SARS-CoV-2 and HSV-1 Induce Amyloid Aggregation in Human CSF

The corona virus (SARS-CoV-2) pandemic and the resulting long-term neurological complications in patients, known as long COVID, have renewed the interest in the correlation between viral infections and neurodegenerative brain disorders. While many viruses can reach the central nervous system (CNS) causing acute or chronic infections (such as herpes simplex virus 1, HSV-1), the lack of a clear mechanistic link between viruses and protein aggregation into amyloids, a characteristic of several neurodegenerative diseases, has rendered such a connection elusive. Recently, we showed that viruses can induce aggregation of purified amyloidogenic proteins via the direct physicochemical mechanism of heterogenous nucleation (HEN). In the current study, we show that the incubation of HSV-1 and SARS-CoV-2 with human cerebrospinal fluid (CSF) leads to the amyloid aggregation of several proteins known to be involved in neurodegenerative diseases, such as: APLP1 (amyloid beta precursor like protein 1), ApoE, clusterin, 2-macroglobulin, PGK-1 (phosphoglycerate kinase 1), ceruloplasmin, nucleolin, 14-3-3, transthyretin and vitronectin. Importantly, UV-inactivation of SARS-CoV-2 does not affect its ability to induce amyloid aggregation, as amyloid formation is dependent on viral surface catalysis via HEN and not its ability to replicate. Our results show that viruses can physically induce amyloid aggregation of proteins in human CSF, and thus providing a potential mechanism that may account for the association between persistent and latent/reactivating brain infections and neurodegenerative diseases.

Global between-countries variance in SARS-CoV-2 mortality is driven by reported prevalence, age distribution, and case detection rate

ObjectiveTo explain the global between-countries variance in number of deaths per million citizens (nDpm) and case fatality rate (CFR) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DesignSystematic analysis. Data sourcesWorldometer, European Centre for Disease Prevention and Control, United Nations Main outcome measuresThe explanators of nDpm and CFR were mathematically hypothesised and tested on publicly-available data from 88 countries with linear regression models on May 1st 2020. The derived explanators - age-adjusted infection fatality rate (IFRadj) and case detection rate (CDR) - were estimated for each country based on a SARS-CoV-2 model of China. The accuracy and agreement of the models with observed data was assessed with R2 and Bland-Altman plots, respectively. Sensitivity analyses involved removal of outliers and testing the models at five retrospective and four prospective time points. ResultsGlobally, IFRadj estimates varied between countries, ranging from below 0.2% in the youngest nations, to above 1.3% in Portugal, Greece, Italy, and Japan. The median estimated global CDR of SARS-CoV-2 infections on April 16th 2020 was 12.9%, suggesting that most of the countries have a much higher number of cases than reported. At least 93% and up to 99% of the variance in nDpm was explained by reported prevalence expressed as cases per million citizens (nCpm), IFRadj, and CDR. IFRadj and CDR accounted for up to 97% of the variance in CFR, but this model was less reliable than the nDpm model, being sensitive to outliers (R2 as low as 67.5%). ConclusionsThe current differences in SARS-CoV-2 mortality between countries are driven mainly by reported prevalence of infections, age distribution, and CDR. The nDpm might be a more stable estimate than CFR in comparing mortality burden between countries.