Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus.
Identifieur interne : 000950 ( PubMed/Corpus ); précédent : 000949; suivant : 000951Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus.
Auteurs : Paul GaleSource :
- Microbial risk analysis [ 2352-3530 ] ; 2018.
Abstract
Assessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (Kd) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, Ka and Kmucin, respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using Ka and Kmucin which may be derived from published Kd values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of Kmucin. At very high Kmucin values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower Kmucin values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p1) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in Ka (due to molecular differences in an emerging virus strain or new host) affect p1, and how these differences in Ka may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of Kd in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.
DOI: 10.1016/j.mran.2018.01.002
PubMed: 32289059
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus.</title><author><name sortKey="Gale, Paul" sort="Gale, Paul" uniqKey="Gale P" first="Paul" last="Gale">Paul Gale</name><affiliation><nlm:affiliation>59 Fairway Avenue, Tilehurst, Reading, Berkshire, UK.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:32289059</idno><idno type="pmid">32289059</idno><idno type="doi">10.1016/j.mran.2018.01.002</idno><idno type="wicri:Area/PubMed/Corpus">000950</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000950</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus.</title><author><name sortKey="Gale, Paul" sort="Gale, Paul" uniqKey="Gale P" first="Paul" last="Gale">Paul Gale</name><affiliation><nlm:affiliation>59 Fairway Avenue, Tilehurst, Reading, Berkshire, UK.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Microbial risk analysis</title><idno type="eISSN">2352-3530</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Assessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (K<sub>d</sub>) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, K<sub>a</sub> and K<sub>mucin</sub>, respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using K<sub>a</sub> and K<sub>mucin</sub> which may be derived from published K<sub>d</sub> values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of K<sub>mucin</sub>. At very high K<sub>mucin</sub> values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower K<sub>mucin</sub> values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p<sub>1</sub>) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in K<sub>a</sub> (due to molecular differences in an emerging virus strain or new host) affect p<sub>1</sub>, and how these differences in K<sub>a</sub> may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of K<sub>d</sub> in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32289059</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2352-3530</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><PubDate><Year>2018</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Microbial risk analysis</Title><ISOAbbreviation>Microb Risk Anal</ISOAbbreviation></Journal><ArticleTitle>Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus.</ArticleTitle><Pagination><MedlinePgn>1-13</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.mran.2018.01.002</ELocationID><Abstract><AbstractText>Assessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (K<sub>d</sub>) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, K<sub>a</sub> and K<sub>mucin</sub>, respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using K<sub>a</sub> and K<sub>mucin</sub> which may be derived from published K<sub>d</sub> values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of K<sub>mucin</sub>. At very high K<sub>mucin</sub> values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower K<sub>mucin</sub> values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p<sub>1</sub>) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in K<sub>a</sub> (due to molecular differences in an emerging virus strain or new host) affect p<sub>1</sub>, and how these differences in K<sub>a</sub> may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of K<sub>d</sub> in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.</AbstractText><CopyrightInformation>© 2018 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gale</LastName><ForeName>Paul</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>59 Fairway Avenue, Tilehurst, Reading, Berkshire, UK.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>01</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Microb Risk Anal</MedlineTA><NlmUniqueID>101728821</NlmUniqueID><ISSNLinking>2352-3522</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Asp, aspartate</Keyword><Keyword MajorTopicYN="N">CRD, carbohydrate-recognition domain</Keyword><Keyword MajorTopicYN="N">Cr, host cell receptor</Keyword><Keyword MajorTopicYN="N">Dose-response</Keyword><Keyword MajorTopicYN="N">EBOV, Zaire ebolavirus</Keyword><Keyword MajorTopicYN="N">Enthalpy</Keyword><Keyword MajorTopicYN="N">Entropy</Keyword><Keyword MajorTopicYN="N">G, Gibbs free energy</Keyword><Keyword MajorTopicYN="N">GI, gastrointestinal</Keyword><Keyword MajorTopicYN="N">GP, glycoprotein</Keyword><Keyword MajorTopicYN="N">H, enthalpy</Keyword><Keyword MajorTopicYN="N">HA, haemagglutinin</Keyword><Keyword MajorTopicYN="N">HBGA, histoblood group antigen</Keyword><Keyword MajorTopicYN="N">HeV, Hendra virus</Keyword><Keyword MajorTopicYN="N">Ka, Kmucin, association constants</Keyword><Keyword MajorTopicYN="N">Kd, dissociation constant for two molecules bound to each other</Keyword><Keyword MajorTopicYN="N">L, Avogadro number</Keyword><Keyword MajorTopicYN="N">M, molar (moles dm−3)</Keyword><Keyword MajorTopicYN="N">MBP, mannose binding protein</Keyword><Keyword MajorTopicYN="N">MERS-CoV, MERS coronavirus</Keyword><Keyword MajorTopicYN="N">MRA, microbiological risk assessment</Keyword><Keyword MajorTopicYN="N">Mucin</Keyword><Keyword MajorTopicYN="N">NPC1, Niemann-Pick C1 protein</Keyword><Keyword MajorTopicYN="N">NiV, Nipah virus</Keyword><Keyword MajorTopicYN="N">NoV, norovirus</Keyword><Keyword MajorTopicYN="N">PL, phospholipid</Keyword><Keyword MajorTopicYN="N">PRR, pathogen recognition receptor</Keyword><Keyword MajorTopicYN="N">Phe, phenylalanine</Keyword><Keyword MajorTopicYN="N">R, ideal gas constant</Keyword><Keyword MajorTopicYN="N">S, entropy</Keyword><Keyword MajorTopicYN="N">SPR, surface plasmon resonance</Keyword><Keyword MajorTopicYN="N">T, temperature</Keyword><Keyword MajorTopicYN="N">TIM-1, T-cell immunoglobulin and mucin domain protein 1</Keyword><Keyword MajorTopicYN="N">VSV, vesicular stomatitis virus</Keyword><Keyword MajorTopicYN="N">Virus</Keyword><Keyword MajorTopicYN="N">k, on/off rate constant</Keyword><Keyword MajorTopicYN="N">n, number of GP/Cr molecular contacts per virus/host cell binding</Keyword><Keyword MajorTopicYN="N">pfu, plaque-forming unit</Keyword><Keyword MajorTopicYN="N">ΔGa, change in Gibbs free energy on association of virus and cell</Keyword><Keyword MajorTopicYN="N">ΔHa, change in enthalpy on association of virus and cell</Keyword><Keyword MajorTopicYN="N">ΔSa, change in entropy on association of virus and cell</Keyword><Keyword MajorTopicYN="N">ΔΔHa, change in ΔHa</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>11</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>12</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>01</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>4</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>4</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>4</Month><Day>1</Day><Hour>0</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32289059</ArticleId><ArticleId IdType="doi">10.1016/j.mran.2018.01.002</ArticleId><ArticleId IdType="pii">S2352-3522(17)30156-1</ArticleId><ArticleId IdType="pmc">PMC7103988</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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