Evidence map›Paper›PMID 31333693›Full record

ArticleFrontiers in plant science2019

Proteomic Data Integration Highlights Central Actors Involved in Einkorn (

Emmanuelle Bancel, Titouan Bonnot, Marlène Davanture, David Alvarez, Michel Zivy, Pierre Martre, Sébastien Déjean, Catherine Ravel

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Review
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 4 institutions in 1 country.

Emmanuelle BancelUMR GDEC, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, Clermont-Ferrand, France.
Titouan BonnotUMR GDEC, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, Clermont-Ferrand, France.
Marlène DavantureUMR GQE, Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS), Agro ParisTech, Université Paris-Sud - Université Paris-Saclay, Gif-sur-Yvette, France.
David AlvarezUMR GDEC, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, Clermont-Ferrand, France.
Michel ZivyUMR GQE, Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS), Agro ParisTech, Université Paris-Sud - Université Paris-Saclay, Gif-sur-Yvette, France.
Pierre MartreUMR GDEC, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, Clermont-Ferrand, France.
Sébastien DéjeanInstitut de Mathématiques de Toulouse, UMR5219 Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Toulouse, France.
Catherine RavelUMR GDEC, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, Clermont-Ferrand, France.
Génétique, Diversité, Écophysiologie des Céréales · FRAgroParisTech · FRUniversité Clermont Auvergne · FRInstitut de Mathématiques de Toulouse · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by Bonnot et al., 2017) concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage. We used the block.splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation.

Indexed as

albumin-globulindata integrationgrain developmentnitrogenproteomicstorage proteinsulfurTriticum monococcum

Identifiers

PMID31333693
PMCPMC6620720
OpenAlexW2952838450

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.