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Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition
Filippo Castiglione; Dario Ghersi; Franco Celada;
Filippo Castiglione; Dario Ghersi; Franco Celada;
Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition
Experimental and computational studies have revealed that T-cell cross-reactivity is a widespread phenomenon that can either be advantageous or detrimental to the host. In particular, detrimental effects can occur whenever the clonal dominance of memory cells is not justified by their infection-clearing capacity. Using an agent-based model of the immune system, we recently predicted the “memory anti-naïve” phenomenon, which occurs when the secondary challenge is similar but not identical to the primary stimulation. In this case, the pre-existingmemory cells formed during the primary infection may be rapidly deployed in spite of their low affinity and can actually prevent a potentially higher affinity naïve response from emerging, resulting in impaired viral clearance. This finding allowed us to propose a mechanistic explanation for the concept of “antigenic sin” originally described in the context of the humoral response. However, the fact that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary mechanisms that can mitigate the effect of the memory anti-naïve phenomenon. In this study we use computer modeling to further elucidate clonal dominance and the memory anti-naïve phenomenon, and to investigate a possible mitigating factor called attrition. Attrition has been described in the experimental and computational literature as a combination of competition for space and apoptosis of lymphocytes via type-I interferon in the early stages of a viral infection. This study systematically explores the relationship between clonal dominance and the mechanism of attrition. Our results suggest that attrition can indeed mitigate the memory anti-naïve effect by enabling the emergence of a diverse, higher affinity naïve response against the secondary challenge. In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction and dominance.
- New York University United States
- University of Nebraska at Omaha United States
Microsoft Academic Graph classification: Biology Low affinity Neuroscience Viral infection Attrition medicine.disease medicine Immune system Original antigenic sin Dominance (genetics)
Library of Congress Subject Headings: lcsh:Immunologic diseases. Allergy lcsh:RC581-607
Immunology, Immunology and Allergy, Original Research, computer modeling, IMMSIM, memory-anti-naïve, attrition, CD8+ response, Computer Simulation, Humans, Immunologic Memory, Interferon Type I, Models, Immunological, T-Lymphocytes, memory-anti-naive
Immunology, Immunology and Allergy, Original Research, computer modeling, IMMSIM, memory-anti-naïve, attrition, CD8+ response, Computer Simulation, Humans, Immunologic Memory, Interferon Type I, Models, Immunological, T-Lymphocytes, memory-anti-naive
Microsoft Academic Graph classification: Biology Low affinity Neuroscience Viral infection Attrition medicine.disease medicine Immune system Original antigenic sin Dominance (genetics)
Library of Congress Subject Headings: lcsh:Immunologic diseases. Allergy lcsh:RC581-607
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1.Selin LK Cornberg M Brehm MA Kim SK Calcagno C Ghersi D. CD8 memory T cells: Cross-reactivity and heterologous immun ity. Semin Immunol. (2004) 16:335–47. 10.1016/j.smim.2004.08.014 15528078 [OpenAIRE] [PubMed] [DOI]
2.de St Groth SF, Webster RG. Disquisitions of original antigenic sin. I. evidence in man. J Exp Med. (1966) 124:331–45. 10.1084/jem.124.3.331 5922742 [OpenAIRE] [PubMed] [DOI]
3.Cheng Y Ghersi D Calcagno C Selin LK Puzone R Celada F. A discrete computer model of the immune system reveals competitive interactions between the humoral and cellular branch and between cross-reacting memory and naive responses. Vaccine. (2009) 27:833–845. 10.1016/j.vaccine.2008.11.109 19101600 [OpenAIRE] [PubMed] [DOI]
4.Pearson YE Cheng Y Selin LK Puzone R Celada F. Systematic simulation of cross-reactivity predicts ambiguity in Tk memory: it may save lives of the infected, but limits specificities vital for further responses. Autoimmunity. (2011) 44:315–27. 10.3109/08916934.2011.523275 21231890 [OpenAIRE] [PubMed] [DOI]
5.Welsh RM Che JW Brehm MA Selin LK. Heterologous immunity between viruses. Immunol Rev. (2010) 235:244–66. 10.1111/j.0105-2896.2010.00897.x 20536568 [OpenAIRE] [PubMed] [DOI]
6.Brack C Hirama M Lenhard-Schuller R Tonegawa S. A complete immunoglobulin gene is created by somatic recombination. Cell. (1978) 15:1–14. 10.1016/0092-8674(78)90078-8 100225 [OpenAIRE] [PubMed] [DOI]
7.Lanzavecchia A. Is the T-cell receptor involved in T-cell killing?Nature. (1986) 319:778–78. 10.1038/319778a0 3485254 [OpenAIRE] [PubMed] [DOI]
8.von Neumann J Theory of Self Reproducing Automata. Champaign, IL: University of Illinois Press (1966).
9.Celada F Selden PE Seiden PE. A computer model of cellular interactions in the immune system. Immunol Today. (1992) 13:56–62. 10.1016/0167-5699(92)90135-T 1575893 [OpenAIRE] [PubMed] [DOI]
10.Celada F Seiden PE. Affinity maturation and hypermutation in a simulation of the humoral immune response. Eur J Immunol. (1996) 26:1350–8. 10.1002/eji.1830260626 8647216 [OpenAIRE] [PubMed] [DOI]
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average views 137 downloads 75 citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average - 137views75downloads
citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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- New York University United States
- University of Nebraska at Omaha United States
Experimental and computational studies have revealed that T-cell cross-reactivity is a widespread phenomenon that can either be advantageous or detrimental to the host. In particular, detrimental effects can occur whenever the clonal dominance of memory cells is not justified by their infection-clearing capacity. Using an agent-based model of the immune system, we recently predicted the “memory anti-naïve” phenomenon, which occurs when the secondary challenge is similar but not identical to the primary stimulation. In this case, the pre-existingmemory cells formed during the primary infection may be rapidly deployed in spite of their low affinity and can actually prevent a potentially higher affinity naïve response from emerging, resulting in impaired viral clearance. This finding allowed us to propose a mechanistic explanation for the concept of “antigenic sin” originally described in the context of the humoral response. However, the fact that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary mechanisms that can mitigate the effect of the memory anti-naïve phenomenon. In this study we use computer modeling to further elucidate clonal dominance and the memory anti-naïve phenomenon, and to investigate a possible mitigating factor called attrition. Attrition has been described in the experimental and computational literature as a combination of competition for space and apoptosis of lymphocytes via type-I interferon in the early stages of a viral infection. This study systematically explores the relationship between clonal dominance and the mechanism of attrition. Our results suggest that attrition can indeed mitigate the memory anti-naïve effect by enabling the emergence of a diverse, higher affinity naïve response against the secondary challenge. In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction and dominance.
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