Natural selection plays a significant role in governing the codon usage bias in the novel SARS-CoV-2 variants of concern (VOC)

Abstract

The ongoing prevailing COVID-19 pandemic caused by SARS-CoV-2 is becoming one of the major global health concerns worldwide. The SARS-CoV-2 genome encodes spike (S) glycoprotein that plays a very crucial role in viral entry into the host cell via binding of its receptor binding domain (RBD) to the host angiotensin converting enzyme 2 (ACE2) receptor. The continuously evolving SARS-CoV-2 genome results in more severe and transmissible variants characterized by the emergence of novel mutations called ‘variants of concern’ (VOC). The currently designated alpha, beta, gamma, delta and omicron VOC are the focus of this study due to their high transmissibility, increased virulence, and concerns for decreased effectiveness of the available vaccines. In VOC, the spike (S) gene and other non-structural protein mutations may affect the efficacies of the approved COVID-19 vaccines. To understand the diversity of SARS-CoV-2, several studies have been performed on a limited number of sequences. However, only a few studies have focused on codon usage bias (CUBs) pattern analysis of all the VOC strains. Therefore, to evaluate the evolutionary divergence of all VOC S-genes, we performed CUBs analysis on 300,354 sequences to understand the evolutionary relationship with its adaptation in different hosts, i.e., humans, bats, and pangolins. Base composition and RSCU analysis revealed the presence of 20 preferred AU-ended and 10 under-preferred GC-ended codons. In addition, CpG was found to be depleted, which may be attributable to the adaptive response by viruses to escape from the host defense process. Moreover, the ENC values revealed a higher bias in codon usage in the VOC S-gene. Further, the neutrality plot analysis demonstrated that S-genes analyzed in this study are under 83.93% influence of natural selection, suggesting its pivotal role in shaping the CUBs. The CUBs pattern of S-genes was found to be very similar among all the VOC strains. Interestingly, we observed that VOC strains followed a trend of antagonistic codon usage with respect to the human host. The identified CUBs divergence would help to understand the virus evolution and its host adaptation, thus help design novel vaccine strategies against the emerging VOC strains. To the best of our knowledge, this is the first report for identifying the evolution of CUBs pattern in all the currently identified VOC.

Host adaptation of codon usage in SARS-CoV-2 from mammals indicates potential natural selection and viral fitness

“…The results (Fig. 2 D) showed that most of the SARS-CoV-2 isolates gave values slightly below the standard curve (R 2 = 0.7562, P = 0.0023), indicating that the codon usage bias was affected by both mutation pressure and evolutionary pressure, consistent with previous reports [ 30 , 31 ]. Neutrality plot analysis showed that the linear regression coefficient of SARS-CoV-2 sequences was -0.1156 (Fig.…”

Natural selection pressure exerted on “Silent” mutations during the evolution of SARS-CoV-2: Evidence from codon usage and RNA structure

“…This result indicates that G+C composition is the dominant factor of SARS-CoV-2’s codon usage, but nature selection also affects SARS-CoV-2’s codon usage. Natural selection may play more important roles in some genes than the genome level (S gene, for example) ( Posani et al, 2022 ; Tyagi et al, 2022 ). The average codon adaptation index (CAI) of all strains at the genome level is 0.7200.…”

Natural Selection Primarily Shaping Codon Usage Bias in Glycine max (soybean) WRKY Genes

“…When the coefficient of GC3 approaches 1, mutational pressure predominantly governs codon usage 33,34 . Conversely, a slope nearing 0 indicates a diminishing impact of mutational pressure, implying a stronger influence of natural selection 35,36 . In this study, analysis of the neutrality plot (Figure 2) revealed a modest yet statistically significant positive correlation between GC12 and GC3, as indicated by the regression equation 𝑦 = 0.388 + 0.165𝑥; 𝑅2 = 0.14; 𝑝 < 0.001.…”