Variation in synonymous evolutionary rates in the SARS-CoV-2 genome

Sun, Qianru; Zeng, Jinfeng; Tang, Kang; Long, Haoyu; Zhang, Chi; Zhang, Jie; Tang, Jing; Xin, Yuting; Zheng, Jialu; Sun, Litao; Liu, Siyang; Du, Xiangjun

doi:10.3389/fmicb.2023.1136386

Cited by 5 publications

(1 citation statement)

References 77 publications

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“…In the past, synonymous mutations were viewed as phenotypically silent but recent studies indicate they can affect viral fitness with potential functional associations [19,20]. These types of mutations may alter RNA secondary structure leading to a higher probability of base pairing [21]. Moreover, there is a different frequency and regularity of mutations in various SARS-CoV-2 genes, where some genes have more non-synonymous mutations than others [22].…”

Section: Discussionmentioning

confidence: 99%

Emerging Mutations of SARS-COV-2 in Kenya

Mbogori,

Thiongo,

Deng

et al. 2024

Preprint

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Background: The rise of new SARS-CoV-2 mutations brought challenges and progress in the global fight against COVID-19. Mutations in spike and accessory genes affect transmission, vaccine efficacy, treatments, testing, and public health strategies. Monitoring emerging variants is crucial to halt virus spread. Methods: 44 nasopharyngeal/oropharyngeal swabs from Kenyan patients were sequenced with the Illumina platform. Galaxy's bioinformatic tools were used for genomic analysis. SARS-CoV-2 genome classification was done using PANGOLIN and mutation annotation with the COVID-19 Annotator tool. Results: The study showed 5 clades to be circulating in the region. 38(86%) were BA.1.1; 2(5%) were BA.1.1.1; 1(2%) was BA.1; 1(2%) was BA.1.14 and 2(5%) were AY.46. These clades had a cumulative of 173 mutations among them with 50 novel mutations. Forty-eight of these novel mutations occurred in a low frequency of 2.3% of the sequences tested while the other two, S:R214R, and NSP2:A555A, were for 43.2% and 18.2% of the cases respectively. Conclusions: The high-frequency novel mutations were synonymous mutations, a phenomenon that was previously viewed as phenotypically silent but recent studies indicate they can affect viral fitness with potential functional associations. These findings add to the understanding of the SARS-CoV-2 virus future evolutionary and immunological dynamics in the region

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Section: Discussionmentioning

confidence: 99%

Emerging Mutations of SARS-COV-2 in Kenya

Mbogori,

Thiongo,

Deng

et al. 2024

Preprint

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SARS-CoV-2 continuously optimizes its codon usage to adapt to human lung environment

Wang,

Li,

Wang

et al. 2023

J Appl Genetics

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Sequence signatures within the genome of SARS-CoV-2 can be used to predict host source

Rudar,

Kruczkiewicz,

Vernygora

et al. 2024

Microbiol Spectr

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We conducted an in silico analysis to better understand the potential factors impacting host adaptation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer, humans, and mink due to the strong evidence of sustained transmission within these hosts. Classification models trained on single nucleotide and amino acid differences between samples effectively identified white-tailed deer-, human-, and mink-derived SARS-CoV-2. For example, the balanced accuracy score of Extremely Randomized Trees classifiers was 0.984 ± 0.006. Eighty-eight commonly identified predictive mutations are found at sites under strong positive and negative selective pressure. A large fraction of sites under selection (86.9%) or identified by machine learning (87.1%) are found in genes other than the spike. Some locations encoded by these gene regions are predicted to be B- and T-cell epitopes or are implicated in modulating the immune response suggesting that host adaptation may involve the evasion of the host immune system, modulation of the class-I major-histocompatibility complex, and the diminished recognition of immune epitopes by CD8+ T cells. Our selection and machine learning analysis also identified that silent mutations, such as C7303T and C9430T, play an important role in discriminating deer-derived samples across multiple clades. Finally, our investigation into the origin of the B.1.641 lineage from white-tailed deer in Canada discovered an additional human sequence from Michigan related to the B.1.641 lineage sampled near the emergence of this lineage. These findings demonstrate that machine-learning approaches can be used in combination with evolutionary genomics to identify factors possibly involved in the cross-species transmission of viruses and the emergence of novel viral lineages. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus capable of infecting and establishing itself in human and wildlife populations, such as white-tailed deer. This fact highlights the importance of developing novel ways to identify genetic factors that contribute to its spread and adaptation to new host species. This is especially important since these populations can serve as reservoirs that potentially facilitate the re-introduction of new variants into human populations. In this study, we apply machine learning and phylogenetic methods to uncover biomarkers of SARS-CoV-2 adaptation in mink and white-tailed deer. We find evidence demonstrating that both non-synonymous and silent mutations can be used to differentiate animal-derived sequences from human-derived ones and each other. This evidence also suggests that host adaptation involves the evasion of the immune system and the suppression of antigen presentation. Finally, the methods developed here are general and can be used to investigate host adaptation in viruses other than SARS-CoV-2.

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Variation in synonymous evolutionary rates in the SARS-CoV-2 genome

Cited by 5 publications

References 77 publications

Emerging Mutations of SARS-COV-2 in Kenya

Emerging Mutations of SARS-COV-2 in Kenya

SARS-CoV-2 continuously optimizes its codon usage to adapt to human lung environment

Sequence signatures within the genome of SARS-CoV-2 can be used to predict host source

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