Evidence for selection on SARS-CoV-2 RNA translation revealed by the evolutionary dynamics of mutations in UTRs and CDSs

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase

Background

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome.

Methods

A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2.

Results

A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency.

Conclusion

Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

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

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.IMPORTANCESevere 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.

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

Viruses need to utilize the resources from host cells to reproduce themselves. RNA translation rate, which is largely determined by codon usage, is the rate-limiting step across the life cycle of viruses. Adapting to the codon usage of hosts would help virus better proliferate. We retrieved the time-course mutation profile of millions of world-wide SARS-CoV-2 sequences. For synonymous mutations, we defined whether a mutation elevate or reduce the relative synonymous codon usage (RSCU). We found that if a synonymous mutation in SARS-CoV-2 increases the RSCU (calculated from human lungs), denoted as delta RSCU > 0, then this mutation is positively selected because the allele frequency (AF) of this mutation increases with time, and vice versa. The results suggest that in SARS-CoV-2, the synonymous mutations that increase codon optimality are beneficial to the virus and are favored by natural selection. For the first time, we used the dynamics of allele frequency to demonstrate that SARS-CoV-2 is continuously optimizing its codon usage to adapt to human lungs. Nevertheless, adaptation to other human tissues cannot be excluded. These results warn us that under this global pandemic, synonymous mutations in SARS-CoV-2 should not be automatically ignored since they indeed change the fitness of the virus.

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

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.

Retrospect of the Two-Year Debate: What Fuels the Evolution of SARS-CoV-2: RNA Editing or Replication Error?

Mutation is one of the mechanisms of the evolutionary divergence of an organism. Under this global COVID-19 pandemic, the fast evolution of SARS-CoV-2 became one of the most worrying issues. Some researchers believed that the hosts' RNA deamination systems (APOBECs and ADARs) are the major source of mutations and have driven the evolution of SARS-CoV-2. However, apart from RNA editing, the RDRP (RNA-dependent RNA polymerase)-mediated replication errors may also contribute to the mutation of SARS-CoV-2 (just like the single-nucleotide polymorphisms/variations in eukaryotes caused by DNA replication errors). Unfortunately, it is technically unable to distinguish RNA editing and replication errors (SNPs) in this RNA virus. Here comes a fundamental question: we indeed observed the fast evolution of SARS-CoV-2, but what exactly fuels its evolution: RNA editing or replication errors? This debate lasts for 2 years. In this piece, we will retrospect the 2-year debate on RNA editing versus SNPs.

The efficacy and safety of SARS-CoV-2 vaccines mRNA1273 and BNT162b2 might be complicated by rampant C-to-U RNA editing

The SARS-CoV-2 RNA vaccines are smartly designed to increase the synonymous codon usage by introducing multiple U-to-C mutations. This design would elevate the translation efficiency of vaccine RNAs. However, we found evidence to reason that the designed cytidines might be converted to uridines again by C-to-U RNA deamination in host cells. This C-to-U mechanism might be a main factor that affects the efficacy and safety of RNA vaccines.

Evidence Supporting That C-to-U RNA Editing Is the Major Force That Drives SARS-CoV-2 Evolution

Mutations of DNA organisms are introduced by replication errors. However, SARS-CoV-2, as an RNA virus, is additionally subjected to rampant RNA editing by hosts. Both resources contributed to SARS-CoV-2 mutation and evolution, but the relative prevalence of the two origins is unknown. We performed comparative genomic analyses at intra-species (world-wide SARS-CoV-2 strains) and inter-species (SARS-CoV-2 and RaTG13 divergence) levels. We made prior predictions of the proportion of each mutation type (nucleotide substitution) under different scenarios and compared the observed versus the expected. C-to-T alteration, representing C-to-U editing, is far more abundant that all other mutation types. Derived allele frequency (DAF) as well as novel mutation rate of C-to-T are the highest in SARS-CoV-2 population, and C-T substitution dominates the divergence sites between SARS-CoV-2 and RaTG13. This is compelling evidence suggesting that C-to-U RNA editing is the major source of SARS-CoV-2 mutation. While replication errors serve as a baseline of novel mutation rate, the C-to-U editing has elevated the mutation rate for orders of magnitudes and accelerates the evolution of the virus.

C-to-U RNA deamination is the driving force accelerating SARS-CoV-2 evolution

Understanding the molecular mechanism underlying the rampant mutation of SARS-CoV-2 would help us control the COVID-19 pandemic. The APOBEC-mediated C-to-U deamination is a major mutation type in the SARS-CoV-2 genome. However, it is unclear whether the novel mutation rate u is higher for C-to-U than for other mutation types, and what the detailed driving force is. By analyzing the time course SARS-CoV-2 global population data, we found that C-to-U has the highest novel mutation rate u among all mutation types and that this u is still increasing with time (du/dt > 0). Novel C-to-U events, rather than other mutation types, have a preference over particular genomic regions. A less local RNA structure is correlated with a high novel C-to-U mutation rate. A cascade model nicely explains the du/dt > 0 for C-to-U deamination. In SARS-CoV-2, the RNA structure serves as the molecular basis of the extremely high and continuously accelerating C-to-U deamination rate. This mechanism is the driving force of the mutation, adaptation, and evolution of SARS-CoV-2. Our findings help us understand the dynamic evolution of the virus mutation rate.

Attenuation and Degeneration of SARS-CoV-2 Despite Adaptive Evolution

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has followed similar trends as other RNA viruses, such as human immunodeficiency virus type 1 and the influenza A virus. Rapid initial diversification was followed by strong competition and a rapid succession of dominant variants. Host-initiated RNA editing has been the primary mechanism for introducing mutations. A significant number of mutations detrimental to viral replication have been quickly purged. Fixed mutations are mostly diversifying mutations selected for host adaptation and immune evasion, with the latter accounting for the majority of the mutations. However, immune evasion often comes at the cost of functionality, and thus, optimal functionality is still far from being accomplished. Instead, selection for antibody-escaping variants and accumulation of near-neutral mutations have led to suboptimal codon usage and reduced replicative capacity, as demonstrated in non-respiratory cell lines. Beneficial adaptation of the virus includes reduced infectivity in lung tissues and increased tropism for the upper airway, resulting in shorter incubation periods, milder diseases, and more efficient transmission between people.

SARS-CoV-2 Variants of Concern and Variations within Their Genome Architecture: Does Nucleotide Distribution and Mutation Rate Alter the Functionality and Evolution of the Virus?

SARS-CoV-2 virus pathogenicity and transmissibility are correlated with the mutations acquired over time, giving rise to variants of concern (VOCs). Mutations can significantly influence the genetic make-up of the virus. Herein, we analyzed the SARS-CoV-2 genomes and sub-genomic nucleotide composition in relation to the mutation rate. Nucleotide percentage distributions of 1397 in-house-sequenced SARS-CoV-2 genomes were enumerated, and comparative analyses (i) within the VOCs and of (ii) recovered and mortality patients were performed. Fisher's test was carried out to highlight the significant mutations, followed by RNA secondary structure prediction and protein modeling for their functional impacts. Subsequently, a uniform dinucleotide composition of AT and GC was found across study cohorts. Notably, the N gene was observed to have a high GC percentage coupled with a relatively higher mutation rate. Functional analysis demonstrated the N gene mutations, C29144T and G29332T, to induce structural changes at the RNA level. Protein secondary structure prediction with N gene missense mutations revealed a differential composition of alpha helices, beta sheets, and coils, whereas the tertiary structure displayed no significant changes. Additionally, the N gene CTD region displayed no mutations. The analysis highlighted the importance of N protein in viral evolution with CTD as a possible target for antiviral drugs.

Hepatitis B virus (HBV) codon adapts well to the gene expression profile of liver cancer: an evolutionary explanation for HBV's oncogenic role

Due to the evolutionary arms race between hosts and viruses, viruses must adapt to host translation systems to rapidly synthesize viral proteins. Highly expressed genes in hosts have a codon bias related to tRNA abundance, the primary RNA translation rate determinant. We calculated the relative synonymous codon usage (RSCU) of three hepatitis viruses (HAV, HBV, and HCV), SARS-CoV-2, 30 human tissues, and hepatocellular carcinoma (HCC). After comparing RSCU between viruses and human tissues, we calculated the codon adaptation index (CAI) of viral and human genes. HBV and HCV showed the highest correlations with HCC and the normal liver, while SARS-CoV-2 had the strongest association with lungs. In addition, based on HCC RSCU, the CAI of HBV and HCV genes was the highest. HBV and HCV preferentially adapt to the tRNA pool in HCC, facilitating viral RNA translation. After an initial trigger, rapid HBV/HCV translation and replication may change normal liver cells into HCC cells. Our findings reveal a novel perspective on virus-mediated oncogenesis.

The Rise and Fall of SARS-CoV-2 Variants and Ongoing Diversification of Omicron

In late December of 2019, high-throughput sequencing technologies enabled rapid identification of SARS-CoV-2 as the etiological agent of COVID-19, and global sequencing efforts are now a critical tool for monitoring the ongoing spread and evolution of this virus. Here, we provide a short retrospective analysis of SARS-CoV-2 variants by analyzing a subset (n = 97,437) of all publicly available SARS-CoV-2 genomes (n = ~11.9 million) that were randomly selected but equally distributed over the course of the pandemic. We plot the appearance of new variants of concern (VOCs) over time and show that the mutation rates in Omicron (BA.1) and Omicron sub-lineages (BA.2–BA.5) are significantly elevated compared to previously identified SARS-CoV-2 variants. Mutations in Omicron are primarily restricted to the spike and nucleocapsid proteins, while 24 other viral proteins—including those involved in SARS-CoV-2 replication—are generally conserved. Collectively, this suggests that the genetic distinction of Omicron primarily arose from selective pressures on the spike, and that the fidelity of replication of this variant has not been altered.

The Sponge Interaction Between Circular RNA and microRNA Serves as a Fast-Evolving Mechanism That Suppresses Non-small Cell Lung Cancer (NSCLC) in Humans

Non-small cell lung cancer (NSCLC) is one of the most lethal cancer types in the world. Currently, the molecular mechanisms and pathways underlying NSCLC oncogenesis are poorly understood. Using multiple Omics data, we systematically explored the differentially expressed circular RNAs (circRNAs) in NSCLC. We also investigated potential microRNA sponges (that absorb circRNAs) in NSCLC and downstream target genes with experimental verifications. hsa_circ_0003497 was down-regulated in NSCLC and played an inhibitory role in tumorigenesis. In contrast, miR-197-3p was up-regulated in NSCLC. hsa_circ_0003497 directly interacts with miR-197-3p and releases a target gene of miR-197-3p termed CTNND1 (a known tumor suppressor gene). Evolutionary analysis reveals fast evolution of this hsa_circ_0003497-miR-197-3p-CTNND1-NSCLC axis in mammals. This work clarified the biological functions and molecular mechanisms of how hsa_circ_0003497 suppresses NSCLC through miR-197-3p and CTNND1. We discovered molecular markers for the prognosis of NSCLC and provided potential intervention targets for its treatment.

uORF-Mediated Translational Regulation of ATF4 Serves as an Evolutionarily Conserved Mechanism Contributing to Non-Small-Cell Lung Cancer (NSCLC) and Stress Response

Diseases and environmental stresses are two distinct challenges for virtually all living organisms. In light of evolution, cellular responses to diseases and stresses might share similar molecular mechanisms, but the detailed regulation pathway is not reported yet.

We obtained the transcriptomes and translatomes from several NSCLC (non-small-cell lung cancer) patients as well as from different species under normal or stress conditions. We found that the translation level of gene ATF4 is remarkably enhanced in NSCLC due to the reduced number of ribosomes binding to its upstream open reading frames (uORFs). We also showed the evolutionary conservation of this uORF-ATF4 regulation in the stress response of other species. Molecular experiments showed that knockdown of ATF4 reduced the cell growth rate while overexpression of ATF4 enhanced cell growth, especially for the ATF4 allele with mutated uORFs. Population genetics analyses in multiple species verified that the mutations that abolish uATGs (start codon of uORFs) are highly deleterious, suggesting the functional importance of uORFs.

Our study proposes an evolutionarily conserved pattern that enhances the ATF4 translation by uORFs upon stress or disease. We generalized the concept of cellular response to diseases and stresses. These two biological processes may share similar molecular mechanisms.

Nothing in SARS-CoV-2 makes sense except in the light of RNA modification?

The expression pattern of RNA deaminases determines the mutation and evolution of SARS-CoV-2.