Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

A protein language model for exploring viral fitness landscapes

Successively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated fitness (i.e., relative effective reproduction number between variants). Modeling the genotype-fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we present CoVFit, a protein language model adapted from ESM-2, designed to predict variant fitness based solely on spike protein sequences. CoVFit was trained on genotype-fitness data derived from viral genome surveillance and functional mutation assays related to immune evasion. CoVFit successively ranked the fitness of unknown future variants harboring nearly 15 mutations with informative accuracy. CoVFit identified 959 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, we show that CoVFit is applicable for predicting viral evolution through single amino acid mutations. Our study gives insight into the SARS-CoV-2 fitness landscape and provides a tool for efficiently identifying SARS-CoV-2 variants with higher epidemic risk.

Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2

Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (Mpro). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which Mpro trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for Mpro closely matching those of NSP10, NSP16, and PLpro. The ANN |R| value for Mpro was 0.564, while NSP10, NSP16, and PLpro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for Mpro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PLpro, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from Mpro. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.

Increased preference for lysine over arginine in spike proteins of SARS-CoV-2 BA.2.86 variant and its daughter lineages

The COVID-19 pandemic offered an unprecedented glimpse into the evolution of its causative virus, SARS-CoV-2. It has been estimated that since its outbreak in late 2019, the virus has explored all possible alternatives in terms of missense mutations for all sites of its polypeptide chain. Spike protein of the virus exhibits the largest sequence variation in particular, with many individual mutations impacting target recognition, cellular entry, and endosomal escape of the virus. Moreover, recent studies unveiled a significant increase in the total charge on the spike protein during the evolution of the virus in the initial period of the pandemic. While this trend has recently come to a halt, we perform a sequence-based analysis of the spike protein of 2665 SARS-CoV-2 variants which shows that mutations in ionizable amino acids continue to occur with the newly emerging variants, with notable differences between lineages from different clades. What is more, we show that within mutations of amino acids which can acquire positive charge, the spike protein of SARS-CoV-2 exhibits a prominent preference for lysine residues over arginine residues. This lysine-to-arginine ratio increased at several points during spike protein evolution, most recently with BA.2.86 and its sublineages, including the recently dominant JN.1, KP.3, and XEC variants. The increased ratio is a consequence of mutations in different structural regions of the spike protein and is now among the highest among viral species in the Coronaviridae family. The impact of high lysine-to-arginine ratio in the spike proteins of BA.2.86 and its daughter lineages on viral fitness remains unclear; we discuss several potential mechanisms that could play a role and that can serve as a starting point for further studies.

A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks

Predicting the evolutionary patterns of emerging and endemic viruses is key for mitigating their spread. In particular, it is critical to rapidly identify mutations with the potential for immune escape or increased disease burden. Knowing which circulating mutations pose a concern can inform treatment or mitigation strategies such as alternative vaccines or targeted social distancing. In 2021, Hie B, Zhong ED, Berger B, Bryson B. 2021 Learning the language of viral evolution and escape. Science 371, 284-288. (doi:10.1126/science.abd7331) proposed that variants of concern can be identified using two quantities extracted from protein language models, grammaticality and semantic change. These quantities are defined by analogy to concepts from natural language processing. Grammaticality is intended to be a measure of whether a variant viral protein is viable, and semantic change is intended to be a measure of potential for immune escape. Here, we systematically test this hypothesis, taking advantage of several high-throughput datasets that have become available, and also comparing this model with several more recently published machine learning models. We find that grammaticality can be a measure of protein viability, though methods that are trained explicitly to predict mutational effects appear to be more effective. By contrast, we do not find compelling evidence that semantic change is a useful tool for identifying immune escape mutations.

SARS-CoV-2: The Interplay Between Evolution and Host Immunity

The persistence of SARS-CoV-2 infections at a global level reflects the repeated emergence of variant strains encoding unique constellations of mutations. These variants have been generated principally because of a dynamic host immune landscape, the countermeasures deployed to combat disease, and selection for enhanced infection of the upper airway and respiratory transmission. The resulting viral diversity creates a challenge for vaccination efforts to maintain efficacy, especially regarding humoral aspects of protection. Here, we review our understanding of how SARS-CoV-2 has evolved during the pandemic, the immune mechanisms that confer protection, and the impact viral evolution has had on transmissibility and adaptive immunity elicited by natural infection and/or vaccination. Evidence suggests that SARS-CoV-2 evolution initially selected variants with increased transmissibility but currently is driven by immune escape. The virus likely will continue to drift to maintain fitness until countermeasures capable of disrupting transmission cycles become widely available.

Predicting pathogen evolution and immune evasion in the age of artificial intelligence

The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.

Few-Shot Viral Variant Detection via Bayesian Active Learning and Biophysics

The early detection of high-fitness viral variants is critical for pandemic response, yet limited experimental resources at the onset of variant emergence hinder effective identification. To address this, we introduce an active learning framework that integrates protein language model ESM3, Gaussian process with uncertainty estimation, and a biophysical model to predict the fitness of novel variants in a few-shot learning setting. By benchmarking on past SARS-CoV-2 data, we demonstrate that our methods accelerates the identification of high-fitness variants by up to fivefold compared to random sampling while requiring experimental characterization of fewer than 1% of possible variants. We also demonstrate that our framework benchmarked on deep mutational scans effectively identifies sites that are frequently mutated during natural viral evolution with a predictive advantage of up to two years compared to baseline strategies, particularly those enabling antibody escape while preserving ACE2 binding. Through systematic analysis of different acquisition strategies, we show that incorporating uncertainty in variant selection enables broader exploration of the sequence landscape, leading to the discovery of evolutionarily distant but potentially dangerous variants. Our results suggest that this framework could serve as an effective early warning system for identifying concerning SARS-CoV-2 variants and potentially emerging viruses with pandemic potential before they achieve widespread circulation.

High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains

Human influenza viruses rapidly acquire mutations in their hemagglutinin (HA) protein that erode neutralization by antibodies from prior exposures. Here, we use a sequencing-based assay to measure neutralization titers for 78 recent H3N2 HA strains against a large set of children and adult sera, measuring ~10,000 total titers. There is substantial person-to-person heterogeneity in the titers against different viral strains, both within and across age cohorts. The growth rates of H3N2 strains in the human population in 2023 are highly correlated with the fraction of sera with low titers against each strain. Notably, strain growth rates are less correlated with neutralization titers against pools of human sera, demonstrating the importance of population heterogeneity in shaping viral evolution. Overall, these results suggest that high-throughput neutralization measurements of human sera against many different viral strains can help explain the evolution of human influenza.

Within-Host Fitness and Antigenicity Shift Are Key Factors Influencing the Prevalence of Within-Host Variations in the SARS-CoV-2 S Gene

Within-host evolution plays a critical role in shaping the diversity of SARS-CoV-2. However, understanding the primary factors contributing to the prevalence of intra-host single nucleotide variants (iSNVs) in the viral population remains elusive. Here, we conducted a comprehensive analysis of over 556,000 SARS-CoV-2 sequencing data and prevalence data of different SARS-CoV-2 S protein amino acid mutations to elucidate key factors influencing the prevalence of iSNVs in the SARS-CoV-2 S gene. Within-host diversity analysis revealed the presence of mutational hotspots within the S gene, mainly located in NTD, RBD, TM, and CT domains. Additionally, we generated a single amino acid resolution selection status map of the S protein. We observed a significant variance in within-host fitness among iSNVs in the S protein. The majority of iSNVs exhibited low to no within-host fitness and displayed low alternate allele frequency (AAF), suggesting that they will be eliminated due to the narrow transmission bottleneck of SARS-CoV-2. Notably, iSNVs with moderate AAFs (0.06-0.12) were found to be more prevalent than those with high AAFs. Furthermore, iSNVs with the potential to alter antigenicity were more prevalent. These findings underscore the significance of within-host fitness and antigenicity shift as two key factors influencing the prevalence of iSNVs in the SARS-CoV-2 S gene.

Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time

In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been selected that are more transmissible and capable of partial immune escape. Currently, models for projecting the evolution of SARS-CoV-2 are based on using dominant genome sequences to forecast whether a known mutation will be prevalent in the future. However, novel variants of SARS-CoV-2 (and other viruses) are driven by evolutionary pressure acting on minor variant genomes, which then become dominant and form a potential next wave of infection. In this study, sequencing data from 96 209 patients, sampled over a 3-year period, were used to analyse patterns of minor variant genomes. These data were used to develop unsupervised machine learning clusters to identify amino acids that had a greater potential for mutation than others in the Spike protein. Being able to identify amino acids that may be present in future variants would better inform the design of longer-lived medical countermeasures and allow a risk-based evaluation of viral properties, including assessment of transmissibility and immune escape, thus providing candidates with early warning signals for when a new variant of SARS-CoV-2 emerges.

Frequency dynamics predict viral fitness, antigenic relationships and epidemic growth

During the COVID-19 pandemic, SARS-CoV-2 variants drove large waves of infections, fueled by increased transmissibility and immune escape. Current models focus on changes in variant frequencies without linking them to underlying transmission mechanisms of intrinsic transmissibility and immune escape. We introduce a framework connecting variant dynamics to these mechanisms, showing how host population immunity interacts with viral transmissibility and immune escape to determine relative variant fitness. We advance a selective pressure metric that provides an early signal of epidemic growth using genetic data alone, crucial with current underreporting of cases. Additionally, we show that a latent immunity space model approximates immunological distances, offering insights into population susceptibility and immune evasion. These insights refine real-time forecasting and lay the groundwork for research into the interplay between viral genetics, immunity, and epidemic growth.

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences. In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. We use SARS-CoV-2 spike protein as the target and use a protein language model to predict novel mutations. Different from existing research which is often limited to mutating the reference sequence for prediction, we propose a parent-child mutation prediction paradigm where a parent sequence is modeled for mutation prediction. Because mutations introduce changing context to the underlying sequence, DNMS models three aspects of the protein sequences: semantic changes, grammatical changes, and attention changes, each modeling protein sequence aspects from shifting of semantics, grammar coherence, and amino-acid interactions in latent space. A ranking approach is proposed to combine all three aspects to capture mutations demonstrating evolving traits, in accordance with real-world SARS-CoV-2 spike protein sequence evolution. DNMS can be adopted for an early warning variant detection system, creating public health awareness of future SARS-CoV-2 mutations.

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

Viral nucleic acid load in the milk of lactating mothers with COVID-19 and the prognosis of infants

The global spread of the novel coronavirus disease 2019, caused by SARS-CoV-2 virus, impacts individuals of all age groups, including lactating women and children. Concerns have been raised regarding the potential transmission of SARS-CoV-2 from mother to child, following the discovery of SARS-CoV-2 RNA in human milk. Therefore, this study aims to investigate whether the Omicron novel coronavirus variants are transmitted through human milk. This study was conducted between March and May 2022 at Children's Medical Center, the First Hospital of Jilin University, Lequn Branch. Fourteen lactating mothers and their breastfed children hospitalized with COVID-19 (Omicron variant) formed mother-child pairs, which constituted the test group. Additional 11 non-breastfed children of the same age hospitalized with COVID-19 (Omicron variant) participated in the study as the control group. Their clinical manifestations were observed, and the milk of lactating mothers with COVID-19 was collected for SARS-CoV-2 RNA detection. Milk samples from each lactating mother were collected consecutively for 2-18 days and subjected to polymerase chain reaction (PCR) testing forSARS-CoV-2 RNA detection. The time span for sample collection ranged from admission to discharged. The symptoms observed in mothers and children infected with the Omicron variant of COVID-19 were primary upper respiratory tract infection, with fever and cough being the main clinical manifestations. In total, 104 breast milk samples were collected from 14 lactating mothers with COVID-19, and all samples were negative for SARS-CoV-2 RNA. This study found no evidence of Omicron variants transmission through breast milk and accepts the safety of breastfeeding for novel coronavirus-positive mothers when contact precautions are taken. Our findings provide additional support for recommendations that lactating women with COVID-19 continue to breastfeed while taking precautions.

Learning the fitness dynamics of pathogens from phylogenies

The dynamics of the genetic diversity of pathogens, including the emergence of lineages with increased fitness, is a foundational concept of disease ecology with key public-health implications. However, the identification of such lineages and estimation of associated fitness remain challenging, and is rarely done outside densely sampled systems1,2. Here we present phylowave, a scalable approach that summarizes changes in population composition in phylogenetic trees, enabling the automatic detection of lineages based on shared fitness and evolutionary relationships. We use our approach on a broad set of viruses and bacteria (SARS-CoV-2, influenza A subtype H3N2, Bordetella pertussis and Mycobacterium tuberculosis), which include both well-studied and understudied threats to human health. We show that phylowave recovers the main known circulating lineages for each pathogen and that it can detect specific amino acid changes linked to fitness changes. Furthermore, phylowave identifies previously undetected lineages with increased fitness, including three co-circulating B. pertussis lineages. Inference using phylowave is robust to uneven and limited observations. This widely applicable approach provides an avenue to monitor evolution in real time to support public-health action and explore fundamental drivers of pathogen fitness.

Unveiling the ghost: machine learning's impact on the landscape of virology

The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more. This review explores how machine learning has been applied to and has impacted the study of viruses, before addressing the strengths and limitations of its techniques and finally highlighting the next steps that are needed for the technology to reach its full potential in this challenging and ever-relevant research area.

Mice carrying the full-length human immunoglobulin loci produce antigen-specific human antibodies with the lambda light chain

The development of antibody drugs through animal immunization typically requires the humanization of host antibodies to address concerns about immunogenicity in humans. However, employing an animal model capable of producing human antibodies presents the opportunity to develop antibody drugs without the need for humanization. Despite the ratio of human immunoglobulin (Ig) κ to Igλ usage being approximately 60%:40%, the majority of approved antibody therapeutics are kappa antibodies, and the development of lambda antibodies as therapeutic agents has lagged behind. Therefore, in this study, we developed mice carrying the IGH and IGL loci (IGHL), which can produce human lambda antibodies, using mouse artificial chromosome (MAC) vectors. We demonstrated that IGHL mice consistently retain the human lambda antibody locus integrated on the MAC across generations and can be induced to produce specific antibodies upon antigen stimulation. These findings provide a promising platform for advancing lambda antibody drugs, which have historically been neglected.

A core network in the SARS-CoV-2 nucleocapsid NTD mediates structural integrity and selective RNA-binding

The SARS-CoV-2 nucleocapsid protein is indispensable for viral RNA genome processing. Although the N-terminal domain (NTD) is suggested to mediate specific RNA-interactions, high-resolution structures with viral RNA are still lacking. Available hybrid structures of the NTD with ssRNA and dsRNA provide valuable insights; however, the precise mechanism of complex formation remains elusive. Similarly, the molecular impact of nucleocapsid NTD mutations that have emerged since 2019 has not yet been fully explored. Using crystallography and solution NMR, we investigate how NTD mutations influence structural integrity and RNA-binding. We find that both features rely on a core network of residues conserved in Betacoronaviruses, crucial for protein stability and communication among flexible loop-regions that facilitate RNA-recognition. Our comprehensive structural analysis demonstrates that contacts within this network guide selective RNA-interactions. We propose that the core network renders the NTD evolutionarily robust in stability and plasticity for its versatile RNA processing roles.

Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention and control. However, detecting high-risk variants through experimental and epidemiological research is time-consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk-Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high-risk SARS-CoV-2 variants. Leveraging over 7.6 million high-quality and complete SARS-CoV-2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk-Detector are validated. First, HiRisk-Detector is evaluated on actual empirical data, successfully detecting all 13 high-risk variants, preceding World Health Organization announcements by 27 days on average. Second, its robustness is tested by reducing sequencing intensity to one-fourth, noting only a minimal delay of 3.8 days, demonstrating its effectiveness. Third, HiRisk-Detector is applied to detect risks among SARS-CoV-2 Omicron variant sub-lineages, confirming its broad applicability and high ROC-AUC and PR-AUC performance. Overall, HiRisk-Detector features powerful capacity for early detection of high-risk variants, bearing great utility for any public emergency caused by infectious diseases or viruses.

Omicron-specific ultra-potent SARS-CoV-2 neutralizing antibodies targeting the N1/N2 loop of Spike N-terminal domain

A multitude of functional mutations continue to emerge on the N-terminal domain (NTD) of the spike protein in SARS-CoV-2 Omicron subvariants. Understanding the immunogenicity of Omicron NTD and the properties of antibodies elicited by it is crucial for comprehending the impact of NTD mutations on viral fitness and guiding vaccine design. In this study, we find that most of NTD-targeting antibodies isolated from individuals with BA.5/BF.7 breakthrough infection (BTI) are ancestral (wild-type or WT)-reactive and non-neutralizing. Surprisingly, we identified five ultra-potent neutralizing antibodies (NAbs) that can only bind to Omicron but not WT NTD. Structural analysis revealed that they bind to a unique epitope on the N1/N2 loop of NTD and interact with the receptor-binding domain (RBD) via the light chain. These Omicron-specific NAbs achieve neutralization through ACE2 competition and blockage of ACE2-mediated S1 shedding. However, BA.2.86 and BA.2.87.1, which carry insertions or deletions on the N1/N2 loop, can evade these antibodies. Together, we provided a detailed map of the NTD-targeting antibody repertoire in the post-Omicron era, demonstrating their vulnerability to NTD mutations enabled by its evolutionary flexibility, despite their potent neutralization. These results revealed the function of the indels in the NTD of BA.2.86/JN.1 sublineage in evading neutralizing antibodies and highlighted the importance of considering the immunogenicity of NTD in vaccine design.

Identification and biophysical characterization of epitope atlas of Porcine Reproductive and Respiratory Syndrome Virus

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) have been a critical threat to swine health since 1987 due to its high mutation rate and substantial economic loss over half a billion dollar in USA. The rapid mutation rate of PRRSV presents a significant challenge in developing an effective vaccine. Even though surveillance and intervention studies have recently (2019) unveiled utilization of PRRSV glycoprotein 5 (GP5; encoded by ORF5 gene) to induce immunogenic reaction and production of neutralizing antibodies in porcine populations, the future viral generations can accrue escape mutations. In this study we identify 63 porcine-PRRSV protein-protein interactions which play primary or ancillary roles in viral entry and infection. Using genome-proteome annotation, protein structure prediction, multiple docking experiments, and binding energy calculations, we identified a list of 75 epitope locations on PRRSV proteins crucial for infection. Additionally, using machine learning-based diffusion model, we designed 56 stable immunogen peptides that contain one or more of these epitopes with their native tertiary structures stabilized through optimized N- and C-terminus flank sequences and interspersed with appropriate linker regions. Our workflow successfully identified numerous known interactions and predicted several novel PRRSV-porcine interactions. By leveraging the structural and sequence insights, this study paves the way for more effective, high-avidity, multi-valent PRRSV vaccines, and leveraging neural networks for immunogen design.

Comparative Evolutionary Epidemiology of SARS-CoV-2 Delta and Omicron Variants in Kuwait

Continuous surveillance is critical for early intervention against emerging novel SARS-CoV-2 variants. Therefore, we investigated and compared the variant-specific evolutionary epidemiology of all the Delta and Omicron sequences collected between 2021 and 2023 in Kuwait. We used Bayesian phylodynamic models to reconstruct, trace, and compare the two variants' demographics, phylogeographic, and host characteristics in shaping their evolutionary epidemiology. The Omicron had a higher evolutionary rate than the Delta. Both variants underwent periods of sequential growth and decline in their effective population sizes, likely linked to intervention measures and environmental and host characteristics. We found that the Delta strains were frequently introduced into Kuwait from East Asian countries between late 2020 and early 2021, while those of the Omicron strains were most likely from Africa and North America between late 2021 and early 2022. For both variants, our analyses revealed significant transmission routes from patients aged between 20 and 50 years on one side and other age groups, refuting the notion that children are superspreaders for the disease. In contrast, we found that sex has no significant role in the evolutionary history of both variants. We uncovered deeper variant-specific epidemiological insights using phylodynamic models and highlighted the need to integrate such models into current and future genomic surveillance programs.

Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization

Introduction

The evolution of SARS-CoV-2 has precipitated the emergence of new mutant strains, some exhibiting enhanced transmissibility and immune evasion capabilities, thus escalating the infection risk and diminishing vaccine efficacy. Given the continuous impact of SARS-CoV-2 mutations on global public health, the economy, and society, a profound comprehension of potential variations is crucial to effectively mitigate the impact of viral evolution. Yet, this task still faces considerable challenges.

Methods

This study introduces DARSEP, a method based on Deep learning Associates with Reinforcement learning for SARS-CoV-2 Evolution Prediction, combined with self-game sequence optimization and RetNet-based model.

Results

DARSEP accurately predicts evolutionary sequences and investigates the virus's evolutionary trajectory. It filters spike protein sequences with optimal fitness values from an extensive mutation space, selectively identifies those with a higher likelihood of evading immune detection, and devises a superior evolutionary analysis model for SARS-CoV-2 spike protein sequences. Comprehensive downstream task evaluations corroborate the model's efficacy in predicting potential mutation sites, elucidating SARS-CoV-2's evolutionary direction, and analyzing the development trends of Omicron variant strains through semantic changes.

Conclusion

Overall, DARSEP enriches our understanding of the dynamic evolution of SARS-CoV-2 and provides robust support for addressing present and future epidemic challenges.

Community-level variability in Bronx COVID-19 hospitalizations associated with differing population immunity during the second year of the pandemic

The Bronx, New York, exhibited unique peaks in the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations compared to national trends. To determine which features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus might underpin this local disease epidemiology, we conducted a comprehensive analysis of the genomic epidemiology of the four dominant strains of SARS-CoV-2 (Alpha, Iota, Delta, and Omicron) responsible for COVID-19 cases in the Bronx between March 2020 and January 2023. Genomic analysis revealed similar viral fitness for Alpha and Iota variants in the Bronx despite nationwide data showing higher cases of Alpha. However, Delta and Omicron variants had increased fitness within the borough. While the transmission dynamics of most variants in the Bronx corresponded with mutational fitness-based predictions of transmissibility, the Delta variant presented as an exception. Epidemiological modeling confirms Delta's advantages of higher transmissibility in Manhattan and Queens, but not the Bronx; wastewater analysis suggests underdetection of cases in the Bronx. The Alpha variant had slightly faster growth but a lower carrying capacity compared to Iota and Delta in all four boroughs, suggesting stronger limitations on Alpha's growth in New York City (NYC). The founder effect of Iota varied between higher vaccinated and lower vaccinated boroughs with longer delay, shorter duration, and lower fitness of the Alpha variant in lower vaccinated boroughs. Amino acid changes in T-cell and antibody epitopes revealed Delta and Iota having larger antigenic variability and antigenic profiles distant from local previously circulating lineages compared to Alpha. In concert with transmission modeling, our data suggest that the limited spread of Alpha may be due to a lack of adaptation to immunity in NYC. Overall, our study demonstrates that localized analyses and integration of orthogonal community-level datasets can provide key insights into the mechanisms of transmission and immunity patterns associated with regional COVID-19 incidence and disease severity that may be missed when analyzing broader datasets.

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R2 of 0.604 [0.593-0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156-5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

Molecular and structural insights into SARS-CoV-2 evolution: from BA.2 to XBB subvariants

Due to the incessant emergence of various SARS-CoV-2 variants with enhanced fitness in the human population, controlling the COVID-19 pandemic has been challenging. Understanding how the virus enhances its fitness during a pandemic could offer valuable insights for more effective control of viral epidemics. In this manuscript, we review the evolution of SARS-CoV-2 from early 2022 to the end of 2023-from Omicron BA.2 to XBB descendants. Focusing on viral evolution during this period, we provide concrete examples that SARS-CoV-2 has increased its fitness by enhancing several functions of the spike (S) protein, including its binding affinity to the ACE2 receptor and its ability to evade humoral immunity. Furthermore, we explore how specific mutations modify these functions of the S protein through structural alterations. This review provides evolutionary, molecular, and structural insights into how SARS-CoV-2 has increased its fitness and repeatedly caused epidemic surges during the pandemic.

A prediction of mutations in infectious viruses using artificial intelligence

Many subtypes of SARS-CoV-2 have emerged since its early stages, with mutations showing regional and racial differences. These mutations significantly affected the infectivity and severity of the virus. This study aimed to predict the mutations that occur during the evolution of SARS-CoV-2 and identify the key characteristics for making these predictions. We collected and organized data on the lineage, date, clade, and mutations of SARS-CoV-2 from publicly available databases and processed them to predict the mutations. In addition, we utilized various artificial intelligence models to predict newly emerging mutations and created various training sets based on clade information. Using only mutation information resulted in low performance of the learning models, whereas incorporating clade differentiation resulted in high performance in machine learning models, including XGBoost (accuracy: 0.999). However, mutations fixed in the receptor-binding motif (RBM) region of Omicron resulted in decreased predictive performance. Using these models, we predicted potential mutation positions for 24C, following the recently emerged 24A and 24B clades. We identified a mutation at position Q493 in the RBM region. Our study developed effective artificial intelligence models and characteristics for predicting new mutations in continuously evolving infectious viruses.

SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations

Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the potential prevalence of new variants in vaccinated populations. The hybrid vaccination combining the wild-type (WT) inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non-Omicron variants and Omicron variants, albeit with EG.5.1 still posing a risk. In conclusion, these findings enhance understanding of population immunity variations and provide valuable insights for future vaccine development and public health strategies.

SARS-CoV-2 Nsp15 antagonizes the cGAS-STING-mediated antiviral innate immune responses

Coronavirus (CoV) Nsp15 is a viral endoribonuclease (EndoU) with a preference for uridine residues. CoV Nsp15 is an innate immune antagonist which prevents dsRNA sensor recognition and stress granule formation by targeting viral and host RNAs. SARS-CoV-2 restricts and delays the host antiviral innate immune responses through multiple viral proteins, but the role of SARS-CoV-2 Nsp15 in innate immune evasion is not completely understood. Here, we generate an EndoU activity knockout rSARS-CoV-2Nsp15-H234A to elucidate the biological functions of Nsp15. Relative to wild-type rSARS-CoV-2, replication of rSARS-CoV-2Nsp15-H234A was significantly decreased in IFN-responsive A549-ACE2 cells but not in its STAT1 knockout counterpart. Transcriptomic analysis revealed upregulation of innate immune response genes in cells infected with rSARS-CoV-2Nsp15-H234A relative to wild-type virus, including cGAS-STING, cytosolic DNA sensors activated by both DNA and RNA viruses. Treatment with STING inhibitors H-151 and SN-011 rescued the attenuated phenotype of rSARS-CoV-2Nsp15-H234A. SARS-CoV-2 Nsp15 inhibited cGAS-STING-mediated IFN-β promoter and NF-κB reporter activity, as well as facilitated the replication of EV-D68 and NDV by diminishing cGAS and STING expression and downstream innate immune responses. Notably, the decline in cGAS and STING was also apparent during SARS-CoV-2 infection. The EndoU activity was essential for SARS-CoV-2 Nsp15-mediated cGAS and STING downregulation, but not all HCoV Nsp15 share the consistent substrate selectivity. In the hamster model, rSARS-CoV-2Nsp15-H234A replicated to lower titers in the nasal turbinates and lungs and induced higher innate immune responses. Collectively, our findings exhibit that SARS-CoV-2 Nsp15 serves as a host innate immune antagonist by targeting host cGAS and STING.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant Rt. These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ∼0.6% median absolute error and ∼6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Molecular characterization, phylogenetic and variation analyses of SARS-CoV-2 strains in India

In the wake of the havoc caused by the COVID-19 pandemic, it is imperative to use the available genomic sequence data to gain insight into the mutational and genomic diversity of SARS-CoV-2. Here we have performed comparative phylogenetic, mutational and genetic diversity analysis on 1962 SARS-CoV-2 genome sequences from seven worst hit Indian states during the third Covid-19 wave, to determine the SARS-CoV-2 strains and mutations in circulation during the third wave and the transmission pattern and disease epidemiology across the states and gain valuable insight into the viral evolution. 6083 Single nucleotide polymorphisms (SNPs) were discovered in the analysis with 93 SNPs common to all states. The genetic relatedness among the statewise multilocus genotypes was visualized by plotting a minimum spanning tree based on Bruvo's distance framework. The phylogenetic tree based on Nei's genetic distance showed distinct clades. The AMOVA results indicated that large proportion of the total genetic variation is distributed within the samples, rather than between the samples within each population and between the populations. Our findings provide insight into the SARS-CoV-2 variants and mutations which dominated the third COVID-19 wave in India and thus provide a basis to monitor and further assess these variants and their sub lineages and mutations for their clinical impact and reaction to existing and newly designed drugs and vaccines. The genetic diversity analysis helps in comprehending the viral transmission scenarios across the Indian states so as to enable the State government and researchers in developing state specific prevention measures for future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00878-7.

Intrahost evolution leading to distinct lineages in the upper and lower respiratory tracts during SARS-CoV-2 prolonged infection

Accumulating evidence points to persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in immunocompromised individuals as a source of novel lineages. While intrahost evolution of the virus in chronically infected patients has previously been reported, existing knowledge is primarily based on samples from the nasopharynx. In this study, we investigate the intrahost evolution and genetic diversity that accumulated during a prolonged SARS-CoV-2 infection with the Omicron BF.7 sublineage, which is estimated to have persisted for >1 year in an immunosuppressed patient. Based on the sequencing of eight samples collected at six time points, we identified 87 intrahost single-nucleotide variants, 2 indels, and a 362-bp deletion. Our analysis revealed distinct viral genotypes in the nasopharyngeal (NP), endotracheal aspirate, and bronchoalveolar lavage samples. This suggests that NP samples may not offer a comprehensive representation of the overall intrahost viral diversity. Our findings not only demonstrate that the Omicron BF.7 sublineage can further diverge from its already exceptionally mutated state but also highlight that patients chronically infected with SARS-CoV-2 can develop genetically specific viral populations across distinct anatomic compartments. This provides novel insights into the intricate nature of viral diversity and evolution dynamics in persistent infections.

Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections

BACKGROUND

The emergence of the SARS-CoV-2 virus has highlighted the importance of genomic epidemiology in understanding the evolution of pathogens and guiding public health interventions. The Omicron variant in particular has underscored the role of epistasis in the evolution of lineages with both higher infectivity and immune escape, and therefore the necessity to update surveillance pipelines to detect them early on.

RESULTS

In this study, we apply a method based on mutual information between positions in a multiple sequence alignment, which is capable of scaling up to millions of samples. We show how it can reliably predict known experimentally validated epistatic interactions, even when using as little as 10,000 sequences, which opens the possibility of making it a near real-time prediction system. We test this possibility by modifying the method to account for the sample collection date and apply it retrospectively to multiple sequence alignments for each month between March 2020 and March 2023. We detected a cornerstone epistatic interaction in the Spike protein between codons 498 and 501 as soon as seven samples with a double mutation were present in the dataset, thus demonstrating the method's sensitivity. We test the ability of the method to make inferences about emerging interactions by testing candidates predicted after March 2023, which we validate experimentally.

CONCLUSIONS

We show how known epistatic interaction in SARS-CoV-2 can be detected with high sensitivity, and how emerging ones can be quickly prioritized for experimental validation, an approach that could be implemented downstream of pandemic genome sequencing efforts.

Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness

Introduction

SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes.

Materials and methods

Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations.

Results

Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure.

Discussion

Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.

The coronavirus nsp15 endoribonuclease: A puzzling protein and pertinent antiviral drug target

The SARS-CoV-2 pandemic has bolstered unprecedented research efforts to better understand the pathogenesis of coronavirus (CoV) infections and develop effective therapeutics. We here focus on non-structural protein nsp15, a hexameric component of the viral replication-transcription complex (RTC). Nsp15 possesses uridine-specific endoribonuclease (EndoU) activity for which some specific cleavage sites were recently identified in viral RNA. By preventing accumulation of viral dsRNA, EndoU helps the virus to evade RNA sensors of the innate immune response. The immune-evading property of nsp15 was firmly established in several CoV animal models and makes it a pertinent target for antiviral therapy. The search for nsp15 inhibitors typically proceeds via compound screenings and is aided by the rapidly evolving insight in the protein structure of nsp15. In this overview, we broadly cover this fascinating protein, starting with its structure, biochemical properties and functions in CoV immune evasion. Next, we summarize the reported studies in which compound screening or a more rational method was used to identify suitable leads for nsp15 inhibitor development. In this way, we hope to raise awareness on the relevance and druggability of this unique CoV protein.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2

Background

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission.

Methods

We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics.

Results

We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein.

Conclusions

This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Dynamic analysis of SARS-CoV-2 evolution based on different countries

Since late 2019, COVID-19 has significantly impacted the world. Understanding the evolution of SARS-CoV-2 is crucial for protecting against future infectious pathogens. In this study, we conducted a comprehensive chronological analysis of SARS-CoV-2 evolution by examining mutation prevalence from the source countries of VOCs: United Kingdom, India, Brazil, South Africa, plus two countries: United States, Russia, utilizing genomic sequences from GISAID. Our methodological approach involved large-scale genomic sequence alignment using MAFFT, Python-based data processing on a high-performance computing platform, and advanced statistical methods the Maximal Information Coefficient (MIC), and also Long Short-Term Memory (LSTM) models for correlation analysis. Our findings elucidate the dynamics of SARS-CoV-2 evolution, highlighting the virus's changing behaviour over various pandemic stages. Key results include the discovery of three temporal mutation patterns-lineage distinct, long-span, and competitive mutations-with varying levels of impact on the virus. Notably, we observed a convergence of advantageous mutations in the spike protein, especially in the later stages of the pandemic, indicating a substantial evolutionary pressure on the virus. One of the most significant revelations is the predominant role of natural immunity over vaccination-induced immunity in driving these evolutionary changes. This emphasizes the critical need for regular vaccine updates to maintain efficacy against evolving strains. In conclusion, our study not only sheds light on the evolutionary trajectory of SARS-CoV-2 but also underscores the urgency for robust, continuous global data collection and sharing. It highlights the necessity for rapid adaptations in medical countermeasures, including vaccine development, to stay ahead of pathogen evolution. This research provides valuable insights for future pandemic preparedness and response strategies.

A Repurposed Drug Interferes with Nucleic Acid to Inhibit the Dual Activities of Coronavirus Nsp13

The recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlighted a critical need to discover more effective antivirals. While therapeutics for SARS-CoV-2 exist, its nonstructural protein 13 (Nsp13) remains a clinically untapped target. Nsp13 is a helicase responsible for unwinding double-stranded RNA during viral replication and is essential for propagation. Like other helicases, Nsp13 has two active sites: a nucleotide binding site that hydrolyzes nucleoside triphosphates (NTPs) and a nucleic acid binding channel that unwinds double-stranded RNA or DNA. Targeting viral helicases with small molecules, as well as the identification of ligand binding pockets, have been ongoing challenges, partly due to the flexible nature of these proteins. Here, we use a virtual screen to identify ligands of Nsp13 from a collection of clinically used drugs. We find that a known ion channel inhibitor, IOWH-032, inhibits the dual ATPase and helicase activities of SARS-CoV-2 Nsp13 at low micromolar concentrations. Kinetic and binding assays, along with computational and mutational analyses, indicate that IOWH-032 interacts with the RNA binding interface, leading to displacement of nucleic acid substrate, but not bound ATP. Evaluation of IOWH-032 with microbial helicases from other superfamilies reveals that it is selective for coronavirus Nsp13. Furthermore, it remains active against mutants representative of observed SARS-CoV-2 variants. Overall, this work provides a new inhibitor for Nsp13 and provides a rationale for a recent observation that IOWH-032 lowers SARS-CoV-2 viral loads in human cells, setting the stage for the discovery of other potent viral helicase modulators.

Early mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants in Chile

Genomic surveillance (GS) programmes were crucial in identifying and quantifying the mutating patterns of SARS-CoV-2 during the COVID-19 pandemic. In this work, we develop a Bayesian framework to quantify the relative transmissibility of different variants tailored for regions with limited GS. We use it to study the relative transmissibility of SARS-CoV-2 variants in Chile. Among the 3443 SARS-CoV-2 genomes collected between January and June 2021, where sampling was designed to be representative, the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. We found that Lambda and Gamma variants' reproduction numbers were 5% (95% CI: [1%, 14%]) and 16% (95% CI: [11%, 21%]) larger than Alpha's, respectively. Besides, we observed a systematic mutation enrichment in the Spike gene for all circulating variants, which strongly correlated with variants' transmissibility during the studied period (r = 0.93, p-value = 0.025). We also characterised the mutational signatures of local samples and their evolution over time and with the progress of vaccination, comparing them with those of samples collected in other regions worldwide. Altogether, our work provides a reliable method for quantifying variant transmissibility under subsampling and emphasises the importance of continuous genomic surveillance.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example

BACKGROUND

At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported.

RESULTS

In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall.

CONCLUSIONS

In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.

Chronic active Epstein-Barr virus infection with reinfection of SARS-CoV-2: a case report

We describe the case of a 57-year-old male with jaundice, abdominal distension and fatigue. He was diagnosed as chronic active Epstein-Barr virus infection (CAEBV) due to intermittent elevated liver enzymes, hepatosplenomegaly and pancytopenia, with persistent positive of EBV biomarkers in blood and also positive in liver tissue. The patient was reinfected by SARS-CoV-2 within 2 months companied with CAEBV. The patient's second infection with SARS-CoV-2 led to the aggravated liver dysfunction with pneumonia and re-admission. After receiving symptomatic treatment, the patient showed significantly improvement of symptoms with partially restoration of liver function. After discharge, the patient's health status continued to deteriorate and eventually died. The instances of SARS-CoV-2 co-infection with the original chronic virus are not uncommon, but the exact mechanism of EBV and SARS-CoV-2 coinfection and the relationship between them are still unclear. Since co-infection of SARS-CoV-2 with original chronic virus might affect each other and lead disease aggravated and complicated, it is necessary to differentiate in the diagnosis of disease and it is important to be aware of the re-infection signs of SARS-CoV-2 in people with chronic virus infection diseases, as well as the risk of co-infection of SARS-CoV-2 with other viruses.

Progressive Evolutionary Dynamics of Gene-Specific ω Led to the Emergence of Novel SARS-CoV-2 Strains Having Super-Infectivity and Virulence with Vaccine Neutralization

An estimation of the proportion of nonsynonymous to synonymous mutation (dn/ds, ω) of the SARS-CoV-2 genome would indicate the evolutionary dynamics necessary to evolve into novel strains with increased infection, virulence, and vaccine neutralization. A temporal estimation of ω of the whole genome, and all twenty-nine SARS-CoV-2 genes of major virulent strains of alpha, delta and omicron demonstrates that the SARS-CoV-2 genome originally emerged (ω ~ 0.04) with a strong purifying selection (ω < 1) and reached (ω ~ 0.85) in omicron towards diversifying selection (ω > 1). A marked increase in the ω occurred in the spike gene from alpha (ω = 0.2) to omicron (ω = 1.97). The ω of the replication machinery genes including RDRP, NSP3, NSP4, NSP7, NSP8, NSP10, NSP13, NSP14, and ORF9 are markedly increased, indicating that these genes/proteins are yet to be evolutionary stabilized and are contributing to the evolution of novel virulent strains. The delta-specific maximum increase in ω in the immunomodulatory genes of NSP8, NSP10, NSP16, ORF4, ORF5, ORF6, ORF7A, and ORF8 compared to alpha or omicron indicates delta-specific vulnerabilities for severe COVID-19 related hospitalization and death. The maximum values of ω are observed for spike (S), NSP4, ORF8 and NSP15, which indicates that the gene-specific temporal estimation of ω identifies specific genes for its super-infectivity and virulency that could be targeted for drug development.

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

Differences and similarities between innate immune evasion strategies of human coronaviruses

So far, seven coronaviruses have emerged in humans. Four recurring endemic coronaviruses cause mild respiratory symptoms. Infections with epidemic Middle East respiratory syndrome-related coronavirus or severe acute respiratory syndrome coronavirus (SARS-CoV)-1 are associated with high mortality rates. SARS-CoV-2 is the causative agent of the coronavirus disease 2019 pandemic. To establish an infection, coronaviruses evade restriction by human innate immune defenses, such as the interferon system, autophagy and the inflammasome. Here, we review similar and distinct innate immune manipulation strategies employed by the seven human coronaviruses. We further discuss the impact on pathogenesis, zoonotic emergence and adaptation. Understanding the nature of the interplay between endemic/epidemic/pandemic coronaviruses and host defenses may help to better assess the pandemic potential of emerging coronaviruses.

Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

A structure-function analysis shows SARS-CoV-2 BA.2.86 balances antibody escape and ACE2 affinity

BA.2.86, a recently described sublineage of SARS-CoV-2 Omicron, contains many mutations in the spike gene. It appears to have originated from BA.2 and is distinct from the XBB variants responsible for many infections in 2023. The global spread and plethora of mutations in BA.2.86 has caused concern that it may possess greater immune-evasive potential, leading to a new wave of infection. Here, we examine the ability of BA.2.86 to evade the antibody response to infection using a panel of vaccinated or naturally infected sera and find that it shows marginally less immune evasion than XBB.1.5. We locate BA.2.86 in the antigenic landscape of recent variants and look at its ability to escape panels of potent monoclonal antibodies generated against contemporary SARS-CoV-2 infections. We demonstrate, and provide a structural explanation for, increased affinity of BA.2.86 to ACE2, which may increase transmissibility.