Page 98 - Molecular features of low-grade developmental brain tumours

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CHAPTER 4
Target prediction tools
RNA-Seq and small RNA-Seq data were integrated using the R package “piano” 53 and custom scripts written in R. The “piano” package is an open-source tool for performing gene set enrichment analysis (GSEA) using a selection of available methods. The whole RNA transcriptome profile and the Reactome 54,55 gene to pathway dataset were passed to “piano”. The Wilcoxon rank-sum test method was used to identify enriched gene-sets amongst the dataset. Significance values were calculated through random gene sampling. Briefly, a random set of genes equal in size to the gene-set being tested was selected and the gene set statistic was recalculated 53. This was repeated 10,000 times to give a discrete null distribution. The gene set p-value was based on the fraction of random gene set statistics that are equal to or more extreme than the original gene set statistic. All p-values were corrected using the Benjamini-Hochberg method. Gene sets with an adjusted p-value<0.05 for non-directional (non-dir), mixed-directional up (mix-dir-up) and mixed-directional down (mix-dir-down) were considered enriched. Next, gene sets that were enriched for DEGs were identified using Fisher’s exact test. Gene sets with a Benjamini-Hochberg adjusted p-value<0.05 were considered enriched for DEGs. Results were visualized using Cytoscape 56. The web-accessible program DAVID (https://david.ncifcrf.gov/) was used to determine enriched pathways (Benjamini-Hochberg adjusted p-value<0.05) from the overlapping DEGs between our study and the study by Martin et al., 2017 23 57,58. Protein-protein interactions were determined for selected DEGs using the STRINGapp in Cytoscape, allowing 50 protein interactions, including scores with a confidence of >0.7 for: databases, text mining, experiments, co-expression, co-occurrence and neighborhood 59.
Gene sets that were potentially modulated by miRNAs were then identified. First, the list of validated miRNA targets for each of the differentially expressed miRNAs was retrieved from miRWalk2 60,61. Each gene set that was enriched for DEGs was then assessed for over- representation of miRNA targets using Fisher’s exact test. Gene sets with a Benjamini- Hochberg adjusted p-value<0.05 were considered enriched for validated miRNA targets. The expression levels of selected differentially expressed miRNAs and DEGs were correlated to identify potentially important miRNA-mRNA interaction partners. Correlations were calculated using Spearman’s rank correlation, statistically significant correlations (adjusted p-value<0.05) of greater than 0.5 and less than -0.5 were deemed as potentially interesting interactions partners.
Real-time quantitative PCR (RT-qPCR) analysis
mRNA expression levels were evaluated as described previously (Bongaarts et al., 2018 62). Briefly, 250 ng of total RNA was reverse-transcribed into cDNA using oligo-dT primers. RT- qPCRs were run according to the manufacturer’s instructions, on a Roche Lightcycler 480 thermocycler (Roche Applied Science, Basel, Switzerland) using LightCycler® 480 SYBR Green I Master (Roche Applied Science, Indianapolis, USA) and primers listed in Supplementary Table 2. The expression of miRNAs was analyzed using Taqman miRNA assays (Applied Biosystems, Foster City, CA, USA). cDNA was generated using the Taqman miRNA reverse
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