Estimating the strength of selection for new SARS-CoV-2 variants

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

Early detection of highly transmissible viral variants using phylogenomics

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.

Estimation of SARS-CoV-2 fitness gains from genomic surveillance data without prior lineage classification

The emergence of SARS-CoV-2 variants with increased fitness has had a strong impact on the epidemiology of COVID-19, with the higher effective reproduction number of the viral variants leading to new epidemic waves. Tracking such variants and their genetic signatures, using data collected through genomic surveillance, is therefore crucial for forecasting likely surges in incidence. Current methods of estimating fitness advantages of variants rely on tracking the changing proportion of a particular lineage over time, but describing successful lineages in a rapidly evolving viral population is a difficult task. We propose a method of estimating fitness gains directly from nucleotide information generated by genomic surveillance, without a priori assigning isolates to lineages from phylogenies, based solely on the abundance of single nucleotide polymorphisms (SNPs). The method is based on mapping changes in the genetic population structure over time. Changes in the abundance of SNPs associated with periods of increasing fitness allow for the unbiased discovery of new variants, thereby obviating a deliberate lineage assignment and phylogenetic inference. We conclude that the method provides a fast and reliable way to estimate fitness advantages of variants without the need for a priori assigning isolates to lineages.

The most exposed regions of SARS-CoV-2 structural proteins are subject to strong positive selection and gene overlap may locally modify this behavior

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic that emerged in 2019 has been an unprecedented event in international science, as it has been possible to sequence millions of genomes, tracking their evolution very closely. This has enabled various types of secondary analyses of these genomes, including the measurement of their sequence selection pressure. In this work, we have been able to measure the selective pressure of all the described SARS-CoV-2 genes, even analyzed by sequence regions, and we show how this type of analysis allows us to separate the genes between those subject to positive selection (usually those that code for surface proteins or those exposed to the host immune system) and those subject to negative selection because they require greater conservation of their structure and function. We have also seen that when another gene with an overlapping reading frame appears within a gene sequence, the overlapping sequence between the two genes evolves under a stronger purifying selection than the average of the non-overlapping regions of the main gene. We propose this type of analysis as a useful tool for locating and analyzing all the genes of a viral genome when an adequate number of sequences are available.IMPORTANCEWe have analyzed the selection pressure of all severe acute respiratory syndrome coronavirus 2 genes by means of the nonsynonymous (Ka) to synonymous (Ks) substitution rate. We found that protein-coding genes are exposed to strong positive selection, especially in the regions of interaction with other molecules (host receptor and genome of the virus itself). However, overlapping coding regions are more protected and show negative selection. This suggests that this measure could be used to study viral gene function as well as overlapping genes.

Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study

The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of new variants is critical for timely optimisation of control strategies. We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to rapidly explore spatial and temporal variations in the estimated transmission advantage. We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29 (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Delta is 1.77 (95% CrI 1.69-1.85) times more transmissible than Alpha (England data). Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.

Phylogenies increase power to detect highly transmissible viral genome variants

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods-using a phylogenetic tree relating viral samples-and count-based methods-using case-counts of variants over time-to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity

BACKGROUND

Throughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread.

METHODS

In this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 16 SARS-CoV-2 variant waves/transitions for 230 countries and sub-country regions, between October 2020 and January 2023. We then clustered geographic locations in terms of their variant behavior across several Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition.

FINDINGS

This work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions.

INTERPRETATION

These results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date.

FUNDING

Laboratory Directed Research and Development (LDRD), Los Alamos National Laboratory.

How public health authorities can use pathogen genomics in health protection practice: a consensus-building Delphi study conducted in the United Kingdom

Pathogen sequencing guided understanding of SARS-CoV-2 evolution during the COVID-19 pandemic. Many health systems developed pathogen genomics services to monitor SARS-CoV-2. There are no agreed guidelines about how pathogen genomic information should be used in public health practice. We undertook a modified Delphi study in three rounds to develop expert consensus statements about how genomic information should be used. Our aim was to inform health protection policy, planning and practice. Participants were from organisations that produced or used pathogen genomics information in the United Kingdom. The first round posed questions derived from a rapid literature review. Responses informed statements for the subsequent rounds. Consensus was accepted when 70 % or more of the responses were strongly agree/agree, or 70 % were disagree/strongly disagree on the five-point Likert scale. Consensus was achieved in 26 (96 %) of 27 statements. We grouped the statements into six categories: monitoring the emergence of new variants; understanding the epidemiological context of genomic data; using genomic data in outbreak risk assessment and risk management; prioritising the use of limited sequencing capacity; sequencing service performance; and sequencing service capability. The expert consensus statements will help guide public health authorities and policymakers to integrate pathogen genomics in health protection practice.

On the Evolutionary Trajectory of SARS-CoV-2: Host Immunity as a Driver of Adaptation in RNA Viruses

Host immunity can exert a complex array of selective pressures on a pathogen, which can drive highly mutable RNA viruses towards viral escape. The plasticity of a virus depends on its rate of mutation, as well as the balance of fitness cost and benefit of mutations, including viral adaptations to the host's immune response. Since its emergence, SARS-CoV-2 has diversified into genetically distinct variants, which are characterised often by clusters of mutations that bolster its capacity to escape human innate and adaptive immunity. Such viral escape is well documented in the context of other pandemic RNA viruses such as the human immunodeficiency virus (HIV) and influenza virus. This review describes the selection pressures the host's antiviral immunity exerts on SARS-CoV-2 and other RNA viruses, resulting in divergence of viral strains into more adapted forms. As RNA viruses obscure themselves from host immunity, they uncover weak points in their own armoury that can inform more comprehensive, long-lasting, and potentially cross-protective vaccine coverage.

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.

Association between viral load and positivization time of a SARS-CoV-2 rapid antigen test in routine nasopharyngeal specimens

BACKGROUND

Rapid SARS-CoV-2 antigen tests are potentially useful tools for screening carriers with high viral load. This study was aimed to assess the potential association between viral load and positivization time of a manual SARS-CoV-2 commercial antigen test in routine nasopharyngeal specimens.

METHODS

In a sample of subjects undergoing routine diagnostic testing, SARS-CoV-2 positivity of nasopharyngeal samples was assayed with both molecular (Altona Diagnostics RealStar SARS-CoV-2 RT-PCR Kit) and antigenic (Roche SARS-CoV-2 Rapid Antigen Test) tests. Positivization time of rapid antigen test was correlated and compared with viral load expressed as mean of SARS-CoV2 E/S genes cycle threshold (Ct) values.

RESULTS

The study sample consisted of 106 patients (median age 48 years, 55 women) with positive results of rapid SARS-CoV-2 antigen testing. A highly significant Spearman's correlation was found between mean SARSCoV-2 E/S genes Ct values and positivization time of manual antigen test (r= 0.70; p<0.001). The positivization time of rapid SARS-CoV-2 antigen test displayed an area under the curve of 0.82 (95%CI, 0.74-0.89) for predicting nasopharyngeal samples with high viral load (i.e., mean Ct <20). A positivization time cut-off of 32 SEC had 94.9% sensitivity and 58.2% specificity for detecting specimens with high viral load. The overall agreement between mean Ct value <20 and positivization time <32 SEC was 70.8%.

CONCLUSIONS

Positivization time of rapid SARS-CoV-2 antigen tests may provide easy and rapid information on viral load, thus making this type of manual assay potentially suitable for quick and reliable detection and isolation of supercarriers.

Global estimates of the fitness advantage of SARS-CoV-2 variant Omicron

New variants of SARS-CoV-2 show remarkable heterogeneity in their relative fitness over both time and space. In this paper we extend the tools available for estimating the selection strength for new SARS-CoV-2 variants to a hierarchical, mixed-effects, renewal equation model. This formulation allows us to estimate selection effects at the global level while incorporating both measured and unmeasured heterogeneity among countries. Applying this model to the spread of Omicron in forty countries, we find evidence for very strong but very heterogeneous selection effects. To test whether this heterogeneity is explained by differences in the immune landscape, we considered several measures of vaccination rates and recent population-level infection as covariates, finding moderately strong, statistically significant effects. We also found a significant positive correlation between the selection advantage of Delta and Omicron at the country level, suggesting that other region-specific explanatory variables of fitness differences do exist. Our method is implemented in the Stan programming language, can be run on standard consumer-grade computing resources, and will be straightforward to apply to future variants.

Boosting the detection performance of severe acute respiratory syndrome coronavirus 2 test through a sensitive optical biosensor with new superior antibody

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged in late 2019 leading to the COVID-19 disease pandemic that triggered socioeconomic turmoil worldwide. A precise, prompt, and affordable diagnostic assay is essential for the detection of SARS-CoV-2 as well as its variants. Antibody against SARS-CoV-2 spike (S) protein was reported as a suitable strategy for therapy and diagnosis of COVID-19. We, therefore, developed a quick and precise phase-sensitive surface plasmon resonance (PS-SPR) biosensor integrated with a novel generated anti-S monoclonal antibody (S-mAb). Our results indicated that the newly generated S-mAb could detect the original SARS-CoV-2 strain along with its variants. In addition, a SARS-CoV-2 pseudovirus, which could be processed in BSL-2 facility was generated for evaluation of sensitivity and specificity of the assays including PS-SPR, homemade target-captured ELISA, spike rapid antigen test (SRAT), and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Experimentally, PS-SPR exerted high sensitivity to detect SARS-CoV-2 pseudovirus at 589 copies/ml, with 7-fold and 70-fold increase in sensitivity when compared with the two conventional immunoassays, including homemade target-captured ELISA (4 × 103 copies/ml) and SRAT (4 × 104 copies/ml), using the identical antibody. Moreover, the PS-SPR was applied in the measurement of mimic clinical samples containing the SARS-CoV-2 pseudovirus mixed with nasal mucosa. The detection limit of PS-SPR is calculated to be 1725 copies/ml, which has higher accuracy than homemade target-captured ELISA (4 × 104 copies/ml) and SRAT (4 × 105 copies/ml) and is comparable with qRT-PCR (1250 copies/ml). Finally, the ability of PS-SPR to detect SARS-CoV-2 in real clinical specimens was further demonstrated, and the assay time was less than 10 min. Taken together, our results indicate that this novel S-mAb integrated into PS-SPR biosensor demonstrates high sensitivity and is time-saving in SARS-CoV-2 virus detection. This study suggests that incorporation of a high specific recognizer in SPR biosensor is an alternative strategy that could be applied in developing other emerging or re-emerging pathogenic detection platforms.

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.

Perspective Chapter: Tracking Trails of SARS CoV-2 - Variants to Therapy

A virus when replicates itself from one generation to another, tends to change a little bit of its structure. These variations are called mutations. History says that SARS CoV-2 originated from the virus reservoirs of animals, specifically non-human mammals like bats and minks. Since then, there are evolutionary changes in its genome due to recombination in divergent strains of different species. Thus, making the virus more robust and smarter to sustain and evade immune responses in humans. Probably, this has led to the 2019 SARS CoV-2 pandemic. This chapter tracks the evolutionary trails of the virus origin, its pathogenesis in humans, and varying variants with the coming times. Eventually, the chapter overviews the available vaccines and therapies to be followed for SARS CoV-2.

Clinical characteristics and immune profile alterations in vaccinated individuals with breakthrough Delta SARS-CoV-2 infections

Despite timely immunization programs, and efficacious vaccines conveying protection against SARS-CoV-2 infection, breakthrough infections in vaccinated individuals have been reported. The Delta variant of concern (VOC) outbreak in Guangzhou resulted in local transmission in vaccinated and non-vaccinated residents, providing a unique opportunity to study the protective effects of the inactivated vaccines in breakthrough infection. Here, we find that the 2-dose vaccinated group has similar peak viral titers and comparable speeds of viral RNA clearance to the non-vaccinated group but accelerated viral suppression in the middle course of the disease. We quantitatively demonstrate that peak viral pneumonia is significantly mitigated in the 2-dose vaccine group (median 0.298%) compared with the non-vaccinated (5.77%) and 1-dose vaccine (3.34%) groups. Pneumonia absorbance is approximately 6 days ahead in the 2-dose group (median 10 days) than in the non-vaccinated group (16 days) (p = 0.003). We also observe reduced cytokine inflammation and markedly undisturbed gene transcription profiles of peripheral blood mononuclear cells (PBMCs) in the 2-dose group. In short, our study demonstrates that prior vaccination substantially restrains pneumonia development, reduces cytokine storms, and facilitates clinical recovery.

SARS-CoV-2 breakthrough infections in vaccinated individuals are a public health concern. Here, the authors analyse the clinical characteristics and profile immune alterations among vaccinated and non-vaccinated residents with Delta SARS-CoV-2 infection in Guangzhou.

Ongoing Positive Selection Drives the Evolution of SARS-CoV-2 Genomes

SARS-CoV-2 is a new RNA virus affecting humans and spreads extensively through world populations since its first outbreak in December, 2019. Whether the transmissibility and pathogenicity of SARS-CoV-2 in humans after zoonotic transfer are actively evolving, and driven by adaptation to the new host and environments is still under debate. Understanding the evolutionary mechanism underlying epidemiological and pathological characteristics of COVID-19 is essential for predicting the epidemic trend, and providing guidance for disease control and treatments. Interrogating novel strategies for identifying natural selection using within-species polymorphisms and 3,674,076 SARS-CoV-2 genome sequences of 169 countries as of December 30, 2021, we demonstrate with population genetic evidence that during the course of SARS-CoV-2 pandemic in humans, 1) SARS-CoV-2 genomes are overall conserved under purifying selection, especially for the 14 genes related to viral RNA replication, transcription, and assembly; 2) ongoing positive selection is actively driving the evolution of 6 genes (e.g., S, ORF3a, and N) that play critical roles in molecular processes involving pathogen-host interactions, including viral invasion into and egress from host cells, and viral inhibition and evasion of host immune response, possibly leading to high transmissibility and mild symptom in SARS-CoV-2 evolution. According to an established haplotype phylogenetic relationship of 138 viral clusters, a spatial and temporal landscape of 556 critical mutations is constructed based on their divergence among viral haplotype clusters or repeatedly increase in frequency within at least 2 clusters, of which multiple mutations potentially conferring alterations in viral transmissibility, pathogenicity, and virulence of SARS-CoV-2 are highlighted, warranting attentions.

Global estimates of the fitness advantage of SARS-CoV-2 variant Omicron

New variants of SARS-CoV-2 show remarkable heterogeneity in their relative fitness both over time and space. In this paper we extend a previously published model for estimating the selection strength for new SARS-CoV-2 variants to a hierarchical, mixed-effects, renewal equation model. This formulation allows us to globally estimate selection effects at different spatial levels while controlling for complex patterns of transmission and jointly inferring the effects of unit-level covariates in the spatial heterogeneity of SARS-CoV-2 selection effects. Applying this model to the spread of Omicron in 40 counties finding evidence for very strong (64%) but very heterogeneous selection effects at the country level. We further considered different measures of vaccination levels and measures of recent population-level infection as possible explanations. However, none of those variables were found to explain a significant proportion of the heterogeneity in country-level selection effects. We did find a significant positive correlation between the selection advantage of Delta and Omicron at the country level, suggesting that region-specific explanatory variables of fitness differences do exist. Our method is implemented in the Stan programming language, can be run on standard commercial-grade computing resources, and should be straightforward to apply to future variants.

Delta Variant of SARS-CoV-2 Replacement in Brazil: A National Epidemiologic Surveillance Program

Coronavirus disease 2019 (COVID-19) pandemic has caused immeasurable impacts on the health and socioeconomic system. The real-time identification and characterization of new Variants of Concern (VOCs) are critical to comprehend its emergence and spread worldwide. In this sense, we carried out a national epidemiological surveillance program in Brazil from April to October 2021. Genotyping by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and sequencing were performed to monitor the dynamics and dissemination of VOCs in samples from 15 federative units. Delta VOC was first detected on June 2021 and took sixteen weeks to replace Gamma. To assess the transmissibility potential of Gamma and Delta VOCs, we studied the dynamics of RT-qPCR cycle threshold (Ct) score in the dominance period of each variant. The data suggest that Delta VOC has a higher transmission rate than Gamma VOC. We also compared relevant symptom patterns in individuals infected with both VOCs. The Delta-infected subjects were less likely to have low oxygen saturation or fatigue, altered results on chest computed tomography, and a propensity for altered X-rays. Altogether, we described the replacement of Gamma by Delta, Delta enhanced transmissibility, and differences in symptom presentation.