Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection

Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.

Fitness, growth and transmissibility of SARS-CoV-2 genetic variants

The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.

Identifying featured indels associated with SARS-CoV-2 fitness

As an RNA virus, severe acute respiratory coronavirus 2 (SARS-CoV-2) is known for frequent substitution mutations, and substitutions in important genome regions are often associated with viral fitness. However, whether indel mutations are related to viral fitness is generally ignored. Here we developed a computational methodology to investigate indels linked to fitness occurring in over 9 million SARS-CoV-2 genomes. Remarkably, by analyzing 31,642,404 deletion records and 1,981,308 insertion records, our pipeline identified 26,765 deletion types and 21,054 insertion types and discovered 65 indel types with a significant association with Pango lineages. We proposed the concept of featured indels representing the population of specific Pango lineages and variants as substitution mutations and termed these 65 indels as featured indels. The selective pressure of all indel types is assessed using the Bayesian model to explore the importance of indels. Our results exhibited higher selective pressure of indels like substitution mutations, which are important for assessing viral fitness and consistent with previous studies in vitro. Evaluation of the growth rate of each viral lineage indicated that indels play key roles in SARS-CoV-2 evolution and deserve more attention as substitution mutations. IMPORTANCE The fitness of indels in pathogen genome evolution has rarely been studied. We developed a computational methodology to investigate the severe acute respiratory coronavirus 2 genomes and analyze over 33 million records of indels systematically, ultimately proposing the concept of featured indels that can represent specific Pango lineages and identifying 65 featured indels. Machine learning model based on Bayesian inference and viral lineage growth rate evaluation suggests that these featured indels exhibit selection pressure comparable to replacement mutations. In conclusion, indels are not negligible for evaluating viral fitness.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.