Hotspot Mutations in SARS-CoV-2

Temporal Dynamics and Genomic Landscape of SARS-CoV-2 After Four Years of Evolution

Introduction Since its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone extensive genomic evolution, impacting public health policies, diagnosis, medication, and vaccine development. This study leverages advanced bioinformatics to assess the virus's temporal and regional genomic evolution from December 2019 to October 2023. Methods Our analysis incorporates 16,575 complete SARS-CoV-2 sequences collected from 214 countries. These samples were comparatively analyzed, with a detailed characterization of nucleic mutations, lineages, distribution, and evolutionary patterns during each year, using the Wuhan-Hu-1 strain as the reference. Results Our analysis has identified a total of 21,580 mutations that we classified into transient mutations, which diminished over time, and persistent mutations with steadily increasing frequencies. This mutation landscape led to a notable surge in the evolutionary rate, rising from 13 mutations per sample in 2020 to 96 by 2023, with minor geographic variations. The phylogenetic analysis unveiled three distinct evolutionary branches, each representing unique viral evolution pathways. These lineages exhibited a tendency for a reduced duration of dominance with a shortening prevalence period over time, as dominant strains were consistently replaced by more fit variants. Notably, the emergence of the Alpha and Delta variants in 2021 was followed by the subsequent dominance of Omicron clade variants that have branched into several recombinant variants in 2022, marking a significant shift in the viral landscape. Conclusion This study sheds light on the dynamic nature of SARS-CoV-2 evolution, emphasizing the importance of continuous and vigilant genomic surveillance. The dominance of recombinant lineages, coupled with the disappearance of local variants, underscores the virus's adaptability.

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.

Unveiling the Biomarkers of Cancer and COVID-19 and Their Regulations in Different Organs by Integrating RNA-Seq Expression and Protein-Protein Interactions

Cancer and COVID-19 have killed millions of people worldwide. COVID-19 is even more dangerous to people with comorbidities such as cancer. Thus, it is imperative to identify the key human genes or biomarkers that can be targeted to develop novel prognosis and therapeutic strategies. The transcriptomic data provided by the next-generation sequencing technique makes this identification very convenient. Hence, mRNA (messenger ribonucleic acid) expression data of 2265 cancer and 282 normal patients were considered, while for COVID-19 assessment, 784 and 425 COVID-19 and normal patients were taken, respectively. Initially, volcano plots were used to identify the up- and down-regulated genes for both cancer and COVID-19. Thereafter, protein-protein interaction (PPI) networks were prepared by combining all the up- and down-regulated genes for each of cancer and COVID-19. Subsequently, such networks were analyzed to identify the top 10 genes with the highest degree of connection to provide the biomarkers. Interestingly, these genes were all up-regulated for cancer, while they were down-regulated for COVID-19. This study had also identified common genes between cancer and COVID-19, all of which were up-regulated in both the diseases. This analysis revealed that FN1 was highly up-regulated in different organs for cancer, while EEF2 was dysregulated in most organs affected by COVID-19. Then, functional enrichment analysis was performed to identify significant biological processes. Finally, the drugs for cancer and COVID-19 biomarkers and the common genes between them were identified using the Enrichr online web tool. These drugs include lucanthone, etoposide, and methotrexate, targeting the biomarkers for cancer, while paclitaxel is an important drug for COVID-19.

Perspective Chapter: Emerging SARS-CoV-2 Variants of Concern (VOCs) and Their Impact on Transmission Rate, Disease Severity and Breakthrough Infections

SARS-CoV-2, like all RNA viruses, evolves over time, and genetic mutations have been linked to increased replication fitness and evolvability. SARS-CoV-2 spreads quickly between countries, resulting in new mutations. SARS-CoV-2 genome sequencing reveals that variants emerge through point mutations, insertions, and deletions. Concerns have been raised about the ability of currently approved vaccines to protect against emerging variants. Viral spike protein is a component of many approved vaccine candidates, and mutations in the S-protein may affect transmission dynamics and the risk of immune escape, resulting this pandemic last-longer in populations. Understanding the evolution of the SARS-CoV-2 virus, as well as its potential relationship with transmissibility, infectivity, and disease severity, may help us predict the consequences of future pandemics. SARS-CoV-2 genome studies have identified a few mutations that could potentially alter the transmissibility and pathogenicity of the SARS-CoV-2 virus. At the moment, it is worth mentioning that a few variants have increased the transmissibility of SARS-CoV-2. The Alpha, Beta, Gamma, Delta, Delta+, and omicron variants are designated as variants of concern (VOCs) by the World Health Organisation and have been linked with an increased risk to the community in terms of transmission, hospitalisation, and mortality. This chapter thoroughly discusses the impact of SARS-CoV-2 mutations, mainly VOCs, on public health by mining many published articles.

Radu E, Matei L, Maria Ruta S, Bleotu C. SARS-CoV-2 Variant Surveillance in Genomic Medicine Era

In the genomic medicine era, the emergence of SARS-CoV-2 was immediately followed by viral genome sequencing and world-wide sequences sharing. Almost in real-time, based on these sequences, resources were developed and applied around the world, such as molecular diagnostic tests, informed public health decisions, and vaccines. Molecular SARS-CoV-2 variant surveillance was a normal approach in this context yet, considering that the viral genome modification occurs commonly in viral replication process, the challenge is to identify the modifications that significantly affect virulence, transmissibility, reduced effectiveness of vaccines and therapeutics or failure of diagnostic tests. However, assessing the importance of the emergence of new mutations and linking them to epidemiological trend, is still a laborious process and faster phenotypic evaluation approaches, in conjunction with genomic data, are required in order to release timely and efficient control measures.

Understanding the mutational frequency in SARS-CoV-2 proteome using structural features

The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5-30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.

COVID-19 diagnostic methods in developing countries

COVID-19 has become one of the few leading causes of death and has evolved into a pandemic that disrupts everyone’s routine, and balanced way of life worldwide, and will continue to do so. To bring an end to this pandemic, scientists had put their all effort into discovering the vaccine for SARS-CoV-2 infection. For their dedication, now, we have a handful of COVID-19 vaccines. Worldwide, millions of people are at risk due to the current pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). Despite the lack of clinically authorized antiviral medications and vaccines for COVID-19, clinical trials of many recognized antiviral agents, their combination, and vaccine development in patients with confirmed COVID-19 are still ongoing. This discovery gave us a chance to get immune to this disease worldwide and end the pandemic. However, the unexpected capacity of mutation of the SARS-CoV-2 virus makes it difficult, like the recent SAS-CoV-2 Omicron variant. Therefore, there is a great necessity to spread the vaccination programs and prevent the spread of this dreadful epidemic by identifying and isolating afflicted patients. Furthermore, several COVID-19 tests are thought to be expensive, time-consuming, and require the use of adequately qualified persons to be carried out efficiently. In addition, we also conversed about how the various COVID-19 testing methods can be implemented for the first time in a developing country and their cost-effectiveness, accuracy, human resources requirements, and laboratory facilities.

The SARS-CoV-2 Alpha variant exhibits comparable fitness to the D614G strain in a Syrian hamster model

Late 2020, SARS-CoV-2 Alpha variant emerged in United Kingdom and gradually replaced G614 strains initially involved in the global spread of the pandemic. In this study, we use a Syrian hamster model to compare a clinical strain of Alpha variant with an ancestral G614 strain. The Alpha variant succeed to infect animals and to induce a pathology that mimics COVID-19. However, both strains replicate to almost the same level and induced a comparable disease and immune response. A slight fitness advantage is noted for the G614 strain during competition and transmission experiments. These data do not corroborate the epidemiological situation observed during the first half of 2021 in humans nor reports that showed a more rapid replication of Alpha variant in human reconstituted bronchial epithelium. This study highlights the need to combine data from different laboratories using various animal models to decipher the biological properties of newly emerging SARS-CoV-2 variants.

The SARS-CoV-2 Alpha variant exhibits similar transmission dynamics to an ancestral D614G variant in a Syrian hamster model, suggesting the limitations of using the hamster as the sole model to assess differences between SARS-CoV-2 strains.

A Review on Evolution of Emerging SARS-CoV-2 Variants based on Spike Glycoprotein

Since the inception of SARS-CoV-2 in December 2019, many variants have emerged over time. Some of these variants have resulted in transmissibility changes of the virus and may also have impact on diagnosis, therapeutics and even vaccines, thereby raising particular concerns in the scientific community. The variants which have mutations in Spike glycoprotein are the primary focus as it is the main target for neutralising antibodies. SARS-CoV-2 is known to infect human through Spike glycoprotein and uses receptor-binding domain (RBD) to bind to the ACE2 receptor in human. Thus, it is of utmost importance to study these variants and their corresponding mutations. Such 12 different important variants identified so far are B.1.1.7 (Alpha), B.1.351 (Beta), B.1.525 (Eta), B.1.427/B.1.429 (Epsilon), B.1.526 (Iota), B.1.617.1 (Kappa), B.1.617.2 (Delta), C.37 (Lambda), P.1 (Gamma), P.2 (Zeta), P.3 (Theta) and the recently discovered B.1.1.529 (Omicron). These variants have 84 unique mutations in Spike glycoprotein. To analyse such mutations, multiple sequence alignment of 77681 SARS-CoV-2 genomes of 98 countries over the period from January 2020 to July 2021 is performed followed by phylogenetic analysis. Also, characteristics of new emerging variants are elaborately discussed. The individual evolution of these mutation points and the respective variants are visualised and their characteristics are also reported. Moreover, to judge the characteristics of the non-synonymous mutation points (substitutions), their biological functions are evaluated by PolyPhen-2 while protein structural stability is evaluated using I-Mutant 2.0.