Androgen-Dependent Exons Figure 5. The association of AR with an alternative internal promoter region within TSC2 correlates with dynamic expression of an alternative TSC2 mRNA. (A) Fluorescent quantification of capillary agarose gel electrophoretic analysis of RT-PCR products amplified from mRNA from LNCaP cells pretreated for 1 hour with vehicle (DMSO) or 1 g/ml cycloheximide (CHX) prior to addition of 10 nM R1881 for 24 hours. The relative normalised expression ratio is the contribution of the PCR product representing the 39 transcript end/59 transcript end. Data from four independent replicates were used to obtain the means 6 SE. (B) Cartoon of intron/exon structure of TSC2 gene, showing alternative promoter upstream of exon 33. The androgen-regulated exons are shown in black, and constitutive exons are indicated in white. The position of both alternative promoters DP1 and DP2 controlling expression of the shorter TSC2 variant mRNA is asterisked. (C) LNCaP cells cultured in steroid-deplete medium for 47 hours were treated with 10 nM R1881 for up to 120 min at 60 min intervals prior to ChIP analysis using anti-AR antisera. Input and immunoprecipitated material were analysed by real-time PCR, using primers for the DP1 (left panel) and DP2 (right panel) regions of the downstream promoter regions upstream of exon 33, as well as a non-AR-binding region within GREB1 (control). Data shown are means 6 SE from three independent experiments. (D) Capillary agarose gel electrophoretic analysis (upper panel) of RT-PCR products amplified from mRNA from cells grown either in steroid-deplete medium (lane 1) or following treatment with 10 nM R1881 (lanes 2–6) at timepoints upto 24 hours as shown. The predicted mRNA isoforms detected are shown to the right of each gel exactly as summarised in Figure 2B. Fluorescent quantification (lower panel) was performed to obtain the relative normalised expression ratio, which is the contribution of the PCR product representing the 39 transcript end/59 transcript end. The gel image (upper panel) is representative of three independent replicates from which data were used to obtain the means 6 SE (lower panel). doi:10.1371/journal.pone.0029088.g005 Clinical samples For details of clinical samples see Materials and Methods S1. RNA purification and array hybridization Total RNA from treatment and control experiments (n = 8) was purified using RNeasy Plus Kit (Qiagen, 74134), which includes genomic DNA (gDNA) eliminator column to remove contaminat- ing DNA accordingly to the manufacturer’s instructions. Ribo- somal RNA (rRNA) reduction was performed using the RiboMi- nus Kit (Invitrogen, K1550-02). RNA quality was verified by analysis on the 2100 Bioanalyzer (Agilent). One microgram of total RNA was labelled according to the GeneChip Whole Transcript (WT) Sense Target Labelling Assay provided by the manufacturer (Affymetrix, 900670) and hybridized to Human Exon 1.0 ST Arrays (Affymetrix, 900649) overnight before scanning in an Affymetrix GCS 3000 7G scanner. Raw data has been deposited at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/ geo/) under accession number GSE 32875 and all details are MIAME compliant. Data analysis Following Quality Assessment of datasets [40], gene expression values were obtained by quantile-normalisation using Robust Multichip Analysis (RMA) with core probesets (228,940 probesets), and antigenomic background probes [40]. After summarization, the Student’s T-test was used to detect differential expression between control and treatment arrays. Only probe sets that were detected above background (defined by detection above back- ground; DABG p#0.01) were included in the analysis. The False discovery rate (FDR) was calculated to make corrections of multiple testing, using the Q-value program [41], with q,0.005 taken to indicate statistical significance. Gene Ontology (GO) annotations for all human genes obtained from http://www. geneontology.org, was compared with annotations for the set of genes up-regulated following androgen treatment. We calculated whether a term occurs proportionally more in the input set than in the entire genome and determined if the difference was significant using a Fisher’s Exact test which returned a P-value. FDR q = 0.05 was used to determine a cut off, and the obtained list of terms was filtered so that terms that were significant only because one (and only one) of their descendant terms was significant were removed. Gene lists were uploaded to the web-based Ingenuity Pathway Analysis (IPA; Ingenuity Systems) software programme, and the ‘‘Core Analysis’’ function was used to study direct and indirect regulatory relationships between genes and their known biological functions. For detection of alternative splicing, the student’s T-test was performed on ‘‘normalized’’ probeset intensities, which were obtained by removal of core meta probeset intensities (gene level estimates) from extended probeset intensities (exon level estimates). Iter-PLIER algorithm was used for gene level estimates, and the PLIER algorithm was used for exon level estimates [40]. To improve the accuracy of gene expression estimation, only those meta probesets with $11 core probes were used. Other genes were excluded from the analysis. 16,309 of 17,881 meta probesets passed this filtering. After the T-test for differential splicing, the following filtering criteria were applied to identify differentially spliced probe sets: a) The gene is expressed in both conditions and DABG p#0.01 at least in three replicates in each condition; b) The reliability of gene level estimates was tested by extracting the probe intensities for each meta probeset, obtaining an average across replicates for each condition, and ensuring good correlation (Pearson product-moment and Spearman’s rank correlation coefficient$0.7) between conditions; c) Gene expression between conditions should not exceed 10 fold (3.32 in the log2 scale); d) The T-test of normalized probe set intensities yields p#0.001 (or p#0.0005); e) The exon must have detectable expression at least in one condition. More specifically, the DABG P-value at the probe set level must be #0.01 in three replicates in either condition. In addition, in this condition, the probe set intensity must be in an appropriate range compared with gene expression level (between 1/5 and 5, or 22.32 and 2.32 in the log2 scale). Differentially spliced probe sets were mapped to alternative splicing events (including cassette exons, alternative 59 and 39 splice sites and mutually exclusive exons), as well as alternative first exons (alternative 59 ends/promoter selection) and alternative last exons (alternative 39 ends/polyadenylation). Alternative splicing events were obtained from the dbCASE database (http://rulai.cshl.edu/ dbCASE) and alternative first and last exons were derived from UCSC known genes. We limited our analysis to alternative transcripts which had full length annotated UCSC/RefSeq sequences with known exon structures [42]. Probe data were visualised in their gene context using the EASANA visualisation module (GenoSplice Technology), and candidate transcripts were inspected using EASANA and the UCSC Genome Browser. We focused our search on cassette exons and changes in 59-ends of transcripts selecting candidates for RT-PCR validation. For positively-validated targets, changes in peptide features of translated proteins resulting from alternative mRNA isoforms were identified using FAST-DB [27,43], identifying conserved domains using the NCBI webpage (http://www.ncbi.nlm.nih.gov/sites/entrez) and the UCSC Genome Browser (http://genome.ucsc.edu/) [24]. qPCR, RT-PCR and sequencing cDNA was generated by reverse transcription of 1 mg of total RNA using the Superscript VILO cDNA synthesis kit (Invitrogen, 11754-050) according to manufacturers instructions. Quantitative PCR (qPCR) (Applied Biosystems 7900HT) was performed in PLoS ONE | www.plosone.org 11 December 2011 | Volume 6 | Issue 12 | e29088