Dataset Information

Negative regulatory roles of DE-ETIOLATED1 in flowering time in Arabidopsis.

ABSTRACT: Arabidopsis flowers early under long days (LD) and late under short days (SD). The repressor of photomorphogenesis DE-ETIOLATED1 (DET1) delays flowering; det1-1 mutants flower early, especially under SD, but the molecular mechanism of DET1 regulation remains unknown. Here we examine the regulatory function of DET1 in repression of flowering. Under SD, the det1-1 mutation causes daytime expression of FKF1 and CO; however, their altered expression has only a small effect on early flowering in det1-1 mutants. Notably, DET1 interacts with GI and binding of GI to the FT promoter increases in det1-1 mutants, suggesting that DET1 mainly restricts GI function, directly promoting FT expression independent of CO expression. Moreover, DET1 interacts with MSI4/FVE, which epigenetically inhibits FLC expression, indicating that the lack of FLC expression in det1-1 mutants likely involves altered histone modifications at the FLC locus. These data demonstrate that DET1 acts in both photoperiod and autonomous pathways to inhibit expression of FT and SOC1. Consistent with this, the early flowering of det1-1 mutants disappears completely in the ft-1 soc1-2 double mutant background. Thus, we propose that DET1 is a strong repressor of flowering and has a pivotal role in maintaining photoperiod sensitivity in the regulation of flowering time.

SUBMITTER: Kang MY

PROVIDER: S-EPMC4428065 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Negative regulatory roles of DE-ETIOLATED1 in flowering time in Arabidopsis.

Kang Min-Young MY Yoo Soo-Cheul SC Kwon Hye-Young HY Lee Byoung-Doo BD Cho Jung-Nam JN Noh Yoo-Sun YS Paek Nam-Chon NC

Scientific reports 20150512

Arabidopsis flowers early under long days (LD) and late under short days (SD). The repressor of photomorphogenesis DE-ETIOLATED1 (DET1) delays flowering; det1-1 mutants flower early, especially under SD, but the molecular mechanism of DET1 regulation remains unknown. Here we examine the regulatory function of DET1 in repression of flowering. Under SD, the det1-1 mutation causes daytime expression of FKF1 and CO; however, their altered expression has only a small effect on early flowering in det1 ...[more]

PMID: 25962685

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Project description:BACKGROUND: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC). RESULTS: We compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation. CONCLUSIONS: Three dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

Project description:Mast flowering is synchronised highly variable flowering by a population of perennial plants over a wide geographical area. High seeding years are seen as a threat to native and endangered species due to high predator density caused by the abundance of seed. An understanding of the molecular pathways that influence masting behaviour in plants could provide better prediction of a forthcoming masting season and enable conservation strategies to be deployed. The goal of this study was to identify candidate flowering genes that might be involved in regulating mast flowering. To achieve this, high-throughput large-scale RNA-sequencing was performed on two masting plant species, Celmisia lyallii (Asteraceae), and Chionochloa pallens (Poaceae) to develop a reference transcriptome for functional and molecular analysis. An average total of 33 million 150 base-paired reads, for both species, were assembled using the Trinity pipeline, resulting in 151,803 and 348,649 transcripts respectively for C. lyallii and C. pallens. For both species, about 56% of the unigenes were annotated with gene descriptions to known proteins followed by Gene Ontology analysis, categorising them on the basis of putative biological processes, molecular function, and cellular localization. A total of 543 transcripts from C. lyallii and 470 transcripts from C. pallens were also mapped to unique flowering-time proteins identified in Arabidopsis thaliana, suggesting the conservation of the flowering network in these wild alpine plants growing in natural field conditions. Expression analysis of several selected homologous flowering-pathway genes showed seasonal and photoperiodic variations. These genes can further be analysed to understand why seasonal cues, such as the increasing photoperiod in spring, that triggers the annual flowering of most plants, are insufficient to always trigger flowering in masting plants and to uncover the molecular basis of how additional cues (such as temperature during the previous growing seasons) then determines flowering in mast years.

Dataset Information

Negative regulatory roles of DE-ETIOLATED1 in flowering time in Arabidopsis.

Publications

Negative regulatory roles of DE-ETIOLATED1 in flowering time in Arabidopsis.

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