Codon usage and evolutionary rates of the 2019-nCoV genes

Maddalena Dilucca; Athanasia Pavlopoulou; Sergio Forcelloni; ANDREA GIANSANTI; Alexandros G Georgakilas

The aim of this study was to carry out a comprehensive analysis of codon usage and composition of 48 coronavirus and explore the possible leading evolutionary determinants of the biases found. [19]. For nCoV we note a different trend T>A>G>C, but in totally AT% is more used than GC% (See 54 Table 3 ). The GC content in 2019-nCoV is 0.373 ± 0.05. 55 2.2. All the sequenced 2019-nCoV genomes share a common codon usage 56 We downloaded the protein-coding sequences of 2019-nCoV from GIDAID database, by did not cluster according to geographic location, thus confirming the common origin of these genomes. 63 Therefore, we will consider a unique vector to represent 2019-nCoV in the following analyses. 2.4. The codon usage of 2019-nCoV is not optimized to the host. 81 To measure the codon usage bias in the coronavirus genomes, we used the effective number 82 of codons (ENC) value and the Competition Adaptation Index (CAI). In Table 2 and 3, we report 83 the ENC and CAI values for all the coronaviruses we considered in this work. To visually enhance 84 the differences among the codon usage of these coronaviruses we calculated Zscore of a single virus 85 in relation to the other members of the CoV subfamily. Interestingly, the ENC value associated to 86 2019-nCoV (51.9 ± 2.59) is lower than the average of all Coronaviruses, meaning that 2019-nCoV uses 87 a broader set of synonymous codons in its coding sequences. We also note that CAI of 2019-nCoV 88 (0.727 ± 0.054) is lower than the average one, underscoring that this coronavirus tend to use codons 89 that are not optimized to the host. We calculate similarity index (SiD) to measure the effect of codon author/funder. All rights reserved. No reuse allowed without permission.