ArticleFrontiers in immunology2013
Inferring HIV Escape Rates from Multi-Locus Genotype Data.
Article in Frontiers in immunology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed, 40 citations in OpenAlex.
- A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection.PLoS pathogens · 2020Article
- Mapping the drivers of within-host pathogen evolution using massive data sets.Nature communications · 2019Article
- Inferring population genetics parameters of evolving viruses using time-series data.Virus evolution · 2019Article
- Mutational load causes stochastic evolutionary outcomes in acute RNA viral infection.Virus evolution · 2019Article
- Dynamics of virus and immune response in multi-epitope network.Journal of mathematical biology · 2018Article
- Estimating the mutational fitness effects distribution during early HIV infection.Virus evolution · 2018Article
- Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.Genetics · 2018Article
- Clonal interference can cause wavelet-like oscillations of multilocus linkage disequilibrium.Journal of the Royal Society, Interface · 2018Article
- Time Intervals in Sequence Sampling, Not Data Modifications, Have a Major Impact on Estimates of HIV Escape Rates.Viruses · 2018Article
- Kinetics of HIV-Specific CTL Responses Plays a Minimal Role in Determining HIV Escape Dynamics.Frontiers in immunology · 2018Article
- Global properties of nested network model with application to multi-epitope HIV/CTL dynamics.Journal of mathematical biology · 2017Article
- Time-Sampled Population Sequencing Reveals the Interplay of Selection and Genetic Drift in Experimental Evolution ofJournal of virology · 2017Article
- Within-Epitope Interactions Can Bias CTL Escape Estimation in Early HIV Infection.Frontiers in immunology · 2017Article
- Article
- Host-Pathogen Coevolution and the Emergence of Broadly Neutralizing Antibodies in Chronic Infections.PLoS genetics · 2016Article
- Investigating the Consequences of Interference between Multiple CD8+ T Cell Escape Mutations in Early HIV Infection.PLoS computational biology · 2016Article
- Fitness Inference from Short-Read Data: Within-Host Evolution of a Reassortant H5N1 Influenza Virus.Molecular biology and evolution · 2015Article
- Broad CTL Response in Early HIV Infection Drives Multiple Concurrent CTL Escapes.PLoS computational biology · 2015Article
- Immuno-epidemiological modeling of HIV-1 predicts high heritability of the set-point virus load, while selection for CTL escape dominates virulence evolution.PLoS computational biology · 2014Article
- Reliable reconstruction of HIV-1 whole genome haplotypes reveals clonal interference and genetic hitchhiking among immune escape variants.Retrovirology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.