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

BACKGROUND: SARS-CoV-2 emerged as a new coronavirus causing COVID-19, and it has been responsible for more than 760 million cases and 6.8 million deaths worldwide until March 2023. Although infected individuals could be asymptomatic, other patients presented heterogeneity and a wide range of symptoms. Therefore, identifying those infected individuals and being able to classify them according to their expected severity could help target health efforts more effectively. METHODOLOGY/PRINCIPAL FINDINGS: Therefore, we wanted to develop a machine learning model to predict those who will develop severe disease at the moment of hospital admission. We recruited 75 individuals and analysed innate and adaptive immune system subsets by flow cytometry. Also, we collected clinical and biochemical information. The objective of the study was to leverage machine learning techniques to identify clinical features associated with disease severity progression. Additionally, the study sought to elucidate the specific cellular subsets involved in the disease following the onset of symptoms. Among the several machine learning models tested, we found that the Elastic Net model was the better to predict the severity score according to a modified WHO classification. This model was able to predict the severity score of 72 out of 75 individuals. Besides, all the machine learning models revealed that CD38+ Treg and CD16+ CD56neg HLA-DR+ NK cells were highly correlated with the severity. CONCLUSIONS/SIGNIFICANCE: The Elastic Net model could stratify the uninfected individuals and the COVID-19 patients from asymptomatic to severe COVID-19 patients. On the other hand, these cellular subsets presented here could help to understand better the induction and progression of the symptoms in COVID-19 individuals.

Treg in inborn errors of immunity: gaps, knowns and future perspectives.

Regulatory T cells (Treg) are essential for immune balance, preventing overreactive responses and autoimmunity. Although traditionally characterized as CD4+CD25+CD127lowFoxP3hi, recent research has revealed diverse Treg subsets such as Tr1, Tr1-like, and CD8 Treg. Treg dysfunction leads to severe autoimmune diseases and immune-mediated inflammatory disorders. Inborn errors of immunity (IEI) are a group of disorders that affect correct functioning of the immune system. IEI include Tregopathies caused by genetic mutations affecting Treg development or function. In addition, Treg dysfunction is also observed in other IEIs, whose underlying mechanisms are largely unknown, thus requiring further research. This review provides a comprehensive overview and discussion of Treg in IEI focused on: A) advances and controversies in the evaluation of Treg extended subphenotypes and function; B) current knowledge and gaps in Treg disturbances in Tregopathies and other IEI including Treg subpopulation changes, genotype-phenotype correlation, Treg changes with disease activity, and available therapies, and C) the potential of Treg cell-based therapies for IEI with immune dysregulation. The aim is to improve both the diagnostic and the therapeutic approaches to IEI when there is involvement of Treg. We performed a non-systematic targeted literature review with a knowledgeable selection of current, high-quality original and review articles on Treg and IEI available since 2003 (with 58% of the articles within the last 6 years) in the PubMed database.