell differentiation for antibody secretion. Available studies indicate a close relationship between virus-specific Tfh cell-mediated immunity and SARS-CoV-2 infection progression. Although several lines of evidence have suggested that Tfh cells contribute to the control of SARS-CoV-2 infection by eliciting neutralizing antibody productions, further studies are needed to elucidate Tfh-mediated effector mechanisms in anti-SARS-CoV-2 immunity. Here, we summarize the functional features and roles of virus-specific Tfh cells in the immunopathogenesis of SARS-CoV-2 infection and in COVID-19 vaccines, and highlight the potential of targeting Tfh cells as therapeutic strategy against SARS-CoV-2 infection.

Role of Th22 Cells in Human Viral Diseases.

Naive CD4+ T cells can differentiate into different cell subsets after receiving antigen stimulation, which secrete corresponding characteristic cytokines and thereby exert biological effects in various diseases. Th22 cells, a novel subset of CD4+ T cells, are different from Th1, Th2, Th17, and Treg cell subsets, which have been discovered in recent years. They can express CCR4, CCR6, and CCR10 molecules and secrete IL-22, IL-13, and TNF-α. They are not able to secrete IL-17, IL-4, and interferon-γ (IFN-γ). IL-22 is considered as a major effector molecule of Th22 cells whose functions and mechanisms of regulating cell differentiation have been constantly improved. In this review, we provide an overview of the origin, differentiation of Th22 cells. Moreover, we also describe the interrelationships between Th22 cells and Th17, Th1, and Th2 cells. Additionally, the role of Th22 cells were discussed in human diseases with virus infection, which will provide novel insight for the prevention and treatment of viral infection in human.

Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.