Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020-March 2021) Using a Statistical Learning Strategy

In silico prediction of immune-escaping hot spots for future COVID-19 vaccine design

The COVID-19 pandemic has had a widespread impact on a global scale, and the evolution of considerable dominants has already taken place. Some variants contained certain key mutations located on the receptor binding domain (RBD) of spike protein, such as E484K and N501Y. It is increasingly worrying that these variants could impair the efficacy of current vaccines or therapies. Therefore, analyzing and predicting the high-risk mutations of SARS-CoV-2 spike glycoprotein is crucial to design future vaccines against the different variants. In this work, we proposed an in silico approach, immune-escaping score (IES), to predict high-risk immune-escaping hot spots on the receptor-binding domain (RBD), implemented through integrated delta binding free energy measured by computational mutagenesis of spike-antibody complexes and mutation frequency calculated from viral genome sequencing data. We identified 23 potentially immune-escaping mutations on the RBD by using IES, nine of which occurred in omicron variants (R346K, K417N, N440K, L452Q, L452R, S477N, T478K, F490S, and N501Y), despite our dataset being curated before the omicron first appeared. The highest immune-escaping score (IES = 1) was found for E484K, which agrees with recent studies stating that the mutation significantly reduced the efficacy of neutralization antibodies. Furthermore, our predicted delta binding free energy and IES show a high correlation with high-throughput deep mutational scanning data (Pearson's r = 0.70) and experimentally measured neutralization titers data (mean Pearson's r = -0.80). In summary, our work presents a new method to identify the potentially immune-escaping mutations on the RBD and provides valuable insights into future COVID-19 vaccine design.

Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations

Importance

Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies.

Objective

To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations.

Design, Setting, and Participants

This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022.

Main Outcomes and Measures

Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants.

Results

Through training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022.

Conclusions and Relevance

In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

Analysis of the mutation dynamics of SARS-CoV-2 genome in the samples from Georgia State of the United States

BACKGROUND

The COVID-19 is caused by a novel coronavirus SARS-CoV-2, which started from China. It spread rapidly throughout the world and was later declared a pandemic by the WHO. Over the course of time, SARS-CoV-2 has mutated for survival advantages, and this led to multiple variants. Multiple studies on mutations identification in SARS-CoV2 have been published covering extensive sample areas. The purpose of this study was to limit the sample area to the Georgia state in the U.S. and to analyze the genome sequences for mutation profiling across the genome and origin of variants.

METHODS

The genome sequences (n = 3,970) were obtained from the NCBI database as of June 12, 2021, with the filter of being complete sequenced genomes, homo-sapiens host, and only from Georgia State of the U.S. NextClade, an online tool was used for the analysis of the sequences using Wuhan-Hu-1/2019 as a reference genome. The algorithm was sequence alignment, translation, mutation calling, phylogenetic placement, clade assignment, and quality control (QC). Thirty-six samples with bad QC were removed from the mutational analysis.

RESULTS

A total 117,743 mutations in the nucleotides were identified (averaging 31.5 mutations per sample). The mutations A23403G, C3037T, C241T, and C14408T were detected in 98% of the samples. Also, a total of 75,517 mutations in the amino acid were identified (averaging 20.2 mutations per sample). The mutations D614G and P314L were identified in >97% samples whereas R203K, G204R, P681H, and N501Y were detected in >50% samples. Analysis also revealed 16 different clades with 20I (49.6%). Clades 20G (24.2%) and 20A (5.5%) being the most abundant, showed that SARS-CoV-2 in the Georgia State originated mainly from Southeast England, other parts of the U.S., and several countries in Western Europe.

CONCLUSION

Looking at the three most common variants in Georgia State of the U.S., we could determine the primary locations of transmission or origin for the virus, and our analyses indicates that majority of the cases originated from Southeast England (Clade 20I), the U.S. itself (Clade 20G), and from Western Europe (Clade 20C).

Application of Statistical Learning to Identify Omicron Mutations in SARS-CoV-2 Viral Genome Sequence Data From Populations in Africa and the United States

IMPORTANCE

With timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time.

OBJECTIVE

To evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance.

DESIGN, SETTING, AND PARTICIPANTS

This case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021.

MAIN OUTCOMES AND MEASURES

The outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated.

RESULTS

In total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021.

CONCLUSIONS AND RELEVANCE

The application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

Virus Diversity, Abundance, and Evolution in Three Different Bat Colonies in Switzerland

Bats are increasingly recognized as reservoirs for many different viruses that threaten public health, such as Hendravirus, Ebolavirus, Nipahvirus, and SARS- and MERS-coronavirus. To assess spillover risk, viromes of bats from different parts of the world have been investigated in the past. As opposed to most of these prior studies, which determined the bat virome at a single time point, the current work was performed to monitor changes over time. Specifically, fecal samples of three endemic Swiss bat colonies consisting of three different bat species were collected over three years and analyzed using next-generation sequencing. Furthermore, single nucleotide variants of selected DNA and RNA viruses were analyzed to investigate virus genome evolution. In total, sequences of 22 different virus families were found, of which 13 are known to infect vertebrates. Most interestingly, in a Vespertilio murinus colony, sequences from a MERS-related beta-coronavirus were consistently detected over three consecutive years, which allowed us to investigate viral genome evolution in a natural reservoir host.

Rapidly Identifying New Coronavirus Mutations of Potential Concern in the Omicron Variant Using an Unsupervised Learning Strategy

Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants (A67V, T95I, G339D, R346K, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, K796Y, N856K, Q954H, N69K, L981F) in the spike protein and a separate core haplotype of 17 polymutants in non-spike genes: (K38, A1892) in nsp3, T492 in nsp4, (P132, V247, T280, S284) in 3C-like proteinase, I189 in nsp6, P323 in RNA-dependent RNA polymerase, I42 in Exonuclease, T9 in envelope protein, (D3, Q19, A63) in membrane glycoprotein, and (P13, R203, G204) in nucleocapsid phosphoprotein. Using these core haplotypes as reference, we have identified four newly emerging polymutants (R346, A701, I1081, N1192) in the spike protein (p-value=9.37*10 -4 , 1.0*10 -15 , 4.76*10 -7 and 1.56*10 -4 , respectively), and five additional polymutants in non-spike genes (D343G in nucleocapsid phosphoprotein, V1069I in nsp3, V94A in nsp4, F694Y in the RNA-dependent RNA polymerase and L106L/F of ORF3a) that exhibit significant increasing trajectories (all p-values < 1.0*10 -15 ). In the absence of relevant clinical data for these newly emerging mutations, it is important to monitor them closely. Two emerging mutations may be of particular concern: the N1192S mutation in spike protein locates in an extremely highly conserved region of all human coronaviruses that is integral to the viral fusion process, and the F694Y mutation in the RNA polymerase may induce conformational changes that could impact Remdesivir binding.

COVID-19 Infection in Children and Infants: Current Status on Therapies and Vaccines

Since the beginning in December 2019, the SARS-CoV-2 outbreak appeared to affect mostly the adult population, sparing the vast majority of children who only showed mild symptoms. The purpose of this investigation is to assess the status on the mechanisms that give children and infants this variation in epidemiology compared to the adult population and its impact on therapies and vaccines that are aimed towards them. A literature review, including in vitro studies, reviews, published guidelines and clinical trials was performed. Clinical trials concerned topics that allowed a descriptive synthesis to be produced. Four underlying mechanisms were found that may play a key role in providing COVID-19 protection in babies. No guidelines are available yet for therapy due to insufficient data; support therapy remains the most used. Only two vaccines are approved by the World Health Organization to be used in children from 12 years of age, and there are currently no efficacy or safety data for children below the age of 12 years. The COVID-19 clinical frame infection is milder in children and adolescents. This section of the population can act as vectors and reservoirs and play a key role in the transmission of the infection; therefore, vaccines are paramount. More evidence is required to guide safely the vaccination campaign.