Evidence map›Paper›PMID 24053558›Full record

ArticleBMC genomics2013

Transcriptome profiling of Gossypium barbadense inoculated with Verticillium dahliae provides a resource for cotton improvement.

Yan Zhang, Xing Fen Wang, Ze Guo Ding, Qing Ma, Gui Rong Zhang, Shu Ling Zhang, Zhi Kun Li, Li Qiang Wu, Gui Yin Zhang, Zhi Ying Ma

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
14.3field-weighted citation impact, top 1% 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

53 citing papers in PubMed, 132 citations in OpenAlex.

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  12. International journal of molecular sciences · 2022
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  17. Transcriptome Analysis and RNA Interference RevealFrontiers in plant science · 2021
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  18. Frontiers in plant science · 2021
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  19. Article
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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

10 authors at 1 institution in 1 country.

Yan ZhangDepartment of Agriculture, North China Key Laboratory for Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding 071001, People's Republic of China. mzhy@hebau.edu.cn.
Xing Fen Wang
Ze Guo Ding
Qing Ma
Gui Rong Zhang
Shu Ling Zhang
Zhi Kun Li
Li Qiang Wu
Gui Yin Zhang
Zhi Ying Ma
Hebei Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVerticillium wilt, caused by the fungal pathogen Verticillium dahliae, is the most severe disease in cotton (Gossypium spp.), causing great lint losses worldwide. Disease management could be achieved in the field if genetically improved, resistant plants were used. However, the interaction between V. dahliae and cotton is a complicated process, and its molecular mechanism remains obscure. To understand better the defense response to this pathogen as a means for obtaining more tolerant cultivars, we monitored the transcriptome profiles of roots from resistant plants of G. barbadense cv. Pima90-53 that were challenged with V. dahliae.

resultsIn all, 46,192 high-quality expressed sequence tags (ESTs) were generated from a full-length cDNA library of G. barbadense. They were clustered and assembled into 23126 unigenes that comprised 2661 contigs and 20465 singletons. Those unigenes were assigned Gene Ontology terms and mapped to 289 KEGG pathways. A total of 3027 unigenes were found to be homologous to known defense-related genes in other plants. They were assigned to the functional classification of plant-pathogen interactions, including disease defenses and signal transduction. The branch of "SA→NPR1→TGA→PR-1→Disease resistance" was first discovered in the interaction of cotton-V. dahliae, indicating that this wilt process includes both biotrophic and necrotrophic stages. In all, 4936 genes coding for putative transcription factors (TF) were identified in our library. The most abundant TF family was the NAC group (527), followed by G2-like (440), MYB (372), BHLH (331), bZIP (271) ERF, C3H, and WRKY. We also analyzed the expression of genes involved in pathogen-associated molecular pattern (PAMP) recognition, the activation of effector-triggered immunity, TFs, and hormone biosynthesis, as well as genes that are pathogenesis-related, or have roles in signaling/regulatory functions and cell wall modification. Their differential expression patterns were compared among mock-/inoculated- and resistant/susceptible cotton. Our results suggest that the cotton defense response has significant transcriptional complexity and that large accumulations of defense-related transcripts may contribute to V. dahliae resistance in cotton. Therefore, these data provide a resource for cotton improvement through molecular breeding approaches.

conclusionsThis study generated a substantial amount of cotton transcript sequences that are related to defense responses against V. dahliae. These genomics resources and knowledge of important related genes contribute to our understanding of host-pathogen interactions and the defense mechanisms utilized by G. barbadense, a non-model plant system. These tools can be applied in establishing a modern breeding program that uses marker-assisted selections and oligonucleotide arrays to identify candidate genes that can be linked to valuable agronomic traits in cotton, including disease resistance.

Indexed as

TranscriptomeVerticilliumBreedingDisease ResistanceExpressed Sequence TagsGene Expression Regulation, PlantGene LibraryGossypiumHost-Pathogen InteractionsMicrosatellite RepeatsMolecular Sequence AnnotationPlant DiseasesPlant RootsRNA, PlantTranscription FactorsRNA, PlantTranscription Factors

Identifiers

PMID24053558
PMCPMC3849602
OpenAlexW2076621201

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.