ABSTRACT
AimsUnravelling autoimmune targets triggered by SARS-CoV-2 infection may provide crucial insights in the physiopathology of the disease and foster the development of potential therapeutic candidate targets and prognostic tools. We aimed at determining i) the association between anti-SARS-CoV-2 and anti-apoA-1 humoral response, ii) their relationship to prognosis, and iii) the degree of linear homology between SARS-CoV-2, apoA-1, and Toll-like receptor-2 (TLR2) epitopes. Methods and ResultsImmunoreactivity against different engineered peptides as well as cytokines were assessed by immunoassays, on a case-control (n=101), an intensive care unit (ICU; n=126) with a 28-days follow-up, and a general population cohort (n=663) with available samples in the pre and post-pandemic period. Using bioinformatics modelling a linear sequence homologies between apoA-1, TLR2, and Spike epitopes were identified. Overall, anti-apoA-1IgG levels were higher in COVID-19 patients or anti-SARS-CoV-2 seropositive individuals than in healthy donors or anti-SARS-CoV-2 seronegative individuals (p<0.0001). Significant and similar associations were noted between anti-apoA-1, anti-SARS-CoV-2IgG, cytokines, and lipid profile. In ICU patients, anti-SARS-CoV-2 and anti-apoA-1 seroconversion rates displayed similar 7-days kinetics, reaching 82% for anti-apoA-1 seropositivity. C-statistics (CS) indicated that anti-Spike/TLR2 mimic-peptide IgGs displayed a significant prognostic accuracy for overall mortality at 28 days (CS: 0.64; p=0.02). In the general population, SARS-CoV-2 exposure increased baseline anti-apoA-1 IgG levels. ConclusionCOVID-19 induces a marked humoral response against the major protein of high-density lipoproteins. As a correlate of poorer prognosis in other clinical settings, such autoimmunity signatures may relate to long-term COVID-19 prognosis assessment and warrant further scrutiny in the current COVID-19 pandemic.
ABSTRACT
BackgroundSARS-CoV-2 test kits are in critical shortage in many countries. This limits large-scale population testing and hinders the effort to identify and isolate infected individuals. ObjectivesHerein, we developed and evaluated multi-stage group testing schemes that test samples in groups of various pool sizes in multiple stages. Through this approach, groups of negative samples can be eliminated with a single test, avoiding the need for individual testing and achieving considerable savings of resources. Study designWe designed and parameterized various multi-stage testing schemes and compared their efficiency at different prevalence rates using computer simulations. ResultsWe found that three-stage testing schemes with pool sizes of maximum 16 samples can test up to three and seven times as many individuals with the same number of test kits for prevalence rates of around 5% and 1%, respectively. We propose an adaptive approach, where the optimal testing scheme is selected based on the expected prevalence rate. ConclusionThese group testing schemes could lead to a major reduction in the number of testing kits required and help improve large-scale population testing in general and in the context of the current COVID-19 pandemic.
