The roles of APOBEC-mediated RNA editing in SARS-CoV-2 mutations, replication and fitness

Kim, Kyumin; Calabrese, Peter; Wang, Shanshan; Qin, Chao; Rao, Youliang; Feng, Pinghui; Chen, Xiaojiang S.

doi:10.1038/s41598-022-19067-x

Cited by 48 publications

(59 citation statements)

References 64 publications

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“…In 44% and 27% of the true positives, there is an adenine or an uracil at P-1, and in 37% of the cases, there is an uracil at P1. This is consistent with evidence that the cytosines of the UC and AC motifs of the SARS-CoV-2 genome are preferentially deaminated by the APOBEC3A and APOBEC1 enzymes [ 12 ]. The importance given to the SHAPE-Seq reactivity comes after that of the nucleotides ( Figure S5 ).…”

Section: Resultssupporting

confidence: 91%

“…This analysis of the most important variables is compatible with a model that mainly predicts cytosines of the ACU pattern as RM in a region with an RNA structure that makes this cytosine more reactive. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures [ 11 , 12 , 14 ].…”

Section: Resultssupporting

confidence: 85%

“…In mammalian species, apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) enzymes deaminate cytosines into uracils (C > U) in single-stranded DNA (ssDNA) and ssRNA [ 11 ]. Recent experiments have demonstrated that APOBEC3A, APOBEC1, and APOBEC3G can effectively cause C > U mutations in the SARS-CoV-2 genome at specific sites [ 12 ]. Cytosines in UC and AC motifs showed the highest mutation rate, modulated by features of the RNA structure around these motifs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent experiments have demonstrated that APOBEC3A, APOBEC1, and APOBEC3G can effectively cause C > U mutations in the SARS-CoV-2 genome at specific sites [ 12 ]. Cytosines in UC and AC motifs showed the highest mutation rate, modulated by features of the RNA structure around these motifs [ 12 ]. This is consistent with previous results [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the random nature of these mutations, predicting SARS-CoV-2 mutations caused by replication errors can be difficult. However, it is feasible to predict mutations driven by the host, such as those caused by host deaminases [ 12 ]. These mutations are expected to be recurrent, i.e., to appear multiple times independently and be present in several SARS-CoV-2 lineages.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Saldivar-Espinoza

Macip

Garcia-Segura

et al. 2022

IJMS

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Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model’s Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Saldivar-Espinoza

Macip

Garcia-Segura

et al. 2022

IJMS

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Long‐term dynamic shifts in genomic base content and evolutionary trajectories of SARS‐CoV‐2 variants

Li,

Zhang,

Wang

et al. 2023

Journal of Medical Virology

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The rapid spread and remarkable mutations of SARS‐CoV‐2 variants, particularly Omicron, necessitate an understanding of their evolutionary characteristics. In this study, we analyzed representative high‐quality whole‐genome sequences of 2008 SARS‐CoV‐2 variants to explore long‐term dynamic changes in genomic base (especially GC) content and variations during viral evolution. Our results demonstrated a highly negative correlation between GC content and variant emergence time (r = −0.765, p < 2.22e−16). Major gene partitions (S, N, ORF1ab) displayed similar trends. Omicron exhibited a significantly lower GC content than non‐Omicron variants (p < 2.22e−16). Notably, we observed a robust negative correlation between C and T content (r = −0.778, p < 2.22e−16) and between G and A content (r = −0.773, p < 2.22e−16). Among all strains, Omicron showed the greatest base variation, with C‐>T mutations being the most frequent (median [interquartile range [IQR]]: 29 (27, 31), 37.67%), succeeded by G‐>A mutations (11 (9, 13), 14.63%). Over a 3‐year span, an annual decline rate of 0.12% in SARS‐CoV‐2 GC content was observed and could become more pronounced in future emerging variants. These findings provided insights into the evolutionary trajectory of SARS‐CoV‐2, underscoring the significance of continuous genomic surveillance for effective prediction of and response to future variants.

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From viral democratic genomes to viral wild bunch of quasispecies

Colson,

Bader,

Fantini

et al. 2023

Journal of Medical Virology

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The tremendous majority of RNA genomes from pathogenic viruses analyzed and deposited in databases are consensus or “democratic” genomes. They represent the genomes most frequently found in the clinical samples of patients but do not account for the huge genetic diversity of coexisting genomes, which is better described as quasispecies. A viral quasispecies is defined as the dynamic distribution of nonidentical but closely related mutants, variants, recombinant, or reassortant viral genomes. Viral quasispecies have collective behavior and dynamics and are the subject of internal interactions that comprise interference, complementation, or cooperation. In the setting of SARS‐CoV‐2 infection, intrahost SARS‐CoV‐2 genetic diversity was recently notably reported for immunocompromised, chronically infected patients, for patients treated with monoclonal antibodies targeting the viral spike protein, and for different body compartments of a single patient. A question that deserves attention is whether such diversity is generated postinfection from a clonal genome in response to selection pressure or is already present at the time of infection as a quasispecies. In the present review, we summarize the data supporting that hosts are infected by a “wild bunch” of viruses rather than by multiple virions sharing the same genome. Each virion in the “wild bunch” may have different virulence and tissue tropisms. As the number of viruses replicated during host infections is huge, a viral quasispecies at any time of infection is wide and is also influenced by host‐specific selection pressure after infection, which accounts for the difficulty in deciphering and predicting the appearance of more fit variants and the evolution of epidemics of novel RNA viruses.

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The roles of APOBEC-mediated RNA editing in SARS-CoV-2 mutations, replication and fitness

Cited by 48 publications

References 64 publications

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Long‐term dynamic shifts in genomic base content and evolutionary trajectories of SARS‐CoV‐2 variants

From viral democratic genomes to viral wild bunch of quasispecies

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