Pankaj Barah1, Naresh Doni Jayavelu2,Simon Rasmussen3, Henrik Bjørn Nielsen3, John
Mundy4and Atle M Bones1*
1Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway N-7491 2Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim,
Norway N-7491
3Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark,
Lyngby, Denmark DK 2800
4Department of Biology, University of Copenhagen, Copenhagen, Denmark DK-2200
*Corresponding author: Atle M. Bones
Department of Biology
Norwegian University of Science and Technology 7491 Trondheim, Norway
Journal area to which this article is submitted (Original research: Full paper) 1) Environment: global change and environmental stress
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Summary
x Low temperature leads to major crop losses every year. Several studies have been conducted, focusing on diversity of cold tolerance level in multiple phenotypically divergent
Arabidopsis thaliana (A. thaliana) ecotypes, but genome-scale understanding is still lacking.
x Here we report the genome-scale transcriptional responses to non-freezing cold stress treatments on ten A. thaliana ecotypes originated from different geographical locations. The ecotypes exhibited considerable variation in global transcript level responses to the cold stress treatment. A total of 6061 transcripts were identified to be significantly cold regulated (p<0.01), which included 498 transcription factors and 315 transposable elements. Ecotype specific transcripts and related gene ontology (GO) categories were identified.
x By using sequence information from the A. thaliana 1001 genomes project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF (C-repeat binding factor) regulon genes.
x Considering the limited availability of experimental information on regulatory interactions in the model plant A. thaliana, we adopted a powerful systems genetics approach, Network Component Analysis (NCA), to re-construct an in-silico transcriptional regulatory network during response to cold stress using the gene expression data from ten ecotypes. Apart from retaining several previously benchmarked regulatory connections, the predicted network model identified new ecotype specific transcription factors and their regulatory interactions. These may be crucial for the geographic adaptation of the ecotypes to cold temperature.
Key words: Arabidopsis, ecotypes, Cold stress, natural variation, adaptation, gene expression, regulatory networks, Arabidopsis 1001 genome, systems biology
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Introduction
As sessile organisms, plants have evolved strategies to survive in unfavourable environmental conditions. Intraspecific variation in responses to environmental stress is well documented among plant species (Mitchell-Olds & Schmitt, 2006; Hall et al., 2008; Alonso-Blanco et al., 2009; Horton et al., 2012). Understanding the molecular basis of such adaptations to environmental conditions has proven useful in selecting better traits or target genes for marker assisted breeding (Alonso-Blanco & Koornneef, 2000). Cold is a naturally occurring hazard to world crop production. Cold stress contributes to poor germination, stunted seedlings, chlorosis, reduced leaf expansion and wilting, and may also lead to tissue necrosis (Sanghera
et al., 2011). Exposure to cold stress may also drastically reduce the reproductive
development of plants. It is thought that plants perceive cold by a receptor at the cell membrane, and that a signal is initiated to activate cold-responsive genes and transcription factors for mediating stress tolerance (Thomashow, 1999; Penfield, 2008). As reported earlier, the CBF pathway plays a major role in cold response, tolerance and acclimation. Nonetheless, there appear to be considerable differences in the sets of cold regulated genes described (Carvallo et al., 2011). Just after few minutes of cold exposure, CBF genes are induced which encode a small family of transcription factors known as CBF1, CBF2, and CBF3 (also known as DREB1B, DREB1C and DREB1A). Cold induction of CBF genes regulates a set of about 100 downstream genes. Among them, the immediate target genes of CBF1-3 contain CRT (C-repeat)/DRE (dehydration responsive element) elements in their promoter regions to which the CBF1-3 proteins bind. The dehydration-responsive element (DRE) is also known as low temperature response element (LTRE) which contributes to cold responsiveness (Yamaguchishinozaki & Shinozaki, 1994). Interestingly, induction of the CBF regulon enhances both cold and drought tolerance (Jaglo-Ottosen et al., 1998). Earlier transcriptome profiling studies have shown that multiple regulatory pathways are activated in
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A. thaliana during cold exposure in addition to the CBF cold-response pathway (Fowler &
Thomashow, 2002).
Natural variation for cold response and tolerance is an important element in the adaptation and geographic distribution of plant species. More generally, there is clear association between the plasticity of gene expression and the adaptability of an organism (Swindell et al., 2007). Several studies have focused on the diversity of cold tolerance levels in divergent A. thaliana ecotypes (Rohde et al., 2004; Alonso-Blanco et al., 2005; McKhann
et al., 2008a; Zhen & Ungerer, 2008b; Zhen & Ungerer, 2008a). McKhann et al. reported that CBF and COR (Cold Regulated) genes responded differently to cold stress in eight
accessions, though they could not find clear correlations between gene expression, sequence polymorphisms and freezing tolerance (McKhann et al., 2008b). Thus, little is known about the molecular basis of the natural variation for freezing tolerance.
Transcriptional profiling in the model Arabidopsis is a major tool to identify plant genes regulated in response to changing environmental conditions (Somerville & Koornneef, 2002). However, variation in experimental conditions and protocols has made it difficult to extract and compare information from data sets produced in different laboratories (Moreau et
al., 2003). To overcome such problems, we subjected 10 ecotypes of A. thaliana were
subjected to 5 individual stress treatments and 6 combinations of these stress treatments under the same experimental set up and profiling protocols (Rasmussen et al., in press). We have considered all the cold experiments conducted on 10 ecotypes from this large dataset to explore genome-scale transcriptome response signatures of A. thaliana during cold stress treatment. By utilising data available from the recently published A. thaliana 1001 genome project, we further analysed sequence polymorphisms in CBF regulon genes (Austin et al., 2011).
5 It is likely that differential expressions, or variation in mRNA stability caused by coding sequence polymorphisms, significantly contribute to natural variation in A. thaliana (Shimizu, 2002). Information about differentially regulated genes during different stress conditions is often available as an outcome of microarray experiments. However, in many cases, little is known about the regulation and interaction of these genes (Keurentjes et al., 2007). Being highly dynamic in nature, any biological system continuously responds to environmental and genetic perturbations. Differential dynamic network mapping may facilitate the exploration of previously unknown interactions (Ideker & Krogan, 2012). While the A. thaliana genome has ~1922 transcription factors (TFs) (Guo et al., 2005), experimentally confirmed regulatory relations are available for less than 100 TFs only (AGRIS database Sept. 10 2012) (Davuluri et al., 2003). Tirosh & Barkai (2011) have explained how regulatory relationships can be deduced from patterns of evolutionary divergence in molecular properties such as gene expression. To this end, several computational algorithms have been developed to identify regulatory modules and their condition-specific regulators from gene expression data (Alter et al., 2000; Segal et al., 2003; Herrgard et al., 2004). Network Component Analysis (NCA) is such an approach, which has been successfully implemented in several species, including A. thaliana, to determine both activities and regulatory influences for a set of transcription factors on target genes in various perspectives (Liao et al., 2003; Kao et al., 2004; Wang, JG et al., 2011). By taking advantage of the NCA method, we have identified ecotype specific regulatory relationships, which provided new information towards understanding the natural variation in cold responses among different ecotypes of the model plant A. thaliana.
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