Journal article Open Access

# Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition

Castiglione, Filippo; Ghersi, Dario; Celada, Franco

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/3351968</identifier>
<creators>
<creator>
<creatorName>Castiglione, Filippo</creatorName>
<givenName>Filippo</givenName>
<familyName>Castiglione</familyName>
<affiliation>National Research Council of Italy</affiliation>
</creator>
<creator>
<creatorName>Ghersi, Dario</creatorName>
<givenName>Dario</givenName>
<familyName>Ghersi</familyName>
<affiliation>College of Information Science and Technology, University of Nebraska</affiliation>
</creator>
<creator>
<givenName>Franco</givenName>
<affiliation>NYU School of Medicine, New York</affiliation>
</creator>
</creators>
<titles>
<title>Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<subjects>
<subject>computer modeling</subject>
<subject>IMMSIM</subject>
<subject>memory-anti-naive</subject>
<subject>attrition</subject>
<subject>CD8+ response</subject>
</subjects>
<dates>
<date dateType="Issued">2019-07-26</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="JournalArticle"/>
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<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3351968</alternateIdentifier>
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<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3389/fimmu.2019.01513</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
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<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Experimental and computational studies have revealed that T-cell cross-reactivity is a&amp;nbsp;widespread phenomenon that can either be advantageous or detrimental to the host.&amp;nbsp;In particular, detrimental effects can occur whenever the clonal dominance of memory&amp;nbsp;cells is not justified by their infection-clearing capacity. Using an agent-based model&amp;nbsp;of the immune system, we recently predicted the &amp;ldquo;memory anti-na&amp;iuml;ve&amp;rdquo; phenomenon,&amp;nbsp;which occurs when the secondary challenge is similar but not identical to the primary&amp;nbsp;stimulation. In this case, the pre-existingmemory cells formed during the primary infection&amp;nbsp;may be rapidly deployed in spite of their low affinity and can actually prevent a potentially&amp;nbsp;higher affinity na&amp;iuml;ve response from emerging, resulting in impaired viral clearance. This&amp;nbsp;finding allowed us to propose a mechanistic explanation for the concept of &amp;ldquo;antigenic&amp;nbsp;sin&amp;rdquo; originally described in the context of the humoral response. However, the fact&amp;nbsp;that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary&amp;nbsp;mechanisms that can mitigate the effect of the memory anti-na&amp;iuml;ve phenomenon. In&amp;nbsp;this study we use computer modeling to further elucidate clonal dominance and the&amp;nbsp;memory anti-na&amp;iuml;ve phenomenon, and to investigate a possible mitigating factor called&amp;nbsp;attrition. Attrition has been described in the experimental and computational literature&amp;nbsp;as a combination of competition for space and apoptosis of lymphocytes via type-I&amp;nbsp;interferon in the early stages of a viral infection. This study systematically explores&amp;nbsp;the relationship between clonal dominance and the mechanism of attrition. Our results&amp;nbsp;suggest that attrition can indeed mitigate the memory anti-na&amp;iuml;ve effect by enabling the&amp;nbsp;emergence of a diverse, higher affinity na&amp;iuml;ve response against the secondary challenge.&lt;br&gt;
In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction&amp;nbsp;and dominance.&amp;nbsp;&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
<awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
</fundingReference>
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