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Mendeley readers
Attention Score in Context
| Title |
Dynamic changes in transcriptome and cell wall composition underlying brassinosteroid-mediated lignification of switchgrass suspension cells
|
|---|---|
| Published in |
Biotechnology for Biofuels and Bioproducts, November 2017
|
| DOI | 10.1186/s13068-017-0954-2 |
| Pubmed ID | |
| Authors |
Xiaolan Rao, Hui Shen, Sivakumar Pattathil, Michael G. Hahn, Ivana Gelineo-Albersheim, Debra Mohnen, Yunqiao Pu, Arthur J. Ragauskas, Xin Chen, Fang Chen, Richard A. Dixon |
| Abstract |
Plant cell walls contribute the majority of plant biomass that can be used to produce transportation fuels. However, the complexity and variability in composition and structure of cell walls, particularly the presence of lignin, negatively impacts their deconstruction for bioenergy. Metabolic and genetic changes associated with secondary wall development in the biofuel crop switchgrass (Panicum virgatum) have yet to be reported. Our previous studies have established a cell suspension system for switchgrass, in which cell wall lignification can be induced by application of brassinolide (BL). We have now collected cell wall composition and microarray-based transcriptome profiles for BL-induced and non-induced suspension cultures to provide an overview of the dynamic changes in transcriptional reprogramming during BL-induced cell wall modification. From this analysis, we have identified changes in candidate genes involved in cell wall precursor synthesis, cellulose, hemicellulose, and pectin formation and ester-linkage generation. We have also identified a large number of transcription factors with expression correlated with lignin biosynthesis genes, among which are candidates for control of syringyl (S) lignin accumulation. Together, this work provides an overview of the dynamic compositional changes during brassinosteroid-induced cell wall remodeling, and identifies candidate genes for future plant genetic engineering to overcome cell wall recalcitrance. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 37% |
| Lecturer | 2 | 7% |
| Student > Master | 2 | 7% |
| Professor > Associate Professor | 2 | 7% |
| Researcher | 2 | 7% |
| Other | 5 | 19% |
| Unknown | 4 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 48% |
| Biochemistry, Genetics and Molecular Biology | 5 | 19% |
| Engineering | 2 | 7% |
| Arts and Humanities | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 4 | 15% |
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 08 December 2017.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#996
of 1,578 outputs
Outputs of similar age
#279,781
of 445,786 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#21
of 41 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 445,786 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.