Cross-sectional study for COVID-19-related mortality predictors in a Brazilian state-wide landscape: the role of demographic factors, symptoms and comorbidities.

OBJECTIVE: The Brazilian state of Paraná has suffered from COVID-19 effects, understanding predictors of increased mortality in health system interventions prevent hospitalisation of patients. We selected the best models to evaluate the association of death with demographic characteristics, symptoms and comorbidities based on three levels of clinical severity for COVID-19: non-hospitalised, hospitalised non-ICU ward and ICU ward. DESIGN: Cross-sectional survey using binomial mixed models. SETTING: COVID-19-positive cases diagnosed by reverse transcription-PCR of municipalities located in Paraná State. PATIENTS: Cases of anonymous datasets of electronic medical records from 1 April 2020 to 31 December 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The best prediction factors were chosen based on criteria after a stepwise analysis using multicollinearity measure, lower Akaike information criterion and goodness-of-fit χ2 tests from univariate to multivariate contexts. RESULTS: Male sex was associated with increased mortality among non-hospitalised patients (OR 1.76, 95% CI 1.47 to 2.11) and non-ICU patients (OR 1.22, 95% CI 1.05 to 1.43) for symptoms and for comorbidities (OR 1.89, 95% CI 1.59 to 2.25, and OR 1.30, 95% CI 1.11 to 1.52, respectively). Higher mortality occurred in patients older than 35 years in non-hospitalised (for symptoms: OR 4.05, 95% CI 1.55 to 10.54; and for comorbidities: OR 3.00, 95% CI 1.24 to 7.27) and in hospitalised over 40 years (for symptoms: OR 2.72, 95% CI 1.08 to 6.87; and for comorbidities: OR 2.66, 95% CI 1.22 to 5.79). Dyspnoea was associated with increased mortality in non-hospitalised (OR 4.14, 95% CI 3.45 to 4.96), non-ICU (OR 2.41, 95% CI 2.04 to 2.84) and ICU (OR 1.38, 95% CI 1.10 to 1.72) patients. Neurological disorders (OR 2.16, 95% CI 1.35 to 3.46), neoplastic (OR 3.22, 95% CI 1.75 to 5.93) and kidney diseases (OR 2.13, 95% CI 1.36 to 3.35) showed the majority of increased mortality for ICU as well in the three levels of severity jointly with heart disease, diabetes and CPOD. CONCLUSIONS: These findings highlight the importance of the predictor's assessment for the implementation of public healthcare policy in response to the COVID-19 pandemic, mainly to understand how non-pharmaceutical measures could mitigate the virus impact over the population.

Evolutionary history of the SARS-CoV-2 Gamma variant of concern (P.1): a perfect storm.

Our goal was to describe in more detail the evolutionary history of Gamma and two derived lineages (P.1.1 and P.1.2), which are part of the arms race that SARS-CoV-2 wages with its host. A total of 4,977 sequences of the Gamma strain of SARS-CoV-2 from Brazil were analyzed. We detected 194 sites under positive selection in 12 genes/ORFs: Spike, N, M, E, ORF1a, ORF1b, ORF3, ORF6, ORF7a, ORF7b, ORF8, and ORF10. Some diagnostic sites for Gamma lacked a signature of positive selection in our study, but these were not fixed, apparently escaping the action of purifying selection. Our network analyses revealed branches leading to expanding haplotypes with sites under selection only detected when P.1.1 and P.1.2 were considered. The P.1.2 exclusive haplotype H_5 originated from a non-synonymous mutational step (H3509Y) in H_1 of ORF1a. The selected allele, 3509Y, represents an adaptive novelty involving ORF1a of P.1. Finally, we discuss how phenomena such as epistasis and antagonistic pleiotropy could limit the emergence of new alleles (and combinations thereof) in SARS-COV-2 lineages, maintaining infectivity in humans, while providing rapid response capabilities to face the arms race triggered by host immuneresponses.

Genome interaction of the virus and the host genes and non-coding RNAs in SARS-CoV-2 infection.

In this review, we highlight the interaction of SARS-CoV-2 virus and host genomes, reporting the current studies on the sequence analysis of SARS-CoV-2 isolates and host genomes from diverse world populations. The main genetic variants that are present in both the virus and host genomes were particularly focused on the ACE2 and TMPRSS2 genes, and their impact on the patients' susceptibility to the virus infection and severity of the disease. Finally, the interaction of the virus and host non-coding RNAs is described in relation to their regulatory roles in target genes and/or signaling pathways critically associated with SARS-CoV-2 infection. Altogether, these studies provide a significant contribution to the knowledge of SARS-CoV-2 mechanisms of infection and COVID-19 pathogenesis. The described genetic variants and molecular factors involved in host/virus genome interactions have significantly contributed to defining patient risk groups, beyond those based on patients' age and comorbidities, and they are promising candidates to be potentially targeted in treatment strategies for COVID-19 and other viral infectious diseases.

Genetic variability in COVID-19-related genes in the Brazilian population.

SARS-CoV-2 utilizes the angiotensin-converting enzyme 2 (ACE2) receptor and transmembrane serine protease (TMPRSS2) to infect human lung cells. Previous studies have suggested that different host ACE2 and TMPRSS2 genetic backgrounds might contribute to differences in the rate of SARS-CoV-2 infection or COVID-19 severity. Recent studies have also shown that variants in 15 genes related to type I interferon immunity to influenza virus might predispose patients toward life-threatening COVID-19 pneumonia. Other genes (SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, IL6, CTSL, ABO, and FURIN) and HLA alleles have also been implicated in the response to infection with SARS-CoV-2. Currently, Brazil has recorded the third-highest number of COVID-19 cases worldwide. We aimed to investigate the genetic variation present in COVID-19-related genes in the Brazilian population. We analyzed 27 candidate genes and HLA alleles in 954 admixed Brazilian exomes. We used the information available in two public databases (http://www.bipmed.org and http://abraom.ib.usp.br/) and additional exomes from individuals born in southeast Brazil, the region of the country with the highest number of COVID-19 patients. Variant allele frequencies were compared with the 1000 Genomes Project phase 3 (1KGP) and gnomAD databases. We detected 395 nonsynonymous variants; of these, 325 were also found in the 1KGP and/or gnomAD. Six of these variants were previously reported to influence the rate of infection or clinical prognosis of COVID-19. The remaining 70 variants were identified exclusively in the Brazilian sample, with a mean allele frequency of 0.0025. In silico analysis revealed that seven of these variants are predicted to affect protein function. Furthermore, we identified HLA alleles previously associated with the COVID-19 response at loci DQB1 and DRB1. Our results showed genetic variability common to other populations and rare and ultrarare variants exclusively found in the Brazilian population. These findings might lead to differences in the rate of infection or response to infection by SARS-CoV-2 and should be further investigated in patients with this disease.