| Title |
Genome-wide analysis of root hair-preferential genes in rice
|
|---|---|
| Published in |
Rice, August 2018
|
| DOI | 10.1186/s12284-018-0241-2 |
| Pubmed ID | |
| Authors |
Sunok Moon, Anil Kumar Nalini Chandran, Gynheung An, Chanhui Lee, Ki-Hong Jung |
| Abstract |
Root hairs are valuable in taking up nutrients and water from the rhizosphere and serving as sites of interactions with soil microorganisms. By increasing the external surface area of the roots or interacting with rhizobacteria, root hairs directly and indirectly promote plant growth and yield. Transcriptome data can be used to understand root-hair development in rice. We performed Agilent 44 K microarray experiments with enriched root-hair samples and identified 409 root hair-preferential genes in rice. The expression patterns of six genes were confirmed using a GUS reporter system and quantitative RT-PCR analysis. Gene Ontology (GO) analysis demonstrated that 13 GO terms, including oxygen transport and cell wall generation, were highly over-represented in those genes. Although comparative analysis between rice and Arabidopsis revealed a large proportion of orthologous pairs, their spatial expression patterns were not conserved. To investigate the molecular network associated with root hair-preferential genes in rice, we analyzed the PPI network as well as coexpression data. Subsequently, we developed a refined network consisting of 24 interactions between 10 genes and 18 of their interactors. Identification of root hair-preferential genes and in depth analysis of those genes will be a useful reference to accelerate the understanding of root-hair development in rice. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 24% |
| Student > Master | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Student > Postgraduate | 2 | 8% |
| Professor | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 6 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 40% |
| Biochemistry, Genetics and Molecular Biology | 5 | 20% |
| Engineering | 1 | 4% |
| Unknown | 9 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 August 2018.
All research outputs
#18,648,325
of 23,102,082 outputs
Outputs from Rice
#254
of 391 outputs
Outputs of similar age
#257,504
of 335,220 outputs
Outputs of similar age from Rice
#6
of 13 outputs
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