Page 105 - Molecular features of low-grade developmental brain tumours

THE CODING AND NON-CODING TRANSCRIPTIONAL LANDSCAPE OF SEGA
under-expressed (Figure. 1c; Supplementary Table 4). Among the 9400 DEGs we have identified 7196 protein-coding genes, 360 long non-coding RNAs, 309 pseudogenes, 1516 genes of which the biotype could not be determined by BioMart and 19 genes that could not be linked to a specific chatagory (Supplementary Table 4). In order to compare the TSC1 mutated SEGA samples with the TSC2 mutated SEGA samples, two differential gene expression analyses were carried out: TSC1 mutated SEGAs compared to control (TSC1-control) and TSC2 mutated SEGAs compared to control (TSC2-control). The majority of the DEGs in both groups (TSC1-control and TSC2-control) were overlapping (5292 genes), whereas 721 genes were only found differentially expressed in TSC1-control and 2816 genes in TSC2-control (Figure 1d). Furthermore, the fold changes between TSC1-control and TSC2-control showed a strong positive correlation (Spearman’s correlation, rho=0.89, p-value<0.01).
To better understand the organization of the protein-coding transcriptome of SEGAs, a gene set enrichment analysis (GSEA; see materials and methods) was performed, identifying 145 pathways enriched in SEGA compared to control tissue (adjusted p-value<0.05). A Fisher’s exact test revealed 92 pathways (adjusted p-value<0.02) enriched for DEGs (adjusted p-value<0.05; Figure 1e; Supplementary Table 3). The SEGA transcriptome profile was associated with pathways including immune system, extracellular matrix organization, metabolism and the MAPK family signaling cascades. These pathways were also found amongst the top 25 pathways containing the highest amount of DEGs (Figure 1f). Furthermore, most of the enriched pathways contained more over-expressed genes then under-expressed genes (Figure 1f; Supplementary Table 3).
Previously, Martin et al., 2017 performed RNA-Seq on 13 SEGA samples, 2 SENs and 8 normal brain tissue samples 23. In order to assess the robustness of our analysis, we overlapped our DEGs set with that of Martin et al., 2017 and performed a pathway analysis (Supplementary Figure 2a,b). We identified 619 over-expressed genes and 777 under- expressed genes in common between both studies, resulting in 32 enriched pathways (Supplementary Figure 2c). Since the MAPK pathway was identified in all pathway analyses performed we decided to focus on this pathway for further analysis.
Higher expression of LAMTOR genes in SEGA compared to control tissue
Previous studies have shown that the Ragulator complex (formed by LAMTOR1, LAMTOR2, LAMTOR3, LAMTOR4 and LAMTOR5) localizes to the late endosomes/lysosomes membrane, where it can activate both the MAPK/ERK pathway and the mTORC1 pathway 29-32,68 (Figure 2a). Based on our RNA-Seq data we found this complex to be over-expressed in SEGA compared to control (Supplementary Table 4). RT-qPCR was used to validate the RNA-Seq data for LAMTOR1, LAMTOR2, LAMTOR3, LAMTOR4 and LAMTOR5. All five genes were found to have higher expression in SEGA compared to control tissue (LAMTOR1: p-value=0.001, LAMTOR2: p-value=0.0011, LAMTOR3: p-value=0.006, LAMTOR4: p-value=0.0325 and LAMTOR5: p-value=0.0026; Figure 2b-f). A protein-protein interaction network for the Ragulator complex was assembled using the STRINGapp in Cytoscape and demonstrated that this complex interacts with proteins related to the MAPK/ERK pathway, including MAPK1 (ERK2), MAPK3
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