CD4+ T Cell Immune Specificity Changes After Vaccination in Healthy And COVID-19 Convalescent Subjects

The immune response promoted by SARS-CoV-2 vaccination is relevant to develop novel vaccines and optimized prevention strategies. We analyzed the adaptive immunity in healthy donors (HD) and convalescent individuals (CD), before and after administering BNT162b2 vaccine. Our results revealed specific changes in CD4+ T cell reactivity profile in vaccinated HD and CD, with an increase in S1 and S2 positive individuals, proportionally higher for S2. On the contrary, NCAP reactivity observed in HD and CD patients was no longer detectable after vaccination. Despite the substantial antibody response in CD, MPro-derived peptides did not elicit CD4+ lymphocyte activation in our assay in either condition. HD presented an increment in anti-S and anti-RBD IgG after first dose vaccination, which increased after the second vaccination. Conversely, anti-S and anti-RBD IgG and IgA titers increased in already positive CD after first dose administration, remaining stable after second dose inoculation. Interestingly, we found a strong significant correlation between S1-induced CD4+ response and anti-S IgA pre-vaccination, which was lost after vaccine administration.This work was supported by grants to AA: FIS PI19/01491 (Fondo de Investigación Sanitaria del Instituto de Salud Carlos III with co-funding from the Fondo Europeo de Desarrollo Regional FEDER) and Sociedad Cooperativa de Viviendas Buen Suceso, S.Coop.Mad. To AA and FS-M: CIBER Cardiovascular from the Instituto de Salud Carlos III (Fondo de Investigación Sanitaria del Instituto de Salud Carlos III with co-funding from the Fondo Europeo de Desarrollo Regional; FEDER). To FS-M: SAF2017-82886-R (Spanish Ministry of Economy and Competitiveness MINECO)), HR17-00016 (“La Caixa” Banking Foundation), “Fondos Supera COVID19” (Banco de Santander and CRUE), “Ayuda Covid 2019” and “Inmunovacter” REACT-UE (Comunidad de Madrid). To MV: Spanish National Research Council (CSIC, project number 202020E079 and CSIC-COVID19-028).Peer reviewe

The age again in the eye of the COVID-19 storm: evidence-based decision making.

One hundred fifty million contagions, more than 3 million deaths and little more than 1 year of COVID-19 have changed our lives and our health management systems forever. Ageing is known to be one of the significant determinants for COVID-19 severity. Two main reasons underlie this: immunosenescence and age correlation with main COVID-19 comorbidities such as hypertension or dyslipidaemia. This study has two aims. The first is to obtain cut-off points for laboratory parameters that can help us in clinical decision-making. The second one is to analyse the effect of pandemic lockdown on epidemiological, clinical, and laboratory parameters concerning the severity of the COVID-19. For these purposes, 257 of SARSCoV2 inpatients during pandemic confinement were included in this study. Moreover, 584 case records from a previously analysed series, were compared with the present study data. Concerning the characteristics of lockdown series, mild cases accounted for 14.4, 54.1% were moderate and 31.5%, severe. There were 32.5% of home contagions, 26.3% community transmissions, 22.5% nursing home contagions, and 8.8% corresponding to frontline worker contagions regarding epidemiological features. Age > 60 and male sex are hereby confirmed as severity determinants. Equally, higher severity was significantly associated with higher IL6, CRP, ferritin, LDH, and leukocyte counts, and a lower percentage of lymphocyte, CD4 and CD8 count. Comparing this cohort with a previous 584-cases series, mild cases were less than those analysed in the first moment of the pandemic and dyslipidaemia became more frequent than before. IL-6, CRP and LDH values above 69 pg/mL, 97 mg/L and 328 U/L respectively, as well as a CD4 T-cell count below 535 cells/μL, were the best cut-offs predicting severity since these parameters offered reliable areas under the curve. Age and sex together with selected laboratory parameters on admission can help us predict COVID-19 severity and, therefore, make clinical and resource management decisions. Demographic features associated with lockdown might affect the homogeneity of the data and the robustness of the results