Global Initiative On Sharing All Influenza Data Research Articles

Genetic Variation of Structural and Functional Genes of SARS-CoV-2 Isolates Circulating in Banyumas (Indonesia)

Scientists are performing various measurements to overcome the COVID-19 pandemic. The genomic mutations of SARS-CoV-2 can change their pathogenicity, infectivity, transmission, and antigenicity. This present study aimed to know a) the genetic variation of structural and functional genes of SARS-CoV-2 circulating in Banyumas and b) the potential of the Cilacap’s Harbour as a human mobility portal on the genetic variations of SARS-CoV-2 circulating in Banyumas, Central Java (Indonesia). Genomic sequence of SARS-CoV-2 isolates were taken from the Global Initiative on Sharing All Influenza Data (GISAID) and the National Center for Biotechnology Information (NCBI) online platforms. A gene cut was carried out from Wuhan reference isolate, fifteen isolates from Banyumas, and two isolates from Cilacap (Central Java) using Unipro UGENE v. 33.0 software, considering the annotation of the Wuhan-1 isolate. Genetic variations were detected among SARS-CoV-2 isolates circulating in Banyumas. The structural protein (envelope, membrane, nucleocapsid) encoding gene and the RdRp gene were highly conserved to Wuhan reference genome (Wuhan-Hu-1). Meanwhile, the ORF and the spike-encoding genes were less identical to the Wuhan reference genome. This study also proposed that human mobility from outside Central Java through Cilacap’s Harbour did not affect the genetic variation of SARS-CoV-2 isolates circulating in Banyumas (Central Java).

Genomic Surveillance of SARS-CoV-2 Variants in the Dominican Republic and Emergence of a Local Lineage.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus that evolves over time, leading to new variants. In the current study, we assessed the genomic epidemiology of SARS-CoV-2 in the Dominican Republic. A total of 1149 SARS-CoV-2 complete genome nucleotide sequences from samples collected between March 2020 and mid-February 2022 in the Dominican Republic were obtained from the Global Initiative on Sharing All Influenza Data (GISAID) database. Phylogenetic relationships and evolution rates were analyzed using the maximum likelihood method and the Bayesian Markov chain Monte Carlo (MCMC) approach. The genotyping details (lineages) were obtained using the Pangolin web application. In addition, the web tools Coronapp, and Genome Detective Viral Tools, among others, were used to monitor epidemiological characteristics. Our results show that the most frequent non-synonymous mutation over the study period was D614G. Of the 1149 samples, 870 (75.74%) were classified into 8 relevant variants according to Pangolin/Scorpio. The first Variants Being Monitored (VBM) were detected in December 2020. Meanwhile, in 2021, the variants of concern Delta and Omicron were identified. The mean mutation rate was estimated to be 1.5523 × 10-3 (95% HPD: 1.2358 × 10-3, 1.8635 × 10-3) nucleotide substitutions per site. We also report the emergence of an autochthonous SARS-CoV-2 lineage, B.1.575.2, that circulated from October 2021 to January 2022, in co-circulation with the variants of concern Delta and Omicron. The impact of B.1.575.2 in the Dominican Republic was minimal, but it then expanded rapidly in Spain. A better understanding of viral evolution and genomic surveillance data will help to inform strategies to mitigate the impact on public health.

Monkeypox virus: phylogenomics, host-pathogen interactome and mutational cascade.

While the world is still recovering from the Covid-19 pandemic, monkeypox virus (MPXV) awaits to cause another global outbreak as a challenge to all of mankind. However, the Covid-19 pandemic has taught us a lesson to speed up the pace of viral genomic research for the implementation of preventive and treatment strategies. One of the important aspects of MPXV that needs immediate insight is its evolutionary lineage based on genomic studies. Utilizing high-quality isolates from the GISAID (Global Initiative on Sharing All Influenza Data) database, primarily sourced from Europe and North America, we employed a SNP-based whole-genome phylogeny method and identified four major clusters among 628 MPXV isolates. Our findings indicate a distinct evolutionary lineage for the first MPXV isolate, and a complex epidemiology and evolution of MPXV strains across various countries. Further analysis of the host-pathogen interaction network revealed key viral proteins, such as E3, SPI-2, K7 and CrmB, that play a significant role in regulating the network and inhibiting the host's cellular innate immune system. Our structural analysis of proteins E3 and CrmB revealed potential disruption of stability due to certain mutations. While this study identified a large number of mutations within the new outbreak clade, it also reflected that we need to move fast with the genomic analysis of newly detected strains from around the world to develop better prevention and treatment methods.

Temporal dynamics and fatality of SARS‐CoV‐2 variants in Bangladesh

Since the beginning of the SARS-CoV-2 pandemic, multiple new variants have emerged posing an increased risk to global public health. This study aimed to investigate SARS-CoV-2 variants, their temporal dynamics, infection rate (IFR) and case fatality rate (CFR) in Bangladesh by analyzing the published genomes. We retrieved 6610 complete whole genome sequences of the SARS-CoV-2 from the GISAID (Global Initiative on Sharing all Influenza Data) platform from March 2020 to October 2022, and performed different in-silico bioinformatics analyses. The clade and Pango lineages were assigned by using Nextclade v2.8.1. SARS-CoV-2 infections and fatality data were collected from the Institute of Epidemiology Disease Control and Research (IEDCR), Bangladesh. The average IFR was calculated from the monthly COVID-19 cases and population size while average CFR was calculated from the number of monthly deaths and number of confirmed COVID-19 cases. SARS-CoV-2 first emerged in Bangladesh on March 3, 2020 and created three pandemic waves so far. The phylogenetic analysis revealed multiple introductions of SARS-CoV-2 variant(s) into Bangladesh with at least 22 Nextstrain clades and 107 Pangolin lineages with respect to the SARS-CoV-2 reference genome of Wuhan/Hu-1/2019. The Delta variant was detected as the most predominant (48.06%) variant followed by Omicron (27.88%), Beta (7.65%), Alpha (1.56%), Eta (0.33%) and Gamma (0.03%) variant. The overall IFR and CFR from circulating variants were 13.59% and 1.45%, respectively. A time-dependent monthly analysis showed significant variations in the IFR (p = 0.012, Kruskal-Wallis test) and CFR (p = 0.032, Kruskal-Wallis test) throughout the study period. We found the highest IFR (14.35%) in 2020 while Delta (20A) and Beta (20H) variants were circulating in Bangladesh. Remarkably, the highest CFR (1.91%) from SARS-CoV-2 variants was recorded in 2021. Our findings highlight the importance of genomic surveillance for careful monitoring of variants of concern emergence to interpret correctly their relative IFR and CFR, and thus, for implementation of strengthened public health and social measures to control the spread of the virus. Furthermore, the results of the present study may provide important context for sequence-based inference in SARS-CoV-2 variant(s) evolution and clinical epidemiology beyond Bangladesh.

The health of influenza surveillance and pandemic preparedness in the wake of the COVID-19 pandemic.

The COVID-19 pandemic is the first to have emerged when Next Generation Sequencing was readily available and it has played the major role in following evolution of the causative agent, Severe Acute Respiratory Syndrome Coronavirus 2. Response to the pandemic was greatly facilitated though use of existing influenza surveillance networks: World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS), focussing largely on human influenza, and the OFFLU network of expertise on avian influenza established by the Food and Agricultural Organization of the United Nations (FAO) and the World Organization for Animal Health (WOAH). Data collection/deposition platforms associated with these networks, notably WHO's FluNet and the Global Initiative on Sharing All Influenza Data (GISAID) were/are being used intensely. Measures introduced to combat COVID-19 resulted in greatly decreased circulation of human seasonal influenza viruses for approximately 2 years, but circulation continued in the animal sector with an upsurge in the spread of highly pathogenic avian influenza subtype H5N1 with large numbers of wild bird deaths, culling of many poultry flocks and sporadic spill over into mammalian species, including humans, thereby increasing pandemic risk potential. While there are proposals/implementations to extend use of GISRS and GISAID to other infectious disease agents (e.g. Respiratory Syncytial Virus and Monkeypox), there is need to ensure that influenza surveillance is maintained and improved in both human and animal sectors in a sustainable manner to be truly prepared (early detection) for the next influenza pandemic.

Genomic surveillance and sequencing of SARS-CoV-2 across South America.

After 2 years of the COVID-19 pandemic, the protocols used to control infection lack attention and analysis. We present data about deposits of complete genomic sequences of SARS-CoV-2 in the Global Initiative on Sharing All Influenza Data (GISAID) database made between January 2021 and May 31, 2022. We build the distribution profile of SARS-CoV-2 variants across South America, highlighting the contribution and influence of each variant over time. Monitoring the genomic sequences in GISAID illustrates negligence in the follow up of infected patients in South America and also the discrepancies between the number of complete genomes deposited throughout the pandemic by developed and developing countries. While Europe and North America account for more than 9 million of the genomes deposited in GISAID, Africa and South America deposited less than 400 000 genome sequences. Genomic surveillance is important for detecting early warning signs of new circulating viruses, assisting in the discovery of new variants and controlling pandemics.

Mutational analysis of the spike protein of SARS-COV-2 isolates revealed atomistic features responsible for higher binding and infectivity.

Introduction: The perpetual appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), and its new variants devastated the public health and social fabric around the world. Understanding the genomic patterns and connecting them to phenotypic attributes is of great interest to devise a treatment strategy to control this pandemic. Materials and Methods: In this regard, computational methods to understand the evolution, dynamics and mutational spectrum of SARS-CoV-2 and its new variants are significantly important. Thus, herein, we used computational methods to screen the genomes of SARS-CoV-2 isolated from Pakistan and connect them to the phenotypic attributes of spike protein; we used stability-function correlation methods, protein-protein docking, and molecular dynamics simulation. Results: Using the Global initiative on sharing all influenza data (GISAID) a total of 21 unique mutations were identified, among which five were reported as stabilizing while 16 were destabilizing revealed through mCSM, DynaMut 2.0, and I-Mutant servers. Protein-protein docking with Angiotensin-converting enzyme 2 (ACE2) and monoclonal antibody (4A8) revealed that mutation G446V in the receptor-binding domain; R102S and G181V in the N-terminal domain (NTD) significantly affected the binding and thus increased the infectivity. The interaction pattern also revealed significant variations in the hydrogen bonding, salt bridges and non-bonded contact networks. The structural-dynamic features of these mutations revealed the global dynamic trend and the finding energy calculation further established that the G446V mutation increases the binding affinity towards ACE2 while R102S and G181V help in evading the host immune response. The other mutations reported supplement these processes indirectly. The binding free energy results revealed that wild type-RBD has a TBE of -60.55kcal/mol while G446V-RBD reported a TBE of -73.49kcal/mol. On the other hand, wild type-NTD reported -67.77kcal/mol of TBE, R102S-NTD reported -51.25kcal/mol of TBE while G181V-NTD reported a TBE of -63.68kcal/mol. Conclusions: In conclusion, the current findings revealed basis for higher infectivity and immune evasion associated with the aforementioned mutations and structure-based drug discovery against such variants.

Taste Detection Threshold in Relation to Pfizer COVID-19 Vaccine.

Background The first hit of the COVID-19 pandemic was reported in December 2019 in Wuhan, Hubei province, China, and wholesale seafood markets were reported to be the source of infection. The development of effective and safe vaccines against SARS-CoV-2 has been extremely fast. The development of the COVID-19 mRNA vaccine started in early January 2020, after the release of the SARS-CoV-2 genetic sequence by the Chinese Center for Disease Control and Prevention and its global dissemination by the Global Initiative on Sharing All Influenza Data (GISAID). Aim The current study aims to evaluate the taste detection threshold after the second shot of Pfizer COVID-19 vaccination at different intervals and after the booster shot. Materials and methods A multistage sampling technique was used to select all Iraqi healthcare workers (HCWs) to whom the inclusion criteria were applied, from Baghdad Medical City and Al-Yarmouk Teaching Hospital participated in this study. The total number of selected subjects was 85. The ages of HCWs ranged from 24 to 60 years. The first study group (G1) comprised 30 HCWs who received the COVID-19 vaccination from Pfizer in two doses. The subjects were recruited into this group two weeks to three months after being vaccinated and had no history of COVID-19 symptoms. The second group (G2) comprised 30 HCWs who received the COVID-19 vaccination from Pfizer in two doses. They were recruited three to six months after they were vaccinated and had no prior history of the virus' symptoms. The third group (G3) was of 25 HCWs who received a third booster dose of COVID-19 vaccination from Pfizer two weeks afterthe booster dose. The taste detection threshold was performed forfour basic tastes: sweet, sour, salt, and bitter. Results The taste detection threshold for (sweets) showed a significant difference between the first and second groups and the second and third groups. The taste detection threshold of sweets was significantly higher in the second group. Conclusions After three to six months from getting the second dose, the taste detection threshold for sweets was assessed as high. This means that the flavor will be harder to perceive. Pfizer COVID-19 immunization may be assumed to be one of the causes of defected taste sensation to sweet flavor.

Genomic analysis of SARS-CoV-2 variants of concern circulating in Hawai'i to facilitate public-health policies.

Using genomics, bioinformatics and statistics, herein we demonstrate the effect of statewide and nationwide quarantine on the introduction of SARS-CoV-2 variants of concern (VOC) in Hawai'i. To define the origins of introduced VOC, we analyzed 260 VOC sequences from Hawai'i, and 301,646 VOC sequences worldwide, deposited in the GenBank and global initiative on sharing all influenza data (GISAID), and constructed phylogenetic trees. The trees define the most recent common ancestor as the origin. Further, the multiple sequence alignment used to generate the phylogenetic trees identified the consensus single nucleotide polymorphisms in the VOC genomes. These consensus sequences allow for VOC comparison and identification of mutations of interest in relation to viral immune evasion and host immune activation. Of note is the P71L substitution within the E protein, the protein sensed by TLR2 to produce cytokines, found in the B.1.351 VOC may diminish the efficacy of some vaccines. Based on the phylogenetic trees, the B.1.1.7, B.1.351, B.1.427, and B.1.429 VOC have been introduced in Hawai'i multiple times since December 2020 from several definable geographic regions. From the first worldwide report of VOC in GenBank and GISAID, to the first arrival of VOC in Hawai'i, averages 320 days with quarantine, and 132 days without quarantine. As such, the effect of quarantine is shown to significantly affect the time to arrival of VOC in Hawai'i. Further, the collective 2020 quarantine of 43-states in the United States demonstrates a profound impact in delaying the arrival of VOC in states that did not practice quarantine, such as Utah. Our data demonstrates that at least 76% of all definable SARS-CoV-2 VOC have entered Hawai'i from California, with the B.1.351 variant in Hawai'i originating exclusively from the United Kingdom. These data provide a foundation for policy-makers and public-health officials to apply precision public health genomics to real-world policies such as mandatory screening and quarantine.

Selective sweeps in SARS-CoV-2 variant competition

The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible-Infected-Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.

Tracking Co-Occurrence of N501Y, P681R, and Other Key Mutations in SARS-CoV-2 Spike for Surveillance

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced five variants of concern (VOC) to date. The important spike mutation ‘N501Y’ is common to Alpha, Beta, Gamma, and Omicron VOC, while the ‘P681R’ is key to Delta’s spread. We have analysed circa 10 million SARS-CoV-2 genome sequences from the world’s largest repository, ‘Global Initiative on Sharing All Influenza Data (GISAID)’, and demonstrated that these two mutations have co-occurred on the spike ‘D614G’ mutation background at least 5767 times from 12 May 2020 to 28 April 2022. In contrast, the Y501-H681 combination, which is common to Alpha and Omicron VOC, is present in circa 1.1 million entries. Over half of the 5767 co-occurrences were in France, Turkey, or US (East Coast), and the rest across 88 other countries; 36.1%, 3.9%, and 4.1% of the co-occurrences were Alpha’s Q.4, Gamma’s P.1.8, and Omicron’s BA.1.1 sub-lineages acquiring the P681R; 4.6% and 3.0% were Delta’s AY.5.7 sub-lineage and B.1.617.2 lineage acquiring the N501Y; the remaining 8.2% were in other variants. Despite the selective advantages individually conferred by N501Y and P681R, the Y501-R681 combination counterintuitively did not outcompete other variants in every instance we have examined. While this is a relief to worldwide public health efforts, in vitro and in vivo studies are urgently required in the absence of a strong in silico explanation for this phenomenon. This study demonstrates a pipeline to analyse combinations of key mutations from public domain information in a systematic manner and provide early warnings of spread. The study here demonstrates the usage of the pipeline using the key mutations N501Y, P681R, and D614G of SARS-CoV-2.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods: A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results: A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a.. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion: Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

A high scale SARS-CoV-2 profiling by its whole-genome sequencing using Oxford Nanopore Technology in Kazakhstan.

Severe acute respiratory syndrome (SARS-CoV-2) is responsible for the worldwide pandemic, COVID-19. The original viral whole-genome was sequenced by a high-throughput sequencing approach from the samples obtained from Wuhan, China. Real-time gene sequencing is the main parameter to manage viral outbreaks because it expands our understanding of virus proliferation, spread, and evolution. Whole-genome sequencing is critical for SARS-CoV-2 variant surveillance, the development of new vaccines and boosters, and the representation of epidemiological situations in the country. A significant increase in the number of COVID-19 cases confirmed in August 2021 in Kazakhstan facilitated a need to establish an effective and proficient system for further study of SARS-CoV-2 genetic variants and the development of future Kazakhstan’s genomic surveillance program. The SARS-CoV-2 whole-genome was sequenced according to SARS-CoV-2 ARTIC protocol (EXP-MRT001) by Oxford Nanopore Technologies at the National Laboratory Astana, Kazakhstan to track viral variants circulating in the country. The 500 samples kindly provided by the Republican Diagnostic Center (UMC-NU) and private laboratory KDL “Olymp” were collected from individuals in Nur-Sultan city diagnosed with COVID-19 from August 2021 to May 2022 using real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). All samples had a cycle threshold (Ct) value below 20 with an average Ct value of 17.03. The overall average value of sequencing depth coverage for samples is 244X. 341 whole-genome sequences that passed quality control were deposited in the Global initiative on sharing all influenza data (GISAID). The BA.1.1 (n = 189), BA.1 (n = 15), BA.2 (n = 3), BA.1.15 (n = 1), BA.1.17.2 (n = 1) omicron lineages, AY.122 (n = 119), B.1.617.2 (n = 8), AY.111 (n = 2), AY.126 (n = 1), AY.4 (n = 1) delta lineages, one sample B.1.1.7 (n = 1) belongs to alpha lineage, and one sample B.1.637 (n = 1) belongs to small sublineage were detected in this study. This is the first study of SARS-CoV-2 whole-genome sequencing by the ONT approach in Kazakhstan, which can be expanded for the investigation of other emerging viral or bacterial infections on the country level.

Molecular epidemiological features of SARS-CoV-2 in Japan, 2020-1.

There were five epidemic waves of coronavirus disease 2019 in Japan between 2020 and 2021. It remains unclear how the domestic waves arose and abated. To better understand this, we analyzed the pangenomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and characterized the molecular epidemiological features of the five epidemic waves in Japan. In this study, we performed deep sequencing to determine the pangenomic SARS-CoV-2 sequences of 1,286 samples collected in two cities far from each other, Tokyo Metropolis and Nagoya. Then, the spatiotemporal genetic changes of the obtained sequences were compared with the sequences available in the Global Initiative on Sharing All Influenza Data (GISAID) database. A total of 873 genotypes carrying different sets of mutations were identified in the five epidemic waves. Phylogenetic analysis demonstrated that sharp displacements of lineages and genotypes occurred between consecutive waves over the 2 years. In addition, a wide variety of genotypes were observed in the early half of each wave, whereas a few genotypes were detected across Japan during an entire wave. Phylogenetically, putative descendant genotypes observed late in each wave displayed regional clustering and evolution in Japan. The genetic diversity of SARS-CoV-2 displayed uneven dynamics during each epidemic wave in Japan. Our findings provide an important molecular epidemiological basis to aid in controlling future SARS-CoV-2 epidemics.

The design of Indonesian SARS‐CoV‐2 primers based on phylogenomic analysis of the SARS‐CoV‐2 clades

Molecular detection needs to be augmented for COVID‐19 detection in Indonesia using the PCR method with primer‐based gene analysis. This is necessary because the RNA of the SARS‐CoV‐2 virus, the causative infectious agent of the pandemic, has been mutated. Therefore, this study aimed to develop a primer design for determining SARS‐CoV‐2 clades in Indonesia using phylogenomic analysis. Data were obtained from 38 GISAID (Global Initiative on Sharing All Influenza Data) viruses and the relationships were analyzed using maximum likelihood (ML) phylogenomic analysis with a substitution model of generalized time‐reversible (GTR) to construct the tree topology. The results showed that the five types of SARS‐CoVs‐2 clades in Indonesia were L, G, GH, GR, and O. It also indicated that the GH region had the highest rate of clade at 50%, with the S clade affecting its formation. Furthermore, the genome sequences of the GH type used to design its primer were based on three genes, namely RdRp, S, and N. The RdRp and N genes were found to be conserved and hardy mutants, while the S gene occurred repeatedly. Several previous studies have stated that the designed primers produced missense mutations compared to another in silico. Therefore, three sets of primers were achieved from the GC contents and clamps, Tm range, and structural secondary indicator standards.

Predicting the number of COVID-19 infections and deaths in USA

BackgroundUncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.ResultsUsing the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R0<1 with an initial infection growth rate entropy of 9.11 bits for the United States (U.S.). At close proximity, the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the U.S., the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented.ConclusionThe proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.

Challenges of the Omicron (B.1.1.529) Variant and Its Lineages: A Global Perspective.

The SARS‐CoV‐2 virus has shown increased ability to mutate over the past two years, especially in the regions of the spike protein and receptor binding sites. Omicron (B.1.1.529) is the fifth variant of concern (VOC) after the emergence of the Alpha, Beta, Gamma, and Delta VOCs of SARS‐CoV‐2. This new variant has now circulated in 128 countries and according to the Global Initiative on Sharing All Influenza Data (GISAID), these 128 countries have shared 650,657 Omicron genome sequences as of 26 January, 2022. In this article, we highlight the real challenges of Omicron and its different lineages.

SARS-CoV-2: Evolution and Emergence of New Viral Variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent responsible for the coronavirus disease 2019 (COVID-19). The high rate of mutation of this virus is associated with a quick emergence of new viral variants that have been rapidly spreading worldwide. Several mutations have been documented in the receptor-binding domain (RBD) of the viral spike protein that increases the interaction between SARS-CoV-2 and its cellular receptor, the angiotensin-converting enzyme 2 (ACE2). Mutations in the spike can increase the viral spread rate, disease severity, and the ability of the virus to evade either the immune protective responses, monoclonal antibody treatments, or the efficacy of current licensed vaccines. This review aimed to highlight the functional virus classification used by the World Health Organization (WHO), Phylogenetic Assignment of Named Global Outbreak (PANGO), Global Initiative on Sharing All Influenza Data (GISAID), and Nextstrain, an open-source project to harness the scientific and public health potential of pathogen genome data, the chronological emergence of viral variants of concern (VOCs) and variants of interest (VOIs), the major findings related to the rate of spread, and the mutations in the spike protein that are involved in the evasion of the host immune responses elicited by prior SARS-CoV-2 infections and by the protection induced by vaccination.

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2’s most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

Collaboration between Clinical and Academic Laboratories for Sequencing SARS-CoV-2 Genomes.

ABSTRACTGenomic sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to provide valuable insight into the ever-changing variant makeup of the COVID-19 pandemic. More than three million SARS-CoV-2 genome sequences have been deposited in Global Initiative on Sharing All Influenza Data (GISAID), but contributions from the United States, particularly through 2020, lagged the global effort. The primary goal of clinical microbiology laboratories is seldom rooted in epidemiologic or public health testing, and many laboratories do not contain in-house sequencing technology. However, we recognized the need for clinical microbiologists to lend expertise, share specimen resources, and partner with academic laboratories and sequencing cores to assist in SARS-CoV-2 epidemiologic sequencing efforts. Here, we describe two clinical and academic laboratory collaborations for SARS-CoV-2 genomic sequencing. We highlight roles of the clinical microbiologists and the academic laboratories, outline best practices, describe two divergent strategies in accomplishing a similar goal, and discuss the challenges with implementing and maintaining such programs.