ABSTRACT
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.
Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Hospitalization , Flow Cytometry , HospitalsABSTRACT
Regulatory T cells (Tregs), which are characterized by the expression of the transcription factor forkhead box P3 (FOXP3), are the main immune cells that induce tolerance and are regulators of immune homeostasis. Natural Treg cells (nTregs), described as CD4+CD25+FOXP3+, are generated in the thymus via activation and cytokine signaling. Transforming growth factor beta type 1 (TGF-ß1) is pivotal to the generation of the nTreg lineage, its maintenance in the thymus, and to generating induced Treg cells (iTregs) in the periphery or in vitro arising from conventional T cells (Tconvs). Here, we tested whether TGF-ß1 treatment, associated with interleukin-2 (IL-2) and CD3/CD28 stimulation, could generate functional Treg-like cells from human thymocytes in vitro, as it does from Tconvs. Additionally, we genetically manipulated the cells for ectopic FOXP3 expression, along with the TGF-ß1 treatment. We demonstrated that TGF-ß1 and ectopic FOXP3, combined with IL-2 and through CD3/CD28 activation, transformed human thymocytes into cells that expressed high levels of Treg-associated markers. However, these cells also presented a lack of homogeneous suppressive function and an unstable proinflammatory cytokine profile. Therefore, thymocyte-derived cells, activated with the same stimuli as Tconvs, were not an appropriate alternative for inducing cells with a Treg-like phenotype and function.
ABSTRACT
Regulatory B cells (Bregs) participate in auto-tolerance maintenance and immune homeostasis. Despite their impact on many diseases and due to the difficulty to define them, knowledge about their origin and their physiological inducers is still unclear. The incomplete understanding about the generation of Bregs and their limited numbers in periphery make it difficult to develop Breg-based therapy. Therefore, identifying factors that promote their development would allow their ex-vivo production in order to create new immunotherapy. This project aims to test the capacity of several cytokines (Interleukin 1-beta (IL-1ß), Granulocyte Macrophage Colony-Stimulating Factor (GM-CSF), and Cluster of differentiation 40 ligand (CD40L)) and bacteria-derived oligodeoxynucleotides (CpG-ODN), alone or in combination, to generate B cells with regulatory phenotype and function. We have demonstrated that the Breg-associated phenotypes were heterogeneous between one and other stimulation conditions. However, the expression of other markers related to Bregs such as IL-10, CD80, CD86, CD71, Programmed cell death-1 (PD-1), and Programmed death-ligand 1 (PD-L1) was increased when cells were stimulated with CpG alone or in combination. Moreover, stimulated B cells presented a suppressive function on autologous activated peripheral blood mononuclear cells (PBMC) proliferation. Therefore, this work is the first step to demonstrate the feasibility to induce functional Breg-like cells in vitro and will then facilitate the way to produce Breg-like cells as a potential future cellular therapy.
