Evidence map›Paper›PMID 24660019›Full record

ArticleJournal of statistical mechanics (Online)2013

Mathematical modeling of escape of HIV from cytotoxic T lymphocyte responses.

Vitaly V Ganusov, Richard A Neher, Alan S Perelson

Open access · greenAbstract read
In one paragraph

Article in Journal of statistical mechanics (Online), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 37 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Modeling the immune response to HIV infection.Current opinion in systems biology · 2018
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Modeling T cell responses to antigenic challenge.Journal of pharmacokinetics and pharmacodynamics · 2014
    Review
  17. Article
  18. Article
  19. Pathogen evolution and the immunological niche.Annals of the New York Academy of Sciences · 2014
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Vitaly V GanusovDepartment of Microbiology, University of Tennessee, Knoxville, TN 37996, USA.
Richard A NeherMax-Planck-Institute for Developmental Biology, 72070 Tübingen, Germany.
Alan S PerelsonTheoretical Biology and Biophysics, Los Alamos National Laboratory, MS K710 Los Alamos, 87545 NM, USA.
Los Alamos National Laboratory · USMax Planck Institute for Developmental Biology · DEUniversity of Tennessee at Knoxville · US

Funding

Modeling Viral and T Lymphocyte DynamicsR01OD011095 · OD · TRIAD NATIONAL SECURITY, LLC · PI PERELSON, ALAN S · 2012 to 2024
$5.0M
Immune System Modeling/HIVR01AI028433 · NIAID · UNIVERSITY OF CALIF-LOS ALAMOS NAT LAB · PI PERELSON, ALAN S · 1990 to 2021
$3.7M
Immune System Modeling/HIVR37AI028433 · NIAID · UNIVERSITY OF CALIF-LOS ALAMOS NAT LAB · PI PERELSON, ALAN S · 2002 to 2011
$2.4M
NIAID NIH HHS R01 AI028433NIAID NIH HHS R37 AI028433NIH HHS R01 OD011095
6 · The paper itself

Abstract

Human immunodeficiency virus (HIV-1 or simply HIV) induces a persistent infection, which in the absence of treatment leads to AIDS and death in almost all infected individuals. HIV infection elicits a vigorous immune response starting about 2-3 weeks post infection that can lower the amount of virus in the body, but which cannot eradicate the virus. How HIV establishes a chronic infection in the face of a strong immune response remains poorly understood. It has been shown that HIV is able to rapidly change its proteins via mutation to evade recognition by virus-specific cytotoxic T lymphocytes (CTLs). Typically, an HIV-infected patient will generate 4-12 CTL responses specific for parts of viral proteins called epitopes. Such CTL responses lead to strong selective pressure to change the viral sequences encoding these epitopes so as to avoid CTL recognition. Indeed, the viral population "escapes" from about half of the CTL responses by mutation in the first year. Here we review experimental data on HIV evolution in response to CTL pressure, mathematical models developed to explain this evolution, and highlight problems associated with the data and previous modeling efforts. We show that estimates of the strength of the epitope-specific CTL response depend on the method used to fit models to experimental data and on the assumptions made regarding how mutants are generated during infection. We illustrate that allowing CTL responses to decay over time may improve the fit to experimental data and provides higher estimates of the killing efficacy of HIV-specific CTLs. We also propose a novel method for simultaneously estimating the killing efficacy of multiple CTL populations specific for different epitopes of HIV using stochastic simulations. Lastly, we show that current estimates of the efficacy at which HIV-specific CTLs clear virus-infected cells can be improved by more frequent sampling of viral sequences and by combining data on sequence evolution with experimentally measured CTL dynamics.

Indexed as

acute HIV infectioncost of escapeCTL responseescape mutationsmathematical model

Identifiers

PMID24660019
PMCPMC3961578
OpenAlexW1988518683

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.