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.

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.

Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases During the Pandemic.

Since the identification of SARS-CoV-2, a large number of genomes have been sequenced with unprecedented speed around the world. This marks a unique opportunity to analyze virus spreading and evolution in a worldwide context. Currently, there is not a useful haplotype description to help to track important and globally scattered mutations. Also, differences in the number of sequenced genomes between countries and/or months make it difficult to identify the emergence of haplotypes in regions where few genomes are sequenced but a large number of cases are reported. We propose an approach based on the normalization by COVID-19 cases of relative frequencies of mutations using all the available data to identify major haplotypes. Furthermore, we can use a similar normalization approach to tracking the temporal and geographic distribution of haplotypes in the world. Using 171,461 genomes, we identify five major haplotypes or operational taxonomic units (OTUs) based on nine high-frequency mutations. OTU_3 characterized by mutations R203K and G204R is currently the most frequent haplotype circulating in four of the six continents analyzed (South America, North America, Europe, Asia, Africa, and Oceania). On the other hand, during almost all months analyzed, OTU_5 characterized by the mutation T85I in nsp2 is the most frequent in North America. Recently (since September), OTU_2 has been established as the most frequent in Europe. OTU_1, the ancestor haplotype, is near to extinction showed by its low number of isolations since May. Also, we analyzed whether age, gender, or patient status is more related to a specific OTU. We did not find OTU's preference for any age group, gender, or patient status. Finally, we discuss structural and functional hypotheses in the most frequently identified mutations, none of those mutations show a clear effect on the transmissibility or pathogenicity.