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RNAseq raw counts FLCN positive vs. FLCN negative renal proximal tubular epithelial cells (RPTEC)

Citation

Glykofridis, Iris; Wolthuis, Rob (2021), RNAseq raw counts FLCN positive vs. FLCN negative renal proximal tubular epithelial cells (RPTEC), Dryad, Dataset, https://doi.org/10.5061/dryad.6djh9w0zs

Abstract

Germline inactivating mutations in Folliculin (FLCN) cause Birt–Hogg–Dubé (BHD) syndrome, a rare autosomal dominant disorder predisposing to kidney tumors. FLCN is a conserved, essential gene linked to diverse cellular processes but the mechanisms by which FLCN prevents kidney cancer remain unknown. Here we show that deleting FLCN activates TFE3, upregulating its downstream E-box genes in human renal tubular epithelial cells (RPTEC/TERT1), including RRAGD and GPNMB, without modifying mTORC1 activity. Surprisingly, deletion of FLCN or its binding partners FNIP1/FNIP2 also induces interferon response genes, but independently of interferon. Mechanistically, FLCN loss promotes STAT2 recruitment to chromatin and slows cellular proliferation. Our integrated analysis identifies STAT1/2 signaling as a novel target of FLCN in renal cells and BHD tumors. STAT1/2 activation appears to counterbalance TFE3-directed hyper-proliferation and may influence the immune response. These findings shed light on unique roles of FLCN in human renal tumorigenesis and pinpoint candidate prognostic biomarkers.

Methods

See material & methods section RNA extraction, sequencing & qRT-PCR of DOI: 10.7554/eLife.61630 

RNA was extracted from dry cell pellet (~1.5E6 cells) according to the High Pure RNA Isolation Kit (Roche, Penzberg, Germany) manual. For Illumina-based sequencing samples were prepped using TruSeq Stranded mRNA Library Preparation Kit according to TruSeq Stranded mRNA Sample Preparation Guide. Sequencing was performed on an Illumina HiSeq 4000 (Illumina, San Diego, California, USA) using run mode SR50. Reads were trimmed using sickle-1.33 (Joshi & Fass, 2011) and aligned to hg19 using hisat2-2.0.4 (Kim, Langmead, & Salzberg, 2015). The alignments were assigned to genes and exons using feature count-1.5.0-p3 (Liao, Smyth, & Shi, 2014) using the gene annotation provided by the iGenomes resource (Illumina, 2020).

Usage Notes

See material & methods section differential expression analysis of RNAseq data of DOI: 10.7554/eLife.61630

We used the R package edgeR (Robinson, McCarthy, & Smyth, 2010) to compare RNA sequencing profiles between FLCNPOS and FLCNNEG replicates, as well as between TP53POS and TP53NEG. This involved reading in the gene-level counts, computing library size normalizing factors using the trimmed-mean of M-values (TMM) method and then fitting a model to estimate the group effect. Obtained p-values were corrected for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) step-up procedure (Benjamini & Hochberg, 1995).

Funding

Cancer Center Amsterdam, Award: CCA2018-5-51