Evidence map›Paper›PMID 27784264›Full record

ArticleBMC evolutionary biology2016

Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution.

Jeewoen Shin, Thomas MacCarthy

Open access · goldAbstract read
In one paragraph

Article in BMC evolutionary biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Coevolutionary theory of hosts and parasites.Journal of evolutionary biology · 2022
    Review
  3. Article
  4. Article
  5. Article
  6. 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

2 authors at 2 institutions in 1 country.

Jeewoen ShinDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Thomas MacCarthyDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. thomas.maccarthy@stonybrook.edu.ORCID 0000-0003-0025-9285
Stony Brook School · USStony Brook University · US

Funding

High-throughput informatics for antibodiesR01GM111741 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI MACCARTHY, THOMAS · 2015 to 2018
$1.1M
NIGMS NIH HHS R01 GM111741
6 · The paper itself

Abstract

backgroundHost resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruses enter host cells by binding cell surface receptors. We adopt a computational modeling approach to study the evolution of the first infection step, where we consider two possible levels of resistance mechanism: at the level of the binding interaction between the host receptor and a virus binding protein, and at the level of receptor protein expression where we use a standard gene regulatory network model. At the population level we adopted the Susceptible-Infected-Susceptible (SIS) model. We used our multi-scale model to understand what conditions might determine the balance between use of resistance mechanisms at the two different levels.

resultsWe explored a range of different conditions (model parameters) that affect host evolutionary dynamics and, in particular, the balance between the use of different resistance mechanisms. These conditions include the complexity of the receptor binding protein-protein interaction, selection pressure on the host population (pathogenicity), and the number of expressed cell-surface receptors. In particular, we found that as the receptor binding complexity (understood as the number of amino acids involved in the interaction between the virus entry protein and the host receptor) increases, viruses tend to become specialists and target one specific receptor. At the same time, on the host side, the potential for resistance shifts from the changes at the level of receptor binding (protein-protein) interaction towards changes at the level of gene regulation, suggesting a mechanism for increased biological complexity.

conclusionsHost resistance and viral pathogenicity depend on quite different evolutionary conditions. Viruses may evolve cell entry strategies that use small receptor binding regions, represented by low complexity binding in our model. Our modeling results suggest that if the virus adopts a strategy based on binding to low complexity sites on the host receptor, the host will select a defense strategy at the protein (receptor) level, rather than at the level of the regulatory network - a virus-host strategy that appears to have been selected most often in nature.

Indexed as

Evolution, MolecularModels, TheoreticalDisease ResistanceGene Regulatory NetworksGenetic VariationHost-Pathogen InteractionsProtein BindingReceptors, Cell SurfaceReceptors, VirusViral ProteinsVirus InternalizationReceptors, Cell SurfaceReceptors, VirusViral ProteinsGene regulatory networkHost cell surface receptorVirus entry

Identifiers

PMID27784264
PMCPMC5080737
OpenAlexW2537884342

What OpenQuestion holds

Textmetadata
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.