Evidence map›Paper›PMID 31185494›Full record

ArticleGigaScience2019

High-resolution computational modeling of immune responses in the gut.

Meghna Verma, Josep Bassaganya-Riera, Andrew Leber, Nuria Tubau-Juni, Stefan Hoops, Vida Abedi, Xi Chen, Raquel Hontecillas

Open access · goldAbstract read
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.7field-weighted citation impact, top 11% 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

12 citing papers in PubMed, 20 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Meghna VermaNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Josep Bassaganya-RieraNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Andrew LeberNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Nuria Tubau-JuniNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Stefan HoopsNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Vida AbediNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Xi ChenGrado Department of Industrial and Systems Engineering, Virginia Tech, 250 Perry St, Blacksburg, VA 24061, USA.
Raquel HontecillasNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.
Biocom · USVirginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHelicobacter pylori causes gastric cancer in 1-2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori-colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing-driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations.

resultsThe outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression-based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells.

conclusionsThe hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.

Indexed as

Computer SimulationModels, BiologicalAnimalsCell ProliferationEpithelial CellsGastrointestinal TractHelicobacter InfectionsHelicobacter pyloriImmune SystemMiceagent-based modeldendritic cellsGaussian processHelicobacter pylorihigh-performance computingimmune systemmacrophagesmetamodelordinary differential equationsensitivity analysis

Identifiers

PMID31185494
PMCPMC6559340
OpenAlexW2951776669

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.