Identification of Gsr1 in Arabidopsis thaliana: A locus inferred to regulate gene expression in response to exogenous glutamine

Euphytica ◽  
2006 ◽  
Vol 151 (3) ◽  
pp. 291-302 ◽  
Author(s):  
R. Meyer ◽  
J. Yuan ◽  
J. Afzal ◽  
M. J. Iqbal ◽  
Mengxia Zhu ◽  
...  
2018 ◽  
Vol 19 (10) ◽  
pp. 3271
Author(s):  
Lin He ◽  
Jingyu Xu ◽  
Yucheng Wang ◽  
Kejun Yang

NAC (NAM, ATAF1/2, and CUC2) transcription factors play important roles in many biological processes, and mainly bind to the NACRS with core sequences “CACG” or “CATGTG” to regulate gene expression. However, whether NAC proteins can bind to other motifs without these core sequences remains unknown. In this study, we employed a Transcription Factor-Centered Yeast one Hybrid (TF-Centered Y1H) screen to study the motifs recognized by ANAC074. In addition to the NACRS core cis-element, we identified that ANAC074 could bind to MybSt1, NRS1, and NRS2. Y1H and GUS assays showed that ANAC074 could bind the promoters of ethylene responsive genes and stress responsive genes via the NRS1, NRS2, or MybSt1 element. ChIP study further confirmed that the bindings of ANAC074 to MybSt1, NRS1, and NRS2 actually occurred in Arabidopsis. Furthermore, ten NAC proteins from different NAC subfamilies in Arabidopsis thaliana were selected and confirmed to bind to the MybSt1, NRS1, and NRS2 motifs, indicating that they are recognized commonly by NACs. These findings will help us to further reveal the functions of NAC proteins.


PLoS Genetics ◽  
2020 ◽  
Vol 16 (4) ◽  
pp. e1008324
Author(s):  
Melody Nicolau ◽  
Nathalie Picault ◽  
Julie Descombin ◽  
Yasaman Jami-Alahmadi ◽  
Suhua Feng ◽  
...  

1992 ◽  
Vol 66 (1) ◽  
pp. 95-105 ◽  
Author(s):  
A M Colberg-Poley ◽  
L D Santomenna ◽  
P P Harlow ◽  
P A Benfield ◽  
D J Tenney

2019 ◽  
Vol 70 (19) ◽  
pp. 5355-5374 ◽  
Author(s):  
Dandan Zang ◽  
Jingxin Wang ◽  
Xin Zhang ◽  
Zhujun Liu ◽  
Yucheng Wang

Abstract Plant heat shock transcription factors (HSFs) are involved in heat and other abiotic stress responses. However, their functions in salt tolerance are little known. In this study, we characterized the function of a HSF from Arabidopsis, AtHSFA7b, in salt tolerance. AtHSFA7b is a nuclear protein with transactivation activity. ChIP-seq combined with an RNA-seq assay indicated that AtHSFA7b preferentially binds to a novel cis-acting element, termed the E-box-like motif, to regulate gene expression; it also binds to the heat shock element motif. Under salt conditions, AtHSFA7b regulates its target genes to mediate serial physiological changes, including maintaining cellular ion homeostasis, reducing water loss rate, decreasing reactive oxygen species accumulation, and adjusting osmotic potential, which ultimately leads to improved salt tolerance. Additionally, most cellulose synthase-like (CSL) and cellulose synthase (CESA) family genes were inhibited by AtHSFA7b; some of them were randomly selected for salt tolerance characterization, and they were mainly found to negatively modulate salt tolerance. By contrast, some transcription factors (TFs) were induced by AtHSFA7b; among them, we randomly identified six TFs that positively regulate salt tolerance. Thus, AtHSFA7b serves as a transactivator that positively mediates salinity tolerance mainly through binding to the E-box-like motif to regulate gene expression.


2006 ◽  
Vol 3 (2) ◽  
pp. 109-122 ◽  
Author(s):  
◽  
Christopher H. Bryant ◽  
Graham J.L. Kemp ◽  
Marija Cvijovic

Summary We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.


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