Additional file 2: of Suppression of PDHX by microRNA-27b deregulates cell metabolism and promotes growth in breast cancer

Table S1. Results of composite miRWalk target prediction. The top 30 are shown. Of these, PPP1CC, CDH11PLK2, JMJD1C, FN1, IRS1, NCOA7, YPEL3, MAP2K4 and PDHX were chosen for validation by luciferase assay. A cut-off was set for each program giving a binary prediction indicated as 1 or 0. These were tallied and those targets predicted by the most algorithms were considered the best potential targets. Raw data used for the survival analysis are included in Additional file 3: Table S2. (XLSX 9 kb

Additional file 1: of Suppression of PDHX by microRNA-27b deregulates cell metabolism and promotes growth in breast cancer

Figure S1. 3’UTRs of PLK2 and PPP1CCC are targeted by miR-27b. Luciferase assays showing the change in luminescence following miR-27b transient transfection verse control of the genes PLK2 (A) and PPP1CCC (B). The putative binding sites are indicated in the boxes below. A cut-off was set for each program giving a binary prediction indicated as 1 or 0. These were tallied and those targets predicted by the most algorithms were considered the best potential targets. Figure S2. Luciferase assays for 3’-UTRs not targeted by miR-27b. While prediction algorithms indicated these seven would be good candidates for miR-27b targeting, the predictions could not be validated experimentally. Figure S3. Scherf Cell line database evaluation of the expression of PDHX in 11 different types of cancer. Data was accessed using Oncomine platform. Figure S4. PDHX expression across a panel of cancer types using the BioExpress gene expression database. Figure S5. PDHX expression according to breast adenocarcinoma subtype within the Curtis Breast Statistics dataset. Data was accessed using Oncomine platform. For the Invasive Ductal Breast Carcinoma, p = 6.0E-4. For Invasive Lobular Breast Carcinoma subtype, p = 5.2E-8. The number of patient samples in each category is indicated in parentheses. Figure S6. XY correlation plots of miR-27b with PDHX (A), C9orf3 with PDHX (B) and C9orf3 with miR-27b (C) by RNA-seq across a panel of 52 tissue types retrieved online from the GTEx database. Figure S7. Graphical representations of the GTEx RNA-seq expression data of miR-27b, PDHX, and C9orf3 across the panel of 52 human tissue types. (PDF 623 kb

Additional file 3: of Suppression of PDHX by microRNA-27b deregulates cell metabolism and promotes growth in breast cancer

Table S2. Raw TCGA data used for survival analysis. Patient clinical information was downloaded for the TCGA patients with breast invasive carcinoma (BRCA) from cBioPortal and PDHX gene expression quantification data was downloaded from Genomic Data Commons Data Portal. (XLSX 104 kb

Additional file 1: of MALAT1 promoted invasiveness of gastric adenocarcinoma

Cancer-related target gene expression by MALAT1 silencing. To analyze the relation among cancer-related target genes by siMALAT1, NanoString nCounter gene expression analysis was carried out. (XLSX 42 kb

Transcriptome analysis of hypoxic cancer cells uncovers intron retention in <i>EIF2B5</i> as a mechanism to inhibit translation

<div><p>Cells adjust to hypoxic stress within the tumor microenvironment by downregulating energy-consuming processes including translation. To delineate mechanisms of cellular adaptation to hypoxia, we performed RNA-Seq of normoxic and hypoxic head and neck cancer cells. These data revealed a significant down regulation of genes known to regulate RNA processing and splicing. Exon-level analyses classified > 1,000 mRNAs as alternatively spliced under hypoxia and uncovered a unique retained intron (RI) in the master regulator of translation initiation, <i>EIF2B5</i>. Notably, this intron was expressed in solid tumors in a stage-dependent manner. We investigated the biological consequence of this RI and demonstrate that its inclusion creates a premature termination codon (PTC), that leads to a 65kDa truncated protein isoform that opposes full-length eIF2Bε to inhibit global translation. Furthermore, expression of 65kDa eIF2Bε led to increased survival of head and neck cancer cells under hypoxia, providing evidence that this isoform enables cells to adapt to conditions of low oxygen. Additional work to uncover <i>-cis</i> and <i>-trans</i> regulators of <i>EIF2B5</i> splicing identified several factors that influence intron retention in <i>EIF2B5</i>: a weak splicing potential at the RI, hypoxia-induced expression and binding of the splicing factor SRSF3, and increased binding of total and phospho-Ser2 RNA polymerase II specifically at the intron retained under hypoxia. Altogether, these data reveal differential splicing as a previously uncharacterized mode of translational control under hypoxia and are supported by a model in which hypoxia-induced changes to cotranscriptional processing lead to selective retention of a PTC-containing intron in <i>EIF2B5</i>.</p></div

Model of hypoxia-induced intron retention in <i>EIF2B5</i> as a mechanism to reduce translation and enhance survival in head and neck cancer cells during periods of prolonged or acute hypoxia.

<p>Upper: Under acute or prolonged hypoxia, increased phosphorylation of Ser2-RNAPII accumulates specifically at <i>EIF2B5</i> intron 12. Binding of SRSF3 is increased under hypoxia at this locus, which contains a weak splice site and alternate downstream splice site. Altogether, oxygen deprivation leads to an accumulation of intron12-containing <i>EIF2B5</i> transcripts, which results in a truncated reading frame due to insertion of a premature termination codon (PTC). Lower: Retention of intron 12 under hypoxia results in a 65kDa isoform of eIF2Bε. This isoform lacks the functional guanine exchange factor (GEF) domain and acts opposite to the full-length isoform to inhibit translation during periods of prolonged hypoxia, which ultimately confers a survival advantage to SQ20B cells under hypoxia.</p

Analysis of RNA binding factor motifs and regulatory sequence features at the <i>EIF2B5</i> intron12 locus.

<p>(A) A nonmotif analysis of other sequence features influencing splicing of <i>EIF2B5</i> exons 12–14. These were data generated using AVISPA [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.ref050" target="_blank">50</a>]. (B) Splicing factor motifs determined to have the largest effects on regulation of the <i>EIF2B5</i> exons 12–14 are shown, with color-coded gene map above and predicted regulatory sites shown below with feature effect value. Black rows highlight a predicted weak splice site before exon 13 and an alternate GTGAG splice site after exon 13. Red arrows signify splicing factors observed as hypoxia-responsive in RNA-Seq data. (C) Immunoblot of SQ20B lysates to assess expression of SRSF3 protein under hypoxia. Nx = normoxia, Hx = 16 h 0.5% O₂ hypoxia. (D) Upper: Immunoblot analysis of knock-down efficiency of SRSF3 in SQ20B cells. Lower: Immunoblot of lysates collected from SQ20B cells treated with 50 nM siRNA under 16 h 0.5% O₂ hypoxia. (E) Upper: Immunoblot results from immunoprecipitation of SQ20B lysate with SRSF3 antibody in normoxic and hypoxic cells. Rabbit IgG was used as a control. Lower: Reverse transcription quantitative PCR (RT-qPCR) analysis of RNA isolated from the immunoprecipitation with SRSF3 using primers for negative (-) control (a region of GAPDH predicted to contain no binding of SRSF3) and primers for the 2 exons flanking <i>EIF2B5</i> intron12 predicted to have SRSF3 binding. Additional data used in the generation of this figure are included in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.s009" target="_blank">S1 Data</a>.</p

Hypoxia-induced retained introns (RIs) for several genes are confirmed by PCR and expression analysis of head and neck tumors.

<p>For each panel, PCR validation of intron retention events using cDNA prepared from oligo-dT–selected mRNA treated with DNAse enzymes is shown. Genes include (A) <i>ANKZF1</i>, (B) <i>EIF2B5</i>, (C) <i>MARS</i>, and (D) <i>TGFB1</i>. Diagrams beside gel images of PCR products depict gene locus models with exons as solid blue and introns as striped rectangles. For each gene, expression analysis for HNSC tumor and matched normal tissue data is shown below—(E) <i>ANKZF1</i>, (F) <i>EIF2B5</i>, (G) <i>MARS</i>, and (H) <i>TGFB1</i>. Data used in the generation of this figure are included in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.s009" target="_blank">S1 Data</a>.</p

Classification of alternatively spliced mRNAs in hypoxic SQ20B cells.

<p>(A) Heatmap of RNA-processing and -splicing factors differentially expressed in hypoxia compared to normoxia (Fold-changes shown, false discovery rate [FDR] < 5%). (B) To the left, plot depicts number of events detected (blue) compared to events with significantly different expression in hypoxic compared to normoxic cells (red) (Bayes Factor ≥ 20, ΔѰ > 10%; Abbreviations: A3SS, alternative 3′ splice site; A5SS, alternative 5′ splice site; AFE, alternative first exon; ALE, alternative last exon; MXE, mutually exclusive exon; RI, retained intron; SE, skipped exon; TUTR, tandem 3′ untranslated region). Specific enrichment for changes in 3 event types are starred: ALE, RI, and TUTR (***<i>P</i> < 0.001, 2-sample test for equality of proportions). To right of graph, exon models of the types of splicing assessed by MISO analysis. (C) Gene ontology figure representing functional enrichment for hypoxia-induced changes in ALE, RI, and TUTR categories. (D) Percent spliced in (Psi) values plotted with hypoxia samples (red) overlaid against corresponding normoxic Psi values (blue). All supporting data used in the generation of this figure are included in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.s009" target="_blank">S1 Data</a>.</p

Detection of hypoxia-mediated changes in phosphorylation and binding of RNA polymerase II (RNAPII).

<p>(A) Immunoblot of phosphorylated forms of RNAPII in nuclear lysates of SQ20B cells. Expression of ATM (Ataxia Telangiectasia Mutated Serine/Threonine kinase) was used as a loading control. (B) Chromatin immunoprecipitation followed by quantitative PCR (qPCR) to determine abundance of total RNAPII or P-Ser2 RNAPII at EIF2B5 intron 12, an upstream negative control intron 10, a negative control region of GAPDH ((-) control), and a RNAPII-positive control region of GAPDH. For (B), total RNAPII data represent average of <i>n</i> = 3 independently conducted experiments (error bars = SEM) and P-Ser2 data are shown as an average of <i>n</i> = 2 independently conducted experiments. (C) Analysis of 3′ splice sites carried out using a First Model Markov method to determine maximum entropy scores, reported as 3′ splice site strength [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.ref056" target="_blank">56</a>]. Hypoxia group = 101 unique 3′ splice sites of introns retained under hypoxia; Random group = 252 hg19 3′ splice sites. Additional data used to create this figure are included in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002623#pbio.2002623.s009" target="_blank">S1 Data</a>.</p