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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genetic Variation of the Human α-2-Heremans-Schmid Glycoprotein
(AHSG) Gene Associated with the Risk of SARS-CoV Infection</title><author><name sortKey="Zhu, Xiaohui" sort="Zhu, Xiaohui" uniqKey="Zhu X" first="Xiaohui" last="Zhu">Xiaohui Zhu</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Wang, Yan" sort="Wang, Yan" uniqKey="Wang Y" first="Yan" last="Wang">Yan Wang</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><addr-line>General Hospital of Beijing Command, PLA, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhang, Hongxing" sort="Zhang, Hongxing" uniqKey="Zhang H" first="Hongxing" last="Zhang">Hongxing Zhang</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Liu, Xuan" sort="Liu, Xuan" uniqKey="Liu X" first="Xuan" last="Liu">Xuan Liu</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Chen, Ting" sort="Chen, Ting" uniqKey="Chen T" first="Ting" last="Chen">Ting Chen</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Yang, Ruifu" sort="Yang, Ruifu" uniqKey="Yang R" first="Ruifu" last="Yang">Ruifu Yang</name><affiliation><nlm:aff id="aff3"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Institute of Microbiology and Epidemiology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Shi, Yuling" sort="Shi, Yuling" uniqKey="Shi Y" first="Yuling" last="Shi">Yuling Shi</name><affiliation><nlm:aff id="aff4"><addr-line>General Hospital of Guangzhou Command, PLA, Guangzhou, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Cao, Wuchun" sort="Cao, Wuchun" uniqKey="Cao W" first="Wuchun" last="Cao">Wuchun Cao</name><affiliation><nlm:aff id="aff3"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Institute of Microbiology and Epidemiology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Li, Ping" sort="Li, Ping" uniqKey="Li P" first="Ping" last="Li">Ping Li</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Ma, Qingjun" sort="Ma, Qingjun" uniqKey="Ma Q" first="Qingjun" last="Ma">Qingjun Ma</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhai, Yun" sort="Zhai, Yun" uniqKey="Zhai Y" first="Yun" last="Zhai">Yun Zhai</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="He, Fuchu" sort="He, Fuchu" uniqKey="He F" first="Fuchu" last="He">Fuchu He</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhou, Gangqiao" sort="Zhou, Gangqiao" uniqKey="Zhou G" first="Gangqiao" last="Zhou">Gangqiao Zhou</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Cao, Cheng" sort="Cao, Cheng" uniqKey="Cao C" first="Cheng" last="Cao">Cheng Cao</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21904596</idno><idno type="pmc">3163911</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163911</idno><idno type="RBID">PMC:3163911</idno><idno type="doi">10.1371/journal.pone.0023730</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">001372</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001372</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Genetic Variation of the Human α-2-Heremans-Schmid Glycoprotein
(AHSG) Gene Associated with the Risk of SARS-CoV Infection</title><author><name sortKey="Zhu, Xiaohui" sort="Zhu, Xiaohui" uniqKey="Zhu X" first="Xiaohui" last="Zhu">Xiaohui Zhu</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Wang, Yan" sort="Wang, Yan" uniqKey="Wang Y" first="Yan" last="Wang">Yan Wang</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><addr-line>General Hospital of Beijing Command, PLA, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhang, Hongxing" sort="Zhang, Hongxing" uniqKey="Zhang H" first="Hongxing" last="Zhang">Hongxing Zhang</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Liu, Xuan" sort="Liu, Xuan" uniqKey="Liu X" first="Xuan" last="Liu">Xuan Liu</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Chen, Ting" sort="Chen, Ting" uniqKey="Chen T" first="Ting" last="Chen">Ting Chen</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Yang, Ruifu" sort="Yang, Ruifu" uniqKey="Yang R" first="Ruifu" last="Yang">Ruifu Yang</name><affiliation><nlm:aff id="aff3"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Institute of Microbiology and Epidemiology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Shi, Yuling" sort="Shi, Yuling" uniqKey="Shi Y" first="Yuling" last="Shi">Yuling Shi</name><affiliation><nlm:aff id="aff4"><addr-line>General Hospital of Guangzhou Command, PLA, Guangzhou, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Cao, Wuchun" sort="Cao, Wuchun" uniqKey="Cao W" first="Wuchun" last="Cao">Wuchun Cao</name><affiliation><nlm:aff id="aff3"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Institute of Microbiology and Epidemiology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Li, Ping" sort="Li, Ping" uniqKey="Li P" first="Ping" last="Li">Ping Li</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Ma, Qingjun" sort="Ma, Qingjun" uniqKey="Ma Q" first="Qingjun" last="Ma">Qingjun Ma</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhai, Yun" sort="Zhai, Yun" uniqKey="Zhai Y" first="Yun" last="Zhai">Yun Zhai</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="He, Fuchu" sort="He, Fuchu" uniqKey="He F" first="Fuchu" last="He">Fuchu He</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zhou, Gangqiao" sort="Zhou, Gangqiao" uniqKey="Zhou G" first="Gangqiao" last="Zhou">Gangqiao Zhou</name><affiliation><nlm:aff id="aff2"><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></nlm:aff></affiliation></author><author><name sortKey="Cao, Cheng" sort="Cao, Cheng" uniqKey="Cao C" first="Cheng" last="Cao">Cheng Cao</name><affiliation><nlm:aff id="aff1"><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Genetic background may play an important role in the process of SARS-CoV
infection and SARS development. We found several proteins that could interact
with the nucleocapsid protein of the SARS coronavirus (SARS-CoV).
α-2-Heremans-Schmid Glycoprotein (AHSG), which is required for macrophage
deactivation by endogenous cations, is associated with inflammatory regulation.
Cytochrome P450 Family 3A (CYP4F3A) is an <italic>ω</italic>-oxidase that
inactivates Leukotriene B4 (LTB4) in human neutrophils and the liver. We
investigated the association between the polymorphisms of these two
inflammation-associated genes and SARS development. The linkage disequilibrium
(LD) maps of these two genes were built with Haploview using data on
CHB+JPT (version 2) from the HapMap. A total of ten tag SNPs were selected
and genotyped. In the Guangzhou cohort study, after adjusting for age and sex,
two <italic>AHSG</italic> SNPs and one <italic>CYP4F3</italic> SNP were found to
be associated with SARS susceptibility: rs2248690 (adjusted odds ratio
[AOR] 2.42; 95% confidence interval [CI] 1.30-4.51);
rs4917 (AOR 1.84; 95% CI 1.02-3.34); and rs3794987 (AOR 2.01; 95%
CI 1.10–3.68). To further validate the association, the ten tag SNPs were
genotyped in the Beijing cohort. After adjusting for age and sex, only rs2248690
(AOR, 1.63; 95% CI, 1.30–2.04) was found to be associated with SARS
susceptibility. The combined analysis of the two studies confirmed tag SNP
rs2248690 in <italic>AHSG</italic> as a susceptibility variant (AOR 1.70;
95% CI 1.37–2.09). The statistical analysis of the rs2248690
genotype data among the patients and healthy controls in the HCW cohort, who
were all similarly exposed to the SARS virus, also supported the findings.
Further, the SNP rs2248690 affected the transcriptional activity of the
<italic>AHSG</italic> promoter and thus regulated the AHSG serum level.
Therefore, our study has demonstrated that the AA genotype of rs2268690, which
leads to a higher AHSG serum concentration, was significantly associated with
protection against SARS development.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Peiris, Js" uniqKey="Peiris J">JS Peiris</name></author><author><name sortKey="Lai, St" uniqKey="Lai S">ST Lai</name></author><author><name sortKey="Poon, Ll" uniqKey="Poon L">LL Poon</name></author><author><name sortKey="Guan, Y" uniqKey="Guan Y">Y Guan</name></author><author><name sortKey="Yam, Ly" uniqKey="Yam L">LY Yam</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ksiazek, Tg" uniqKey="Ksiazek T">TG Ksiazek</name></author><author><name sortKey="Erdman, D" uniqKey="Erdman D">D Erdman</name></author><author><name sortKey="Goldsmith, Cs" uniqKey="Goldsmith C">CS Goldsmith</name></author><author><name sortKey="Zaki, Sr" uniqKey="Zaki S">SR Zaki</name></author><author><name sortKey="Peret, T" uniqKey="Peret T">T Peret</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Drosten, 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21904596</article-id><article-id pub-id-type="pmc">3163911</article-id><article-id pub-id-type="publisher-id">PONE-D-11-03160</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0023730</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology</subject><subj-group><subject>Biochemistry</subject><subj-group><subject>Glycobiology</subject><subj-group><subject>Glycoproteins</subject></subj-group></subj-group></subj-group><subj-group><subject>Genetics</subject><subj-group><subject>Human Genetics</subject><subj-group><subject>Genetic Association Studies</subject></subj-group></subj-group><subj-group><subject>Population Genetics</subject><subj-group><subject>Genetic Polymorphism</subject></subj-group></subj-group><subj-group><subject>Genetic Mutation</subject></subj-group></subj-group><subj-group><subject>Immunology</subject><subj-group><subject>Immunity</subject><subj-group><subject>Inflammation</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine</subject><subj-group><subject>Clinical Genetics</subject></subj-group><subj-group><subject>Clinical Immunology</subject><subj-group><subject>Immunity</subject><subj-group><subject>Inflammation</subject></subj-group></subj-group></subj-group><subj-group><subject>Clinical Research Design</subject><subj-group><subject>Cohort Studies</subject></subj-group></subj-group><subj-group><subject>Epidemiology</subject></subj-group><subj-group><subject>Infectious Diseases</subject><subj-group><subject>Viral Diseases</subject><subj-group><subject>SARS</subject></subj-group></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Genetic Variation of the Human α-2-Heremans-Schmid Glycoprotein
(AHSG) Gene Associated with the Risk of SARS-CoV Infection</article-title><alt-title alt-title-type="running-head">Variation of the AHSG Gene Associated with
SARS</alt-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Zhu</surname><given-names>Xiaohui</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Wang</surname><given-names>Yan</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Zhang</surname><given-names>Hongxing</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff6"><sup>6</sup></xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Liu</surname><given-names>Xuan</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chen</surname><given-names>Ting</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Yang</surname><given-names>Ruifu</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Shi</surname><given-names>Yuling</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cao</surname><given-names>Wuchun</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Li</surname><given-names>Ping</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Ma</surname><given-names>Qingjun</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zhai</surname><given-names>Yun</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff6"><sup>6</sup></xref></contrib><contrib contrib-type="author"><name><surname>He</surname><given-names>Fuchu</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff6"><sup>6</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Gangqiao</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cao</surname><given-names>Cheng</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>State Key Laboratory of Pathogen and Bio-Security, Beijing Institute of Biotechnology, Beijing, China</addr-line></aff><aff id="aff2"><label>2</label><addr-line>State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China</addr-line></aff><aff id="aff3"><label>3</label><addr-line>State Key Laboratory of Pathogen and Bio-Security, Institute of Microbiology and Epidemiology, Beijing, China</addr-line></aff><aff id="aff4"><label>4</label><addr-line>General Hospital of Guangzhou Command, PLA, Guangzhou, China</addr-line></aff><aff id="aff5"><label>5</label><addr-line>General Hospital of Beijing Command, PLA, Beijing, China</addr-line></aff><aff id="aff6"><label>6</label><addr-line>National Engineering Research Center for Protein Drugs, Beijing, China</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Thiel</surname><given-names>Volker</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">Kantonal Hospital St. Gallen, Switzerland</aff><author-notes><corresp id="cor1">* E-mail: <email>zhougq114@gmail.com</email> (GZ); <email>caoc@nic.bmi.ac.cn</email> (CC)</corresp><fn fn-type="con"><p>Conceived and designed the experiments: QM GZ CC. Performed the experiments:
YW XZ XL PL TC. Analyzed the data: XZ YW HZ. Contributed
reagents/materials/analysis tools: HZ YZ RY YS WC FH. Wrote the paper: XZ YW
CC.</p></fn></author-notes><pub-date pub-type="collection"><year>2011</year></pub-date><pub-date pub-type="epub"><day>17</day><month>8</month><year>2011</year></pub-date><volume>6</volume><issue>8</issue><elocation-id>e23730</elocation-id><history><date date-type="received"><day>13</day><month>2</month><year>2011</year></date><date date-type="accepted"><day>26</day><month>7</month><year>2011</year></date></history><permissions><copyright-statement>Zhu et al.</copyright-statement><copyright-year>2011</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p></license></permissions><abstract><p>Genetic background may play an important role in the process of SARS-CoV
infection and SARS development. We found several proteins that could interact
with the nucleocapsid protein of the SARS coronavirus (SARS-CoV).
α-2-Heremans-Schmid Glycoprotein (AHSG), which is required for macrophage
deactivation by endogenous cations, is associated with inflammatory regulation.
Cytochrome P450 Family 3A (CYP4F3A) is an <italic>ω</italic>-oxidase that
inactivates Leukotriene B4 (LTB4) in human neutrophils and the liver. We
investigated the association between the polymorphisms of these two
inflammation-associated genes and SARS development. The linkage disequilibrium
(LD) maps of these two genes were built with Haploview using data on
CHB+JPT (version 2) from the HapMap. A total of ten tag SNPs were selected
and genotyped. In the Guangzhou cohort study, after adjusting for age and sex,
two <italic>AHSG</italic> SNPs and one <italic>CYP4F3</italic> SNP were found to
be associated with SARS susceptibility: rs2248690 (adjusted odds ratio
[AOR] 2.42; 95% confidence interval [CI] 1.30-4.51);
rs4917 (AOR 1.84; 95% CI 1.02-3.34); and rs3794987 (AOR 2.01; 95%
CI 1.10–3.68). To further validate the association, the ten tag SNPs were
genotyped in the Beijing cohort. After adjusting for age and sex, only rs2248690
(AOR, 1.63; 95% CI, 1.30–2.04) was found to be associated with SARS
susceptibility. The combined analysis of the two studies confirmed tag SNP
rs2248690 in <italic>AHSG</italic> as a susceptibility variant (AOR 1.70;
95% CI 1.37–2.09). The statistical analysis of the rs2248690
genotype data among the patients and healthy controls in the HCW cohort, who
were all similarly exposed to the SARS virus, also supported the findings.
Further, the SNP rs2248690 affected the transcriptional activity of the
<italic>AHSG</italic> promoter and thus regulated the AHSG serum level.
Therefore, our study has demonstrated that the AA genotype of rs2268690, which
leads to a higher AHSG serum concentration, was significantly associated with
protection against SARS development.</p></abstract><counts><page-count count="8"></page-count></counts></article-meta></front><body><sec id="s1"><title>Introduction</title><p>Severe acute respiratory syndrome (SARS) is an acute respiratory disease resulting
from the infection of a previously undescribed coronavirus (SARS-CoV) that spreads
through airborne transmission <xref rid="pone.0023730-Peiris1" ref-type="bibr">[1]</xref>–<xref rid="pone.0023730-Drosten1" ref-type="bibr">[3]</xref>. Rapid transmission, high infectivity and unpredictable
clinical progression with a fatality ratio of approximately 9.6% made SARS a
global threat in 2003. However, the pathogenesis of this infectious agent is still
not fully understood. Asymptomatic and mildly symptomatic SARS-CoV infections, which
represent more than 10% of all SARS-CoV infections, have been reported in
many places, including Hong Kong, Taiwan, Guangdong Province of China, and
Singapore<xref rid="pone.0023730-Woo1" ref-type="bibr">[4]</xref>–<xref rid="pone.0023730-WilderSmith1" ref-type="bibr">[8]</xref>. Clinical and laboratory
investigations have shown that the host genetic background is an important factor
that determines the susceptibility to and pathogenicity of SARS infection. We have
demonstrated that genetic haplotypes associated with low serum mannose-binding
lectin (<italic>MBL</italic>) are also associated with SARS, and our findings were
confirmed by other independent studies <xref rid="pone.0023730-Zhang1" ref-type="bibr">[9]</xref>, <xref rid="pone.0023730-Ip1" ref-type="bibr">[10]</xref>. Other susceptible genes, including
chemokine (C–C motif) ligand 5 (<italic>CCL5</italic>) <xref rid="pone.0023730-Ng1" ref-type="bibr">[11]</xref>, interferon gamma (<italic>IFN
γ</italic>) <xref rid="pone.0023730-Chong1" ref-type="bibr">[12]</xref>, 2′–5′ oligoadenylate synthetase 1
(<italic>OAS</italic>) and myxovirus resistance 1 (<italic>MX1</italic>) <xref rid="pone.0023730-He1" ref-type="bibr">[13]</xref>, have also been
reported. The involvement of the major histocompatibility complex class I gene
(<italic>HLA</italic>) <xref rid="pone.0023730-Lin1" ref-type="bibr">[14]</xref>–<xref rid="pone.0023730-Xiong1" ref-type="bibr">[16]</xref> and the L-SIGN gene (<italic>CLEC4M</italic>) <xref rid="pone.0023730-Chan1" ref-type="bibr">[17]</xref>–<xref rid="pone.0023730-Tang1" ref-type="bibr">[19]</xref> are still
disputed.</p><p>Several reports have suggested that the nucleocapsid protein of SARS-CoV, which is
expressed at the early stage of viral infection and detectable in the serum <xref rid="pone.0023730-Che1" ref-type="bibr">[20]</xref>, is associated
with severe pulmonary inflammation by inducing massive pro-inflammatory cytokine
production <xref rid="pone.0023730-Yasui1" ref-type="bibr">[21]</xref>–<xref rid="pone.0023730-Zhu1" ref-type="bibr">[23]</xref>. In an effort to elucidate this mechanism, human cellular
proteins that interact with the nucleocapsid protein were screened by a yeast
two-hybrid assay in our laboratory. The translation elongation factor 1α (EEF1A)
<xref rid="pone.0023730-Zhou1" ref-type="bibr">[24]</xref>, cytochrome
p450 subfamily 4F polypeptide 3 (CYP4F3, Gene ID 4051, GenBank ID NG_007964.1) and
alpha-2-HS-glycoprotein (AHSG, Gene ID 197, GenBank ID NG_011436.1) were found to
interact with the nucleocapsid protein of SARS-CoV (<bold>Unpublished
data</bold>).</p><p>AHSG is required for macrophage deactivation by endogenous cations. Low serum AHSG
levels may cause the uncontrolled production of proinflammatory cytokines <xref rid="pone.0023730-Lebreton1" ref-type="bibr">[25]</xref>, <xref rid="pone.0023730-Ombrellino1" ref-type="bibr">[26]</xref>. Mice
deficient in AHSG showed hypersensitivity to LPS-induced inflammation <xref rid="pone.0023730-Li2" ref-type="bibr">[27]</xref>. Cytochrome P450
Family 3A (CYP4F3A) is an <italic>ω</italic>-oxidase that inactivates the
potent inflammatory factor Leukotriene B4 (LTB4) in human neutrophils and the liver
<xref rid="pone.0023730-Murphy1" ref-type="bibr">[28]</xref>. LTB4 is
likely a major determining factor in tissue damage because it can amplify the
inflammatory reaction by recruiting leukocytes and inducing their degranulation,
which makes the removal of excess LTB4 important for the prevention of immunologic
damage <xref rid="pone.0023730-Kannan1" ref-type="bibr">[29]</xref>.
Considering that CYP4F3 and AHSG are involved in innate immunity and inflammatory
processes, we investigated whether single-nucleotide polymorphisms (SNPs) in these
genes were associated with susceptibility to viral infection.</p></sec><sec id="s2"><title>Results</title><sec id="s2a"><title>SARS-Cov Nucleocapsid Protein Associates with AHSG</title><p>We previously noted in a yeast two-hybrid analysis that SARS-Cov nucleocapsid
protein associates with CYP4F3A, EEF1A and AHSG (<bold>data not shown</bold>).
To substantiate the interaction between SARS-Cov N and AHSG, human serum was
incubated with GST or GST-N fusion proteins. The adsorbates were analyzed by
immunoblotting with anti-AHSG antibody. It was demonstrated that AHSG binds to
GST-N, but not GST (<xref ref-type="fig" rid="pone-0023730-g001"><bold>Figure
1</bold></xref>).</p><fig id="pone-0023730-g001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.g001</object-id><label>Figure 1</label><caption><title>The nucleocapsid protein associates with serum AHSG</title><p>. Human serum (100 µl) was incubated with agarose beads conjugated
to GST-N protein or GST at 4°C for 2 h. The beads were washed three
times with PBS supplemented with 0.1% NP40. The bound proteins
were subjected to SDS-PAGE followed by immunoblot with anti-AHSG
antibody.</p></caption><graphic xlink:href="pone.0023730.g001"></graphic></fig></sec><sec id="s2b"><title>Study Participants and Demographics</title><p>We obtained peripheral blood or serum samples from SARS patients who were
diagnosed by WHO standards from two different cities in China. Of the SARS
patients, 67 were from the Eighth People's Hospital in southern China City,
Guangzhou, and 624 were from Xiaotangshan Hospital (a hospital temporarily set
up to fight SARS in Beijing, China) or Ditan Hospital (Beijing, China). All of
the SARS samples were collected during the 2003 SARS epidemic. Analysis by ELISA
and IFA confirmed all SARS patients to be seropositive for anti-SARS antibodies,
and the specificity of the ELISA assay was higher than 99% <xref rid="pone.0023730-Che1" ref-type="bibr">[20]</xref>, <xref rid="pone.0023730-Liu1" ref-type="bibr">[30]</xref>, <xref rid="pone.0023730-Shi1" ref-type="bibr">[31]</xref>. For
comparison, we obtained two groups of control samples from Guangzhou (192
people) and Beijing (791 people). The Guangzhou control samples were collected
during the 2003 SARS epidemic while the Beijing control samples were collected
after the epidemic. All samples were collected from people who had never
developed SARS. To assess the influence of the exposure, blood samples from
health care workers (HCW) that worked in SARS wards in the Second Affiliated
Hospital of Senyaxian Medical School in 2003 were obtained. Of these samples, 40
were from HCW who developed SARS during the course of hospital duty and 122 were
from health care workers free of the disease. DNA was extracted from all the
samples selected. All cases and controls were Han Chinese. We selected enough
samples to perform three progressive case-control studies.</p><p>Chi-square tests were carried out to evaluate whether the cases and controls of
the three studies were matched. We found that the cases and controls from
Beijing were well matched for sex and age, while there were more females in the
case cohort of the Guangzhou non-HCW population. Most of the HCW population was
composed of female nurses between the ages of 16 and 30 (<xref ref-type="table" rid="pone-0023730-t001"><bold>Table 1</bold></xref>).</p><table-wrap id="pone-0023730-t001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.t001</object-id><label>Table 1</label><caption><title>The Demographic Characteristics of the SARS Patients and
Controls.</title></caption><alternatives><graphic id="pone-0023730-t001-1" xlink:href="pone.0023730.t001"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"></td><td colspan="3" align="left" rowspan="1">Guangzhou Non-HCW Population</td><td colspan="3" align="left" rowspan="1">Beijing Population</td><td colspan="3" align="left" rowspan="1">HCW Population</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SARS patients</td><td align="left" rowspan="1" colspan="1">Controls</td><td align="left" rowspan="1" colspan="1"><italic>P</italic></td><td align="left" rowspan="1" colspan="1">SARS patients</td><td align="left" rowspan="1" colspan="1">Controls</td><td align="left" rowspan="1" colspan="1"><italic>P</italic></td><td align="left" rowspan="1" colspan="1">SARS patients</td><td align="left" rowspan="1" colspan="1">Controls</td><td align="left" rowspan="1" colspan="1"><italic>P</italic></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">(N = 67) No. (%)</td><td align="left" rowspan="1" colspan="1">(N = 192) No. (%)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">(N = 624) No. (%)</td><td align="left" rowspan="1" colspan="1">(N = 791) No. (%)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">(N = 40) No. (%)</td><td align="left" rowspan="1" colspan="1">(N = 122) No. (%)</td><td align="left" rowspan="1" colspan="1"></td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Sex</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male</td><td align="left" rowspan="1" colspan="1">29 (43.28)</td><td align="left" rowspan="1" colspan="1">99 (51.56)</td><td align="left" rowspan="1" colspan="1">0.24</td><td align="left" rowspan="1" colspan="1">312 (50.00)</td><td align="left" rowspan="1" colspan="1">412 (52.09)</td><td align="left" rowspan="1" colspan="1">0.44</td><td align="left" rowspan="1" colspan="1">3 (7.50)</td><td align="left" rowspan="1" colspan="1">12 (9.84)</td><td align="left" rowspan="1" colspan="1">0.66</td></tr><tr><td align="left" rowspan="1" colspan="1">Female</td><td align="left" rowspan="1" colspan="1">38 (56.72)</td><td align="left" rowspan="1" colspan="1">93 (48.44)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">312 (50.00)</td><td align="left" rowspan="1" colspan="1">379 (47.91)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">37 (92.50)</td><td align="left" rowspan="1" colspan="1">110 (90.16)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Age, y</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">16–30</td><td align="left" rowspan="1" colspan="1">21 (31.34)</td><td align="left" rowspan="1" colspan="1">57 (29.69)</td><td align="left" rowspan="1" colspan="1">0.78</td><td align="left" rowspan="1" colspan="1">253 (40.54)</td><td align="left" rowspan="1" colspan="1">339 (42.86)</td><td align="left" rowspan="1" colspan="1">0.26</td><td align="left" rowspan="1" colspan="1">29 (72.50)</td><td align="left" rowspan="1" colspan="1">85 (69.67)</td><td align="left" rowspan="1" colspan="1">0.96</td></tr><tr><td align="left" rowspan="1" colspan="1">31–40</td><td align="left" rowspan="1" colspan="1">23 (34.33)</td><td align="left" rowspan="1" colspan="1">58 (30.21)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">152 (24.36)</td><td align="left" rowspan="1" colspan="1">163 (20.61)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">9 (22.50)</td><td align="left" rowspan="1" colspan="1">31 (25.41)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">41–50</td><td align="left" rowspan="1" colspan="1">13 (19.40)</td><td align="left" rowspan="1" colspan="1">49 (25.52)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">131 (21.00)</td><td align="left" rowspan="1" colspan="1">159 (20.10)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1 (2.50)</td><td align="left" rowspan="1" colspan="1">4 (3.28)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">>50</td><td align="left" rowspan="1" colspan="1">10 (14.93)</td><td align="left" rowspan="1" colspan="1">28 (14.58)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">88 (14.10)</td><td align="left" rowspan="1" colspan="1">130 (16.43)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1 (2.50)</td><td align="left" rowspan="1" colspan="1">2 (1.64)</td><td align="left" rowspan="1" colspan="1"></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt101"><label></label><p><italic>P</italic> was calculated using the χ<sup>2</sup>test.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s2c"><title><italic>AHSG</italic> SNPs and SARS Development</title><p>We downloaded the SNP genotype data for CHB+JPT (version 2) from the HapMap
database and built a linkage disequilibrium (LD) map of <italic>AHSG</italic>with the Haploview software (version 4.0). The pairwise tagging algorithm
implemented in the Tagger program of Haploview (r<sup>2</sup> threshold was 0.8)
was used to select the tag SNPs for assessment. SNPs that were reported to
affect the AHSG level or that had been associated with other diseases were also
selected. Finally, we chose to genotype rs2248690 (5′-flanking region),
rs2077119 (5′-flanking region), rs4917 (exon 6), rs2593813 (intron 1) and
rs4918 (exon 7) in the AHSG gene. The genotyping and statistical results are
presented in <bold><xref ref-type="supplementary-material" rid="pone.0023730.s001">Table S1</xref></bold> and <bold><xref ref-type="supplementary-material" rid="pone.0023730.s002">Table
S2</xref></bold> under the dominant and additive models, respectively.
The genotyping and statistical analyses of the Guangzhou non-HCW population
revealed that two tag SNPs were associated with SARS. The rs2248690
<italic>TT/AT</italic> genotype was more associated with increased
susceptibility to SARS than the <italic>AA</italic> genotype
(OR = 2.42; 95% CI, 1.30–4.51; <xref ref-type="table" rid="pone-0023730-t002"><bold>Table 2</bold></xref><bold>and <xref ref-type="supplementary-material" rid="pone.0023730.s001">Table
S1</xref></bold>). The <italic>TT/CT</italic> genotype of rs4917 was
associated with an increased possibility of developing clinically apparent SARS
(OR = 1.84; 95% CI, 1.02–3.34; <xref ref-type="table" rid="pone-0023730-t003"><bold>Table 3</bold></xref><bold>and <xref ref-type="supplementary-material" rid="pone.0023730.s001">Table
S1</xref></bold>). In the validation study (Beijing population), only
the rs2248690 polymorphism was significantly associated with SARS development
(relative to the <italic>TT/AT</italic> genotype, 1.63; 95% CI,
1.30–2.04; <xref ref-type="table" rid="pone-0023730-t002"><bold>Table
2</bold></xref><bold> and <xref ref-type="supplementary-material" rid="pone.0023730.s001">Table S1</xref></bold>). Because the Beijing and
Guangzhou sample groups had homogenous demographic and genetic parameters (Han
Chinese), a joint analysis was performed. The combined analysis of the two
studies under the dominant model is presented in <bold><xref ref-type="supplementary-material" rid="pone.0023730.s003">Table
S3</xref></bold>. After combining data from the two cohorts, the
<italic>TT/AT</italic> genotype of rs2248690 had a frequency of 27.5%
in the control population and a significantly higher frequency of 39.1%
in the SARS patients (OR = 1.70; 95% CI,
1.37–2.09; <xref ref-type="table" rid="pone-0023730-t002"><bold>Table
2</bold></xref><bold> and <xref ref-type="supplementary-material" rid="pone.0023730.s003">Table S3</xref></bold>). After adjusting for sex
and age, a non-significant association was observed between rs4917 and SARS
susceptibility (OR = 1.22; 95% CI, 1.02–1.54;
<italic>P</italic> = 0.08). Because rs4917 and
rs2248690 are in high linkage disequilibrium (0.62), the association between
rs4917 and SARS can be easily understood. A logistic regression analysis was
applied to adjust the linkage between rs4917 and rs2248690. The result showed
that rs2248690 was associated with SARS development
(OR = 1.58; 95% CI, 1.16–2.17;
<italic>p</italic> = 0.004), while rs4917 was not
associated with SARS (<italic>p</italic> = 0.54). To remove
the influence of the exposure factor to SARS infection and risk, cases and
controls from the HCW population were genotyped at rs2248690. Though the
<italic>P-</italic>value is higher than 0.05, the distribution of different
genotypes between the HCW cases and HCW controls is similar to the distribution
between the non-HCW cases and non-HCW controls with a difference of
approximately 10%. The statistical analysis of the rs2248690 genotype
data from all cases and HCW controls confirmed this finding
(OR = 1.68; 95% CI, 1.38–2.06; <xref ref-type="table" rid="pone-0023730-t004"><bold>Table 4</bold></xref>). We
tested the deviation from the Hardy-Weinberg equilibrium in both cases and
controls, within each cohort and within the overall study population; we found
that there was no statistically significant deviation from equilibrium for any
SNP.</p><table-wrap id="pone-0023730-t002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.t002</object-id><label>Table 2</label><caption><title>The association results for rs2248690 in two independent samples and
the combined sample.</title></caption><alternatives><graphic id="pone-0023730-t002-2" xlink:href="pone.0023730.t002"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td colspan="2" align="left" rowspan="1">Cohort and Group</td><td colspan="5" align="left" rowspan="1">Genotype</td><td colspan="4" align="left" rowspan="1">Allele</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AA (%)</td><td align="left" rowspan="1" colspan="1">AT (%)</td><td align="left" rowspan="1" colspan="1">TT (%)</td><td align="left" rowspan="1" colspan="1">OR<sup>a</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>a</sup></italic></td><td align="left" rowspan="1" colspan="1">A (%)</td><td align="left" rowspan="1" colspan="1">T (%)</td><td align="left" rowspan="1" colspan="1">OR<sup>b</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>b</sup></italic></td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">GZ Cohort</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">145 (75.52)</td><td align="left" rowspan="1" colspan="1">46 (23.96)</td><td align="left" rowspan="1" colspan="1">1 (0.52)</td><td align="left" rowspan="1" colspan="1">2.42 (1.30–4.51)</td><td align="left" rowspan="1" colspan="1">0.005</td><td align="left" rowspan="1" colspan="1">336 (87.50)</td><td align="left" rowspan="1" colspan="1">48 (12.50)</td><td align="left" rowspan="1" colspan="1">1.93 (1.16–3.22)</td><td align="left" rowspan="1" colspan="1">0.016</td></tr><tr><td align="left" rowspan="1" colspan="1">(Non-HCW)</td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">40 (59.70)</td><td align="left" rowspan="1" colspan="1">25 (37.31)</td><td align="left" rowspan="1" colspan="1">2 (2.99)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">105 (78.36)</td><td align="left" rowspan="1" colspan="1">29 (21.64)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">BJ Cohort</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">545 (71.71)</td><td align="left" rowspan="1" colspan="1">191 (25.13)</td><td align="left" rowspan="1" colspan="1">24 (3.16)</td><td align="left" rowspan="1" colspan="1">1.63 (1.30–2.04)</td><td align="left" rowspan="1" colspan="1"><0.001</td><td align="left" rowspan="1" colspan="1">1281 (84.28)</td><td align="left" rowspan="1" colspan="1">239 (15.72)</td><td align="left" rowspan="1" colspan="1">1.45 (1.20–1.77)</td><td align="left" rowspan="1" colspan="1"><0.001</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">369 (60.99)</td><td align="left" rowspan="1" colspan="1">214 (35.37)</td><td align="left" rowspan="1" colspan="1">22 (3.64)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">952 (78.68)</td><td align="left" rowspan="1" colspan="1">258 (21.32)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Combined</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">690 (72.48)</td><td align="left" rowspan="1" colspan="1">237 (24.89)</td><td align="left" rowspan="1" colspan="1">25 (2.63)</td><td align="left" rowspan="1" colspan="1">1.69 (1.37–2.09)</td><td align="left" rowspan="1" colspan="1"><0.001</td><td align="left" rowspan="1" colspan="1">1617 (84.93)</td><td align="left" rowspan="1" colspan="1">287 (15.07)</td><td align="left" rowspan="1" colspan="1">1.53 (1.28–1.83)</td><td align="left" rowspan="1" colspan="1"><0.001</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">409 (60.86)</td><td align="left" rowspan="1" colspan="1">239 (35.57)</td><td align="left" rowspan="1" colspan="1">24 (3.57)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1057 (78.65)</td><td align="left" rowspan="1" colspan="1">287 (21.35)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt102"><label></label><p>CI, confidence interval; BJ, Beijing; GZ, Guangzhou.</p></fn><fn id="nt103"><label></label><p>a. Case vs. Control. A combined group of the minor-allele homozygotes
and heterozygotes was compared with the major-allele homozygotes
group ((AT+TT) vs. AA); multivariate unconditional logistic
regression was used.</p></fn><fn id="nt104"><label></label><p>b. Case vs. Control. The frequency of the A allele was compared to
the frequency of the T allele; the CMH test was used.</p></fn></table-wrap-foot></table-wrap><table-wrap id="pone-0023730-t003" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.t003</object-id><label>Table 3</label><caption><title>The association results for rs4917 in two independent samples and the
combined sample.</title></caption><alternatives><graphic id="pone-0023730-t003-3" xlink:href="pone.0023730.t003"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td colspan="2" align="left" rowspan="1">Cohort and Group</td><td colspan="5" align="left" rowspan="1">Genotype</td><td colspan="4" align="left" rowspan="1">Allele</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">CC (%)</td><td align="left" rowspan="1" colspan="1">CT (%)</td><td align="left" rowspan="1" colspan="1">TT (%)</td><td align="left" rowspan="1" colspan="1">OR<sup>a</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>a</sup></italic></td><td align="left" rowspan="1" colspan="1">C (%)</td><td align="left" rowspan="1" colspan="1">T (%)</td><td align="left" rowspan="1" colspan="1">OR<sup>b</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>b</sup></italic></td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">GZ Cohort</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">98 (56.65)</td><td align="left" rowspan="1" colspan="1">69 (39.88)</td><td align="left" rowspan="1" colspan="1">6 (3.47)</td><td align="left" rowspan="1" colspan="1">1.84 (1.02–3.34)</td><td align="left" rowspan="1" colspan="1">0.04</td><td align="left" rowspan="1" colspan="1">265 (76.59)</td><td align="left" rowspan="1" colspan="1">81 (23.41)</td><td align="left" rowspan="1" colspan="1">1.49 (0.95–2.33)</td><td align="left" rowspan="1" colspan="1">0.10</td></tr><tr><td align="left" rowspan="1" colspan="1">(Non-HCW)</td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">28 (43.75)</td><td align="left" rowspan="1" colspan="1">32 (50.00)</td><td align="left" rowspan="1" colspan="1">4 (6.25)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">88 (68.75)</td><td align="left" rowspan="1" colspan="1">40 (31.25)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">BJ Cohort</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">391 (53.64)</td><td align="left" rowspan="1" colspan="1">282 (38.68)</td><td align="left" rowspan="1" colspan="1">56 (7.68)</td><td align="left" rowspan="1" colspan="1">1.21 (0.98–1.51)</td><td align="left" rowspan="1" colspan="1">0.08</td><td align="left" rowspan="1" colspan="1">1064 (72.98)</td><td align="left" rowspan="1" colspan="1">394 (27.02)</td><td align="left" rowspan="1" colspan="1">1.12 (0.95–1.33)</td><td align="left" rowspan="1" colspan="1">0.18</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">299 (48.07)</td><td align="left" rowspan="1" colspan="1">281 (45.18)</td><td align="left" rowspan="1" colspan="1">42 (6.75)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">879 (70.66)</td><td align="left" rowspan="1" colspan="1">365 (29.34)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Combined</td><td align="left" rowspan="1" colspan="1">Control</td><td align="left" rowspan="1" colspan="1">489 (54.21)</td><td align="left" rowspan="1" colspan="1">351 (38.91)</td><td align="left" rowspan="1" colspan="1">62 (6.87)</td><td align="left" rowspan="1" colspan="1">1.22 (1.02–1.54)</td><td align="left" rowspan="1" colspan="1">0.08</td><td align="left" rowspan="1" colspan="1">1329 (73.67)</td><td align="left" rowspan="1" colspan="1">475 (26.33)</td><td align="left" rowspan="1" colspan="1">1.17 (1.00–1.37)</td><td align="left" rowspan="1" colspan="1">0.05</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SARS</td><td align="left" rowspan="1" colspan="1">327 (47.67)</td><td align="left" rowspan="1" colspan="1">313 (45.63)</td><td align="left" rowspan="1" colspan="1">46 (6.71)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">967 (70.48)</td><td align="left" rowspan="1" colspan="1">405 (29.52)</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt105"><label></label><p>CI, confidence interval; BJ, Beijing; GZ, Guangzhou; HCW, health care
workers.</p></fn><fn id="nt106"><label></label><p>a. Case vs. Control. A combined group of the minor-allele homozygotes
and heterozygotes was compared with the major-allele homozygotes
group ((CT+TT) vs. CC); multivariate unconditional logistic
regression was used.</p></fn><fn id="nt107"><label></label><p>b. Case vs. Control. The frequency of the C allele was compared to
the frequency of the T allele; the CMH test was used.</p></fn></table-wrap-foot></table-wrap><table-wrap id="pone-0023730-t004" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.t004</object-id><label>Table 4</label><caption><title>Evaluation of the Exposure Factor.</title></caption><alternatives><graphic id="pone-0023730-t004-4" xlink:href="pone.0023730.t004"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">Cohorts compared</td><td align="left" rowspan="1" colspan="1">Genotype</td><td align="left" rowspan="1" colspan="1">No. Cases (%)</td><td align="left" rowspan="1" colspan="1">No. Controls (%)</td><td align="left" rowspan="1" colspan="1">Crude OR<sup>a</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>a</sup></italic> Value</td><td align="left" rowspan="1" colspan="1">Adjusted OR<sup>b</sup> (95% CI)</td><td align="left" rowspan="1" colspan="1"><italic>P<sup>b</sup></italic> Value</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">HCW Cases vs. HCW Controls</td><td align="left" rowspan="1" colspan="1">AA</td><td align="left" rowspan="1" colspan="1">25 (62.50)</td><td align="left" rowspan="1" colspan="1">88 (72.10)</td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TT/AT</td><td align="left" rowspan="1" colspan="1">15 (37.50)</td><td align="left" rowspan="1" colspan="1">34 (27.90)</td><td align="left" rowspan="1" colspan="1">1.55 (0.73–3.30)</td><td align="left" rowspan="1" colspan="1">0.32</td><td align="left" rowspan="1" colspan="1">1.54 (0.72–3.28)</td><td align="left" rowspan="1" colspan="1">0.27</td></tr><tr><td align="left" rowspan="1" colspan="1">GZ Cases vs. HCW Controls</td><td align="left" rowspan="1" colspan="1">AA</td><td align="left" rowspan="1" colspan="1">65 (60.75)</td><td align="left" rowspan="1" colspan="1">88 (72.13)</td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TT/AT</td><td align="left" rowspan="1" colspan="1">42 (39.25)</td><td align="left" rowspan="1" colspan="1">34 (27.87)</td><td align="left" rowspan="1" colspan="1">1.67 (0.96–2.91)</td><td align="left" rowspan="1" colspan="1">0.09</td><td align="left" rowspan="1" colspan="1">1.73 (0.94–3.18)</td><td align="left" rowspan="1" colspan="1">0.08</td></tr><tr><td align="left" rowspan="1" colspan="1">All Cases vs. HCW Controls</td><td align="left" rowspan="1" colspan="1">AA</td><td align="left" rowspan="1" colspan="1">434 (60.96)</td><td align="left" rowspan="1" colspan="1">88 (72.13)</td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TT/AT</td><td align="left" rowspan="1" colspan="1">278 (39.04)</td><td align="left" rowspan="1" colspan="1">34 (27.87)</td><td align="left" rowspan="1" colspan="1">1.66 (1.09–2.53)</td><td align="left" rowspan="1" colspan="1">0.02</td><td align="left" rowspan="1" colspan="1">1.58 (1.00–2.47)</td><td align="left" rowspan="1" colspan="1">0.05</td></tr><tr><td align="left" rowspan="1" colspan="1">All Cases vs. All controls</td><td align="left" rowspan="1" colspan="1">AA</td><td align="left" rowspan="1" colspan="1">434 (60.96)</td><td align="left" rowspan="1" colspan="1">778 (72.44)</td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">1[Reference]</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TT/AT</td><td align="left" rowspan="1" colspan="1">278 (39.04)</td><td align="left" rowspan="1" colspan="1">296 (27.56)</td><td align="left" rowspan="1" colspan="1">1.68 (1.38–2.06)</td><td align="left" rowspan="1" colspan="1"><0.001</td><td align="left" rowspan="1" colspan="1">1.68 (1.38–2.06)</td><td align="left" rowspan="1" colspan="1"><0.001</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt108"><label></label><p>Abbreviations used: CI, confidence interval; OR, odds ratio; HCW,
health care workers; GZ, Guangzhou. All OR and <italic>P</italic>values are from the reference group but against the other
category.</p></fn><fn id="nt109"><label></label><p>a Values calculated using the CMH test.</p></fn><fn id="nt110"><label></label><p>b Values adjusted for age and sex using the multivariate
unconditional logistic regression.</p></fn></table-wrap-foot></table-wrap><p>Based on these results, we conclude that the <italic>TT/AT</italic> genotype of
rs2248690 is associated with the increased likelihood of developing SARS, while
the <italic>AA</italic> genotype is associated with protection against SARS.</p></sec><sec id="s2d"><title>rs2248690 is associated with AHSG serum concentration</title><p>AHSG is a serum protein, and it has been reported that there is an association
between <italic>AHSG</italic> polymorphisms (rs4917 and rs4918) and AHSG serum
concentration levels <xref rid="pone.0023730-Osawa1" ref-type="bibr">[32]</xref>, <xref rid="pone.0023730-Osawa2" ref-type="bibr">[33]</xref>. However, no convincing multivariate analysis has
been performed to identify the most associated variants. To uncover potential
functional changes associated with the <italic>AHSG</italic> rs2248690
polymorphism, 192 healthy subjects from Beijing were genotyped, and their AHSG
serum concentrations were determined. As expected, there was an association
between the rs2248690 genotype and the AHSG serum concentrations (<xref ref-type="table" rid="pone-0023730-t005"><bold>Table 5</bold></xref>). The
order of the average AHSG serum concentrations was as follows:
<italic>−799AA</italic> ><italic>−799AT</italic>><italic>−799TT</italic>. Consistently, rs4917 and rs4918 were
associated with different serum levels of AHSG. Furthermore, a stepwise
regression analysis clearly showed that rs2248690 was an independent contributor
to the variability in AHSG serum concentration (<xref ref-type="table" rid="pone-0023730-t005"><bold>Table 5</bold></xref>). These results show
that LD with rs2248690 causes the association of rs4917 with AHSG serum
concentration.</p><table-wrap id="pone-0023730-t005" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.t005</object-id><label>Table 5</label><caption><title>Variants Affecting AHSG Serum Levels.</title></caption><alternatives><graphic id="pone-0023730-t005-5" xlink:href="pone.0023730.t005"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">Variables</td><td align="left" rowspan="1" colspan="1"><italic>β</italic></td><td align="left" rowspan="1" colspan="1"><italic>T</italic></td><td align="left" rowspan="1" colspan="1"><italic>P</italic><xref ref-type="table-fn" rid="nt111">a</xref></td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">rs2248690</td><td align="left" rowspan="1" colspan="1">−0.737</td><td align="left" rowspan="1" colspan="1">−14.336</td><td align="left" rowspan="1" colspan="1">0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">rs4917</td><td align="left" rowspan="1" colspan="1">0.014</td><td align="left" rowspan="1" colspan="1">0.209</td><td align="left" rowspan="1" colspan="1">0.84</td></tr><tr><td align="left" rowspan="1" colspan="1">rs4918</td><td align="left" rowspan="1" colspan="1">−0.011</td><td align="left" rowspan="1" colspan="1">−0.162</td><td align="left" rowspan="1" colspan="1">0.87</td></tr><tr><td align="left" rowspan="1" colspan="1">Sex</td><td align="left" rowspan="1" colspan="1">−0.057</td><td align="left" rowspan="1" colspan="1">−1.11</td><td align="left" rowspan="1" colspan="1">0.27</td></tr><tr><td align="left" rowspan="1" colspan="1">Age</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="left" rowspan="1" colspan="1">0.046</td><td align="left" rowspan="1" colspan="1">0.96</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>rs2248690 Genotype</bold></td><td align="left" rowspan="1" colspan="1"><bold>N (%)</bold></td><td align="left" rowspan="1" colspan="1"><bold>AHSG concentration (mean ± S.E.M)
µg/ml</bold></td><td align="left" rowspan="1" colspan="1"><bold><italic>P</italic></bold><bold>(ANOVA)</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">AA</td><td align="left" rowspan="1" colspan="1">142 (74.0%)</td><td align="left" rowspan="1" colspan="1">583±33</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">AT</td><td align="left" rowspan="1" colspan="1">45 (23.4%)</td><td align="left" rowspan="1" colspan="1">541±25</td><td align="left" rowspan="1" colspan="1"><0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">TT</td><td align="left" rowspan="1" colspan="1">5 (2.6%)</td><td align="left" rowspan="1" colspan="1">492±28</td><td align="left" rowspan="1" colspan="1"></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt111"><label>a</label><p>Stepwise multivariate regression analysis.</p></fn></table-wrap-foot></table-wrap><p>Because rs2248690 is located in the promoter region of the AHSG gene (−799
bp upstream of the transcription start site), various alleles of this SNP may
yield phenotypic differences in <italic>AHSG</italic> transcription levels. It
has been reported that the A allele of rs2248690 has a reduced binding affinity
for transcription factor AP1 <xref rid="pone.0023730-Inoue1" ref-type="bibr">[34]</xref>, which is a repressor of AHSG expression <xref rid="pone.0023730-Bushel1" ref-type="bibr">[35]</xref>, <xref rid="pone.0023730-Schreiber1" ref-type="bibr">[36]</xref>. We
observed allele-associated differences in the <italic>AHSG</italic>promoter's transcriptional activity in two different cell lines using a
luciferase assay. The sequence that contained −<italic>799T</italic>,
which is overrepresented in SARS patients, showed significantly lower promoter
activity (<xref ref-type="fig" rid="pone-0023730-g002"><bold>Figure
2</bold></xref>).</p><fig id="pone-0023730-g002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0023730.g002</object-id><label>Figure 2</label><caption><title>The rs2248690(−<italic>799A/T</italic>) polymorphism regulates
AHSG promoter activity.</title><p>Left, a schematic of two reporter gene constructs. Right, luciferase
expression of the constructs in different cells. Each value represents
the mean ± S.D. of five independent experiments that were
performed in triplicate. *, P<0.05.</p></caption><graphic xlink:href="pone.0023730.g002"></graphic></fig></sec><sec id="s2e"><title><italic>CYP4F3</italic> SNPs and SARS Susceptibility</title><p>We also built an LD map of <italic>CYP4F3</italic> and selected five tag SNPs for
assessment (<bold><xref ref-type="supplementary-material" rid="pone.0023730.s001">Table S1</xref>, <xref ref-type="supplementary-material" rid="pone.0023730.s002">Table
S2</xref> and <xref ref-type="supplementary-material" rid="pone.0023730.s003">Table S3</xref></bold>). In the non-HCW
Guangzhou population, the rs3794987 <italic>GG/AG</italic> genotype was
associated with an increased susceptibility to SARS
(OR = 2.01; 95% CI, 1.10–3.68). However, the
results of this association were not replicated in the Beijing population. The
combined analysis of the two studies does not show any association of the
<italic>CYP4F3</italic> SNPs analyzed with SARS susceptibility.</p></sec></sec><sec id="s3"><title>Discussion</title><p>After the interaction between AHSG and the SARS-CoV nucleocapsid protein was
identified and validated, we chose <italic>AHSG</italic> as a candidate gene in
subsequent case-control analyses. We found an association between one SNP in
<italic>AHSG</italic> (rs2248690) and the development of SARS in two separate
case-control studies as well as in the combined analysis of both studies after
adjusting for age and sex. Considering the exposure factor, the intercomparison of
the HCW-controls and the other cases validated the association we observed between
rs2248690 and SARS disease because the HCW-controls, who had worked in SARS wards,
were exposed to SARS-CoV at least as much as the other cases. Additionally, this
polymorphism, which was located in the promoter of <italic>AHSG</italic>, affects
AHSG serum levels by altering the transcriptional activity. The genotype that
conferred protection against SARS, rs2248690 <italic>AA</italic>, is associated with
higher AHSG serum levels.</p><p>Our case-control studies included 1833 individuals and revealed a significant
association between rs2248690 and the SARS disease. More specifically, individuals
with the <italic>AA</italic> genotype have a 41% lower risk of developing
SARS than those with the <italic>TT/AT</italic> genotype. The rs2248690 SNP is
located in the 5′ flanking region of <italic>AHSG</italic> at position
−799 within the promoter region. We have shown that this variant affects AHSG
serum levels by altering transcriptional activity. Other SNPs in the
<italic>AHSG</italic>, such as rs4917 and rs4918, have also been shown to be
associated with AHSG serum levels and diseases affected by AHSG serum levels <xref rid="pone.0023730-Osawa2" ref-type="bibr">[33]</xref>. In this study,
we demonstrated that rs2248690 is the dominant factor affecting AHSG
concentration.</p><p>AHSG is a circulating negative inflammatory acute-phase glycoprotein. The
pathophysiological sequence of infection is mediated by macrophage-derived
pro-inflammatory cytokines. The current literature suggests that the availability of
AHSG to macrophages is critical in regulating the innate immune response in tissue
injury and infection because AHSG is required for macrophage deactivation by
endogenous cations <xref rid="pone.0023730-Wang1" ref-type="bibr">[37]</xref>. Decreased levels of human fetuin have been observed in
patients with acute lymphocytic Hodgkin's and non-Hodgkin's lymphomas,
rheumatoid arthritis, acute alcoholic hepatitis and trauma <xref rid="pone.0023730-Murphy1" ref-type="bibr">[28]</xref>, <xref rid="pone.0023730-Kalabay1" ref-type="bibr">[38]</xref>–<xref rid="pone.0023730-Kalabay2" ref-type="bibr">[41]</xref>. Under conditions where
macrophage-associated fetuin levels are decreased, macrophage deactivation by
endogenous counter-regulators may be impaired, leading to the uncontrolled
overproduction of pro-inflammatory cytokines <xref rid="pone.0023730-Kannan1" ref-type="bibr">[29]</xref>, <xref rid="pone.0023730-Kalabay1" ref-type="bibr">[38]</xref>. A recent work showed that
mice deficient in AHSG are hypersensitive to LPS-induced inflammation <xref rid="pone.0023730-Li2" ref-type="bibr">[27]</xref>. Because the
development of SARS is always associated with a severe inflammatory reaction, low
AHSG serum levels could result in severe symptoms after a SARS-CoV infection.
Considering that there might be many asymptomatic SARS-CoV infections in the control
group, it is plausible that relatively higher serum concentrations of AHSG can
orchestrate an unimpaired counter-regulation between macrophage deactivation and
endogenous pro-inflammatory cytokines. Therefore, viruses can be cleared without
severe inflammation and obvious symptoms. However, we observed that AHSG interacts
with the SARS-CoV nucleocapsid protein (<xref ref-type="fig" rid="pone-0023730-g001"><bold>Figure 1</bold></xref>), which may induce massive pro-inflammatory
cytokine production upon infection <xref rid="pone.0023730-Yasui1" ref-type="bibr">[21]</xref>. AHSG may neutralize the pro-inflammatory effect of the
SARS nucleocapsid protein because AHSG is so abundant in the serum (0.2–0.55
mg/ml). Considering that AHSG is a circulating glycoprotein synthesized in human
liver tissue and the nucleocapsid protein is “wrapped” by membrane
proteins in the SARS-CoV virion, interactions between AHSG and the nucleocapsid
protein are less likely to affect the viral infectivity and replication of the
SARS-CoV.</p><p>To our knowledge, this is the first study reporting an association of
<italic>AHSG</italic> polymorphisms with SARS. Individuals with polymorphisms
that contribute to high concentrations of AHSG may be less likely to develop SARS.
These findings improve our understanding of SARS pathogenesis and provide a genetic
basis for SARS prognosis. Future studies should further investigate the mechanisms
of AHSG-based resistance to SARS and other infectious diseases.</p></sec><sec sec-type="materials|methods" id="s4"><title>Materials and Methods</title><sec id="s4a"><title>Ethics Statement</title><p>Written, informed consent was obtained from all of the participants or their
guardians; genetic analysis was covered in the consent. This study was performed
with the approval of the Ethics Committees of all the hospitals from which
samples were obtained and it was also approved by the the Ethics Committees of
Chinese National Human Genome Center, Beijing Institute of Biotechnology and
Beijing Proteome Research Center. The study was conducted according to the
principles of the Declaration of Helsinki.</p></sec><sec id="s4b"><title>Study Participants</title><p>The first case-control study was performed to evaluate potential associations
between <italic>AHSG</italic> and <italic>CYP4F3</italic> polymorphisms with
SARS. The second case-control study was performed to confirm the findings from
the first study. The HCW case-control study was used to evaluate the influence
of the exposure rate and to reconfirm the findings of positively associated
SNPs. The association of the <italic>AHSG</italic> genotype and AHSG serum
concentrations was evaluated in 192 healthy Beijing Han Chinese.</p></sec><sec id="s4c"><title>Case-Control Study 1</title><p>Serum samples from 67 SARS patients diagnosed by WHO standards were collected
from the Eighth People's Hospital of Guangzhou in 2003. Analysis by
enzyme-linked immunosorbent assay (ELISA) and immunofluorescent assay (IFA)
confirmed that all of the SARS patients were seropositive for SARS antibodies.
The specificity of the ELISA assay was higher than 99% <xref rid="pone.0023730-Che1" ref-type="bibr">[20]</xref>, <xref rid="pone.0023730-Liu1" ref-type="bibr">[30]</xref>, <xref rid="pone.0023730-Shi1" ref-type="bibr">[31]</xref>. Serum samples
from 192 age-, sex-, and ethnicity-matched controls were collected from healthy
people in Guangzhou during the same period. All cases and controls were Han
Chinese.</p></sec><sec id="s4d"><title>Case-Control Study 2</title><p>Blood samples from 624 patients diagnosed by WHO standards were collected from
Xiaotangshan Hospital (a hospital temporarily set up to fight SARS in Beijing,
China) and Ditan Hospital (Beijing, China) during the SARS epidemic in 2003. All
SARS patients selected were confirmed to be seropositive by both an
enzyme-linked immunosorbent assay (ELISA) and an immunofluorescent assay (IFA)
<xref rid="pone.0023730-Che1" ref-type="bibr">[20]</xref>, <xref rid="pone.0023730-Liu1" ref-type="bibr">[30]</xref>, <xref rid="pone.0023730-Shi1" ref-type="bibr">[31]</xref>. Information
regarding sex, ethnic status, and age at SARS diagnosis was recorded. A total of
791 age-, sex-, and ethnicity-matched adults were recruited as control subjects
after the 2003 SARS epidemic, and none of these control subjects ever developed
SARS (<xref ref-type="table" rid="pone-0023730-t001"><bold>Table
1</bold></xref>). Of these samples, 352 infected patients (from Xiaotangshan
Hospital) and 410 controls were from the BPRC (Beijing Proteome Research Center)
of China and were previously employed in a study that showed no association
between <italic>CLEC4M</italic> homozygosity and protection against SARS
coronavirus <xref rid="pone.0023730-Zhi1" ref-type="bibr">[18]</xref>.
All cases and controls were Han Chinese. The remaining 272 samples of SARS
patients were collected from the Ditan Hospital.</p></sec><sec id="s4e"><title>HCW Population Case-Control Study</title><p>Blood samples were obtained from 40 health care workers that developed SARS
during the course of hospital duty and 122 health care workers who had worked in
SARS wards but remained free of the disease. All of these HCW workers worked in
SARS wards in the Second Affiliated Hospital of Senyaxian Medical School in
2003. Information regarding sex and age was recorded. The HCW cases and controls
were Han Chinese.</p></sec><sec id="s4f"><title>rs2248690 Genotype and AHSG Serum Concentration study</title><p>Blood samples from 192 healthy Beijing Chinese were collected from the 307
Hospital of PLA (People's Liberation Army) in Beijing, and sera were
collected. Serum AHSG levels were assessed by ELISA, and the rs2248690 loci were
genotyped as described below.</p></sec><sec id="s4g"><title>DNA Isolation</title><p>Peripheral blood samples were obtained from Guangzhou HCW and Beijing cohorts. A
total of 200 µl of each blood sample was mixed and incubated with
1×RBC lysis buffer (1.5 M ammonium chloride, 100 mM potassium bicarbonate
and 10 mM EDTA). Subsequently, the lymphocytes were obtained by centrifugation.
Serum samples were obtained from the Guangzhou non-HCW cases and controls.
Genomic DNA from all the peripheral blood leukocytes and plasma samples was
extracted using the QIAamp DNA Blood Mini Kit (Qiagen, Valencia, CA) and stored
at −20°C. The resulting DNA was quantified using a standard
spectrophotometric reading on a DU 650 spectrophotometer (Beckman
Coulter,Fullerton, CA). Each sample was diluted to a final concentration of 10
ng/ml.</p></sec><sec id="s4h"><title>Selection of SNPs</title><p>For the association study, we downloaded the SNP genotype data for CHB+JPT
(version 2) from the HapMap database and built LD maps of these two genes with
the Haploview software. A pairwise tagging algorithm implemented in the Tagger
program of Haploview (with an r<sup>2</sup> threshold of 0.8) was used to select
the tag SNPs. SNPs that were reported to affect AHSG/CYP4F3 levels or had been
associated with other diseases were also selected. Finally, we chose to genotype
rs2248690 (5′-flanking region), rs2077119 (5′-flanking region),
rs4917 (exon 6), rs2593813 (intron 1), and rs4918 (exon 7) in the AHSG gene. The
<italic>CYP4F3</italic> gene has more than 30 SNPs in three linkage
disequilibrium blocks. We chose rs3794987 (5′-flanking region), rs1159776
(5′-flanking region), rs4646519 (intron 11), and rs1290625 (intron 2) for
genotyping.</p></sec><sec id="s4i"><title>Genotyping</title><p>Genotyping was completed using the SNPstream ultra-high throughput genotyping
system (Beckman Coulter, Fullerton, CA) according to the manufacturer's
instructions. Briefly, the method combines solution-phase multiplex single
nucleotide extension (SNE) with a solid-phase sorting of labeled SNE primers by
hybridization to a chip that contains 384 4×4 arrays of 12 oligonucleotide
tags and 4 oligonucleotides for positive and negative controls. Each SNE primer
contained 1 of the 20 oligonucleotide tags at its 5′ end, and the SNE
reactions were performed in 12-plex. The microarray plate was imaged using a SNP
scope reader (Beckman Coulter, Fullerton, CA). The two-color system allowed the
detection of the SNP by comparing the signals from the two fluorescent dyes. The
image signals were subsequently transferred to genotyping software that
translated the images of the arrays into genotype calls. The error rate
(0–1.2%) was determined by DNA sequencing (3730, Applied Biosystem
Inc) for 10% of the SNPs examined in the present study.</p></sec><sec id="s4j"><title>Statistical Analysis</title><p>Chi-square tests were used to compare the genotypic frequencies and demographic
distributions between the cases and controls. The exact test was applied to
evaluate whether the population was in Hardy–Weinberg equilibrium by the
Markov chain method; P-values less than 0.05 were considered statistically
significant. The tag SNPs were chosen on a pairwise basis, and the linkage
disequilibrium (LD) calculation was performed on a confidence interval (CI)
basis using the Haploview 4.0 software.</p><p>Fisher's exact test and multivariate unconditional logistic regression were
used to estimate the crude and adjusted odds ratios for each sex and age
category. The odds ratio and a 95% CI were used to measure the strength
of association in the genetic risk association study. The additive and dominant
models were used for this analysis. In the additive model, each SNP was
genotyped and separated into three categories with the homozygous major allele
genotype chosen as the reference group. For the dominant model, each SNP was
modeled as a dichotomous variable with the homozygous major allele genotype as
the reference group and the other two genotypes combined into one category.</p><p>ANOVA and stepwise multivariate regression analyses were performed to identify
the SNPs associated with AHSG serum levels. These statistical analyses were
performed using SAS (version 8.0).</p></sec><sec id="s4k"><title>Serum AHSG assay</title><p>Serum AHSG concentrations were measured in 192 subjects by ELISA. Briefly, the
sera were diluted 10,000-fold and pipetted into the wells of a microtiter plate
whose surfaces were coated with polyclonal anti-human AHSG antibodies (R&D).
After incubation, horseradish peroxidase (HRP) -conjugated polyclonal anti-human
AHSG antibodies were added to the wells. Following the incubation and washing
steps, the absorbance was measured using an automated plate reader at 450 nm.
The concentrations of the diluted sera were read from the standard curve of
human AHSG, and the concentrations of the experimental samples were calculated.
All samples were measured in parallel and in duplicate. The intra- and
inter-assay coefficients of variations were below 10%. The statistical
analyses were done using ANOVA.</p></sec><sec id="s4l"><title>Promoter assays</title><p>Two reporter plasmids encompassing −1091 to +22 bp of the human AHSG
promoter were generated by PCR from two genomic DNA samples with either
−799A OR -799T, using the primers P1:
5′-cgcagatctGGTCTCCATGAGAGGGCTTC-3′ and P2:
5′-cgcaagcttCAGGCGTGCAGGTGGTTG-3′, respectively. The PCR product was
digested with BglII and HindIII and ligated into a pGL3-basic promoter-probing
vector (Promega) containing the firefly luciferase gene as a reporter. The
constructs were sequenced to ensure correct cloning.</p><p>HepG2 and HL7702 cells were seeded at 1×10<sup>5</sup> cells per well,
respectively, in 12-well plates and transfected with pGL3-basic (a promoterless
control) or pGL3-basic containing a particular allele of the
<italic>AHSG</italic> promoter. The pRL-SV40 plasmid (Promega) was
cotransfected as a normalizing control. All transfections were carried out in
triplicate. At 24 h post-incubation, the cells were collected and analyzed for
luciferase activity using the Dual-Luciferase Reporter Assay System (Promega).
The data shown represent three independent experiments performed in triplicate.
The statistical analyses were performed using the Student's
<italic>t</italic>-test.</p></sec><sec id="s4m"><title>Protein-binding assays</title><p>For the GST pull-down experiments, human serum were incubated for 2 h at 4°C
with 5 µg of purified GST or GST fusion proteins bound to glutathione
beads (GE Healthcare, Little Chalfont, Buckinghamshire, UK). The adsorbates were
washed with lysis buffer and subjected to SDS-PAGE and immunoblot analysis.
Purified AHSG protein was included as a loading control.</p></sec></sec><sec sec-type="supplementary-material" id="s5"><title>Supporting Information</title><supplementary-material content-type="local-data" id="pone.0023730.s001"><label>Table S1</label><caption><p><bold>The Dominant Model of Crude and Adjusted Odd Ratios (ORs) by
</bold><bold><italic>AHSG</italic></bold><bold> and
</bold><bold><italic>CYP4F3</italic></bold><bold> Single-Nucleotide
Polymorphism (SNP) Genotypes.</bold></p><p>(DOC)</p></caption><media xlink:href="pone.0023730.s001.doc"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0023730.s002"><label>Table S2</label><caption><p><bold>The Additive model of Crude and Adjusted Odd Ratios (ORs) by
</bold><bold><italic>AHSG</italic></bold><bold> and
</bold><bold><italic>CYP4F3</italic></bold><bold> Single-Nucleotide
Polymorphism (SNP) Genotypes.</bold></p><p>(DOC)</p></caption><media xlink:href="pone.0023730.s002.doc"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0023730.s003"><label>Table S3</label><caption><p><bold>Combinatorial Analysis Under the Dominant Model.</bold></p><p>(DOC)</p></caption><media xlink:href="pone.0023730.s003.doc"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><fn-group><fn fn-type="COI-statement"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="financial-disclosure"><p><bold>Funding: </bold>This work was supported by the China High-Tech Program (863 program) 2007AA022204
and Grant 2008ZX10004-009 from the National Programs on Infectious Disease. The
funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.</p></fn></fn-group><ref-list><title>References</title><ref id="pone.0023730-Peiris1"><label>1</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Peiris</surname><given-names>JS</given-names></name><name><surname>Lai</surname><given-names>ST</given-names></name><name><surname>Poon</surname><given-names>LL</given-names></name><name><surname>Guan</surname><given-names>Y</given-names></name><name><surname>Yam</surname><given-names>LY</given-names></name><etal></etal></person-group><year>2003</year><article-title>Coronavirus as a possible cause of severe acute respiratory
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