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Within-Epitope Interactions Can Bias CTL Escape Estimation in Early HIV Infection

Overview of attention for article published in Frontiers in immunology, May 2017
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Title
Within-Epitope Interactions Can Bias CTL Escape Estimation in Early HIV Infection
Published in
Frontiers in immunology, May 2017
DOI 10.3389/fimmu.2017.00423
Pubmed ID
Authors

Victor Garcia, Marcus W. Feldman

Abstract

As human immunodeficiency virus (HIV) begins to replicate within hosts, immune responses are elicited against it. Escape mutations in viral epitopes-immunogenic peptide parts presented on the surface of infected cells-allow HIV to partially evade these responses, and thus rapidly go to fixation. The faster they go to fixation, i.e., the higher their escape rate, the larger the selective pressure exerted by the immune system is assumed to be. This relation underpins the rationale for using escapes to assess the strength of immune responses. However, escape rate estimates are often obtained by employing an aggregation procedure, where several mutations that affect the same epitope are aggregated into a single, composite epitope mutation. The aggregation procedure thus rests upon the assumption that all within-epitope mutations have indistinguishable effects on immune recognition. In this study, we investigate how violation of this assumption affects escape rate estimates. To this end, we extend a previously developed simulation model of HIV that accounts for mutation, selection, and recombination to include different distributions of fitness effects (DFEs) and inter-mutational genomic distances. We use this discrete time Wright-Fisher based model to simulate early within-host evolution of HIV for DFEs and apply standard estimation methods to infer the escape rates. We then compare true with estimated escape rate values. We also compare escape rate values obtained by applying the aggregation procedure with values estimated without use of that procedure. We find that across the DFEs analyzed, the aggregation procedure alters the detectability of escape mutations: large-effect mutations are overrepresented while small-effect mutations are concealed. The effect of the aggregation procedure is similar to extracting the largest-effect mutation appearing within an epitope. Furthermore, the more pronounced the over-exponential decay of the DFEs, the more severely true escape rates are underestimated. We conclude that the aggregation procedure has two main consequences. On the one hand, it leads to a misrepresentation of the DFE of fixed mutations. On the other hand, it conceals within-epitope interactions that may generate irregularities in mutation frequency trajectories that are thus left unexplained.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Master 2 17%
Student > Ph. D. Student 2 17%
Other 1 8%
Professor 1 8%
Other 1 8%
Unknown 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Agricultural and Biological Sciences 3 25%
Veterinary Science and Veterinary Medicine 1 8%
Psychology 1 8%
Unknown 4 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2024.
All research outputs
#7,556,285
of 25,932,719 outputs
Outputs from Frontiers in immunology
#8,885
of 32,608 outputs
Outputs of similar age
#110,246
of 328,392 outputs
Outputs of similar age from Frontiers in immunology
#164
of 403 outputs
Altmetric has tracked 25,932,719 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 32,608 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,392 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 403 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.