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Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism

Overview of attention for article published in BMC Genomics, February 2016
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Title
Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism
Published in
BMC Genomics, February 2016
DOI 10.1186/s12864-016-2476-x
Pubmed ID
Authors

Shaojun Zhang, Wenzhu Yang, Qianqian Zhao, Xiaojin Zhou, Ling Jiang, Shuai Ma, Xiaoqing Liu, Ye Li, Chunyi Zhang, Yunliu Fan, Rumei Chen

Abstract

D-myo-inositol phosphates (IPs) are a series of phosphate esters. Myo-inositol hexakisphosphate (phytic acid, IP6) is the most abundant IP and has negative effects on animal and human nutrition. IPs play important roles in plant development, stress responses, and signal transduction. However, the metabolic pathways and possible regulatory mechanisms of IPs in maize are unclear. In this study, the B73 (high in phytic acid) and Qi319 (low in phytic acid) lines were selected for RNA-Seq analysis from 427 inbred lines based on a screening of IP levels. By integrating the metabolite data with the RNA-Seq data at three different kernel developmental stages (12, 21 and 30 days after pollination), co-regulatory networks were constructed to explore IP metabolism and its interactions with other pathways. Differentially expressed gene analyses showed that the expression of MIPS and ITPK was related to differences in IP metabolism in Qi319 and B73. Moreover, WRKY and ethylene-responsive transcription factors (TFs) were common among the differentially expressed TFs, and are likely to be involved in the regulation of IP metabolism. Six co-regulatory networks were constructed, and three were chosen for further analysis. Based on network analyses, we proposed that the GA pathway interacts with the IP pathway through the ubiquitination pathway, and that Ca(2+) signaling functions as a bridge between IPs and other pathways. IP pools were found to be transported by specific ATP-binding cassette (ABC) transporters. Finally, three candidate genes (Mf3, DH2 and CB5) were identified and validated using Arabidopsis lines with mutations in orthologous genes or RNA interference (RNAi)-transgenic maize lines. Some mutant or RNAi lines exhibited seeds with a low-phytic-acid phenotype, indicating perturbation of IP metabolism. Mf3 likely encodes an enzyme involved in IP synthesis, DH2 encodes a transporter responsible for IP transport across organs and CB5 encodes a transporter involved in IP co-transport into vesicles. This study provides new insights into IP metabolism and regulation, and facilitates our development of a better understanding of the functions of IPs and how they interact with other pathways involved in plant development and stress responses. Three new genes were discovered and preliminarily validated, thereby increasing our knowledge of IP metabolism.

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The data shown below were collected from the profiles of 6 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 2%
Brazil 1 2%
Unknown 47 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 33%
Researcher 11 22%
Student > Master 5 10%
Student > Postgraduate 4 8%
Student > Doctoral Student 3 6%
Other 6 12%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 55%
Biochemistry, Genetics and Molecular Biology 10 20%
Pharmacology, Toxicology and Pharmaceutical Science 3 6%
Computer Science 1 2%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 6 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 February 2016.
All research outputs
#13,225,592
of 22,851,489 outputs
Outputs from BMC Genomics
#4,770
of 10,658 outputs
Outputs of similar age
#139,015
of 298,866 outputs
Outputs of similar age from BMC Genomics
#111
of 234 outputs
Altmetric has tracked 22,851,489 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,658 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 53% 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 298,866 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 234 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.