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Infiltration-RNAseq: transcriptome profiling of Agrobacterium-mediated infiltration of transcription factors to discover gene function and expression networks in plants

Overview of attention for article published in Plant Methods, October 2016
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Title
Infiltration-RNAseq: transcriptome profiling of Agrobacterium-mediated infiltration of transcription factors to discover gene function and expression networks in plants
Published in
Plant Methods, October 2016
DOI 10.1186/s13007-016-0141-7
Pubmed ID
Authors

Donna M. Bond, Nick W. Albert, Robyn H. Lee, Gareth B. Gillard, Chris M. Brown, Roger P. Hellens, Richard C. Macknight

Abstract

Transcription factors (TFs) coordinate precise gene expression patterns that give rise to distinct phenotypic outputs. The identification of genes and transcriptional networks regulated by a TF often requires stable transformation and expression changes in plant cells. However, the production of stable transformants can be slow and laborious with no guarantee of success. Furthermore, transgenic plants overexpressing a TF of interest can present pleiotropic phenotypes and/or result in a high number of indirect gene expression changes. Therefore, fast, efficient, high-throughput methods for assaying TF function are needed. Agroinfiltration is a simple plant biology method that allows transient gene expression. It is a rapid and powerful tool for the functional characterisation of TF genes in planta. High throughput RNA sequencing is now a widely used method for analysing gene expression profiles (transcriptomes). By coupling TF agroinfiltration with RNA sequencing (named here as Infiltration-RNAseq), gene expression networks and gene function can be identified within a few weeks rather than many months. As a proof of concept, we agroinfiltrated Medicago truncatula leaves with M. truncatula LEGUME ANTHOCYANIN PRODUCITION 1 (MtLAP1), a MYB transcription factor involved in the regulation of the anthocyanin pathway, and assessed the resulting transcriptome. Leaves infiltrated with MtLAP1 turned red indicating the production of anthocyanin pigment. Consistent with this, genes encoding enzymes in the anthocyanin biosynthetic pathway, and known transcriptional activators and repressors of the anthocyanin biosynthetic pathway, were upregulated. A novel observation was the induction of a R3-MYB transcriptional repressor that likely provides transcriptional feedback inhibition to prevent the deleterious effects of excess anthocyanins on photosynthesis. Infiltration-RNAseq is a fast and convenient method for profiling TF-mediated gene expression changes. We utilised this method to identify TF-mediated transcriptional changes and TF target genes in M. truncatula and Nicotiana benthamiana. This included the identification of target genes of a TF not normally expressed in leaves, and targets of TFs from other plant species. Infiltration-RNAseq can be easily adapted to other plant species where agroinfiltration protocols have been optimised. The ability to identify downstream genes, including positive and negative transcriptional regulators, will result in a greater understanding of TF function.

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The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 94 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 20%
Student > Ph. D. Student 14 15%
Student > Master 13 14%
Student > Bachelor 11 12%
Other 6 6%
Other 15 16%
Unknown 17 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 56%
Biochemistry, Genetics and Molecular Biology 18 19%
Computer Science 3 3%
Chemical Engineering 1 1%
Unknown 20 21%
Attention Score in Context

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 12 December 2019.
All research outputs
#17,823,285
of 22,896,955 outputs
Outputs from Plant Methods
#900
of 1,083 outputs
Outputs of similar age
#225,392
of 315,882 outputs
Outputs of similar age from Plant Methods
#6
of 8 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,083 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 11th percentile – i.e., 11% 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 315,882 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.