Tiling microarray analysis of gene expression profile of Escherichia coli galT mutant cells under D-galactose-stressed condition
Ontology highlight
ABSTRACT:
ORGANISM(S): Escherichia coli
SUBMITTER: Andrei Trostel
PROVIDER: E-GEOD-42821 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Leafy spurge is a model for studying well-defined phases of dormancy in underground adventitious buds (UABs) of herbaceous perennial weeds, which is a primary factor allowing many invasive perennial weeds to escape conventional control measures. A 12-week ramp down in both temperature (27M-BM-0C M-bM-^FM-^R 10M-BM-0C) and photoperiod (16 h M-bM-^FM-^R 8 h light) is required to induce a transition from para- to endo-dormancy in UABs of leafy spurge. To evaluate the effects of photoperiod and temperature on molecular networks associated with this transition, we compared global transcriptome data-sets obtained from UABs of leafy spurge exposed to a ramp down in both temperature and photoperiod (RDtp) vs. a ramp down in temperature (RDt) alone. 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We previously established conditions for induction and release of dormancy phases under controlled environments (DoM-DM-^_ramacM-DM-1 et al. 2010; Foley et al. 2009). To induce endodormancy, paradormant plants were subjected to a ramp-down in temperature (27M-BM-0C M-bM-^FM-^R 10M-BM-0C) and photoperiod (16 h M-bM-^FM-^R 8 h light), i.e., RDtp, for 12 weeks (Fig. 1, Exp-1). To distinguish the individual effects of temperature and photoperiod on molecular networks involved in endodormancy induction (see Fig. 1, Exp-2), we compared three-month old paradormant plants subjected to a ramp-down in temperature (27M-BM-0C M-bM-^FM-^R 10M-BM-0C) under constant photoperiod (16 h light) for 12 weeks (i.e., RDt) to RDtp plants. Additionally, a set of paradormant plants were kept under constant temperature and light (27M-BM-0C, 16 h light) as a control. At the end of each treatment, the aerial portion of the plant was decapitated to determine the dormancy status of the crown buds by their vegetative growth rate in the greenhouse, as described by Foley et al. (2009). New vegetative shoot growth was recorded weekly; results were analyzed using the generalized linear mixed model (PROC GLIMMIX) procedure of SAS 9.2, and 95% confidence intervals for treatment by week means were generated. Transcriptome analyses At the end of each treatment, crown buds were collected from intact plants to study transcriptome profiles and were stored at -80M-BM-0C. RNA extraction, cDNA synthesis and fluorescent labeling, microarray hybridization using ~23K element arrays, and spot intensity analyses were performed as previously described (DoM-DM-^_ramacM-DM-1 et al. 2010). Transcriptome data obtained from Exp-1 and -2 (Fig. 1) was used for various bioinformatics analysis. GeneMaths XT 2.1 software was used to normalize the arrays, to conduct statistical analyses, and to generate Venn diagrams as previously described by DoM-DM-^_ramacM-DM-1 et al. (2010). Expression data for both Exp-1 and -2 are deposited at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) as GEO dataset query GSE19217 and GSE??, respectively. The entire data set was further analyzed using Ariadne Pathway Studio 7 Software-Resnet Plant Version 2.1 (Ariadne Genomics Inc., Rockville, MD, USA) to obtain Gene Set Enrichment Analysis (GSEA) and Sub Network Enrichment Analysis (SNEA). GSEA is used to determine if predefined sets of genes are over-represented (P<0.05) between treatments; focusing on gene sets based on AraCyc metabolic pathways http://pmn.plantcyc.org/ARA/class-instances?object=Pathways, and gene ontology (GO) (http://www.geneontology.org/). SNEA algorithm was used to identify the networks by highlighting over-represented ontologies based on published gene regulation hierarchies, protein:protein interactions, or protein modification targets. Furthermore, these networks were visualized using the Union Selected Pathway function of the software for expression targets, binding partners, protein modification targets, and also custom advanced function to generate sub-networks as neighbors of proteins based on expression, regulation, molecular transport, protein modification, promoter binding, molecular synthesis, chemical reaction, and direct regulation.
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