Abstract:We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study.
“…On the other hand, the study of Kim et al MicroScale Thermophoresis analysis revealed that the NSP12 P323L mutation stabilized the NSP12-NSP7-NSP8 complex interaction [ 37 ]. Periwal et al also suggesetd that the P323L had a stabilizing effect relative to the wild type protein [ 61 ]. Fig.…”
Background
At the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.
Methods
10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.
Results
Six mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively.
Conclusion
Therefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.
“…On the other hand, the study of Kim et al MicroScale Thermophoresis analysis revealed that the NSP12 P323L mutation stabilized the NSP12-NSP7-NSP8 complex interaction [ 37 ]. Periwal et al also suggesetd that the P323L had a stabilizing effect relative to the wild type protein [ 61 ]. Fig.…”
Background
At the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.
Methods
10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.
Results
Six mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively.
Conclusion
Therefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.
“…Although the F106F mutation was considered silent, it was associated with an altered miR-197-5p target sequence, a host-specific miRNA necessary to defend against coronaviruses [ 48 ]. Studies have demonstrated a strong correlation between these non-structural protein mutations and the spike mutation, indicating a potential interaction that could impact viral pathogenesis and evolution [ 49 ].…”
The SARS-CoV-2 infection that caused the COVID-19 pandemic has become a significant public health concern. New variants with distinct mutations have emerged, potentially impacting its infectivity, immune evasion capacity, and vaccine response. A whole-genome sequencing study of 292 SARS-CoV-2 isolates collected from selected regions of Indonesia between January and October 2021 was performed to identify the distribution of SARS-CoV-2 variants and common mutations in Indonesia. During January–April 2021, Indonesian lineages B.1.466.2 and B.1.470 dominated, but from May 2021, Delta’s AY.23 lineage outcompeted them. An analysis of 7515 published sequences from January 2021 to June 2022 revealed a decline in Delta in November 2021, followed by the emergence of Omicron variants in December 2021. We identified C241T (5′UTR), P314L (NSP12b), F106F (NSP3), and D614G (Spike) mutations in all sequences. The other common substitutions included P681R (76.4%) and T478K (60%) in Spike, D377Y in Nucleocapsid (61%), and I82T in Membrane (60%) proteins. Breakthrough infection and prolonged viral shedding cases were associated with Delta variants carrying the Spike T19R, G142D, L452R, T478K, D614G, P681R, D950N, and V1264L mutations. The dynamic of SARS-CoV-2 variants in Indonesia highlights the importance of continuous genomic surveillance in monitoring and identifying potential strains leading to disease outbreaks.
“…While substitutions within SARS-CoV-2 may act at a structural level to stabilize RNA structure ( 25 ) or viral proteins such as NSP12 ( 26 ), these may also facilitate protein-protein interactions both between viral proteins and with host proteins. Several studies have investigated the cellular interactome of the SARS-CoV-2 proteome including NSP12 [e.g., reference ( 15 )].…”
SARS-CoV-2 emerged in the human population in late 2019, and human-to-human transmission has dominated the evolutionary landscape and driven the selection of different lineages. The first major change that resulted in increased transmission was the D614G substitution in the spike protein. This was accompanied by the P323L substitution in the viral RNA-dependent RNA polymerase (NSP12). Together with D614G, these changes are the root of the predominant global SARS-CoV-2 landscape. Here, we found that NSP12 formed an interactome with cellular proteins. The functioning of NSP12 was dependent on the T-complex protein ring complex, a molecular chaperone. In contrast, there was a differential association between NSP12 variants and components of a phosphatase complex (PP2/PP2A and STRN3). The virus expressing NSP12
L323
was less sensitive to perturbations in PP2A and supports the paradigm that ongoing genotype to phenotype adaptation of SARS-CoV-2 in humans is not exclusively restricted to the spike protein.
IMPORTANCE
SARS-CoV-2 has caused a worldwide health and economic crisis. During the course of the pandemic, genetic changes occurred in the virus, which have resulted in new properties of the virus—particularly around gains in transmission and the ability to partially evade either natural or vaccine-acquired immunity. Some of these viruses have been labeled Variants of Concern (VoCs). At the root of all VoCs are two mutations, one in the viral spike protein that has been very well characterized and the other in the virus polymerase (NSP12). This is the viral protein responsible for replicating the genome. We show that NSP12 associates with host cell proteins that act as a scaffold to facilitate the function of this protein. Furthermore, we found that different variants of NSP12 interact with host cell proteins in subtle and different ways, which affect function.
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