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Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis

Overview of attention for article published in Plant Cell, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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9 X users
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3 Facebook pages

Citations

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101 Dimensions

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187 Mendeley
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Title
Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis
Published in
Plant Cell, February 2016
DOI 10.1105/tpc.15.00910
Pubmed ID
Authors

Ulrike Bechtold, Christopher A. Penfold, Dafyd J. Jenkins, Roxane Legaie, Jonathan D. Moore, Tracy Lawson, Jack S.A. Matthews, Silvere R.M. Vialet-Chabrand, Laura Baxter, Sunitha Subramaniam, Richard Hickman, Hannah Florance, Christine Sambles, Deborah L. Salmon, Regina Feil, Laura Bowden, Claire Hill, Neil R. Baker, John E. Lunn, Bärbel Finkenstädt, Andrew Mead, Vicky Buchanan-Wollaston, Jim Beynon, David A. Rand, David L. Wild, Katherine J. Denby, Sascha Ott, Nicholas Smirnoff, Philip M. Mullineaux

Abstract

Water availability is the biggest single limitation on plant productivity worldwide. In Arabidopsis, changes in metabolism and gene expression drive increased drought tolerance and initiate diverse drought avoidance and escape responses. To address regulatory processes that link these responses together, we set out to identify novel genes that govern early responses to drought. To do this, a high-resolution time series transcriptomics dataset was produced, coupled with detailed physiological and metabolic analyses of plants subjected to a slow transition from well-watered to drought conditions. 1815 drought-responsive differentially expressed genes were identified. The major early changes in gene expression coincided with a drop in carbon assimilation, and only in the late stages with an increase in foliar abscisic acid content. In order to identify gene regulatory networks (GRNs) mediating the transition between the early and late stages of drought, we used Bayesian network modelling of differentially expressed transcription factor (TF) genes. This approach identified AGAMOUS-LIKE22 as key hub gene in a TF GRN. It has previously been shown that AGL22 is involved in the transition from vegetative state to flowering but here we show that AGL22 expression influences steady state photosynthetic rates and lifetime water use. This suggests that AGL22 uniquely regulates a transcriptional network during drought stress, linking changes in primary metabolism and the initiation of stress responses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 187 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 <1%
Finland 1 <1%
Canada 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 182 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 21%
Researcher 33 18%
Student > Master 21 11%
Student > Doctoral Student 16 9%
Professor 10 5%
Other 37 20%
Unknown 31 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 103 55%
Biochemistry, Genetics and Molecular Biology 31 17%
Computer Science 3 2%
Environmental Science 2 1%
Medicine and Dentistry 2 1%
Other 11 6%
Unknown 35 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 21 September 2016.
All research outputs
#5,268,271
of 25,584,565 outputs
Outputs from Plant Cell
#2,528
of 7,069 outputs
Outputs of similar age
#85,238
of 406,999 outputs
Outputs of similar age from Plant Cell
#23
of 56 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,069 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has gotten more attention than average, scoring higher than 64% of its peers.
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 406,999 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.