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OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants

Overview of attention for article published in BMC Genomics, December 2013
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Title
OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants
Published in
BMC Genomics, December 2013
DOI 10.1186/1471-2164-14-893
Pubmed ID
Authors

Vaibhav Srivastava, Ogonna Obudulu, Joakim Bygdell, Tommy Löfstedt, Patrik Rydén, Robert Nilsson, Maria Ahnlund, Annika Johansson, Pär Jonsson, Eva Freyhult, Johanna Qvarnström, Jan Karlsson, Michael Melzer, Thomas Moritz, Johan Trygg, Torgeir R Hvidsten, Gunnar Wingsle

Abstract

Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 2%
Sweden 2 2%
Israel 1 <1%
South Africa 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
Germany 1 <1%
United States 1 <1%
Unknown 106 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 27%
Researcher 29 25%
Other 10 9%
Student > Master 8 7%
Student > Bachelor 7 6%
Other 20 17%
Unknown 11 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 44%
Biochemistry, Genetics and Molecular Biology 10 9%
Chemistry 10 9%
Computer Science 8 7%
Engineering 5 4%
Other 13 11%
Unknown 19 16%

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 22 July 2014.
All research outputs
#2,904,975
of 4,507,509 outputs
Outputs from BMC Genomics
#2,892
of 4,113 outputs
Outputs of similar age
#67,706
of 105,260 outputs
Outputs of similar age from BMC Genomics
#164
of 242 outputs
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So far Altmetric has tracked 4,113 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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