{"id":4077,"date":"2020-03-06T18:05:45","date_gmt":"2020-03-06T09:05:45","guid":{"rendered":"http:\/\/www.bicyclogy.com\/?p=4077"},"modified":"2021-04-24T22:58:52","modified_gmt":"2021-04-24T13:58:52","slug":"2020%e5%b9%b43%e6%9c%886%e6%97%a5","status":"publish","type":"post","link":"https:\/\/www.bicyclogy.com\/?p=4077","title":{"rendered":"2020\u5e743\u67086\u65e5"},"content":{"rendered":"

RESEARCH ARTICLE
\nMICROBIOLOGY
\nOn the origin and continuing evolution of SARS-CoV-2
\nXiaolu Tang1,7
\n, Changcheng Wu1,7
\n, Xiang Li2,3,4,7
\n, Yuhe Song2,5,7
\n, Xinmin Yao1
\n, Xinkai Wu1
\n,
\nYuange Duan1
\n, Hong Zhang1
\n, Yirong Wang1
\n, Zhaohui Qian6
\n, Jie Cui2,3,*, and Jian Lu1,*
\n1. State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics,
\nSchool of Life Sciences, Peking University, Beijing, 100871, China
\n2. CAS Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai,
\nChinese Academy of Sciences, China
\n3. Center for Biosafety Mega-Science, Chinese Academy of Sciences, China
\n4. University of Chinese Academy of Sciences, China
\n5. School of Life Sciences, Shanghai University, China
\n6. NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology,
\nChinese Academy of Medical Sciences and Peking Union Medical College, Beijing
\n7. These authors contributed equally to this work.
\n*Corresponding authors:
\nJian Lu, Email: LUJ@pku.edu.cn
\nJie Cui, Email: jcui@ips.ac.cn
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\nABSTRACT
\nThe SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since
\nimpacted a large portion of China and raised major global concern. Herein, we investigated
\nthe extent of molecular divergence between SARS-CoV-2 and other related coronaviruses.
\nAlthough we found only 4% variability in genomic nucleotides between SARS-CoV-2 and a
\nbat SARS-related coronavirus (SARSr-CoV; RaTG13), the difference at neutral sites was
\n17%, suggesting the divergence between the two viruses is much larger than previously
\nestimated. Our results suggest that the development of new variations in functional sites in the
\nreceptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin
\nSARSr-CoVs are likely caused by mutations and natural selection besides recombination.
\nPopulation genetic analyses of 103 SARS-CoV-2 genomes indicated that these viruses
\nevolved into two major types (designated L and S), that are well defined by two different
\nSNPs that show nearly complete linkage across the viral strains sequenced to date. Although
\nthe L type (~70%) is more prevalent than the S type (~30%), the S type was found to be the
\nancestral version. Whereas the L type was more prevalent in the early stages of the outbreak
\nin Wuhan, the frequency of the L type decreased after early January 2020. Human
\nintervention may have placed more severe selective pressure on the L type, which might be
\nmore aggressive and spread more quickly. On the other hand, the S type, which is
\nevolutionarily older and less aggressive, might have increased in relative frequency due to
\nrelatively weaker selective pressure. These findings strongly support an urgent need for
\nfurther immediate, comprehensive studies that combine genomic data, epidemiological data,
\nand chart records of the clinical symptoms of patients with coronavirus disease 2019
\n(COVID-19).
\nKeywords: SARS-CoV-2, virus, molecular evolution, population genetics
\nReceived: 25-Feb-2020; Revised: 28-Feb-2020; Accepted: 29-Feb-2020.
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\nINTRODUCTION
\nThe coronavirus disease 2019 (COVID-19) epidemic started in late December 2019 in Wuhan,
\nthe capital of Central China’s Hubei Province. Since then, it has rapidly spread across China
\nand in other countries, raising major global concerns. The etiological agent is a novel
\ncoronavirus, SARS-CoV-2, named for the similarity of its symptoms to those induced by the
\nsevere acute respiratory syndrome. As of February 28, 2020, 78,959 cases of SARS-CoV-2
\ninfection have been confirmed in China, with 2,791 deaths. Worryingly, there have also been
\nmore than 3,664 confirmed cases outside of China in 46 countries and areas
\n(https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/situation-reports\/),<\/a>
\nraising significant doubts about the likelihood of successful containment. Further, the
\ngenomic sequences of SARS-CoV-2 viruses isolated from a number of patients share
\nsequence identity higher than 99.9%, suggesting a very recent host shift into humans [1-3].
\nCoronaviruses are naturally hosted and evolutionarily shaped by bats [4, 5]. Indeed, it has
\nbeen postulated that most of the coronaviruses in humans are derived from the bat reservoir [6,
\n7]. Unsurprisingly, several teams have recently confirmed the genetic similarity between
\nSARS-CoV-2 and a bat betacoronavirus of the sub-genus Sarbecovirus [8-13]. The
\nwhole-genome sequence identity of the novel virus has 96.2% similarity to a bat
\nSARS-related coronavirus (SARSr-CoV; RaTG13) collected in Yunnan province, China [2,
\n14], but is not very similar to the genomes of SARS-CoV (about 79%) or MERS-CoV (about
\n50%) [1, 15]. It has also been confirmed that the SARS-CoV-2 uses the same receptor, the
\nangiotensin converting enzyme II (ACE2), as the SARS-CoV [11]. Although the specific
\nroute of transmission from natural reservoirs to humans remains unclear [5, 13], several
\nstudies have shown that pangolins may have provided a partial spike gene to SARS-CoV-2;
\nthe critical functional sites in the spike protein of SAR-CoV-2 are nearly identical to one
\nidentified in a virus isolated from a pangolin [16-18].
\nDespite these recent discoveries, several fundamental issues related to the evolutionary
\npatterns and driving forces behind this outbreak of SARS-CoV-2 remain unexplored [19].
\nHerein, we investigated the extent of molecular divergence between SARS-CoV-2 and other
\nrelated coronaviruses and carried out population genetic analyses of 103 sequenced genomes
\nof SARS-CoV-2. This work provides new insights into the factors driving the evolution of
\nSARS-CoV-2 and its pattern of spread through the human population.
\nRESULTS
\nMolecular phylogeny and divergence between SARS-CoV-2 and related coronaviruses.
\nFor each annotated ORF in the reference genome of SARS-CoV-2 (NC_045512), we
\nextracted the orthologous sequences in human SARS-CoV, four bat
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\nSARS-related coronaviruses (SARSr-CoV: RaTG13, ZXC21, ZC45, and BM48-31), one
\nPangolin SARSr-CoV from Guangdong (GD) [17], and six Pangolin SARSr-CoV genomes
\nfrom Guangxi (GX) [18] (Table S1). We aligned the coding sequences (CDSs) based on the
\nprotein alignments (see Materials and Methods). Most ORFs annotated from SARS-CoV-2
\nwere found to be conserved in other viruses, except for ORF8 and ORF10 (Table 1). The
\nprotein sequence of SARS-CoV-2 ORF8 shared very low similarity with sequences in
\nSARS-CoV and BM48-31, and ORF10 had a premature stop codon in both SARS-CoV and
\nBM48-31 (Fig. S1). A one-base deletion caused a frame-shift mutation in ORF10 of ZXC21
\n(Fig. S1).
\nTo investigate the phylogenetic relationships between these viruses at the genomic scale, we
\nconcatenated coding regions (CDSs) of the nine conserved ORFs (orf1ab, E, M, N, S, ORF3a,
\nORF6, ORF7a, and ORF7b) and reconstructed the phylogenetic tree using the synonymous
\nsites (Fig. 1A). We also used CODEML in the PAML [20] to infer the ancestral sequence of
\neach node and calculated the dN (nonsynonymous substitutions per nonsynonymous site), dS
\n(synonymous substitutions per synonymous site), and dN\/dS (\u03c9) values for each branch (Fig.
\n1A). In parallel, we also calculated the pairwise dN, dS, and \u03c9 values between SARS-CoV-2
\nand another virus (Table 1).
\nThe genome-wide phylogenetic tree indicated that SARS-CoV-2 was closest to RaTG13,
\nfollowed by GD Pangolin SARSr-CoV, then by GX Pangolin SARSr-CoVs, then by ZC45
\nand ZXC21, then by human SARS-CoV, and finally by BM48-31(Fig. 1A). Notably, we
\nfound that the nucleotide divergence at synonymous sites between SARS-CoV-2 and other
\nviruses was much higher than previously anticipated. For example, although the overall
\ngenomic nucleotides overall differ ~4% between SARS-CoV-2 and RaTG13, the genomic
\naverage dS was 0.17, which means the divergence at the neutral sites is 17% between these
\ntwo viruses (Table 1). This is because the nonsynonymous sites are usually under stronger
\nnegative selection than synonymous sites, and calculating sequence differences without
\nseparating these two classes of sites may underestimate the extent of molecular divergence by
\nseveral folds.
\nNotably, the dS value varied considerably across genes in SARS-CoV-2 and the other viruses
\nanalyzed. In particular, the spike gene (S) consistently exhibited larger dS values than other
\ngenes (Table 1). This pattern became clear when we calculated the dS value for each branch
\nin Fig. 1A for the spike gene versus the concatenated sequences of the remaining genes (Fig.
\nS2). In each branch, the dS of spike was 2.22 \u00b1 1.35 (mean \u00b1 SD) times as large as that of the
\nother genes. This extremely elevated dS value of spike could be caused either by a high
\nmutation rate or by natural selection that favors synonymous substitutions. Synonymous
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\nsubstitutions may serve as another layer of genetic regulation, guiding the efficiency of
\nmRNA translation by changing codon usage [21]. If positive selection is the driving force for
\nthe higher synonymous substation rate seen in spike, we expect the frequency of optimal
\ncodons (FOP) of spike to be different from that of other genes. However, our codon usage
\nbias analysis (Table S2) suggests the FOP of spike was only slightly higher than that of the
\ngenomic average (0.717 versus 0.698, see Materials and Methods). Thus, we believe that the
\nelevated synonymous substitution rate measured in spike is more likely caused by higher
\nmutational rates; however, the underlying molecular mechanism remains unclear.
\nBoth SARS-CoV and SARS-CoV-2 bind to ACE2 through the RBD of spike protein in order
\nto initiate membrane fusion and enter human cells [1, 2, 22-26]. Five out of the six critical
\namino acid (AA) residues in RBD were different between SARS-CoV-2 and SARS-CoV (Fig.
\n1B), and a 3D structural analysis indicated that the spike of SARS-CoV-2 has a higher
\nbinding affinity to ACE2 than SARS-CoV [23]. Intriguingly, these same six critical AAs are
\nidentical between GD Pangolin-CoV and SARS-CoV-2 [16]. In contrast, although the
\ngenomes of SARS-CoV-2 and RaTG13 are more similar overall, only one out of the six
\nfunctional sites are identical between the two viruses (Fig. 1B). It has been proposed that the
\nSARS-CoV-2 RBD region of the spike protein might have resulted from recent recombination
\nevents in pangolins [16-18]. Although several ancient recombination events have been
\ndescribed in spike [27, 28], it also seems likely that the identical functional sites in
\nSARS-CoV-2 and GD Pangolin-CoV may actually the result of coincidental convergent
\nevolution [18].
\nIf the functional AA residues in the SARS-CoV-2 RBD region were acquired from GD
\nPangolin-CoV in a very recent recombination event, we would expect the nucleotide
\nsequences of this region to be nearly identical between the two viruses. However, for the CDS
\nsequences that span five critical AA sites in the SARS-CoV-2 spike (ranging from codon 484
\nto 507, covering five adjacent functional sites: F486, Q493, S494, N501, and Y505; Fig. S3),
\nwe estimated dS = 0.411, dN = 0.019, and \u03c9= 0.046 between SARS-CoV-2 and GD
\nPangolin-CoV. By assuming the synonymous substitution rate (u) of 1.67-4.67 x 10-3
\n\/site\/year,
\nas estimated in SARS-CoV [29], the recombination\/introgression, if it occurred at all, would
\nbe estimated to happen approximately 19.8-55.4 years ago. Here, the formula
\nwas used to calculate divergence time; note that the increased mutational rate of
\nspike was considered for this calculation. Thus, it seems very unlikely that SARS-CoV-2
\noriginated from the GD Pangolin-CoV due to a very recent recombination event.
\nAlternatively, it seems more likely that a high mutation rate in spike, coupled with strong
\nnatural selection, has shaped the identical functional AA residues between these two viruses,
\nas proposed previously [18]. Although these sites are maintained in SARS-CoV-2 and GD
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\nPangolin-CoV, mutations may have changed the residues in the RaTG13 lineage after it
\ndiverged from SARS-CoV-2 (the blue arrow in Fig. 1A). In summary, it seems that the shared
\nidentity of critical AA sites between SARS-CoV-2 and GD Pangolin-CoV might be due to
\nrandom mutations coupled with natural selection, and not necessarily recombination.
\nSelective constraints and positive selection during the evolution of SARS-CoV-2 and
\nrelated coronaviruses
\nThe genome-wide \u03c9 value between SARS-CoV-2 and other viruses ranged from 0.044 to
\n0.124 (Table 1), indicative of strong negative selection on the nonsynonymous sites. In other
\nwords, 87.6% to 95.6% of the nonsynonymous mutations were removed by negative selection
\nduring viral evolution. To determine the extent of positive selection, we concatenated the
\nCDS sequences of 9 conserved ORFs in all the viruses in Fig. 1A and fitted the M7 (beta:
\nneutral and negative selection) and M8 (beta + \u03c9>1:neutral, negative selection, and positive
\nselection) model using CODEML (Materials and Methods). The M8 model (lnL =
\n-104,813.732, np =18) was a significantly better fit than the M7 (lnL = -105,063.284, np = 16)
\nmodel (P < 10-10), suggesting that some AA substitutions were favored by positive Darwinian
\nselection (but not necessarily in the SARS-CoV-2 lineage).Under the M8 model, 98.48% (p0)
\nof the nonsynonymous substitutions were estimated under neutral evolution or purifying
\nselection (0\u2a7d\u03c9\u2a7d1), and 1.52% (p1) of the nonsynonymous substitutions were under positive
\nselection (\u03c9 = 1.50). A Bayes Empirical Bayes (BEB) analysis suggested that 10 AA sites
\nshowed strong signals of positive selection, and, interestingly, three of those were located in
\nthe RBD of spike, including at one critical site (Fig. 1C and Fig. S4). Thus, although these
\ncoronaviruses were generally under very strong negative selection, positive selection was also
\nresponsible for the evolution of protein sequences. The putatively positively-selected sites
\nmight serve as candidates for further functional studies.
\nMutations in 103 SARS-CoV-2 genomes
\nWe downloaded 103 publicly available SARS-CoV-2 genomes, aligned the sequences, and
\nidentified the genetic variants. For ease of visualization, we marked each virus strain based on
\nthe location and date the virus was isolated with the format of “Location_Date\u201d throughout
\nthis study (see Table S1 for details; Each ID did not contain information of the patient’s race
\nor ethnicity). Although SARS-CoV-2 is an RNA virus, for simplicity, we presented our
\nresults based on DNA sequencing results throughout this study (i.e., the nucleotide T
\n(thymine) means U (uracil) in SARS-CoV-2). For each variant, the ancestral state was
\ninferred based on the genome and CDS alignments of SARS-CoV-2 (NC_045512), RaTG13,
\nand GD Pangolin-CoV (Materials and Methods). In total, we identified mutations in 149 sites
\nacross the 103 sequenced strains. Ancestral states for 43 synonymous, 83 non-synonymous,
\nand two stop-gain mutations were unambiguously inferred. The frequency spectra of
\nsynonymous and nonsynonymous mutations are shown in Fig. 2.
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\nMost derived mutations were singletons (67.4% (29\/43) of synonymous mutations and 84.3%
\n(70\/83) of nonsynonymous mutations), indicating either a recent origin [30] or population
\ngrowth [31]. In general, the derived alleles of synonymous mutations were significantly
\nskewed towards higher frequencies than those of nonsynonymous ones (P < 0.01, Wilcoxon
\nrank-sum test; Fig. 2), suggesting the nonsynonymous mutations tended to be selected against.
\nHowever, 16.3% (7 out of 43) synonymous mutations, and one nonsynonymous (ORF8
\n(L84S, 28,144)) mutation had a derived frequency of \u2265 70% across the SARS-CoV2 strains.
\nThe nonsynonymous mutations that had derived alleles in at least two SARS-CoV-2 strains
\naffected six proteins: orf1ab (A117T, I1607V, L3606F, I6075T), S (H49Y, V367F), ORF3a
\n(G251V), ORF7a (P34S), ORF8 (V62L, S84L), and N (S194L, S202N, P344S).
\nTwo major types of SARS-CoV-2 are defined by two SNPs that show complete linkage
\nTo detect the possible recombination among SARS-CoV2 viruses, we used Haploview [32] to
\nanalyze and visualize the patterns of linkage disequilibrium (LD) between variants with minor
\nalleles in at least two SARS-CoV-2 strains (Fig. 3A). Since most mutations were at very low
\nfrequencies, it is not surprising that many pairs had a very low r
\n2
\nor LOD value (Fig. 3B-C).
\nConsistent with another recent report [31], we did not find evidence of recombination
\nbetween the SARS-CoV2 strains.
\nHowever, we found that SNPs at location 8,782 (orf1ab: T8517C, synonymous) and 28,144
\n(ORF8: C251T, S84L) showed significant linkage, with an r
\n2
\nvalue of 0.954 (Fig. 3B, red)
\nand a LOD value of 50.13 (Fig. 3C, red). Among the 103 SARS-CoV-2 virus strains, 101 of
\nthem exhibited complete linkage between the two SNPs: 72 strains exhibited a \u201cCT\u201d
\nhaplotype (defined as \u201cL\u201d type because T28,144 is in the codon of Leucine) and 29 strains
\nexhibited a \u201cTC\u201d haplotype (defined as \u201cS\u201d type because C28,144 is in the codon of Serine)
\nat these two sites. Thus, we categorized the SARS-CoV-2 viruses into two major types, with
\nL being the major type (~70%) and S being the minor type (~30%).
\nThe evolutionary history of L and S types of SARS-CoV-2
\nAlthough we defined the L and S types based on two tightly linked SNPs, strikingly, the
\nseparation between the L (blue) and S (red) types was maintained when we reconstructed the
\nhaplotype networks using all the SNPs in the SARS-CoV-2 genomes (Fig. 4A; the number of
\nmutations between two neighboring haplotypes was inferred parsimoniously). This analysis
\nfurther supports the idea that the two linked SNPs at sites 8,782 and 28,144 adequately define
\nthe L and S types of SARS-CoV-2.
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\nTo determine whether L or S type is ancestral, we examined the genomic alignments of
\nSARS-CoV-2 and other highly related viruses. Strikingly, nucleotides of the S type at sites
\n8,782 and 28,144 were identical to the orthologous sites in the most closely related viruses
\n(Fig. 4B). Remarkably, both sites were highly conserved in other viruses as well. Hence,
\nalthough the L type (~70%) was more prevalent than the S type (~30%) in the SARS-CoV-2
\nviruses we examined, the S type is actually the ancestral version of SARS-CoV-2.
\nTo further examine the relationship among the strains in the L and S types, we reconstructed a
\nphylogenetic tree of all the 103 SARS-CoV-2 viruses based on their whole-genome sequences.
\nOur phylogenetic tree also clearly shows the separation of the two types (Fig. 5). Viruses of
\nthe L type (blue) first clustered together, and likewise, viruses of the S type (red) were also
\nmore closely related to each other. Therefore, our whole-genome comparisons further confirm
\nthe separation of the L and S types.
\nThus far, we found that, although the L type is derived from the S type, L (~70%) is more
\nprevalent than S (~30%) among the sequenced SARS-CoV-2 genomes we examined. This
\npattern suggests that L has a higher transmission rate than the S type. Furthermore, our
\nmutational load analysis indicated that the L type had accumulated a significantly higher
\nnumber of derived mutations than S type (P < 0.0001, Wilcoxon rank-sum test; Fig. S5). We
\npropose that, although the L type newly evolved from the ancient S type, it transmits faster or
\nreplicates faster in human populations, causing it to accumulate more mutations than the S
\ntype. Thus, our results suggest the L might be more aggressive than the S type due to the
\npotentially higher transmission and\/or replication rates.
\nTo test whether the two types of SARS-CoV-2 had differences in temporal and spatial
\ndistributions, we stratified the viruses based on the locations and dates they were isolated
\n(Table S1). Among the 27 viruses isolated from Wuhan, 26 (96.3%) were L type, and only 1
\n(3.7%) was S type. However, among the other 73 viruses isolated outside Wuhan, 45 (61.6%)
\nwere L type, and 28 (38.4%) were S type. This comparison suggests that the L type is
\nsignificantly more prevalent in Wuhan than in other places (P = 0.0004, Fisher\u2019s exact test,
\nFig. 6 and Table S3). All of the 26 samples isolated before January 7, 2020, were from
\nWuhan, and among the 74 samples collected from January 7, 2020, only one was from
\nWuhan, 33 were from other places in China, and 40 were from patients outside China. Thus,
\nit is not surprising that the L type was significantly more prevalent before January 7, 2020
\n(96.2%, 25 L and 1 S) than after January 7, 2020 (62.2%, 46 L and 28 S) (P = 0.0008,
\nFisher\u2019s exact test, Fig. 6 and Table S3).
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\nIf the L type is more aggressive than the S type, why did the relative frequency of the L type
\ndecrease compared to the S type in other places after the initial breakout in Wuhan? One
\npossible explanation is that, since January 2020, the Chinese central and local governments
\nhave taken rapid and comprehensive prevention and control measures. These human
\nintervention efforts might have caused severe selective pressure against the L type, which
\nmight be more aggressive and spread more quickly. The S type, on the other hand, might have
\nexperienced weaker selective pressure by human intervention, leading to an increase in its
\nrelative abundance among the SARS-CoV-2 viruses. Thus, we hypothesized that the two
\ntypes of SARS-CoV-2 viruses might have experienced different selective pressures due to
\ndifferent epidemiological features. Of note, the above analyses were based on very patchy
\nSARS-CoV-2 genomes that were collected from different locations and time points. More
\ncomprehensive genomic data is required for further testing of our hypothesis.
\nHeteroplasmy of SARS-CoV-2 viruses in patients
\nIt is currently unclear how the L type specifically evolved from the S type during the
\ndevelopment of SARS-CoV-2. However, we found that the sequence of viruses isolated from
\none patient that lived in the United States on January 21 (USA_2020\/01\/21.a, GISAID ID:
\nEPI_ISL_404253) had the genotype Y (C or T) at both positions 8,782 and 28,144, differing
\nfrom the general trend of having either C or T. Although novel mutations could lead to this
\nresult, the most parsimonious explanation is that this patient may have been infected by both
\nthe L and S types (Fig. 7A). The sample of USA_2020\/01\/21.a was collected from a
\n63-year-old female patient living in Chicago (from GISAID). Based on the report from the
\nUnited States Centers for Disease Control and Prevention
\n(https:\/\/www.cdc.gov\/media\/releases\/2020\/p0124-second-travel-coronavirus.html),<\/a> we
\ninferred this patient returned to the United States from Wuhan on January 13, 2020. However,
\nwhether the co-existence of L and S types in this patient was due to multiple-time infections
\nduring her visit to Wuhan is currently unclear. Notably, the viruses identified from a patient
\nin Australia on January 28, 2020 (Australia_2020\/01\/28.a, GISAID ID: EPI_ISL_407894)
\nhad multiple degenerate nucleotides. This sample was collected from a 44-year-old male
\npatient in Gold Cost, Australia (from GISAID). Based on the report from the Courier Mail
\n(January 30, 2020), we inferred this patient had the history of traveling from Wuhan to the
\nGold Coast before the diagnosis of infection. As shown in Fig. 7B, we inferred this patient
\nmight have been infected by at least two different strains of SARS-CoV-2 (Fig. 7B).
\nTo further investigate the heteroplasmy of SARS-CoV-2 viruses in patients, we searched 12
\ndeep-sequencing libraries of SARS-CoV-2 genomes that were deposited in the Sequence
\nRead Archive (SRA) (Table S4, Materials and Methods). We found 17 genomic sites that
\nshowed evidence of heteroplasmy of SARS-CoV-2 virus in five patients, but we did not find
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\nany other instances of the co-existence of L and S types in any patient (Table 2). These
\nfindings evince the developing complexity of the evolution of SARS-CoV-2 infections.
\nFurther studies investigating how the different alleles of SARS-CoV-2 viruses compete with
\neach other will be of significant value.
\nDISCUSSION
\nIn this study, we investigated the patterns of molecular divergence between SARS-CoV-2 and
\nother related coronaviruses. Although the genomic analyses suggested that SARS-CoV-2 was
\nclosest to RaTG13, their difference at neutral sites was much higher than previously realized.
\nOur results provide novel insights into tracing the intermediate natural host of SARS-CoV-2.
\nWith population genetic analyses of 103 genomes of SARS-CoV-2, we found that
\nSARS-CoV-2 viruses evolved into two major types (L and S types), and the two types were
\nwell defined by just two SNPs that show nearly complete linkage across SARS-CoV-2 strains.
\nAlthough the L type (~70%) was more prevalent than the S type (~30%) in the SARS-CoV-2
\nviruses we examined, our evolutionary analyses suggested the S type was most likely the
\nmore ancient version of SARS-CoV-2. Our results also support the idea that the L type is
\nmore aggressive than the S type.
\nSince nonsynonymous sites are usually under stronger negative selection than synonymous
\nsites, calculating sequence differences without separating these two classes of sites could lead
\nto a potentially significant underestimate of the degree of molecular divergence. For example,
\nalthough the overall nucleotides only differed by ~4% between SARS-CoV-2 and RaTG13,
\nthe genomic average dS value, which is usually a neutral proxy, was 0.17 between these two
\nviruses (Table 1). Of note, the genome-wide dS value is 0.012 between humans and
\nchimpanzees [33], and 0.08 between humans and rhesus macaques [34]. Thus, the neutral
\nmolecular divergence between SARS-CoV-2 and RaTG13 is 14 times larger than that
\nbetween humans and chimpanzees, and twice as large as that between humans and macaques.
\nThe genomic average dS value between SARS-CoV-2 and GD Pangolin-CoV is 0.475, which
\nis comparable to that between humans and mice (0.5) [35], and the dS value between
\nSARS-CoV-2 and GX Pangolin-Cov is even larger (0.722). The scale of these measures
\nsuggests that we should perhaps consider the difference in the neutral evolving site rather than
\nthe difference in all nucleotide sequences when tracing the origin and natural intermediate
\nhost of SARS-CoV-2.
\nOur analyses of molecular evolution and population genetics suggested that some amino acid
\nchanges might be favored by natural selection during the evolution of SARS-CoV-2 and other
\nrelated viruses. However, negative selection appears to be the predominant force acting on
\nthese viruses. Interestingly, the virus isolated from one patient in Shenzhen on January 13,
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\n2020 (SZ_2020\/01\/13.a, GISAID ID: EPI_ISL_406592) had C at both positions 8,782 and
\n28,144 in the genome, belonging to neither L nor S type (Fig. 4A and 5). Notably, this strain
\nhad one stop-gain mutation in orf1ab and had accumulated 20 silent and 5 nonsynonymous
\nmutations after diverging from the ancestor haplotype (Fig. 4A). Thus, it is possible that
\nfunctional constraints on the genomic sequence were weakened after the disruption of orf1ab
\nin this strain. Notably, on viruses isolated from a patient living in South Korean
\n(Skorea_2020\/01.a, GISAID: EPI_ISL_411929), acquired six nonsynonymous mutations that
\nwere different from the most recent common ancestor of SARS-CoV-2: orf1ab (M902I and
\nT6891M), S (S221W), ORF3a (W128L and G251V), and E (L37H). If these changes are not
\ndue to sequencing errors, it would be interesting to test whether and how these mutations
\naffect the transmission and pathogenesis of SARS-CoV-2.
\nIn this work, we propose that SARS-CoV-2 can be divided into two major types (L and S
\ntypes): the S type is ancestral, and the L type evolved from S type. Intriguingly, the S and L
\ntypes can be clearly defined by just two tightly linked SNPs at positions 8,782 (orf1ab:
\nT8517C, synonymous) and 28,144 (ORF8: C251T, S84L). However, it is currently unclear
\nwhether L type evolved from the S type in humans or in the intermediate hosts. It is also
\nunclear whether the L type is more virulent than the S type. orf1ab, which encodes
\nreplicase\/transcriptase, is required for viral genome replication and might also be important
\nfor viral pathogenesis [36]. Although the T8517C mutation in orf1ab does not change the
\nprotein sequence (it changes the codon AGT (Ser) to AGC (Ser)), we hypothesized this
\nmutation might affect orf1ab translation since AGT is preferred while AGC is unpreferred
\n(Table S2). ORF8 promotes the expression of ATF6, the ER unfolded protein response factor,
\nin human cells [37]. Thus, it will be interesting to investigate the function of the S84L AA
\nchange in ORF8, as well as the combinatory effect of these two mutations in SARS-CoV-2
\npathogenesis.
\nIn summary, our analyses of 103 sequenced SARS-CoV-2 genomes suggest that the L type is
\nmore aggressive than the S type and that human interference may have shifted the relative
\nabundance of L and S type soon after the SARS-CoV-2 outbreak. As previously noted [19],
\nthe data examined in this study are still very limited, and follow-up analyses of a larger set of
\ndata are needed to have a better understanding of the evolution and epidemiology of
\nSARS-CoV-2. There is a strong need for further immediate, comprehensive studies that
\ncombine genomic data, epidemiological data, and chart records of the clinical symptoms of
\npatients with SARS-CoV-2.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nMATERIALS AND METHODS
\nMolecular evolution of SARS-CoV-2 and other related viruses
\nThe set of 103 complete genome sequences were downloaded from GISAID (Global Initiative
\non Sharing All Influenza Data; https:\/\/www.gisaid.org\/)<\/a> with acknowledgment, GenBank
\n(https:\/\/www.ncbi.nlm.nih.gov\/genbank),<\/a> and NMDC (http:\/\/nmdc.cn\/#\/nCoV).<\/a> Sequences
\nand annotations of the reference genome of SARS-CoV-2 (NC_045512) and other related
\nviruses were downloaded from GenBank or GISAID (Table S1). The genomic sequences of
\nSARS-CoV-2 were aligned using MUSCLE v3.8.31 [38].
\nThe annotated CDSs of other viruses were downloaded from GenBank. To avoid missing
\nannotations in other viruses, we also annotated the ORFs using CDSs annotated in
\nSARS-CoV-2 using Exonerate (–model protein2genome:bestfit –score 5 -g y) [39]. The
\nprotein sequences of SARS-CoV-2 and other related viruses were aligned with MUSCLE
\nv3.8.31 [38], and the codon alignments were made based on the protein alignment with
\nRevTrans [40]. The codon alignments of the conserved ORFs were further concatenated for
\ndown-stream evolutionary analysis. The phylogenetic tree was constructed by the
\nneighbor-joining method in MEGA-X [41] using the parameters of Kimura 2-parameter
\nmodel, and only the third positions of codons were considered. YN00 from PAML v4.9a [20]
\nwas used to calculate the pairwise divergence between SARS-CoV-2 and other viruses for
\neach individual gene or for the concatenated sequences. The free-ratio model in CODEML in
\nthe PAML [20] package was used to calculate the dN, dS, and \u03c9 values for each branch.
\nPositively selected amino acids
\nPositive selection was detected using EasyCodeML [42], a recently published wrapper of
\nCODEML [20]. The M7 and M8 models were compared. In the M7 model, \u03c9 follows a beta
\ndistribution such that 0\u2a7d\u03c9\u2a7d1, and in the M8 model, a proportion p0 of sites have \u03c9 drawn
\nfrom the beta distribution, and the remaining sites with proportion p1 are positively selected
\nand have \u03c91>1. The LRTs between M7 and M8 models were conducted by comparing twice
\nthe difference in log-likelihood values (2 ln \u0394l) against a \u03c7
\n2
\n-distribution (df=2). The positively
\nselected sites were identified with the Bayes Empirical Bayes (BEB) score larger than 0.95.
\nHaplotype network
\nDnaSP v6.12.03 [43] was used to generate multi-sequence aligned haplotype data, and
\nPopART v1.7 [44] was used to draw haplotype networks based on the haplotypes generated
\nby DnaSP. RAxML v8.2.12 [45] was used to build the maximum likelihood phylogenetic tree
\nof 103 aligned SARS-CoV-2 genomes with theparameters \u201c-p 1234 -m GTRCAT\u201d.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nSNP calling process
\nWe downloaded 12 SARS-CoV-2 metagenomic sequencing libraries (Table S2), and mapped
\nthe NGS reads to the reference genome of SARS-CoV-2 (NC_045512) using BWA
\n(0.7.17-r1188) [46] with the default parameters. SNP calling was done using bcftools mpileup
\n(bcftools 1.9) [47].
\nCodon usage bias analysis
\nWe calculated the RSCU (Relative Synonymous Codon Usage) value of each codon in the
\nSARS-CoV-2 reference genome (NC_045512). The RSCU value for each codon was the
\nobserved frequency of this codon divided by its expected frequency under equal usage among
\nthe amino acid [48]. The codons with RSCU > 1 were defined as preferred codons, and those
\nwith RSCU < 1 were defined as unpreferred codons. The FOP (frequency of optimal codons)
\nvalue of each gene was calculated as the number of preferred codons divided by the total
\nnumber of preferred and unpreferred codons.
\nConflict of interest
\nThe authors declare that they have no conflicts of interest.
\nAcknowledgments
\nThe authors thank the researchers who generated and shared the sequencing data from
\nGISAID (https:\/\/www.gisaid.org\/)<\/a> on which this research is based. We thank Dr. Chung-I Wu,
\nHong Wu, Hongya Gu, Liping Wei, Xuemei Lu, Weiwei Zhai, Guodong Wang, Xiaodong Su,
\nKeping Hu, and Leiliang Zhang for suggestive comments to this study. This work was
\nsupported by grants from the National Natural Science Foundation of China (No. 91731301)
\nto J.L. JC is supported by CAS Pioneer Hundred Talents Program.
\nReferences
\n1. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019
\nnovel coronavirus: implications for virus origins and receptor binding. Lancet. 2020. Epub 2020\/02\/03.
\ndoi: 10.1016\/S0140-6736(20)30251-8. PubMed PMID: 32007145.
\n2. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with
\na new coronavirus of probable bat origin. Nature. 2020. doi: 10.1038\/s41586-020-2012-7. PubMed
\nPMID: 32015507.
\n3. Ren L-L, Wang Y-M, Wu Z-Q, Xiang Z-C, Guo L, Xu T, et al. Identification of a novel coronavirus
\ncausing severe pneumonia in human: a descriptive study. Chinese Medical Journal. 2020.
\n4. Cui J, Li F, Shi Z-L. Origin and evolution of pathogenic coronaviruses. Nature Reviews
\nMicrobiology. 2019;17(3):181-92. doi: 10.1038\/s41579-018-0118-9.
\n5. Li X, Song Y, Wong G, Cui J. Bat origin of a new human coronavirus: there and back again. Science
\nChina Life Sciences. 2020. doi: 10.1007\/s11427-020-1645-7.
\n6. Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, et al. Bats are natural reservoirs of SARS-like
\ncoronaviruses. Science. 2005;310(5748):676-9. Epub 2005\/10\/01. doi: 10.1126\/science.1118391.
\nPubMed PMID: 16195424.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\n7. Dominguez SR, O’Shea TJ, Oko LM, Holmes KV. Detection of group 1 coronaviruses in bats in
\nNorth America. Emerg Infect Dis. 2007;13(9):1295-300. Epub 2008\/02\/07. doi:
\n10.3201\/eid1309.070491. PubMed PMID: 18252098; PubMed Central PMCID: PMCPMC2857301.
\n8. Wu A, Peng Y, Huang B, Ding X, Wang X, Niu P, et al. Genome Composition and Divergence of the
\nNovel Coronavirus (2019-nCoV) Originating in China. Cell Host Microbe. 2020. Epub 2020\/02\/09. doi:
\n10.1016\/j.chom.2020.02.001. PubMed PMID: 32035028.
\n9. Xu X, Chen P, Wang J, Feng J, Zhou H, Li X, et al. Evolution of the novel coronavirus from the
\nongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China
\nLife Sci. 2020. Epub 2020\/02\/06. doi: 10.1007\/s11427-020-1637-5. PubMed PMID: 32009228.
\n10. Benvenuto D, Giovanetti M, Ciccozzi A, Spoto S, Angeletti S, Ciccozzi M. The 2019-new
\ncoronavirus epidemic: Evidence for virus evolution. J Med Virol. 2020. Epub 2020\/01\/30. doi:
\n10.1002\/jmv.25688. PubMed PMID: 31994738.
\n11. Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al. Discovery of a novel coronavirus
\nassociated with the recent pneumonia outbreak in humans and its potential bat origin. bioRxiv. 2020.
\n12. Chan JF, Kok KH, Zhu Z, Chu H, To KK, Yuan S, et al. Genomic characterization of the 2019 novel
\nhuman-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan.
\nEmerg Microbes Infect. 2020;9(1):221-36. Epub 2020\/01\/29. doi: 10.1080\/22221751.2020.1719902.
\nPubMed PMID: 31987001.
\n13. Wei X, Li X, Cui J. Evolutionary Perspectives on Novel Coronaviruses Identified in Pneumonia
\nCases in China. National Science Review. 2020.
\n14. Paraskevis D, Kostaki EG, Magiorkinis G, Panayiotakopoulos G, Sourvinos G, Tsiodras S.
\nFull-genome evolutionary analysis of the novel corona virus (2019-nCoV) rejects the hypothesis of
\nemergence as a result of a recent recombination event. Infect Genet Evol. 2020;79:104212. Epub
\n2020\/02\/01. doi: 10.1016\/j.meegid.2020.104212. PubMed PMID: 32004758.
\n15. Gralinski LE, Menachery VD. Return of the Coronavirus: 2019-nCoV. Viruses. 2020;12(2). Epub
\n2020\/01\/30. doi: 10.3390\/v12020135. PubMed PMID: 31991541.
\n16. Wong MC, Cregeen SJJ, Ajami NJ, Petrosino JF. Evidence of recombination in coronaviruses
\nimplicating pangolin origins of nCoV-2019. bioRxiv. 2020.
\n17. Xiao K, Zhai J, Feng Y, Zhou N, Zhang X, Zou J-J, et al. Isolation and Characterization of
\n2019-nCoV-like Coronavirus from Malayan Pangolins. bioRxiv. 2020:2020.02.17.951335. doi:
\n10.1101\/2020.02.17.951335.
\n18. Lam TT-Y, Shum MH-H, Zhu H-C, Tong Y-G, Ni X-B, Liao Y-S, et al. Identification of 2019-nCoV
\nrelated coronaviruses in Malayan pangolins in southern China. bioRxiv. 2020:2020.02.13.945485. doi:
\n10.1101\/2020.02.13.945485.
\n19. Wu C-I, Poo M-m. Moral imperative for the immediate release of 2019-nCoV sequence data.
\nNational Science Review. 2020. doi: 10.1093\/nsr\/nwaa030.
\n20. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24(8):1586-91.
\nEpub 2007\/05\/08. doi: 10.1093\/molbev\/msm088. PubMed PMID: 17483113.
\n21. Hanson G, Coller J. Codon optimality, bias and usage in translation and mRNA decay. Nature
\nreviews Molecular cell biology. 2018;19(1):20-30. Epub 2017\/10\/11. doi: 10.1038\/nrm.2017.91.
\nPubMed PMID: 29018283.
\n22. Wan Y, Shang J, Graham R, Baric RS, Li F. Receptor recognition by novel coronavirus from Wuhan:
\nAn analysis based on decade-long structural studies of SARS. J Virol. 2020. Epub 2020\/01\/31. doi:
\n10.1128\/JVI.00127-20. PubMed PMID: 31996437.
\n23. Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh C-L, Abiona O, et al. Cryo-EM Structure of the
\n2019-nCoV Spike in the Prefusion Conformation. bioRxiv. 2020:2020.02.11.944462. doi:
\n10.1101\/2020.02.11.944462.
\n24. Ou X, Liu Y, Lei X, Li P, Mi D, Ren L, et al. Characterization of spike glycoprotein of 2019-nCoV on
\nvirus entry and its immune cross-reactivity with spike glycoprotein of SARS-CoV.
\n2020:10.21203\/rs.2.4016\/v1. doi: 10.21203\/rs.2.24016\/v1.
\n25. Qu X-X, Hao P, Song X-J, Jiang S-M, Liu Y-X, Wang P-G, et al. Identification of Two Critical Amino
\nAcid Residues of the Severe Acute Respiratory Syndrome Coronavirus Spike Protein for Its Variation in
\nZoonotic Tropism Transition via a Double Substitution Strategy. Journal of Biological Chemistry.
\n2005;280(33):29588-95.
\n26. Ren W, Qu X, Li W, Han Z, Yu M, Zhou P, et al. Difference in Receptor Usage between Severe
\nAcute Respiratory Syndrome (SARS) Coronavirus and SARS-Like Coronavirus of Bat Origin. Journal of
\nVirology. 2008;82(4):1899. doi: 10.1128\/JVI.01085-07.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\n27. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human
\nrespiratory disease in China. Nature. 2020. Epub 2020\/02\/06. doi: 10.1038\/s41586-020-2008-3.
\nPubMed PMID: 32015508.
\n28. Ji W, Wang W, Zhao X, Zai J, Li X. Homologous recombination within the spike glycoprotein of the
\nnewly identified coronavirus may boost cross\u2010species transmission from snake to human. Journal of
\nmedical virology. 2020.
\n29. Zhao Z, Li H, Wu X, Zhong Y, Zhang K, Zhang Y-P, et al. Moderate mutation rate in the SARS
\ncoronavirus genome and its implications. BMC Evolutionary Biology. 2004;4(1):21. doi:
\n10.1186\/1471-2148-4-21.
\n30. Zhang C, Wang M. Origin time and epidemic dynamics of the 2019 novel coronavirus. bioRxiv.
\n2020.
\n31. Yu W-B, Tang G-D, Zhang L, Corlett RT. Decoding evolution and transmissions of novel
\npneumonia coronavirus using the whole genomic data. ChinaXiv. 2020:202002.00033. doi:
\n10.12074\/202002.00033.
\n32. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype
\nmaps. Bioinformatics. 2005;21(2):263-5. Epub 2004\/08\/07. doi: 10.1093\/bioinformatics\/bth457.
\nPubMed PMID: 15297300.
\n33. Waterson RH, Lander ES, Wilson RK, The Chimpanzee S, Analysis C. Initial sequence of the
\nchimpanzee genome and comparison with the human genome. Nature. 2005;437(7055):69-87. doi:
\n10.1038\/nature04072.
\n34. Gibbs RA, Rogers J, Katze MG, Bumgarner R, Weinstock GM, Mardis ER, et al. Evolutionary and
\nBiomedical Insights from the Rhesus Macaque Genome. Science. 2007;316(5822):222. doi:
\n10.1126\/science.1139247.
\n35. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, et al. Initial sequencing and
\ncomparative analysis of the mouse genome. Nature. 2002;420(6915):520-62. Epub 2002\/12\/06. doi:
\n10.1038\/nature01262. PubMed PMID: 12466850.
\n36. Graham RL, Sparks JS, Eckerle LD, Sims AC, Denison MR. SARS coronavirus replicase proteins in
\npathogenesis. Virus Res. 2008;133(1):88-100. Epub 2007\/04\/03. doi: 10.1016\/j.virusres.2007.02.017.
\nPubMed PMID: 17397959; PubMed Central PMCID: PMCPMC2637536.
\n37. Hu B, Zeng L-P, Yang X-L, Ge X-Y, Zhang W, Li B, et al. Discovery of a rich gene pool of bat
\nSARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLOS
\nPathogens. 2017;13(11):e1006698. doi: 10.1371\/journal.ppat.1006698.
\n38. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput.
\nNucleic Acids Res. 2004;32(5):1792-7. Epub 2004\/03\/23. doi: 10.1093\/nar\/gkh340. PubMed PMID:
\n15034147; PubMed Central PMCID: PMCPMC390337.
\n39. Slater GS, Birney E. Automated generation of heuristics for biological sequence comparison.
\nBMC Bioinformatics. 2005;6:31. doi: 10.1186\/1471-2105-6-31. PubMed PMID: 15713233; PubMed
\nCentral PMCID: PMCPMC553969.
\n40. Wernersson R, Pedersen AG. RevTrans: Multiple alignment of coding DNA from aligned amino
\nacid sequences. Nucleic Acids Res. 2003;31(13):3537-9. Epub 2003\/06\/26. PubMed PMID: 12824361;
\nPubMed Central PMCID: PMCPMC169015.
\n41. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular Evolutionary Genetics Analysis
\nacross Computing Platforms. Mol Biol Evol. 2018;35(6):1547-9. Epub 2018\/05\/04. doi:
\n10.1093\/molbev\/msy096. PubMed PMID: 29722887; PubMed Central PMCID: PMCPMC5967553.
\n42. Gao F, Chen C, Arab DA, Du Z, He Y, Ho SYW. EasyCodeML: A visual tool for analysis of selection
\nusing CodeML. Ecol Evol. 2019;9(7):3891-8. Epub 2019\/04\/25. doi: 10.1002\/ece3.5015. PubMed PMID:
\n31015974; PubMed Central PMCID: PMCPMC6467853.
\n43. Rozas J, Ferrer-Mata A, Sanchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al.
\nDnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets. Mol Biol Evol.
\n2017;34(12):3299-302. doi: 10.1093\/molbev\/msx248. PubMed PMID: 29029172.
\n44. Leigh JW, Bryant D. popart: full-feature software for haplotype network construction. Methods
\nin Ecology and Evolution. 2015;6(9):1110-6. doi: 10.1111\/2041-210x.12410.
\n45. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large
\nphylogenies. Bioinformatics. 2014;30(9):1312-3. Epub 2014\/01\/24. doi:
\n10.1093\/bioinformatics\/btu033. PubMed PMID: 24451623; PubMed Central PMCID:
\nPMCPMC3998144.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\n46. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.
\nBioinformatics. 2009;25(14):1754-60. Epub 2009\/05\/20. doi: 10.1093\/bioinformatics\/btp324.
\nPubMed PMID: 19451168; PubMed Central PMCID: PMCPMC2705234.
\n47. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment\/Map
\nformat and SAMtools. Bioinformatics. 2009;25(16):2078-9. Epub 2009\/06\/10. doi:
\n10.1093\/bioinformatics\/btp352. PubMed PMID: 19505943; PubMed Central PMCID:
\nPMCPMC2723002.
\n48. Sharp PM, Li WH. Codon usage in regulatory genes in Escherichia coli does not reflect selection
\nfor ‘rare’ codons. Nucleic Acids Res. 1986;14(19):7737-49. Epub 1986\/10\/10. doi:
\n10.1093\/nar\/14.19.7737. PubMed PMID: 3534792; PubMed Central PMCID: PMCPMC311793.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 1. Molecular divergence and selective pressures during the evolution of
\nSARS-CoV-2 and related viruses.
\nA. The phylogenetic tree of SARS-CoV-2 and the related Coronaviruses. The branch length
\n(dS) is presented, and the dN\/dS (\u03c9) value is given in the parenthesis. The phylogenetic tree
\nwas reconstructed with the synonymous sites in the concatenated CDSs of nine conserved
\nORFs (orf1ab, E, M, N, S, ORF3a, ORF6, ORF7a and ORF7b).
\nB. Conservation of 6 critical amino acid residues in the spike (S) protein. The critical active
\nsites are Y442, L472, N479, D480, T487, and Y491 in SARS-CoV, and they correspond to
\nL455, F486, Q493, S494, N501, and Y505 in SARS-CoV-2 (marked with inverted triangles),
\nrespectively.
\nC. Three candidate positively selected sites (marked with inverted triangles) in the
\nreceptor-binding domain (RBD) of spike protein (S:439N, S:483V and S:493Q) and the
\nsurrounding 10 amino acids.
\nB
\nGX Pangolin-CoV_P2V
\nGX Pangolin-CoV_P5E
\nGX Pangolin-CoV_P1E
\nGX Pangolin-CoV_P5L
\nGX Pangolin-CoV_P4L
\nGX Pangolin-CoV_P3B
\nGD Pangolin-CoV
\nBat SARSr-CoV ZXC21
\nBat SARSr-CoV ZC45
\n0.058(0.049)
\n0.051(0.090)
\n0.083(0.031)
\nBat RaTG13
\nSARS-CoV-2
\n0.094(0.036)
\nSARS-CoV
\nBat SARSr-CoV BM48-31
\n0.412(0.076)
\n0.644(0.045)
\n0.313(0.034)
\n0.428(0.098)
\n0.183(0.061)
\n0.158(0.034)
\n0.091(0.041)
\n0.196(0.053)
\nC
\nA
\nSARS-CoV-2
\nBat RaTG13
\nGD Pangolin-CoV
\nGX Pangolin-CoV_P2V
\nGX Pangolin-CoV_P5E
\nGX Pangolin-CoV_P1E
\nGX Pangolin-CoV_P5L
\nGX Pangolin-CoV_P4L
\nGX Pangolin-CoV_P3B
\nBat SARSr-CoV ZXC21
\nBat SARSr-CoV ZC45
\nSARS-CoV
\nBat SARSr-BM48-31
\nS:455L S:486F 493Q494S 501N 505Y
\nSARS-CoV-2
\nBat RaTG13
\nGD Pangolin-CoV
\nGX Pangolin-CoV_P5L
\nBat SARSr-CoV ZXC21
\nBat SARSr-CoV ZC45
\nSARS-CoV
\nBat SARSr-BM48-31
\nS:439N, RBD S:483V, RBD S:493Q, RBD, active site Downloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 2. The frequency spectra of derived mutations in 103 SARS-CoV-2 viruses. Note
\nthe derived alleles of synonymous mutations are skewed towards higher frequencies than
\nthose of nonsynonymous mutations.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 3. Linkage disequilibrium between SNPs in the SARS-CoV-2 viruses.
\nA. LD plot of any two SNP pairs among the 29 sites that have minor alleles in at least two
\nstrains. The number near slashes at the top of the image shows the coordinate of sites in the
\ngenome. Color in the square is given by standard (D’\/LOD), and the number in square is r
\n2
\nvalue.
\nB. The r
\n2
\nof each pair of SNPs (y-axis) against the genomic distance between that pair
\n(x-axis).
\nC. The LOD of each pair of SNPs (y-axis) against the genomic distance between that pair
\n(x-axis).
\nNote that in both B and C, the red point represents the LD between SNPs at 8,782 and 28,144.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 4. Haplotype analysis of SARS-CoV-2 viruses.
\nA. The haplotype networks of SARS-CoV-2 viruses. Blue represents the L type, and red is the
\nS type. The orange arrow indicates that the L type evolved from the S type. Note that in this
\nstudy, we marked each sample with a unique ID that starting with the geological location,
\nfollowed by the date the virus was isolated (see Table S1 for details). Each ID did not contain
\ninformation of the patient’s race or ethnicity. ZJ, Zhejiang; YN, Yunnan; WH, Wuhan; USA,
\nUnited States of America; TW, Taiwan; SZ, Shenzhen; SD, Shandong; SC, Sichuan; JX,
\nJiangxi; JS, Jiangsu; HZ, Hangzhou; GZ, Guangzhou; GD, Guangdong; FS, Foshan; CQ,
\nChongqing.
\nB. Evolution of the L and S types of SARS-CoV-2 viruses. Genome sequence alignments
\nwith the seven most closely related viruses indicated that the S type was most likely the
\nancient version of SARS-CoV-2. \u201c.\u201d, The nucleotide sequence is identical; \u201c-\u201d, gap.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 5. The unrooted phylogenetic tree of the 103 SARS-CoV-2 genomes. The ID of
\neach sample is the same as in Fig. 4A. Note WH_2019\/12\/31.a represents the reference
\ngenome (NC_045512). Note SZ_2020\/01\/13.a had C at both positions 8,782 and 28,144 in
\nthe genome, belonging to neither L nor S type.
\n5.0E-5
\nJapan_2020\/01\/25.a
\nAustralia_2020\/01\/30.a
\nFrance_2020\/01\/29.a
\nWH_2020\/01\/01.f
\nFS_2020\/01\/22.b
\nNepal_2020\/01\/13.a
\nUSA_2020\/01\/25.a
\nUSA_2020\/01\/22.b
\nGD_2020\/01\/14.a
\nFrance_2020\/01\/23.b
\nSC_2020\/01\/15.a
\nWH_2019\/12\/30.e
\nUSA_2020\/01\/29.b
\nGD_2020\/01\/15.c
\nWH_2020\/01\/01.a
\nTW_2020\/01\/31.a
\nAustralia_2020\/01\/24.a
\nWH_2020\/01\/02\/.b
\nFS_2020\/01\/22.a
\nWH_2019\/12\/26.a
\nEngland_2020\/01\/29.a
\nWH_2020\/01\/01.e
\nGZ_2020\/01\/22.a
\nGermany_2020\/01\/28.a
\nGD_2020\/01\/15.b
\nFS_2020\/01\/22.c
\nSZ_2020\/01\/13.a
\nUSA_2020\/01\/28.a
\nWH_2020\/01\/05.a
\nHZ_2020\/01\/19.a
\nJapan_2020\/01\/31.b
\nThailand_2020\/01\/13.a
\nSZ_2020\/01\/13.b
\nGD_2020\/01\/22.a
\nEngland_2020\/01\/29.b
\nThailand_2020\/01\/08.a
\nWH_2019\/12\/30.n
\nWH_2020\/01\/01.b
\nWH_2019\/12\/30.d
\nUSA_2020\/01\/27.a
\nUSA_2020\/01\/19.a
\nUSA_2020\/01\/21.a
\nSD_2020\/01\/19.a
\nJX_2020\/01\/11.a
\nSydney_2020\/01\/25.a
\nGD_2020\/01\/23.a
\nVietnam_2020\/01\/24.a
\nJapan_2020\/01\/29.a
\nJapan_2020\/01\/31.a
\nBelgium_2020\/02\/03.a
\nUSA_2020\/01\/22.a
\nHZ_2020\/01\/20.a
\nWH_2019\/12\/31.a
\nWH_2020\/01\/07.a
\nSkorea_2020\/01.a
\nFrance_2020\/01\/23.a
\nWH_2019\/12\/30.l
\nUSA_2020\/01\/25.b
\nWH_2019\/12\/30.c
\nCQ_2020\/01\/21.a
\nWH_2019\/12\/30.m
\nWH_2020\/01\/01.c
\nSingapore_2020\/01\/23.a
\nWH_2019\/12\/30.b
\nCQ_2020\/01\/18.a
\nUSA_2020\/01\/23.a
\nSZ_2020\/01\/16.a
\nAustralia_2020\/01\/28.a
\nSZ_2020\/01\/10.a
\nYN_2020\/01\/17.a
\nZJ_2020\/01\/17.a
\nSZ_2020\/01\/16.b
\nGD_2020\/01\/18.a WH_2019\/12\/30.i
\nWH_2019\/12\/24.a
\nYN_2020\/01\/17.b
\nJapan_2020\/01\/29.b
\nWH_2019\/12\/30.a
\nSingapore_2020\/01\/25.a
\nWH_2019\/12\/30.h
\nUSA_2020\/01\/29.d
\nSZ_2020\/01\/11.a
\nKorea_2020\/01\/25.a
\nWH_2020\/01\/01.d
\nWH_2019\/12\/30.g
\nWH_2020\/01\/02\/.a
\nZJ_2020\/01\/16.a
\nTW_2020\/01\/23.a
\nFrance_2020\/01\/29.b Singapore_2020\/02\/01.a
\nTW_2020\/02\/05.a
\nCQ_2020\/01\/23.a
\nWH_2019\/12\/30.j
\nWH_2019\/12\/30.k
\nUSA_2020\/01\/29.c
\nSydney_2020\/01\/22.a
\nAustralia_2020\/01\/25.a
\nJS_2020\/01\/19.a
\nGD_2020\/01\/17.a
\nUSA_2020\/01\/29.a
\nGD_2020\/01\/15.a
\nUSA_2020\/01\/31.a
\nWH_2019\/12\/30.f
\nS Type
\nL Type Downloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nFigure 6. The two types of SARS-CoV-2 showed differences in temporal and spatial
\ndistributions.
\nFigure 7. The heteroplasmy of SARS-CoV-2 viruses in human patients.
\nA. The viruses isolated from a patient that lived in the United States (USA_2020\/01\/21.a,
\nGISAID ID: EPI_ISL_404253) had the genotype Y (C or T) at both 8,782 and 28,144. The
\nmost likely explanation is that this patient was infected by both the L and S types. Note the
\nreference is L type.
\nB. The viruses Australia_2020\/01\/28.a (GISAID ID:EPI_ISL_407894) identified from a
\npatient in Australia had multiple degenerated nucleotides, and the best explanation is that this
\npatient was infected by at least two different strains of SARS-CoV-2 viruses.
\n0
\n20
\n40
\n60
\nWuhan Outside
\nWuhan
\nBefore
\nJan.7 2020
\nFrom
\nJan.7 2020
\nNumber of strains
\nS Type
\nL Type
\n96.3% 96.2%
\n61.6% 62.2%
\n38.4% 37.8%
\n3.7% 3.8%
\nP = 0.0004 P = 0.0008 Downloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nTable 1 The molecular divergence between SARS-CoV-2 and related viruses
\nGene
\nAligned
\nLength
\n(nt)
\nRaTG13
\nGD
\nPangolin-Co
\nV
\nGX
\nPangolin-C
\noV
\nSARSr-CoV
\nZC45 SARSCoV
\nSARSr-CoV
\nBM48-31
\nGenomic
\nAverage
\n28734 0.008\/0.17
\n(0.044)
\n0.026\/0.475
\n(0.054)
\n0.055\/0.722
\n(0.076)
\n0.044\/0.549
\n(0.081)
\n0.113\/0.926
\n(0.122)
\n0.143\/1.15
\n(0.124)
\nORF10 114 0.011\/0
\n(NA)
\n0.011\/0
\n(NA)
\n0.072\/0.044
\n(1.637)
\n0.011\/0
\n(NA)
\n– –
\nORF3a 825 0.009\/0.157
\n(0.06)
\n0.019\/0.291
\n(0.065)
\n0.066\/0.518
\n(0.128)
\n0.052\/0.508
\n(0.102)
\n0.188\/0.918
\n(0.205)
\n0.271\/0.923
\n(0.294)
\nORF6 183 0\/0.098
\n(0)
\n0.014\/0.217
\n(0.062)
\n0.038\/0.491
\n(0.077)
\n0.027\/0.173
\n(0.158)
\n0.191\/0.913
\n(0.209)
\n0.393\/1.512
\n(0.26)
\nORF7a 363 0.011\/0.177
\n(0.061)
\n0.018\/0.275
\n(0.066)
\n0.073\/0.477
\n(0.153)
\n0.066\/0.351
\n(0.188)
\n0.088\/0.697
\n(0.126)
\n0.337\/1.14
\n(0.296)
\nORF7b 129 0.01\/0
\n(NA)
\n0.02\/0.455
\n(0.043)
\n0.17\/0.436
\n(0.39)
\n0.029\/0.181
\n(0.162)
\n0.155\/0.401
\n(0.387)
\n0.264\/NA
\n(NA)
\nORF8 363 0.021\/0.07
\n(0.303)
\n0.032\/0.303
\n(0.105)
\n0.099\/1.015
\n(0.098)
\n0.03\/0.603
\n(0.05)
\n– –
\nE 225 0\/0.018
\n(0)
\n0\/0.037
\n(0)
\n0.006\/0.096
\n(0.063)
\n0\/0.056
\n(0)
\n0.027\/0.166
\n(0.164)
\n0.043\/0.352
\n(0.121)
\nM 666 0.004\/0.186
\n(0.021)
\n0.014\/0.298
\n(0.046)
\n0.025\/0.372
\n(0.067)
\n0.016\/0.283
\n(0.055)
\n0.07\/0.576
\n(0.121)
\n0.109\/1.292
\n(0.085)
\nN 1257 0.005\/0.131
\n(0.039)
\n0.011\/0.144
\n(0.076)
\n0.04\/0.304
\n(0.132)
\n0.036\/0.333
\n(0.108)
\n0.059\/0.381
\n(0.155)
\n0.102\/1.197
\n(0.085)
\norf1a 13215 0.009\/0.167
\n(0.054)
\n0.026\/0.488
\n(0.053)
\n0.073\/0.811
\n(0.09)
\n0.026\/0.405
\n(0.063)
\n0.148\/1.141
\n(0.129)
\n0.174\/1.199
\n(0.145)
\norf1ab 21288 0.007\/0.152
\n(0.044)
\n0.019\/0.495
\n(0.039)
\n0.055\/0.776
\n(0.071)
\n0.031\/0.527
\n(0.058)
\n0.105\/0.962
\n(0.109)
\n0.125\/1.108
\n(0.113)
\nS (spike) 3819 0.014\/0.321
\n(0.043)
\n0.075\/0.69
\n(0.108)
\n0.06\/0.86
\n(0.07)
\n0.138\/1.063
\n(0.13)
\n0.172\/1.265
\n(0.136)
\n0.217\/1.518
\n(0.143)
\nFor each gene, the dN and dS values between SARS-CoV-2 and another virus are given, and
\nthe dN\/dS (\u03c9) ratio is given in the parenthesis.
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020
\nTable
\n2. The heteroplasmy of SARS
\n-CoV
\n-2 viruses in human patients
\nAccession
\nnumber
\nGenomic position
\nRef
\nallele
\nAlt
\nallele
\nRef
\nreads
\nAlt
\nreads
\nLocation_date GISAID ID
\nSRR10903401 1821
\nG
\nA 52
\n5 WH_2020\/01\/02.a EPI_ISL_406716
\nSRR10903401 19164
\nC
\nT 40 12 WH_2020\/01\/02.a EPI_ISL_406716
\nSRR10903401 24323
\nA
\nC 102 67 WH_2020\/01\/02.a EPI_ISL_406716
\nSRR10903401 26314
\nG
\nA 15
\n2 WH_2020\/01\/02.a EPI_ISL_406716
\nSRR10903401 26590
\nT
\nC 10
\n2 WH_2020\/01\/02.a EPI_ISL_406716
\nSRR10903402 11563
\nC
\nT 164 26 WH_2020
\n\/01
\n\/02.b EPI_ISL_406717
\nSRR11092057 9064 TTAT TT 13
\n2 WH_2019
\n\/12
\n\/30.e EPI_ISL_402124
\nSRR11092057 17825
\nC
\nT 19
\n5 WH_2019
\n\/12
\n\/30.e EPI_ISL_402124
\nSRR11092059 4795
\nC
\nT 10
\n4 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 6360
\nA
\nG 39
\n5 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 7042
\nG
\nA
\n5
\n3 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 12153
\nC
\nT 15 13 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 15921
\nG
\nT 19
\n2 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 16474
\nA
\nG 11
\n2 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092059 20344
\nC
\nT 19
\n2 WH_2019\/12\/30.h EPI_ISL_402130
\nSRR11092062 565
\nT
\nC 64 23 WH_2019
\n\/12
\n\/30.e EPI_ISL_402124
\nSRR11092062 17825
\nC
\nT 141 34 WH_2019
\n\/12
\n\/30.e EPI_ISL_402124
\nSRR11092063 29441
\nC
\nA
\n6
\n2 WH_2019
\n\/12
\n\/30.d EPI_ISL_402127
\nDownloaded from https:\/\/academic.oup.com\/nsr\/advance-article-abstract\/doi\/10.1093\/nsr\/nwaa036\/5775463<\/a> by guest on 06 March 2020<\/p>\n","protected":false},"excerpt":{"rendered":"

RESEARCH ARTICLE MICROBIOLOGY On the origin and continuing evolution of SARS-CoV-2 Xiaolu Tang1,7 , Changcheng … \u7d9a\u304d\u3092\u8aad\u3080 2020\u5e743\u67086\u65e5<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[68,17],"tags":[],"class_list":["post-4077","post","type-post","status-publish","format-standard","hentry","category-group_2","category-covid"],"jetpack_publicize_connections":[],"acf":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/posts\/4077","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4077"}],"version-history":[{"count":2,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/posts\/4077\/revisions"}],"predecessor-version":[{"id":4981,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=\/wp\/v2\/posts\/4077\/revisions\/4981"}],"wp:attachment":[{"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4077"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4077"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bicyclogy.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4077"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}