Unknown,Transcriptomics,Genomics,Proteomics

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MtDASH Gain and loss of function mutants

ABSTRACT: Comparison between transcriptomes of dash mutants vs WT plants and ectopic expression of 35S::DASH in hairy roots versus empty vector Loss of function: 3 WT plants A17 at 8 days after pollination and 3 dash mutant plants at 8 days afer pollination. Gain of function transformants: 3 transformed plants containing empty vector and 4 transformed plants containing 35S::DASH Total RNA was extracted using a modified cetyltrimethylammonium bromide method (Verdier et al., 2008). 5ug of total RNA from each sample was purified (RNeasy MinElute Cleanup kit; Qiagen) according to the manufacturer?s instructions. RNA was quantified and evaluated for purity using an ND-1000 Nanodrop Spectrophotometer (NanoDrop Technologies) and a Bioanalyzer 2100 (Agilent). The Affymetrix M. truncatula GeneChip Array (Affymetrix) was used for expression analysis during seed development. RNA from three (for dash mutant analysis) and four (for DASH ectopic expression analysis) independent biological replicates were analysed for each time point. Probe synthesis/labelling was carried out from 500 ng of RNA using the GeneChip 3?IVT express kit, according to the manufacturer?s instructions (Affymetrix). Array hybridization, scanning, and data normalization were performed as described by Benedito et al., (2008). Each file from the hybridized Affymetrix array was exported from GeneChip Operating Software version 1.4 (Affymetrix) and imported into Robust Multiarray Average Express (Irizarry et al., 2003) for global normalization. Presence/absence call for each probe set to remove background noise was obtained using dCHIP (Li and Wong, 2001). To identify probe sets differentially expressed in dash mutant vs WT control, the R package Anapuce (J. Aubert, UMR 518 AgroParisTech/INRA) was used. For each probe set, a paired t-test was performed on the log2 expression data from three arrays (3 independent biological repeats for 8 dap data), assuming that the variance of the log expression was the same for all transcripts per genotype. Spots with extreme specific variance, too low or too high, were excluded from the statistical analysis (details on the procedure given in Gagnot et al., 2008). P-values were adjusted by the Benjamini-Hochberg method (Benjamini and Hochberg, 1995), which controls the family-wise error rate.

ORGANISM(S): Medicago truncatula

SUBMITTER: Jerome Verdier

PROVIDER: E-GEOD-58117 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

DASH transcription factor impacts Medicago truncatula seed size by its action on embryo morphogenesis and auxin homeostasis.

Noguero Mélanie M Le Signor Christine C Vernoud Vanessa V Bandyopadhyay Kaustav K Sanchez Myriam M Fu Chunxiang C Torres-Jerez Ivone I Wen Jiangqi J Mysore Kirankumar S KS Gallardo Karine K Udvardi Michael M Thompson Richard R Verdier Jerome J

The Plant journal : for cell and molecular biology 20150201 3

The endosperm plays a pivotal role in the integration between component tissues of molecular signals controlling seed development. It has been shown to participate in the regulation of embryo morphogenesis and ultimately seed size determination. However, the molecular mechanisms that modulate seed size are still poorly understood especially in legumes. DASH (DOF Acting in Seed embryogenesis and Hormone accumulation) is a DOF transcription factor (TF) expressed during embryogenesis in the chalaza ...[more]

PMID: 25492260

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Project description:Comparison of polysomal profiles of murine adult olig2 cortical progenitors, murine tumor olig2 cells derived from hPDGF-B-driven glioblastomas, and murine olig2 proliferative recruited glioma cells contributing to the tumor mass but not derived from the cell of origin An unbiased way to define a cell population lies in identification of its gene expression signature and subsequent comparison to gene expression signatures of known normal or cancer cells to define its position on the axis of tumorigenesis. To quantify similarities and differences in the expression profiles of recruited cells and tumor cells using microarray analysis, we used the bacTRAP technology that allows immunoprecipitation of polysomes from specific cell types in vivo (Heiman et al 2008, Doyle et al 2008). We compared polysomal expression profiles of hPDGFb-driven Ntv-a Ink4a/Arf+/- olig2 mouse glioma cells representing a histologically defined glioma population, recruited olig2 cells derived from Ink4a/Arf+/- olig2 RP-eGFP mice transplanted with non-fluorescent glioma cells, and normal adult cortical olig2 mouse progenitors, using Affymetrix 430 mouse 2.0 chips and Genespring GX10 software (Doyle et al 2008, Heiman et al 2008). Translational profiles of recruited olig2 cells and glioma olig2 cells were similar to each other and clearly distinct from the normal olig2 progenitors, majority of the differences accounted for by the “statistically significantly changed” set of mRNAs (ANOVA, p<0.05), and sample clustering reduced upon removal of the ANOVA-tested mRNA set. Unsupervised hierarchical mRNA clustering likewise indicated that recruited olig2 cells clustered more closely to glioma olig2 cells. The caveat is that olig2 expression in the adult normal brain of bacTRAP olig2 RP-eGFP mice labels both progenitors and mature oligodendrocytes, while recruited and tumor olig2 cells are almost entirely progenitors; therefore, some of the differences seen in the comparative analysis of normal, tumor and recruited olig2 cells may in part result from the shift in relative abundance of different progenitor and mature olig2 cell populations. Normal olig2 progenitors were collected from three replicates of pooled cortices of three olig2 RP-eGFP bacTRAP reporter mice. Mouse gliomas induced by injection of a non-fluorescent RCAS-hPDGFb (Shih et al 2004) or by transplantation of muring glioma cells derived from Ntv-a gliomas induced by the non-fluorescent RCAS-hPDGFb were collected from Ntv-a olig2 RP-eGFP bacTRAP reporter mice and immunoprecipitated; each tumor was processed as a separate sample. Briefly, mouse tissues were collected into ice-cold cyclohexamide-containing buffer, homogenized, cells were lysed in NP-40 and DHPC-containing buffer, centrifuged at 20,000g for 15 min, supernatant incubated with anti-eGFP-conjugated protein G beads 30 min at 4C, washed and RNA collected using Trizol reagent as per manufacturer’s instructions (Doyle et al., 2008; Heiman et al., 2008). RNA was purified, concentrated using Qiagen RNeasy kit, its quality confirmed by Agilent Bioanalyzer, 15ng per sample amplified using Affymetrix Two-Cycle Amplification kit and hybridized to Affymetrix 430 mouse 2.0 chips as described in Doyle et al., 2008; Heiman et al., 2008. Data was analyzed using Genespring GX10 software, as described in the text and in Doyle et al., 2008; Heiman et al., 2008. Briefly, samples were normalized using GCRMA, filtered to remove probe sets with low intensity, and analyzed using ANOVA (p<0.05) with SNK post-hoc and Benjamini and Hochberg FDR multiple testing correction. Probe quality, Pearson’s correlation, hierarchical clustering, heatmaps, gene profile plots were generated using Genespring GX10 software. Pathway analysis was performed using Ingenuity as per manufacturer’s instructions.

Project description:Mouse ErythroLeukaemic (MEL) cell lines were maintained in DMEM supplemented with 10% fetal calf serum and penicillin/streptomycin. MEL cells in the log phase of growth (0.5 to 1 millions cells / mL) were induced to differentiate with 2% dimethylsulfoxide (DMSO) for four days. Total RNA was isolated in triplicate from non-induced and DMSO-induced MEL cells using RNeasy Mini Kit (Qiagen). Purity and quality of isolated RNA were assessed by RNA 6000 Nano assay on a 2100 Bioanalyzer (Agilent Technologies, Santa 6 Clara, CA). All samples showed a RIN>8 and were all used for subsequent labeling. RNA (300 ng) was used for production of end-labelled biotinylated ssDNA. Labelled ssDNA was hybridized to the GeneChip Mouse Gene 1.0 ST array oligonucleotide microarray (Affymetrix, Santa Clara, CA) according to manufacturers recommendations. To examine the quality of the various arrays, measured intensity values were analyzed using Gene Expression Console (Affymetrix, Santa Clara, CA). The analyses indicated a high quality of all samples and overall comparability. The microarray data was normalized by quantile normalization, and followed by a summarization using Tukeys median polish algorithm (Irizarry et al., 2003) in Expression Console from Affymetrix (Santa Clara, CA). Data was further processed using packages within the Bioconductor project in the R statistical programming environment (Gentleman et al., 2004). In order to normalize between experimental batches and receive a dataset with variance independent of absolute expression levels data were renormalized applying vsn2 function (Huber et al., 2002). The log2 intensities for each probe were used for further analysis. Differentially expressed genes were determined applying the limma Bioconductor package and using the functions lmFit and eBayes with default settings. Resulting p-values were adjusted for multiple testing according to Benjamini-Hochberg (Hochberg and Benjamini, 1990). All calculations were performed in the R statistical programming environment (Huber et al., 2002) version 2.8.0. Hierarchical clustering (Eisen et al., 1998) was performed using MeV4.0.01 (Saeed et al., 2003) applying euclidean distance and average linkage.

Dataset Information

MtDASH Gain and loss of function mutants

Publications

DASH transcription factor impacts Medicago truncatula seed size by its action on embryo morphogenesis and auxin homeostasis.

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