Evidence map›Paper›PMID 30696429›Full record

ArticleBMC systems biology2019

How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response.

Natacha Go, Suzanne Touzeau, Zeenath Islam, Catherine Belloc, Andrea Doeschl-Wilson

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Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

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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

5 citing papers in PubMed.

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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

5 authors.

Natacha GoBIOEPAR, INRA, Oniris, Route de Gachet, CS 40706, Nantes, France. natacha.go@laposte.net.ORCID 0000-0002-9638-9078
Suzanne TouzeauBIOCORE, Inria, INRA, CNRS, UPMC Univ Paris 06, Université Côte d'Azur, 2004 route des Lucioles, BP 93, Sophia Antipolis, France.
Zeenath IslamDivision of Genetics and Genomics, The Roslin Institute, Easter Bush, Midlothian, UK.
Catherine BellocBIOEPAR, INRA, Oniris, Route de Gachet, CS 40706, Nantes, France.
Andrea Doeschl-WilsonDivision of Genetics and Genomics, The Roslin Institute, Easter Bush, Midlothian, UK.

Funding

Biotechnology and Biological Sciences Research Council
6 · The paper itself

Abstract

backgroundUnderstanding what determines the between-host variability in infection dynamics is a key issue to better control the infection spread. In particular, pathogen clearance is desirable over rebounds for the health of the infected individual and its contact group. In this context, the Porcine Respiratory and Reproductive Syndrome virus (PRRSv) is of particular interest. Numerous studies have shown that pigs similarly infected with this highly ubiquitous virus elicit diverse response profiles. Whilst some manage to clear the virus within a few weeks, others experience prolonged infection with a rebound. Despite much speculation, the underlying mechanisms responsible for this undesirable rebound phenomenon remain unclear.

resultsWe aimed at identifying immune mechanisms that can reproduce and explain the rebound patterns observed in PRRSv infection using a mathematical modelling approach of the within-host dynamics. As diverse mechanisms were found to influence PRRSv infection, we established a model that details the major mechanisms and their regulations at the between-cell scale. We developed an ABC-like optimisation method to fit our model to an extensive set of experimental data, consisting of non-rebounder and rebounder viremia profiles. We compared, between both profiles, the estimated parameter values, the resulting immune dynamics and the efficacies of the underlying immune mechanisms. Exploring the influence of these mechanisms, we showed that rebound was promoted by high apoptosis, high cell infection and low cytolysis by Cytotoxic T Lymphocytes, while increasing neutralisation was very efficient to prevent rebounds.

conclusionsOur paper provides an original model of the immune response and an appropriate systematic fitting method, whose interest extends beyond PRRS infection. It gives the first mechanistic explanation for emergence of rebounds during PRRSv infection. Moreover, results suggest that vaccines or genetic selection promoting strong neutralising and cytolytic responses, ideally associated with low apoptotic activity and cell permissiveness, would prevent rebound.

Indexed as

Models, ImmunologicalAnimalsApoptosisPorcine respiratory and reproductive syndrome virusSwineViremiaABC-like optimisation methodImmunological modelPRRSvRebounder viremia profile

Identifiers

PMID30696429
PMCPMC6352383

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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.