Computational protocol: Transcriptional changes measured in rice roots after exposure to arsenite-contaminated sediments

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[…] The DNA-microarray analysis was performed using 0.2 μg high quality total cRNA generated with the Low Input Quick Amp Labeling Kit, One-Color according to the manufactures protocol. The sample preparation and the hybridization were performed according to Agilent’s protocols for one-color microarray-based gene expression analysis. The scanning of the chips was performed on the Agilent Technologies Scanner G2505C according to the manufacturer’s protocol. Expression data were extracted with the Agilent Feature Extraction software. Raw data were uploaded in the ArrayExpress Archive of Functional Genomics Database (Experiment ArrayExpress accession: E-MTAB-5430) (Parkinson et al. ). For group-comparisons, raw data of the respective samples were quantile normalized. Quantile normalized data were processed as log2-fold change (LFC). All following statistical analyses were performed with the TIGR MultiExperiment Viewer (MeV) software (Saeed et al. ). DEGs were identified by means of the three statistical methods: linear models for microarray data (LIMMA), (two-class unpaired) significance analysis of microarrays (SAM), and T tests. Genes were only selected as DEGs if they were identified as differentially expressed by all statistical methods. This approach resulted in the rejection of false-positive genes, which were selected as significant due to outlier values of single replicates. The cutoff for the SAM was set to a value of 50 ± 3 genes that were included in the further analysis. The calculated false discovery rate was always 0%. The T test was calculated using the Welch approximation with p values based on a t distribution and an overall threshold value of α = 0.001. The significance was determined by means of the adjusted Bonferroni correction. From the identified DEGs, genes were selected as CBGs if they showed high relative expression changes while ensuring a low variance between the replicates. Expression differences of Aslow or Ashigh versus the references were visualized in principal component scatterplots (PCA) with the centering mode based on the median. To gain a better insight into arenite’s mechanism of action in rice plants, the DEGs for Aslow and Ashigh were associated with GO-terms for biological functions using the rice array platform (Cao et al. ). Prior to this, the genes had to be transformed from the RAP ID to the corresponding pub_locus IDs using the Rice Annotation Project Database’s ID converter (Sakai et al. ). Only GO-terms with hyper p values below p = 0.01 were regarded as significantly induced. [...] To obtain dose-response curves of the candidate biomarker genes, their expression was analyzed by means of qPCR over a range of seven arsenite concentrations as described above. First strand cDNA was prepared from DNase-treated total RNA in a total volume of 20 μl with the Affinity Script qPCR cDNA Synthesis Kit according to the manufacturer’s protocol. Two microliters of the DNase-treated RNA suspension were used for cDNA Synthesis. RNA amounts for cDNA synthesis ranged from 3 to 31 ng of RNA according to recommendations in the manufacturer’s protocol (3 pg–3 μg total RNA). The qPCR analyses were carried out using the MX3005P device (Stratagene, La Jolla, CA, USA). The reactions were prepared according to the manufacturer’s protocol for Brilliant III SYBR Green QPCR Master Mix (Stratagene) containing 2 μl prediluted cDNA in a 25 μl reaction. Information on primer sequences, as well as concentrations and temperature profiles used for the qPCR are given in Tables and . The amplification was followed by a melting curve analysis to confirm PCR product specificity. During the optimization of the gene specific qPCR protocols, product specificity and absence of primer dimers were further confirmed using the Agilent DNA 1000 Kit run on Bioanalyzer (Agilent). The experimental threshold (Cq) was calculated using the algorithm enhancements provided by the MxPro Mx3005P v3.00 software. The baseline was adjusted manually to the lag phase of the curve. All samples were run in technical triplicates and the mean value of each triplicate was used for further calculations. PCR efficiencies were calculated based on the kinetics of individual PCR reactions for each replicate. The expression changes given as relative expression ratio (RER) were determined as efficiency corrected calculation models based on multiple samples.(Pfaffl ) The “eukaryotic elongation factor 1 alpha” (eEF1a) was used for data normalization. Primer sequences for eEF1a were obtained from (Jain et al. ). Correlations between the RER for each gene and the inhibition of root and shoot elongation for rice plants grown on artificial arsenite-spiked sediment were conducted by means of spearman rank correlation. The results of this analysis are given as correlation matrix (Table ). The dose-response curves for the five candidate biomarker were fitted using a 3-parametric sigmoidal curve fitting. The statistical analyses of qPCR data were conducted by means of Systat Software’s SigmaPlot 12.3. […]

Pipeline specifications

Software tools Agilent Feature Extraction, limma, SigmaPlot
Databases ArrayExpress
Applications Miscellaneous, Gene expression microarray analysis
Organisms Oryza sativa
Chemicals Arsenic