Evidence map›Paper›PMID 23943779›Full record

ArticleMolecular & cellular proteomics : MCP2013

Application of an improved proteomics method for abundant protein cleanup: molecular and genomic mechanisms study in plant defense.

Yixiang Zhang, Peng Gao, Zhuo Xing, Shumei Jin, Zhide Chen, Lantao Liu, Nasie Constantino, Xinwang Wang, Weibing Shi, Joshua S Yuan and 1 more

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

10 citing papers in PubMed.

  1. Article
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  4. Review
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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

11 authors.

Yixiang ZhangTexas A&M Agrilife Synthetic and Systems Biology Innovation Hub (SSBiH), Department of Plant Pathology and Microbiology, Texas A&M University, College Station, Texas 77843;
Peng Gao
Zhuo Xing
Shumei Jin
Zhide Chen
Lantao Liu
Nasie Constantino
Xinwang Wang
Weibing Shi
Joshua S Yuan
Susie Y Dai

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High abundance proteins like ribulose-1,5-bisphosphate carboxylase oxygenase (Rubisco) impose a consistent challenge for the whole proteome characterization using shot-gun proteomics. To address this challenge, we developed and evaluated Polyethyleneimine Assisted Rubisco Cleanup (PARC) as a new method by combining both abundant protein removal and fractionation. The new approach was applied to a plant insect interaction study to validate the platform and investigate mechanisms for plant defense against herbivorous insects. Our results indicated that PARC can effectively remove Rubisco, improve the protein identification, and discover almost three times more differentially regulated proteins. The significantly enhanced shot-gun proteomics performance was translated into in-depth proteomic and molecular mechanisms for plant insect interaction, where carbon re-distribution was used to play an essential role. Moreover, the transcriptomic validation also confirmed the reliability of PARC analysis. Finally, functional studies were carried out for two differentially regulated genes as revealed by PARC analysis. Insect resistance was induced by over-expressing either jacalin-like or cupin-like genes in rice. The results further highlighted that PARC can serve as an effective strategy for proteomics analysis and gene discovery.

Indexed as

AnimalsGenome, PlantGenomicsHost-Pathogen InteractionsOryzaPlant DiseasesPlant ProteinsPlantsPlants, Genetically ModifiedProteomicsRibulose-Bisphosphate CarboxylaseSpodopteraPlant ProteinsRibulose-Bisphosphate Carboxylase

Identifiers

PMID23943779
PMCPMC3820953

What OpenQuestion holds

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Registered trials

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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.