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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Analysis of synonymous codon usage in SARS <italic>Coronavirus</italic> and other viruses in the <italic>Nidovirales</italic></title><author><name sortKey="Gu, Wanjun" sort="Gu, Wanjun" uniqKey="Gu W" first="Wanjun" last="Gu">Wanjun Gu</name></author><author><name sortKey="Zhou, Tong" sort="Zhou, Tong" uniqKey="Zhou T" first="Tong" last="Zhou">Tong Zhou</name></author><author><name sortKey="Ma, Jianmin" sort="Ma, Jianmin" uniqKey="Ma J" first="Jianmin" last="Ma">Jianmin Ma</name></author><author><name sortKey="Sun, Xiao" sort="Sun, Xiao" uniqKey="Sun X" first="Xiao" last="Sun">Xiao Sun</name></author><author><name sortKey="Lu, Zuhong" sort="Lu, Zuhong" uniqKey="Lu Z" first="Zuhong" last="Lu">Zuhong Lu</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">15041183</idno><idno type="pmc">7127446</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127446</idno><idno type="RBID">PMC:7127446</idno><idno type="doi">10.1016/j.virusres.2004.01.006</idno><date when="2004">2004</date><idno type="wicri:Area/Pmc/Corpus">001584</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001584</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Analysis of synonymous codon usage in SARS <italic>Coronavirus</italic> and other viruses in the <italic>Nidovirales</italic></title><author><name sortKey="Gu, Wanjun" sort="Gu, Wanjun" uniqKey="Gu W" first="Wanjun" last="Gu">Wanjun Gu</name></author><author><name sortKey="Zhou, Tong" sort="Zhou, Tong" uniqKey="Zhou T" first="Tong" last="Zhou">Tong Zhou</name></author><author><name sortKey="Ma, Jianmin" sort="Ma, Jianmin" uniqKey="Ma J" first="Jianmin" last="Ma">Jianmin Ma</name></author><author><name sortKey="Sun, Xiao" sort="Sun, Xiao" uniqKey="Sun X" first="Xiao" last="Sun">Xiao Sun</name></author><author><name sortKey="Lu, Zuhong" sort="Lu, Zuhong" uniqKey="Lu Z" first="Zuhong" last="Lu">Zuhong Lu</name></author></analytic><series><title level="j">Virus Research</title><idno type="ISSN">0168-1702</idno><idno type="eISSN">1872-7492</idno><imprint><date when="2004">2004</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In this study, we calculated the codon usage bias in severe acute respiratory syndrome <italic>Coronavirus</italic> (SARSCoV) and performed a comparative analysis of synonymous codon usage patterns in SARSCoV and 10 other evolutionary related viruses in the <italic>Nidovirales</italic>. Although there is a significant variation in codon usage bias among different SARSCoV genes, codon usage bias in SARSCoV is a little slight, which is mainly determined by the base compositions on the third codon position. By comparing synonymous codon usage patterns in different viruses, we observed that synonymous codon usage pattern in these virus genes was virus specific and phylogenetically conserved, but it was not host specific. Phylogenetic analysis based on codon usage pattern suggested that SARSCoV was diverged far from all three known groups of <italic>Coronavirus</italic>. Compositional constraints could explain most of the variation of synonymous codon usage among these virus genes, while gene function is also correlated to synonymous codon usages to a certain extent. However, translational selection and gene length have no effect on the variations of synonymous codon usage in these virus genes.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Chan Yeung, M" uniqKey="Chan Yeung M">M Chan-Yeung</name></author><author><name sortKey="Yu, W C" uniqKey="Yu W">W.C Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chiapello, H" uniqKey="Chiapello H">H Chiapello</name></author><author><name sortKey="Lisacek, F" uniqKey="Lisacek F">F Lisacek</name></author><author><name sortKey="Caboche, M" uniqKey="Caboche M">M Caboche</name></author><author><name sortKey="Henaut, A" uniqKey="Henaut A">A Henaut</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chiapello, H" uniqKey="Chiapello H">H Chiapello</name></author><author><name sortKey="Ollivier, E" uniqKey="Ollivier E">E Ollivier</name></author><author><name sortKey="Landes Devauchelle, C" uniqKey="Landes Devauchelle C">C Landes-Devauchelle</name></author><author><name sortKey="Nitschke, P" uniqKey="Nitschke P">P Nitschke</name></author><author><name sortKey="Risler, J L" uniqKey="Risler J">J.L Risler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chiusano, M L" uniqKey="Chiusano M">M.L Chiusano</name></author><author><name sortKey="D Nofrio, G" uniqKey="D Nofrio G">G D’Onofrio</name></author><author><name sortKey="Alvarez Valin, F" uniqKey="Alvarez Valin F">F Alvarez-Valin</name></author><author><name sortKey="Jabbari, K" uniqKey="Jabbari K">K Jabbari</name></author><author><name sortKey="Colonna, G" uniqKey="Colonna G">G Colonna</name></author><author><name sortKey="Bernardi, G" uniqKey="Bernardi G">G Bernardi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chiusano, M L" uniqKey="Chiusano M">M.L Chiusano</name></author><author><name sortKey="Alvarez Valin, F" uniqKey="Alvarez Valin F">F 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Majumdar</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gouy, M" uniqKey="Gouy M">M Gouy</name></author><author><name sortKey="Gautier, C" uniqKey="Gautier C">C Gautier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Grantham, R" uniqKey="Grantham R">R Grantham</name></author><author><name sortKey="Gautier, C" uniqKey="Gautier C">C Gautier</name></author><author><name sortKey="Gouy, M" uniqKey="Gouy M">M Gouy</name></author><author><name sortKey="Mercier, R" uniqKey="Mercier R">R Mercier</name></author><author><name sortKey="Pave, A" uniqKey="Pave A">A Pave</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Grantham, R" uniqKey="Grantham R">R Grantham</name></author><author><name sortKey="Gautier, C" uniqKey="Gautier C">C Gautier</name></author><author><name sortKey="Gouy, M" uniqKey="Gouy M">M Gouy</name></author><author><name sortKey="Jacobzone, M" uniqKey="Jacobzone M">M Jacobzone</name></author><author><name sortKey="Mercier, R" uniqKey="Mercier R">R Mercier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gupta, S K" uniqKey="Gupta S">S.K Gupta</name></author><author><name sortKey="Majumdar, S" uniqKey="Majumdar S">S Majumdar</name></author><author><name sortKey="Bhattacharya, T K" uniqKey="Bhattacharya T">T.K Bhattacharya</name></author><author><name sortKey="Ghosh, T C" uniqKey="Ghosh T">T.C Ghosh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ikemura, T" uniqKey="Ikemura T">T Ikemura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ikemura, T" uniqKey="Ikemura T">T Ikemura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jenkins, G M" uniqKey="Jenkins G">G.M Jenkins</name></author><author><name sortKey="Pagel, M" uniqKey="Pagel M">M Pagel</name></author><author><name sortKey="Gould, E A" uniqKey="Gould E">E.A Gould</name></author><author><name sortKey="Zanotto, P M D A" uniqKey="Zanotto P">P.M.d.A Zanotto</name></author><author><name sortKey="Holmes, E C" uniqKey="Holmes E">E.C Holmes</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jenkins, G M" uniqKey="Jenkins G">G.M Jenkins</name></author><author><name sortKey="Holmes, E C" uniqKey="Holmes E">E.C Holmes</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Karlin, S" uniqKey="Karlin S">S Karlin</name></author><author><name sortKey="Mrazek, J" uniqKey="Mrazek J">J Mrazek</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lesnik, T" uniqKey="Lesnik T">T Lesnik</name></author><author><name sortKey="Solomovici, J" uniqKey="Solomovici J">J Solomovici</name></author><author><name sortKey="Deana, A" uniqKey="Deana A">A Deana</name></author><author><name sortKey="Ehrlich, R" uniqKey="Ehrlich R">R Ehrlich</name></author><author><name 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Majumdar</name></author><author><name sortKey="Gupta, S K" uniqKey="Gupta S">S.K Gupta</name></author><author><name sortKey="Sundararaj, V S" uniqKey="Sundararaj V">V.S Sundararaj</name></author><author><name sortKey="Ghosh, T C" uniqKey="Ghosh T">T.C Ghosh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marais, G" uniqKey="Marais G">G Marais</name></author><author><name sortKey="Duret, L" uniqKey="Duret L">L Duret</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Marra, M A" uniqKey="Marra M">M.A Marra</name></author><author><name sortKey="Jones, S J" uniqKey="Jones S">S.J Jones</name></author><author><name sortKey="Astell, C R" uniqKey="Astell C">C.R Astell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Martin, A" uniqKey="Martin A">A Martin</name></author><author><name sortKey="Bertranpetit, J" uniqKey="Bertranpetit J">J 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Bredenbeek</name></author><author><name sortKey="Dobbe, J C" uniqKey="Dobbe J">J.C Dobbe</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wright, F" uniqKey="Wright F">F Wright</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Xie, T" uniqKey="Xie T">T Xie</name></author><author><name sortKey="Ding, D F" uniqKey="Ding D">D.F Ding</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Virus Res</journal-id><journal-id journal-id-type="iso-abbrev">Virus Res</journal-id><journal-title-group><journal-title>Virus Research</journal-title></journal-title-group><issn pub-type="ppub">0168-1702</issn><issn pub-type="epub">1872-7492</issn><publisher><publisher-name>Elsevier B.V.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">15041183</article-id><article-id pub-id-type="pmc">7127446</article-id><article-id pub-id-type="publisher-id">S0168-1702(04)00020-6</article-id><article-id pub-id-type="doi">10.1016/j.virusres.2004.01.006</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Analysis of synonymous codon usage in SARS <italic>Coronavirus</italic> and other viruses in the <italic>Nidovirales</italic></article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Gu</surname><given-names>Wanjun</given-names></name></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Tong</given-names></name></contrib><contrib contrib-type="author"><name><surname>Ma</surname><given-names>Jianmin</given-names></name></contrib><contrib contrib-type="author"><name><surname>Sun</surname><given-names>Xiao</given-names></name></contrib><contrib contrib-type="author"><name><surname>Lu</surname><given-names>Zuhong</given-names></name><email>zhlu@seu.edu.cn</email><xref rid="COR1" ref-type="corresp">∗</xref></contrib></contrib-group><aff>Key Laboratory of Molecular and Biomolecular Electronics, Southeast University, Ministry of Education, Nanjing, Jiangsu 210096, China</aff><author-notes><corresp id="COR1"><label>*</label>Corresponding author. Tel.: +86-25-83619983; fax: +86-25-83619983. <email>zhlu@seu.edu.cn</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>25</day><month>2</month><year>2004</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><month>5</month><year>2004</year></pub-date><pub-date pub-type="epub"><day>25</day><month>2</month><year>2004</year></pub-date><volume>101</volume><issue>2</issue><fpage>155</fpage><lpage>161</lpage><history><date date-type="received"><day>25</day><month>10</month><year>2003</year></date><date date-type="rev-recd"><day>9</day><month>1</month><year>2004</year></date><date date-type="accepted"><day>9</day><month>1</month><year>2004</year></date></history><permissions><copyright-statement>Copyright © 2004 Elsevier B.V. All rights reserved.</copyright-statement><copyright-year>2004</copyright-year><copyright-holder>Elsevier B.V.</copyright-holder><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract><p>In this study, we calculated the codon usage bias in severe acute respiratory syndrome <italic>Coronavirus</italic> (SARSCoV) and performed a comparative analysis of synonymous codon usage patterns in SARSCoV and 10 other evolutionary related viruses in the <italic>Nidovirales</italic>. Although there is a significant variation in codon usage bias among different SARSCoV genes, codon usage bias in SARSCoV is a little slight, which is mainly determined by the base compositions on the third codon position. By comparing synonymous codon usage patterns in different viruses, we observed that synonymous codon usage pattern in these virus genes was virus specific and phylogenetically conserved, but it was not host specific. Phylogenetic analysis based on codon usage pattern suggested that SARSCoV was diverged far from all three known groups of <italic>Coronavirus</italic>. Compositional constraints could explain most of the variation of synonymous codon usage among these virus genes, while gene function is also correlated to synonymous codon usages to a certain extent. However, translational selection and gene length have no effect on the variations of synonymous codon usage in these virus genes.</p></abstract><kwd-group><title>Abbreviations</title><kwd>bp, base pair</kwd><kwd>SARSCoV, severe acute respiratory syndrome <italic>Coronavirus</italic></kwd><kwd>RSCU, relative synonymous codon usage</kwd><kwd>ENC, effective number of codons</kwd><kwd>CA, correspondence analysis</kwd><kwd>GC<sub>3S</sub>, the frequency of G+C at the synonymous third position of sense codons</kwd><kwd>A<sub>3S</sub>, T<sub>3S</sub>, G<sub>3S</sub> and C<sub>3S</sub>, the adenine, thymine, guanine and cytosine content at synonymous third positions</kwd><kwd>ORF, open reading frame</kwd><kwd>PCR, polymerase chain reaction</kwd><kwd>S.D., standard deviation</kwd></kwd-group><kwd-group><title>Keywords</title><kwd>Synonymous codon usage</kwd><kwd>Mutational bias</kwd><kwd>Selection pressure</kwd><kwd>Base composition</kwd><kwd>SARS</kwd><kwd><italic>Coronavirus</italic></kwd></kwd-group></article-meta></front><body><sec><label>1</label><title>Introduction</title><p>Synonymous codons are not used equally both within and between genomes (<xref rid="BIB15" ref-type="bibr">Grantham et al., 1980</xref>, <xref rid="BIB31" ref-type="bibr">Martin et al., 1989</xref>, <xref rid="BIB25" ref-type="bibr">Lloyd and Sharp, 1992</xref>). Compositional constraints and natural selection are thought to be the two main factors accounting for codon usage variation among genes in different organisms (<xref rid="BIB22" ref-type="bibr">Karlin and Mrazek, 1996</xref>, <xref rid="BIB38" ref-type="bibr">Sharp et al., 1986</xref>, <xref rid="BIB23" ref-type="bibr">Lesnik et al., 2000</xref>). The diverse patterns of codon usage in mammals may arise from compositional constraints of the genomes (<xref rid="BIB22" ref-type="bibr">Karlin and Mrazek, 1996</xref>, <xref rid="BIB12" ref-type="bibr">Francino and Ochman, 1999</xref>, <xref rid="BIB27" ref-type="bibr">Majumdar et al., 1999</xref>, <xref rid="BIB13" ref-type="bibr">Ghosh et al., 2000</xref>). In contrast, in some unicellular organisms, such as <italic>Escherichia coli</italic> and <italic>Saccharomyces cerevisiae</italic>, high expressed genes have a strong selective preference for codons with a high concentration of the corresponding acceptor tRNA molecule, whereas low expressed genes displayed a more uniform pattern of codon usage (<xref rid="BIB14" ref-type="bibr">Gouy and Gautier, 1982</xref>, <xref rid="BIB16" ref-type="bibr">Grantham et al., 1981</xref>, <xref rid="BIB18" ref-type="bibr">Ikemura, 1981</xref>, <xref rid="BIB19" ref-type="bibr">Ikemura, 1985</xref>; <xref rid="BIB38" ref-type="bibr">Sharp et al., 1986</xref>, <xref rid="BIB23" ref-type="bibr">Lesnik et al., 2000</xref>). Moreover, mutational pressure rather than translational selection is the most important determinant of the codon bias in some human RNA viruses (<xref rid="BIB24" ref-type="bibr">Levin and Whittome, 2000</xref>, <xref rid="BIB20" ref-type="bibr">Jenkins et al., 2001</xref>, <xref rid="BIB21" ref-type="bibr">Jenkins and Holmes, 2003</xref>). Furthermore, replicational and transcriptional selection is responsible for the codon usage variation among the genes of <italic>Borrelia burgdorferi</italic> (<xref rid="BIB32" ref-type="bibr">McInerney, 1998</xref>). In some other researches, codon usage was also found to be related to gene function (<xref rid="BIB2" ref-type="bibr">Chiapello et al., 1998</xref>, <xref rid="BIB10" ref-type="bibr">Epstein et al., 2000</xref>, <xref rid="BIB26" ref-type="bibr">Ma et al., 2002</xref>), protein secondary structure (<xref rid="BIB4" ref-type="bibr">Chiusano et al., 1999</xref>, <xref rid="BIB5" ref-type="bibr">Chiusano et al., 2000</xref>, <xref rid="BIB34" ref-type="bibr">Oresic and Shalloway, 1998</xref>, <xref rid="BIB41" ref-type="bibr">Xie and Ding, 1998</xref>, <xref rid="BIB17" ref-type="bibr">Gupta et al., 2000</xref>), cellular location of gene products (<xref rid="BIB3" ref-type="bibr">Chiapello et al., 1999</xref>) and gene length (<xref rid="BIB6" ref-type="bibr">Coghlan and Wolfe, 2000</xref>, <xref rid="BIB28" ref-type="bibr">Marais and Duret, 2001</xref>, <xref rid="BIB33" ref-type="bibr">Moriyama and Powell, 1998</xref>).</p><p>Severe acute respiratory syndrome (SARS) is a respiratory disease that was recently reported in Asia, North America and Europe (<xref rid="BIB1" ref-type="bibr">Chan-Yeung and Yu, 2003</xref>, <xref rid="BIB9" ref-type="bibr">Drazen, 2003</xref>). Although genome sequence of severe acute respiratory syndrome <italic>Coronavirus</italic> (SARSCoV) has been published and many studies have been performed on SARSCoV in recent months (<xref rid="BIB35" ref-type="bibr">Paul et al., 2003</xref>, <xref rid="BIB36" ref-type="bibr">Qin et al., 2003</xref>, <xref rid="BIB30" ref-type="bibr">Marra et al., 2003</xref>, <xref rid="BIB39" ref-type="bibr">Snijder et al., 2003</xref>), little genomic analysis is available on this virus. Codon usage data of SARSCoV might give some clues to the features of SARSCoV genome and some evolutionary information of this virus. Here, we analyzed the codon usage data of this virus and other viruses in the order <italic>Nidovirales</italic>. The key evolutionary determinants of codon usage bias in these viruses were also investigated.</p></sec><sec><label>2</label><title>Materials and methods</title><sec><label>2.1</label><title>Materials</title><p>SARSCoV is a large, enveloped, positive-stranded RNA virus, which belongs to order <italic>Nidovirales</italic>, family Coronaviridae, genus <italic>Coronavirus</italic> in virus taxonomy (<xref rid="BIB30" ref-type="bibr">Marra et al., 2003</xref>). The complete genome and coding sequences of SARSCoV TOR2 isolation were obtained from GenBank (Version 134.0). To keep the statistical significance of codon usage bias, only sequences with length above 150 bps were analyzed (<xref rid="TBL1" ref-type="table">Table 1</xref>). To compare the codon usage pattern among different viruses, coding genes of 10 other viruses belonging to order <italic>Nidovirales</italic> (six viruses in the genus <italic>Coronavirus</italic>, four viruses in the genus <italic>Arterivirus</italic>) were also parsed from GenBank (Version 134.0) (<xref rid="TBL2" ref-type="table">Table 2</xref>).<table-wrap position="float" id="TBL1"><label>Table 1</label><caption><p>Identified ORFs (length > 150 bps) in the SARSCoV (TOR2 isolation) genome<xref rid="TBLFN1" ref-type="table-fn">a</xref>, <xref rid="TBLFN2" ref-type="table-fn">b</xref></p></caption><table frame="hsides" rules="groups"><tbody><tr><td>Gene product</td><td><italic>L</italic><xref rid="TBLFN1" ref-type="table-fn">a</xref></td><td>ENC</td><td>GC<sub>3S</sub> (%)</td><td><italic>f</italic><sub>1</sub>′<xref rid="TBLFN2" ref-type="table-fn">b</xref></td></tr><tr><td colspan="5"><hr></hr></td></tr><tr><td>Putative orf1ab polyprotein</td><td align="char">21222</td><td align="char">48.47</td><td align="char">32.20</td><td align="char">−0.60</td></tr><tr><td>Orf1a polyprotein</td><td align="char">13149</td><td align="char">48.24</td><td align="char">33.10</td><td align="char">−0.57</td></tr><tr><td>Putative spike glycoprotein</td><td align="char">3468</td><td align="char">45.73</td><td align="char">28.30</td><td align="char">−0.85</td></tr><tr><td>Putative uncharacterized protein</td><td align="char">825</td><td align="char">47.66</td><td align="char">34.50</td><td align="char">−0.37</td></tr><tr><td>Putative uncharacterized protein</td><td align="char">465</td><td align="char">42.80</td><td align="char">45.10</td><td align="char">1.34</td></tr><tr><td>Putative small envelope protein E</td><td align="char">231</td><td align="char">59.06</td><td align="char">38.70</td><td align="char">0.34</td></tr><tr><td>Putative protein M</td><td align="char">666</td><td align="char">59.04</td><td align="char">42.50</td><td align="char">0.51</td></tr><tr><td>Putative uncharacterized protein</td><td align="char">192</td><td align="char">42.19</td><td align="char">28.80</td><td align="char">−1.08</td></tr><tr><td>Putative uncharacterized protein</td><td align="char">269</td><td align="char">43.05</td><td align="char">30.60</td><td align="char">−0.55</td></tr><tr><td>Putative nucleocapsid protein</td><td align="char">1269</td><td align="char">54.16</td><td align="char">37.60</td><td align="char">0.49</td></tr><tr><td>Putative uncharacterized protein</td><td align="char">297</td><td align="char">46.62</td><td align="char">58.10</td><td align="char">1.87</td></tr></tbody></table><table-wrap-foot><fn id="TBLFN1"><label>a</label><p><italic>L</italic> represents the length of identified ORF.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN2"><label>b</label><p><italic>f</italic><sub>1</sub>′ represent the first axis values of each gene in CA.</p></fn></table-wrap-foot></table-wrap><table-wrap position="float" id="TBL2"><label>Table 2</label><caption><p>Phylogenetic breakdown, accession number, GC<sub>3S</sub> and the first two axis values in CA of 11 selected viruses in order <italic>Nidovirales</italic><xref rid="TBLFN3" ref-type="table-fn">a</xref>, <xref rid="TBLFN4" ref-type="table-fn">b</xref></p></caption><table frame="hsides" rules="groups"><tbody><tr><td>Organism<xref rid="TBLFN3" ref-type="table-fn">a</xref></td><td>Accession number</td><td>GC<sub>3S</sub>(%)</td><td><italic>f</italic><sub>1</sub>′<xref rid="TBLFN4" ref-type="table-fn">b</xref></td><td><italic>f</italic><sub>2</sub>′<xref rid="TBLFN4" ref-type="table-fn">b</xref></td></tr><tr><td colspan="5"><hr></hr></td></tr><tr><td colspan="5"><italic>Coronavirus</italic></td></tr><tr><td> HCoV 229E</td><td>NC_002645</td><td>30.89</td><td align="char">−0.84</td><td align="char">−0.16</td></tr><tr><td> PEDV</td><td>NC_003436</td><td>37.32</td><td align="char">−0.04</td><td align="char">0.42</td></tr><tr><td> TGV</td><td>NC_002306</td><td>27.02</td><td align="char">−0.99</td><td align="char">−0.08</td></tr><tr><td> BCoV</td><td>NC_003045</td><td>29.43</td><td align="char">−0.75</td><td align="char">0.48</td></tr><tr><td> MHV</td><td>NC_001846</td><td>38.30</td><td align="char">−0.16</td><td align="char">0.27</td></tr><tr><td> AIBV</td><td>NC_001451</td><td>26.09</td><td align="char">−0.90</td><td align="char">−1.30</td></tr><tr><td> SARSCoV</td><td>NC_004718</td><td>37.23</td><td align="char">0.05</td><td align="char">0.36</td></tr><tr><td colspan="5">
<italic>Arterivirus</italic></td><td>
</td><td>
</td><td>
</td><td>
</td></tr><tr><td> EAV</td><td>NC_002532</td><td>47.28</td><td>0.80</td><td align="char">0.47</td></tr><tr><td> LDEV</td><td>NC_002534</td><td>45.18</td><td>0.53</td><td align="char">0.43</td></tr><tr><td> PRRSV</td><td>NC_001961</td><td>53.76</td><td>1.31</td><td align="char">0.55</td></tr><tr><td> SHFV</td><td>NC_003092</td><td>48.43</td><td>1.09</td><td align="char">−0.14</td></tr></tbody></table><table-wrap-foot><fn id="TBLFN3"><label>a</label><p><italic>Organism abbreviation</italic>: HCoV 229E, human <italic>Coronavirus</italic> 229E; PEDV, porcine epidemic diarrhea virus; TGV, transmissible gastroenteritis virus; BCoV, bovine <italic>Coronavirus</italic>; MHV, murine hepatitis virus; AIBV, avian infectious bronchitis virus; SARSCoV, SARS <italic>Coronavirus</italic>; EAV, equine arteritis virus; LDEV, lactate dehydrogenase elevating virus; PRRSV, porcine reproductive and respiratory syndrome virus; SHFV, simian hemorrhagic fever virus.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN4"><label>b</label><p><italic>f</italic><sub>1</sub>′ and <italic>f</italic><sub>2</sub>′, respectively, represent the first axis mean value and the second axis mean value in CA of each genome.</p></fn></table-wrap-foot></table-wrap></p></sec><sec><label>2.2</label><title>Methods</title><sec><label>2.2.1</label><title>Synonymous codon usage measures (RSCU)</title><p>Relative synonymous codon usage values of each codon in a gene were used to examine the synonymous codon usage without the confounding influence of amino acid composition (<xref rid="BIB37" ref-type="bibr">Sharp and Li, 1986</xref>). N<sub>3S</sub>, the frequency of base N at synonymous third codon positions, was also used to calculate the extent of base composition bias. Additionally, the effective number of codons of a gene (ENC) was used to quantify the codon usage bias of a gene (<xref rid="BIB40" ref-type="bibr">Wright, 1990</xref>), which is the best overall estimator of absolute synonymous codon usage bias (<xref rid="BIB7" ref-type="bibr">Comeron and Aguade, 1998</xref>). ENC value ranges from 20 (when only one codon is used per amino acid) to 61 (when all synonymous codons are equally used for each amino acid).</p></sec><sec><label>2.2.2</label><title>Correspondence analysis (CA)</title><p>Correspondence analysis was used to investigate the major trend in codon usage variation among genes. Each gene is represented as a 59 dimensional vector, and each dimension corresponds to the RSCU value of one sense codon (excluding AUG, UGG and three stop codons).</p><p>CA based on RSCU values relies on two main steps (<xref rid="BIB29" ref-type="bibr">Mardia et al., 1979</xref>). The first step is to measure the similarities in codon usage using the squared Euclidean distance among all genes, and the resulting distance table will be used to compute the coordinates of the genes in a multidimensional space. The second step provides the visualization of these Euclidean distances through positioning genes by successive orthogonal projections of the cloud of points. Essentially, this process consists in finding the linear transformations <italic>f</italic><sub>1</sub>′,<italic>f</italic><sub>2</sub>′,…,<italic>f</italic><sub>58</sub>′ of the original variables <italic>f</italic><sub>1</sub>′,<italic>f</italic><sub>2</sub>′,…,<italic>f</italic><sub>59</sub>′. The <italic>f</italic>′-variables are calculated and ordered according to the values of relative variance. <italic>f</italic><sub>1</sub>′ is the maximum value; <italic>f</italic><sub>2</sub>′ is the next value and is by construction not correlated with <italic>f</italic><sub>1</sub>′. The same applies to <italic>f</italic><sub>3</sub>′,<italic>f</italic><sub>4</sub>′, and so on, until <italic>f</italic><sub>58</sub>′. So, genes with similar codon usage are neighbors on the components of projection.</p></sec><sec><label>2.2.3</label><title>Statistical methods</title><p>Linear regression analysis was used to find the correlation between codon usage bias and nucleotide composition. One tailed <italic>t</italic>-test was used to compare the variation of codon usage between different gene groups (<xref rid="BIB11" ref-type="bibr">Ewens and Grant, 2001</xref>). As a null hypothesis, it is assumed that mean values of codon usage indices in different gene groups is statistically the same. Under the null assumption, t-statistic could be calculated. Then, <italic>P</italic>-value is derived and it is taken as significance when <italic>P</italic>-value is below 0.05.</p><p>A C++ program was developed to calculate the codon usage indices for each gene. CA and other statistical analysis were performed with statistical software SPSS 11.0.</p></sec></sec></sec><sec><label>3</label><title>Results</title><sec><label>3.1</label><title>Synonymous codon usage in SARSCoV</title><p>The details of coding genes in SARSCoV and the overall RSCU values of 61 sense codons in SARSCoV were, respectively, shown in <xref rid="TBL1" ref-type="table">Table 1</xref>, <xref rid="TBL3" ref-type="table">Table 3</xref>. All preferentially used codons in SARSCoV are all A-ended or U-ended codons (<xref rid="TBL3" ref-type="table">Table 3</xref>). SARSCoV is a GC poor genome with GC content of 37.52%. Due to compositional constraints, it is expected that A-ended and/or U-ended codons should be preferentially used in this genome. To study the codon usage variation among different SARSCoV genes, ENC and GC<sub>3S</sub> values of different SARSCoV genes were calculated (<xref rid="TBL1" ref-type="table">Table 1</xref>). ENC values of different SARSCoV genes vary from 42.19 to 59.06, with a mean value of 48.99 and S.D. of 6.41. Because all ENC values of SARSCoV genes are much higher (ENC>40), codon usage bias in SARSCoV genome is a little slight. However, there is a marked variation in codon usage pattern among different SARSCoV genes (S.D.=6.41). Similarly, GC<sub>3S</sub> values of each SARSCoV gene also confirm the heterogeneity of synonymous codon usage among different SARSCoV genes, which range from 28.3 to 58.1% with a mean of 37.23 and S.D. of 8.78%.<table-wrap position="float" id="TBL3"><label>Table 3</label><caption><p>Synonymous codon usage in SARSCoV<xref rid="TBLFN5" ref-type="table-fn">a</xref>, <xref rid="TBLFN6" ref-type="table-fn">b</xref>, <xref rid="TBLFN7" ref-type="table-fn">c</xref></p></caption><table frame="hsides" rules="groups"><tbody><tr><td>AA<xref rid="TBLFN5" ref-type="table-fn">a</xref></td><td>Codon</td><td>RSCU</td><td><italic>N</italic><xref rid="TBLFN6" ref-type="table-fn">b</xref></td><td>AA<xref rid="TBLFN5" ref-type="table-fn">a</xref></td><td>Codon</td><td>RSCU</td><td><italic>N</italic><xref rid="TBLFN6" ref-type="table-fn">b</xref></td></tr><tr><td colspan="8"><hr></hr></td></tr><tr><td>Ala</td><td><bold>GCU</bold></td><td align="char"><bold>2.08</bold></td><td align="char">531</td><td>Ile</td><td><bold>AUU</bold></td><td align="char"><bold>1.72</bold></td><td align="char">410</td></tr><tr><td></td><td>GCC</td><td align="char">0.58</td><td align="char">147</td><td></td><td>AUC</td><td align="char">0.67</td><td align="char">159</td></tr><tr><td></td><td>GCA</td><td align="char">1.13</td><td align="char">288</td><td></td><td>AUA</td><td align="char">0.62</td><td align="char">148</td></tr><tr><td></td><td>GCG</td><td align="char">0.22</td><td align="char">55</td><td>Cys</td><td><bold>UGU</bold></td><td align="char"><bold>1.27</bold></td><td align="char">280</td></tr><tr><td>
Gly</td><td>
GGG</td><td align="char">
0.17</td><td align="char">
37</td><td></td><td>
UGC</td><td align="char">
0.73</td><td align="char">
160</td></tr><tr><td></td><td>GGA</td><td align="char">0.85</td><td align="char">182</td><td>Thr</td><td><bold>ACU</bold></td><td align="char"><bold>1.66</bold></td><td align="char">427</td></tr><tr><td></td><td>GGC</td><td align="char">0.95</td><td align="char">202</td><td></td><td>ACC</td><td align="char">0.59</td><td align="char">153</td></tr><tr><td></td><td><bold>GGU</bold></td><td align="char"><bold>2.02</bold></td><td align="char">431</td><td></td><td>ACG</td><td align="char">0.18</td><td align="char">46</td></tr><tr><td>
Val</td><td>
<bold>GUU</bold></td><td align="char">
<bold>1.71</bold></td><td align="char">
479</td><td></td><td>
ACA</td><td align="char">
1.57</td><td align="char">
406</td></tr><tr><td></td><td>GUC</td><td align="char">0.67</td><td align="char">188</td><td>Asn</td><td><bold>AAU</bold></td><td align="char"><bold>1.24</bold></td><td align="char">449</td></tr><tr><td></td><td>GUA</td><td align="char">0.83</td><td align="char">232</td><td></td><td>AAC</td><td align="char">0.76</td><td align="char">277</td></tr><tr><td></td><td>GUG</td><td align="char">0.78</td><td align="char">219</td><td>Gln</td><td><bold>CAA</bold></td><td align="char"><bold>1.16</bold></td><td align="char">298</td></tr><tr><td>
Leu</td><td>
UUA</td><td align="char">
1.04</td><td align="char">
238</td><td></td><td>
CAG</td><td align="char">
0.84</td><td align="char">
214</td></tr><tr><td></td><td>UUG</td><td align="char">1.10</td><td align="char">251</td><td>Tyr</td><td><bold>UAU</bold></td><td align="char"><bold>1.12</bold></td><td align="char">345</td></tr><tr><td></td><td><bold>CUU</bold></td><td align="char"><bold>1.79</bold></td><td align="char">409</td><td></td><td>UAC</td><td align="char">0.88</td><td align="char">270</td></tr><tr><td></td><td>CUC</td><td align="char">0.83</td><td align="char">191</td><td>His</td><td><bold>CAU</bold></td><td align="char"><bold>1.29</bold></td><td align="char">187</td></tr><tr><td></td><td>CUA</td><td align="char">0.64</td><td align="char">147</td><td></td><td>CAC</td><td align="char">0.71</td><td align="char">103</td></tr><tr><td></td><td>CUG</td><td align="char">0.60</td><td align="char">138</td><td>Asp</td><td><bold>GAU</bold></td><td align="char"><bold>1.24</bold></td><td align="char">463</td></tr><tr><td>
Phe</td><td>
UUC</td><td align="char">
0.77</td><td align="char">
260</td><td></td><td>
GAC</td><td align="char">
0.76</td><td align="char">
282</td></tr><tr><td></td><td><bold>UUU</bold></td><td align="char"><bold>1.23</bold></td><td align="char">414</td><td>Glu</td><td><bold>GAA</bold></td><td align="char"><bold>1.04</bold></td><td align="char">354</td></tr><tr><td>
Pro</td><td>
<bold>CCU</bold></td><td align="char">
<bold>1.74</bold></td><td align="char">
247</td><td></td><td>
GAG</td><td align="char">
0.96</td><td align="char">
326</td></tr><tr><td></td><td>CCC</td><td align="char">0.40</td><td align="char">57</td><td>Lys</td><td><bold>AAA</bold></td><td align="char"><bold>1.04</bold></td><td align="char">421</td></tr><tr><td></td><td>CCA</td><td align="char">1.70</td><td align="char">241</td><td></td><td>AAG</td><td align="char">0.96</td><td align="char">388</td></tr><tr><td></td><td>CCG</td><td align="char">0.16</td><td align="char">22</td><td>Arg</td><td>CGU</td><td align="char">1.77</td><td align="char">153</td></tr><tr><td>
Ser</td><td>
<bold>UCU</bold></td><td align="char">
<bold>1.96</bold></td><td align="char">
310</td><td align="char"></td><td>
CGC</td><td align="char">
0.72</td><td align="char">
62</td></tr><tr><td></td><td>UCC</td><td align="char">0.42</td><td align="char">67</td><td></td><td>CGA</td><td align="char">0.44</td><td align="char">38</td></tr><tr><td></td><td>UCA</td><td align="char">1.70</td><td align="char">270</td><td></td><td>CGG</td><td align="char">0.09</td><td align="char">8</td></tr><tr><td></td><td>UCG</td><td align="char">0.23</td><td align="char">36</td><td></td><td><bold>AGA</bold></td><td align="char"><bold>2.08</bold></td><td align="char">180</td></tr><tr><td></td><td>AGU</td><td align="char">1.17</td><td align="char">186</td><td></td><td>AGG</td><td align="char">0.90</td><td align="char">78</td></tr><tr><td></td><td>AGC</td><td align="char">0.52</td><td align="char">82</td><td></td><td></td><td align="char"></td><td align="char"></td></tr></tbody></table><table-wrap-foot><fn id="TBLFN5"><label>a</label><p>AA is the abbreviation of amino acid.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN6"><label>b</label><p><italic>N</italic> represents the number of occurrence of each sense codon.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN7"><label>c</label><p>The preferentially used codons for each amino acid are displayed in bold.</p></fn></table-wrap-foot></table-wrap></p></sec><sec><label>3.2</label><title>Synonymous codon usage in different viruses is virus specific, but not host specific</title><p>CA was implemented for all identified ORFs from each of the 11 virus genomes as a single dataset, which consists of 103 coding sequences. CA detected one major trend in the first axis which accounted for 15.40% of the total variation, and none of the other axes individually accounted for more than 7.60% of the total variation. A plot of the first axis and the second axis of each gene was shown in <xref rid="FIG1" ref-type="fig">Fig. 1</xref>. Although this graph is a little complex with some overlap among genes from different genomes, it is clear that genes from a particular genome tend to cluster together. The separation of one virus genome from other virus genomes is determined to be significant on both axes (<italic>t</italic>-test, <italic>P</italic>-value <10<sup>−15</sup> on the first axis and <italic>P</italic>-value <10<sup>−3</sup> on the second axis). So, similar to codon usage in mammals and bacteria, synonymous codon usage in these viruses is also virus specific.<fig id="FIG1"><label>Fig. 1</label><caption><p>A plot of the values of the first axis and the second axis of each gene in CA (abbreviations of the viruses: AIBV, avian infectious bronchitis virus; BCoV, bovine <italic>Coronavirus</italic>; EAV, equine arteritis virus; HCoV 229E, human <italic>Coronavirus</italic> 229E; LDEV, lactate dehydrogenase elevating virus; MHV, murine hepatitis virus; PEDV, porcine epidemic diarrhea virus; PRRSV, porcine reproductive and respiratory syndrome virus; SARSCoV, SARS <italic>Coronavirus</italic>; SHFV, simian hemorrhagic fever virus; TGV, transmissible gastroenteritis virus. <italic>f</italic><sub>1</sub>′ and <italic>f</italic><sub>2</sub>′, respectively, represent the values of the first and the second axis of each gene in CA).</p></caption><graphic xlink:href="gr1"></graphic></fig></p><p>To show whether there is a correlation between virus codon usage and its host, these 103 virus genes were divided into several groups according to the virus host. For example, because both SARSCoV genes and human <italic>Coronavirus</italic> 229E infect human, genes in these two viruses were incorporated as a group. Next, <italic>t</italic>-test was also used to test whether the separation of different viral genes which infect different hosts is significant. The <italic>P</italic>-value is 0.57 on the first axis and is 0.08 on the second axis, which suggested that codon usage in different virus genes was not host specific.</p></sec><sec><label>3.3</label><title>Phylogenetic analysis of these viruses based on codon usage pattern</title><p>In <xref rid="FIG1" ref-type="fig">Fig. 1</xref>, all virus genes in the genus <italic>Coronavirus</italic> were plotted in red. At the same time, all viral genes in the genus <italic>Arterivirus</italic> were plotted in blue. <italic>Coronavirus</italic> genes are mainly located on the left side of the plot, while a majority of <italic>Arterivirus</italic> genes are located on the right side. The separation of <italic>Coronavirus</italic> genes and <italic>Arterivirus</italic> genes on the first axis is statistically significant (<italic>t</italic>-test, <italic>P</italic>-value <10<sup>−15</sup>). Hence, synonymous codon usage appears to be conserved between phylogenetically related viruses.</p><p>Also, SARSCoV genes were widely extended in the first axis (<xref rid="FIG1" ref-type="fig">Fig. 1</xref>). Six of eleven SARSCoV genes were located in the cluster of <italic>Coronavirus</italic> genes, while the other five SARSCoV genes were located in the cluster of <italic>Arterivirus</italic> genes. Therefore, SARSCoV might have been diverged far from all three known <italic>Coronavirus</italic> groups. Comparing with all other viruses in the genus <italic>Coronavirus</italic>, it might be more evolutionary related to the genus <italic>Arterivirus</italic>.</p></sec><sec><label>3.4</label><title>Mutational bias is the main factor determines the codon usage variation among different viruses</title><p>Linear regression analysis was implemented to find whether there is some correlation between synonymous codon usage bias and nucleotide compositions. The <italic>R</italic><sup>2</sup> value and significance level of these regression analyses was listed in <xref rid="TBL4" ref-type="table">Table 4</xref>. The first axis value of each gene in CA is closely correlated with all the base compositions on the third codon position, while the second axis of each gene is correlated with some base compositions on the third codon position to a certain extent. Therefore, compositional constraint mainly determines the variation of synonymous codon usage among these virus genes.<table-wrap position="float" id="TBL4"><label>Table 4</label><caption><p>Summary of linear regression analysis between the first two axes in CA and the nucleotide contents on the third codon position in all selected virus genes<xref rid="TBLFN8" ref-type="table-fn">a</xref></p></caption><table frame="hsides" rules="groups"><tbody><tr><td>Base composition</td><td><italic>f</italic><sub>1</sub>′<xref rid="TBLFN9" ref-type="table-fn">b</xref></td><td><italic>f</italic><sub>2</sub>′<xref rid="TBLFN9" ref-type="table-fn">b</xref></td></tr><tr><td colspan="3"><hr></hr></td></tr><tr><td>A<sub>3S</sub></td><td>0.791<xref rid="TBLFN11" ref-type="table-fn">****</xref></td><td>0.085<xref rid="TBLFN10" ref-type="table-fn">*</xref></td></tr><tr><td>T<sub>3S</sub></td><td>0.239<xref rid="TBLFN11" ref-type="table-fn">****</xref></td><td>0.444<xref rid="TBLFN11" ref-type="table-fn">****</xref></td></tr><tr><td>G<sub>3S</sub></td><td>0.484<xref rid="TBLFN11" ref-type="table-fn">****</xref></td><td>0.082<xref rid="TBLFN10" ref-type="table-fn">*</xref></td></tr><tr><td>C<sub>3S</sub></td><td>0.720<xref rid="TBLFN11" ref-type="table-fn">****</xref></td><td>0.0001<sup>NS</sup></td></tr><tr><td>GC<sub>3S</sub></td><td>0.936<xref rid="TBLFN11" ref-type="table-fn">****</xref></td><td>0.018<sup>NS</sup></td></tr></tbody></table><table-wrap-foot><fn><p>NS in superscript represent non-significant.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN8"><label>a</label><p>Value in this table is the <italic>R</italic><sup>2</sup> value of each linear regression analysis.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN9"><label>b</label><p><italic>f</italic><sub>1</sub>′ and <italic>f</italic><sub>2</sub>′, respectively, represent the values of the first and the second axis of each gene in CA.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN10"><label>*</label><p><italic>P</italic>-value <0.01.</p></fn></table-wrap-foot><table-wrap-foot><fn id="TBLFN11"><label>****</label><p><italic>P</italic>-value <0.00001.</p></fn></table-wrap-foot></table-wrap></p><p>Furthermore, we plotted the first axis values in CA and GC<sub>3S</sub> values of each gene (<xref rid="FIG2" ref-type="fig">Fig. 2</xref>). The GC<sub>3S</sub> mean value of genes in coronaviruses ranges from 26.09 to 37.32, and it ranges from 45.18 to 53.76 in arteriviruses (<xref rid="TBL2" ref-type="table">Table 2</xref>). Although codon usage bias appears to be conserved between evolutionary related viruses (Section 3.3), the patterns of codon usage in different virus genes also appear to be a direct function of the GC content on the third codon position of these genes.<fig id="FIG2"><label>Fig. 2</label><caption><p>A dot plot of the first axis value in correspondence analysis and GC<sub>3S</sub> of each gene (<italic>f</italic><sub>1</sub>′ denotes the first axis value in correspondence analysis of each gene, and GC<sub>3S</sub> denotes the G+C content on the third synonymous codon position of each gene).</p></caption><graphic xlink:href="gr2"></graphic></fig></p></sec><sec><label>3.5</label><title>Gene function also drives the codon usage variation among different viruses</title><p>The plot of ENC and GC<sub>3S</sub> is another effective way to explore codon usage variation among genes (<xref rid="BIB40" ref-type="bibr">Wright, 1990</xref>). ENC values of each virus gene were plotted against its corresponding GC<sub>3S</sub> (<xref rid="FIG3" ref-type="fig">Fig. 3</xref>). The solid line represents the curve if codon usage is only determined by GC content on the third codon position. A large proportion of points lie near to the solid line on the left region of this distribution. It also suggests that mutational bias is the main factor determines the codon usage variation among these genes. However, there are also some points lying below the expected curve. Hence, other than mutational bias, there might be some additional factors drive the codon usage variation among these genes.<fig id="FIG3"><label>Fig. 3</label><caption><p>ENC vs. GC<sub>3S</sub> plot of all virus genes (ENC denotes the effective number of codon of each gene, and GC<sub>3S</sub> denotes the G+C content on the third synonymous codon position of each gene. The solid line represents the relationship between GG3S and ENC under random codon usage assumption).</p></caption><graphic xlink:href="gr3"></graphic></fig></p><p>To show whether translational selection or gene function were correlated with the observed variation in codon bias, all virus genes were grouped into several classes according to gene function. Because most of these viruses contain genes coding for RNA polymerase, envelop protein and structural glycoprotein, these three gene groups were selected to find whether there is some correlation between codon usage and gene function. One tailed <italic>t</italic>-test was then performed on ENC values of these genes with the hypothesis that there is no correlation between codon usage bias and gene function. Some associations have been found. Average codon usage bias is higher in RNA polymerase gene group than in envelop gene group (<italic>t</italic>-test, <italic>P</italic>-value=0.031), and it is higher in polymerase gene group than in structural glycoprotein gene group (<italic>t</italic>-test, <italic>P</italic>-value=0.002). But, there is no association between codon usage in structural glycoprotein gene group and envelop protein gene group (<italic>t</italic>-test, <italic>P</italic>-value=0.74). Because the structural glycoprotein and envelop protein are all structural proteins in these viruses and RNA polymerase is a nonstructural protein, it is clear that codon usage in structural genes is significantly diverged from that in nonstructural genes. On the other hand, structural genes are generally highly expressed than nonstructural genes. So, if translational selection was also contributed to codon usage bias in these genes, codon usage bias in structural genes should be higher than in RNA polymerase genes. However, RNA polymerase genes (ENC=49.25) were found to have greater codon usage bias than structural genes (ENC=54.60 for envelop gene and ENC=55.33 for structural glycoprotein). Hence, codon usage bias in these virus genes is not related to gene expression level. Furthermore, we also performed a linear regression analysis on ENC value and gene length of each gene. But, there was no significant correlation between codon usage and gene length in these virus genes (<italic>P</italic>-value>0.05). So, gene function, rather than translational selection and gene length, is another factor accounting for codon usage variation among these virus genes.</p></sec></sec><sec><label>4</label><title>Discussion</title><p>Our analysis revealed that synonymous codon usage bias in SARSCoV was less biased, which was mainly determined by the base compositions on the third codon position. Comparative analysis of codon usage bias in the order <italic>Nidovirales</italic> also suggested that codon usage in these viruses was virus specific and mutational bias was the main factor drives the codon usage variation among these viruses. Gene function was also related to codon usage bias in these viruses to some extent. But, translational selection and gene length might have no effect on the codon usage pattern in these viruses. Some published results has shown that the overall extent of codon usage bias in RNA viruses is low and there is little variation in bias between genes (<xref rid="BIB24" ref-type="bibr">Levin and Whittome, 2000</xref>, <xref rid="BIB20" ref-type="bibr">Jenkins et al., 2001</xref>, <xref rid="BIB21" ref-type="bibr">Jenkins and Holmes, 2003</xref>). Although SARSCoV is a newly detected RNA virus infecting human, the synonymous codon usage pattern in SARSCoV we described here is also in accordance with these published codon usage pattern of human RNA viruses (<xref rid="BIB21" ref-type="bibr">Jenkins and Holmes, 2003</xref>). Because mutation rates in RNA viruses are much higher than those in DNA viruses (<xref rid="BIB8" ref-type="bibr">Drake and Holland, 1999</xref>), it is understandable that mutation pressure is the main determinant of codon usage bias in SARSCoV. Our analysis also revealed that there was no host specific codon usage pattern in these viruses. So, host genome might have no obvious effect on the evolution of these viruses.</p><p>Some phylogenetic analysis of SARSCoV (<xref rid="BIB36" ref-type="bibr">Qin et al., 2003</xref>, <xref rid="BIB30" ref-type="bibr">Marra et al., 2003</xref>) has shown that SARSCoV does not closely resemble any of the three previously known groups in genus <italic>Coronavirus</italic>. But <xref rid="BIB39" ref-type="bibr">Snijder et al. (2003)</xref> has proposed that SARSCoV is most closely related to group 2 <italic>Coronavirus</italic>es. Based on different codon usage patterns in different coronaviruses, we revealed that codon usage patterns of each virus was phylogenetically distinct and SARSCoV might have been diverged far from all three known <italic>Coronavirus</italic> groups, which is in accordance with the results <xref rid="BIB36" ref-type="bibr">Qin et al. (2003)</xref> and <xref rid="BIB30" ref-type="bibr">Marra et al. (2003)</xref> proposed.</p><p>Codon usage patterns and the phylogenetic results we proposed here are useful to understand the processes governing the evolution of SARSCoV, especially the roles played by mutation pressure and natural selection. 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