ABSTRACT
Background: SARS CoV 2 infection alters the immunological profiles of natural killer (NK) cells. However, whether NK anti-viral functions (direct cytotoxicity and/or antibody-dependent cell cytotoxicity (ADCC)) are impaired during severe COVID-19 and what host factors modulate these functions remain unclear. Method(s): Using functional assays, we examined the ability of NK cells from SARS-CoV-2 negative controls (n=12), mild COVID-19 patients (n=26), and hospitalized COVID-19 patients (n=41) to elicit direct cytotoxicity and ADCC [NK degranulation by flow] against cells expressing SARS-CoV-2 antigens. SARS-CoV- 2 N antigen plasma load was measured using an ultra-sensitive Simoa assay. We also phenotypically characterized the baseline expression of NK activating (CD16 and NKG2C), maturation (CD57), and inhibitory (NKG2A and the glyco-immune negative checkpoint Siglec-9) by flow cytometry. Finally, an anti-Siglec-9 blocking antibody was used to examine the impact of Siglec-9 expression on anti-SARS-CoV-2-specific ADCC [degranulation and target cell lysis]. Result(s): NK cells from hospitalized COVID-19 patients degranulate less against SARS-CoV-2-antigen-expressing cells (in direct cytolytic and ADCC assays) than did cells from mild COVID-19 patients or negative controls (Fig. 1A). The lower NK degranulation was associated with higher plasma levels of SARS-CoV-2 N-antigen (P<=0.02). Phenotypic and functional analyses showed that NK cells expressing Siglec-9 elicited higher ADCC than Siglec-9- NK cells (P<0.05;Fig. 1B). Consistently, Siglec-9+ NK cells expressed an activated and mature phenotype with higher expression of CD16, CD57, and NKG2C, and lower expression of NKG2A, than Siglec-9- NK cells (P<=0.03). These data are consistent with the concept that the NK cell subpopulation expressing Siglec-9 is highly activated and cytotoxic. However, the Siglec-9 molecule itself is an inhibitory receptor that restrains NK cytotoxicity during cancer and other infections. Indeed, blocking Siglec-9 significantly enhanced the ADCC-mediated NK degranulation and lysis of SARS-CoV-2-antigen-positive target cells (P<=0.05;Fig. 1C). Conclusion(s): These data support a model (Fig. 1D) in which the Siglec-9+ CD56dim NK subpopulation is cytotoxic even while being restrained by the inhibitory effects of Siglec-9. However, alleviating the Siglec-9-mediated restriction on NK cytotoxicity can further improve NK immune surveillance and presents an opportunity to develop novel immunotherapeutic tools against SARS-CoV-2 infected cells. (Figure Presented).
ABSTRACT
The lymphocyte count was used as a marker in COVID-19. To investigate the relationship between lymphocyte count at admission and COVID-19 severity, we conducted a detailed study and meta-analysis using a machine learning (ML) approach-based artificial immune system. We'd also like to see if patient characteristics such as age and health have an impact on the association between lymphocyte count and COVID-19. To provide an approximation, we apply the supervised machine learning technique. We use the supervised machine learning method (Immunos –R) to give an approach to build an artificial immune network to get accurate analyses response in T cells (cd4+cd8) without doing in-vitro laboratory studies. Accuracy rate for lower lymphocyte count(T lymphocytes cells subset using Immunos –R) mean difference is 84 % for lymphopenia . © 2022 American Institute of Physics Inc.. All rights reserved.
ABSTRACT
A natural immune system has a remarkable ability to recognise self from nonself antigens in human body. The immune system has a natural ability to learn, recognise and respond accordingly to protect human body against viruses and diseases. B-cells produce detectable antibodies and immunoglobulins and are responsible for humoral antibody immunity (IgM, IgG).Memory cells play an important role in this entire reactive process. With the help of these memory cells, the immune system recognises and responds immediately to the second entry of infection.I propose a supervised machine learning method (Immunos-R) to give an approach to build an immune response model to get an accurate antibody immunological response to SARS-CoV-2 without doing in-vitro laboratory studies.Accuracy rate for antibody immunological response (B lymphocytes cells subset) is 96.0% at fourth split and lowest at 8th split 68.0% © 2022 American Institute of Physics Inc.. All rights reserved.