What do we know about the function of SARS-CoV-2 proteins?

The COVID-19 pandemic has highlighted the importance in the understanding of the biology of SARS-CoV-2. After more than two years since the first report of COVID-19, it remains crucial to continue studying how SARS-CoV-2 proteins interact with the host metabolism to cause COVID-19. In this review, we summarize the findings regarding the functions of the 16 non-structural, 6 accessory and 4 structural SARS-CoV-2 proteins. We place less emphasis on the spike protein, which has been the subject of several recent reviews. Furthermore, comprehensive reviews about COVID-19 therapeutic have been also published. Therefore, we do not delve into details on these topics; instead we direct the readers to those other reviews. To avoid confusions with what we know about proteins from other coronaviruses, we exclusively report findings that have been experimentally confirmed in SARS-CoV-2. We have identified host mechanisms that appear to be the primary targets of SARS-CoV-2 proteins, including gene expression and immune response pathways such as ribosome translation, JAK/STAT, RIG-1/MDA5 and NF-kß pathways. Additionally, we emphasize the multiple functions exhibited by SARS-CoV-2 proteins, along with the limited information available for some of these proteins. Our aim with this review is to assist researchers and contribute to the ongoing comprehension of SARS-CoV-2's pathogenesis.

Phylodynamic of SARS-CoV-2 during the second wave of COVID-19 in Peru.

At over 0.6% of the population, Peru has one of the highest SARS-CoV-2 mortality rate in the world. Much effort to sequence genomes has been done in this country since mid-2020. However, an adequate analysis of the dynamics of the variants of concern and interest (VOCIs) is missing. We investigated the dynamics of the COVID-19 pandemic in Peru with a focus on the second wave, which had the greatest case fatality rate. The second wave in Peru was dominated by Lambda and Gamma. Analysis of the origin of Lambda shows that it most likely emerged in Peru before the second wave (June-November, 2020). After its emergence it reached Argentina and Chile from Peru where it was locally transmitted. During the second wave in Peru, we identify the coexistence of two Lambda and three Gamma sublineages. Lambda sublineages emerged in the center of Peru whereas the Gamma sublineages more likely originated in the north-east and mid-east. Importantly, it is observed that the center of Peru played a prominent role in transmitting SARS-CoV-2 to other regions within Peru.

Dynamics of SARS-CoV-2 mutations reveals regional-specificity and similar trends of N501 and high-frequency mutation N501Y in different levels of control measures.

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has spread globally, causing more than 161.5 million cases and 3.3 million deaths to date. Surveillance and monitoring of new mutations in the virus' genome are crucial to our understanding of the adaptation of SARS-CoV-2. Moreover, how the temporal dynamics of these mutations is influenced by control measures and non-pharmaceutical interventions (NPIs) is poorly understood. Using 1,058,020 SARS-CoV-2 from sequenced COVID-19 cases from 98 countries (totaling 714 country-month combinations), we perform a normalization by COVID-19 cases to calculate the relative frequency of SARS-CoV-2 mutations and explore their dynamics over time. We found 115 mutations estimated to be present in more than 3% of global COVID-19 cases and determined three types of mutation dynamics: high-frequency, medium-frequency, and low-frequency. Classification of mutations based on temporal dynamics enable us to examine viral adaptation and evaluate the effects of implemented control measures in virus evolution during the pandemic. We showed that medium-frequency mutations are characterized by high prevalence in specific regions and/or in constant competition with other mutations in several regions. Finally, taking N501Y mutation as representative of high-frequency mutations, we showed that level of control measure stringency negatively correlates with the effective reproduction number of SARS-CoV-2 with high-frequency or not-high-frequency and both follows similar trends in different levels of stringency.