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Bayesian phylodynamic inferences on the temporal evolution and global transmission of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread widely from China to the world. Although the viral genome has been well characterized, the evolutionary origin and global transmission dynamics of SARS-CoV-2 remain poorly investigated. To address this, we retrieved 313 SARS-CoV-2 genomes from the GISAID database (https://www.gisaid.org), from which 99 genomes generated from original clinical specimens with exact collection dates from 16 countries were selected and enrolled for Bayesian phylodynamic analysis. Here we show that the time to the Most Recent Common Ancestor (tMRCA) of SARS-CoV-2 is Dec 11, 2019 (95%HPD, Nov 21 - Dec 24). Two clades of global circulating strains of SARS-CoV-2 were suggested by Bayesian Maximum Clade Credibility (MCC) tree. The USA circulating strains of SARS-CoV-2 seemed to be from both of the two clades, the UK and Australia circulating strains were from Clade 1, the circulating strains in Singapore, Japan, Germany, France, and Italy were from Clade 2. Although we have not found any obvious bottle-neck-effect from the Bayesian Skyline Plot of the viral population dynamics reconstruction, a sharp reduction of the lower 95% HPD of the relative genetic diversity was observed from Feb 5, 2020, suggesting a possible initiation of a bottle-neck-effect. Thirteen (6 synonymous and 7 non-synonymous) mutations in the viral genome were observed, including two clade-specific mutations (C8782T and T1844C in Clade 1 rather than Clade 2) and eleven sub-clade specific mutations. All of the observed mutations occurred in the USA circulating strains, except one mutation T18488C only occurred in the UK circulating strains. A non-synonymous mutation in the 3’-UTR was also observed, suggesting an altered RNA replication capacity of SARS-CoV-2. We thus came to the conclusion that continuous evolution occurred in almost all regions of the SARS-CoV-2 genome and potentially in a country-specific manner. Further efforts on monitoring the genomic mutations of SARS-CoV-2 from different countries are recommended.


Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first recognized in humans in late 2019 in Wuhan (the capital of middle China's Hubei province) 1-3, has spread worldwide, developing a global pandemic 4. The initial transmission of SARS-CoV-2 has been limited in the national wide of China during the first two month 5, while a global spread is establishing with more than 134, 000 laboratory confirmed infections and 5300 deaths from 123 countries by March 14, 2020 (data released by WHO).
However, two key mutations were recently identified, potentially contributing to the sub-lineage classification of SARS-CoV-2 11. Although the genome structure of SARS-CoV-2 has been well documented, the temporal evolution and global transmission of the virus remains poorly investigated.
Herein, we retrieved 313 SARS-CoV-2 genomes from the GISAID database, from which 99 genomes were selected with exact collection dates before Feb 29, 2019. We analyzed the 99 viral genomes by Bayesian phylodynamic approaches to infer the origin time and global transmission of SARS-CoV-2.

Viral genome sequence acquisition and alignment
We retrieved 313 complete SARS-CoV-2 genomes (> 29000 bp) uploaded to the GISAID database (www.gisaid.org) with exact collection dates before Feb 29, 2019. After removal of the viral genome with multiple stretches of NNNs (> 0.1% of overall sequence), or with multiple gaps aligned against the reference genome (GenBank Acc. Num. MN908947.3), a total of 99 SARS-CoV-2 complete genomes from original clinical specimens with exact collection date were selected and enrolled for further analysis (Table S1). The enrolled SARS-CoV-2 genomes were aligned by ClustalW implemented in MEGA 6.0 12.

Molecular clock and evolutionary dynamics analysis
We used BEAST v1.10.4 13 to perform Markov Chain Monte Carlo (MCMC) analysis to infer the molecular clock and evolutionary dynamics of SARS-CoV-2. The program BEAUti (Bayesian Evolutionary analysis Utility, included in BEAST software package) was applied for setting the evolutionary model and parameters for the MCMC analysis. An exponential growth coalescent tree prior was selected as the demographic model to estimate the epidemic growth rate, whist a Bayesian 4 skyline coalescent tree prior was selected to infer the viral population dynamics. The tip date was set as the actual sampling time of the corresponding clinical specimen. The best-fit nucleotide

Temporal evolution of SARS-CoV-2
To gain insight into the temporal evolutionary dynamics of SARS-CoV-2, we performed Markov Chain Monte Carlo (MCMC) algorithms implemented in BEAST 1.10.4 package with the 99 enrolled SARS-CoV-2 genomes. Generalized Time Reversible (GTR) with invariant sites as site heterogeneity model (GTR+I) was selected as the best-fit nucleotide substitution model by the Akaike Information Criterion (AIC) implemented in jModelTest. The estimated mean evolutionary rate of SARS-CoV-2 was estimated to be 6.14 × 10-6 subs/site/day (95% HPD: 3.61 × 10 -6 -8.68 × 10 -6 subs/site/day), corresponding to 2.24 × 10 -3 subs/site/year (95% HPD: 1.32 × 10 -3 -3.17 × 10 -3 subs/site/year). Potentially due to the strong interventions of Wuhan city lockdown by the Chinese government, the Wuhan circulating viral strains were well controlled, while the viral strains outside Wuhan spread to worldwide. To emphasize the viral global transmission from outside Wuhan of China, we excluded the Wuhan circulating strains and observed that the circulating viral strains outside Wuhan could be separated into four sub-clades (Figure 1a). The two sub-clades from Clade 1 was diverged at Jan 1, 2020 (95%HPD, Dec 27, 2019 -Jan 5, 2020), while the two sub-clades from Clade 2 was diverged at Jan 8, 2020 (95%HPD, Jan 3 -Jan 13). With respect to the country-specific strains of SARS-CoV-2, we observed that the circulating strains in USA were from both of the two clades, the UK and Australia circulating strains were from Clade 1, the circulating strains in Singapore, Japan, Germany, France and Italy seemed to be from Clade 2 (Figure 1a, Table S1).

Population dynamics of SARS-CoV-2
To infer the population growth dynamics of SARS-CoV-2, the viral relative genetic diversity was Despite the BSP was generated from a limited sample size, the results suggested a possible initiation of a bottle-neck-effect in the population size of SARS-CoV-2, indicating more infected cases will occur in the near future due to the increased mutations in the virus genome.

Clade-/Sub-clade specific genomic mutations of SARS-CoV-2
Despite the SARS-CoV-2 remains relative stable, thirteen clade/sub-clade-specific mutations were observed in the present study (Figure 1a). The mutations at nt 8782 and nt 28144 were clade specific, 6 i.e., C8782T and T28144C were only occurred in Clade 1, rather than in Clade 2. Only a viral strain Despite only 4% genomic nucleotide variability occurred between SARS-CoV-2 and a bat SARS-related coronavirus (RaTG13) 7, the circulating strains of SARS-CoV-2 was believed to evolve into two major types (L and S type) supported by a population genetics approach 11. The two types of SARS-CoV-2 were well defined by two mutations at nt 8782 and nt 28144. In the present study, we also observed two major clades (Figure 1) of the circulating strains of SARS-CoV-2 by a time-scaled Maximum Clade Credibility (MCC) tree. The two clades could be well defined by mutations C8782T and T28144C.
Meanwhile, the viral strains in Clade 1 and Clade 2 were almost equivalent to the viral strains in S type and L type, respectively. Because the MCC tree was based on a Bayesian phylogenetic inference, differing from the haplotype-based population genetics approach, we thus concluded that the circulating SARS-CoV-2 has evolved into two groups, that were well defined by two mutations C8782T and T28144C.
Besides the two clade-specific mutations, we also observed eleven sub-clade specific mutations. All of the sub-clade specific mutations could be found in the USA circulating SARS-CoV-2 strains (Table 1).
While one non-synonymous mutation T18488C was only observed in the UK circulating strains. Two non-synonymous mutations G28878A and G29742A were only observed in the circulating strains in Australia and USA. These results suggested that differential evolution trends of SARS-CoV-2 may be occurred in different countries. Notably, one non-synonymous mutation G29742A occurred in 3'UTR of the viral genome, suggesting a possible altered RNA replication capacity in the Australia and USA circulating strains of SARS-CoV-2 20,21.
In conclusion, we performed Bayesian MCMC analysis on 99 SARS-CoV-2 genomes from original clinical specimen with exact collection dates to infer the temporal evolutionary dynamics and global 8 transmission of the virus. Our results suggested that the tMRCA of the virus is Dec 11, 2019. Two major clades of the circulating strains were observed in a maximum clade credibility tree, which were well defined by two mutations G28878A and G29742A. Eleven sub-clade specific mutations were also observed, all of which could be found in the USA circulating strains. One mutation (G29742A) occurred in the 3'-UTR of SARS-CoV-2. Our results suggested that the circulating SARS-CoV-2 is under continuing evolution. Further more attention should be paid on the genomic mutations of SARS-CoV-2.

Data availability
The SARS-CoV-2 genomes enrolled in the present study could be retrieved from the GISAID database (https://www.gisaid.org) with the corresponding information included in Table S1.

Declaration of Competing Interest
The authors declare that they have no conflicts of interest.

Supplementary Files
This is a list of supplementary files associated with this preprint. Click to download. Table S1.pdf