Work Description

Date: 19 July, 2023

Dataset Title: Dynamics of TERT Regulation via Alternative Splicing in Stem Cells and Cancer Cells

Dataset Creators: J.J. Kim & A.T. Ludlow

Dataset Contact: Andrew Ludlow atludlow@umich.edu

Funding: K99/ROO CA197672-01A1, NCI

Key Points:
- We set out to identify regulated and dysregulated TERT splice variant expression as a means to identify a potential cancer therapeutic approach.
- We determined if the TERT AS was regulated by NOVA1-PTBP1-PTBP2 axis, during stem cell differentiation into neural progenitor cells, and indeed observe it was.
- We made a serendipitous observation that stem cell density impacted TERT splice variant expression but that cancer cells did not seem to utilize this mechanism.
- Based on public database analysis, correlational analysis, and experimental observations, we identified splicing factors (SFs) that may have cell type-specific roles in TERT AS regulation and telomerase activity.

Research Overview:
Part of the regulation of telomerase activity includes the alternative splicing (AS) of the catalytic subunit telomerase reverse transcriptase (TERT). Although a therapeutic window for telomerase/TERT inhibition exists between cancer cells and somatic cells, stem cells express TERT and rely on telomerase activity for physiological replacement of cells. Therefore, identifying differences in TERT regulation between stem cells and cancer cells is essential for developing telomerase inhibition-based cancer therapies that reduce damage to stem cells. In this study, we measured TERT splice variant expression and telomerase activity in induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs), and non-small cell lung cancer cells (NSCLC, Calu-6 cells). We observed that a NOVA1-PTBP1-PTBP2 axis regulates TERT alternative splicing (AS) in iPSCs and their differentiation into NPCs. We also found that splice-switching of TERT, which regulates telomerase activity, is induced by different cell densities in stem cells but not cancer cells. Lastly, we identified cell type-specific splicing factors that regulate TERT AS. Overall, our findings represent an important step forward in understanding the regulation of TERT AS in stem cells and cancer cells. These data and subsequent studies may reveal a splicing factor(s) or their binding site(s) that could be targeted with small molecule drugs or antisense oligonucleotides, respectively, to reduce telomerase activity in cancer cells and promote durable cancer remissions.

Methodology:
Detailed methodology is described in the manuscript (Kim et al., PLOS ONE, 2023). We utilized human cell lines (human induced pluripotent (iPSCs) and non-small cell lung cancer cells Calu-6) to study the regulation and dysregulation of TERT alternative splicing. We used gene manipulation by short interfering RNAs to reduce protein levels of key splicing factors to understand TERT splicing regulation. We measured splicing factors by western blot in cells of varying densities to elucidate the correlation to TERT splicing variants. Finally we used The Cancer Genome Atlas data, our labs hTERT mini gene screening data, and our collected data to determine cell type specific splicing regulators of TERT.

Files contained here:
The folders contains 20 excel and prism files related to figures and supplementary figures. Titles are corresponding to the figures. In addition, 8 csv files with RSEM values of 8 specific genes from tumor cells and tumor-adjacent normal cells are included.
Files were generated and edited by Microsoft Excel 2016 (.xlsx and .csv) and GraphPad Prism 9 (.pzfx).

List of files:
'Fig_1F__S1Fig_L_M.pzfx' - TERT splice isoform DDPCR data from iPSCS NOVA1, PTBP1, PTBP2 manipulations. Raw values and statistical analyses for Fig 1F and S1Fig L, M
'Fig_1G.pzfx' - ddTRAP (telomerase enzyme activity assay) data for iPSCs with NOVA-PTBP1-PTBP2 manipulations. Raw values and statistical analyses for Fig 1G
'Fig_1_B_C__S1FigB-H.pzfx' - TERT splice isoform percentage analysis in ipscs and NPCs during differentiation. Telomerase activity during differentiation. Raw values and statistical analyses for Fig 1B, C and S1Fig B-H
'Fig_2B-D__S2FigA-H.pzfx' - TERT and telomerase ddPCR data in iPSCs plated at different densities. Raw values and statistical analyses for Fig 2B-D and S2Fig A-H
'Fig_3B-D.pzfx' - TERT ddPCR data in Calu-6 cells plated at different densities. Raw values and statistical analyses for Fig 3B-D
'Fig_3B-K.xlsx' - TERT ddPCR data in Calu-6 cells plated at different densities. Raw values for Fig 3B-K
'Fig_3E-K.pzfx' -TERT ddPCR data in Calu-6 cells plated at different densities. Raw values and statistical analyses for Fig 3E-K
'Fig_3L.pzfx' - Telomerase ddPCR data in Calu-6 cells plated at different densities. Raw values and statistical analyses for Fig 3L
'Fig_3L.xlsx' - Telomerase ddPCR data in Calu-6 cells plated at different densities. Raw values for Fig 3L
'Fig_4A.pzfx' - TERT mini gene screen data. Raw values and statistical analyses for Fig 4A
'Fig_4B-F_S3_Fig_A-D.pzfx' - TCGA gene expression data for splicing factors related to TERT in LUAD compared to tumor-adjacent normal tissues. Raw values and statistical analyses for Fig 4B-F and S3 Fig A-D
'Fig_5A-F__S4_Fig_A-C.pzfx' - Splicing factor and TERT mRNA variant correlations. Raw values and statistical analyses for Fig 5A-F and S4 Fig A-C
'Fig_5_and_S4Fig_A-D_master_file.xlsx' - Splicing factor and TERT mRNA variant correlations. Raw values for Fig 5 and S4 Fig A-D master file
'Fig_6A-D__S5_Fig_A-C.pzfx' - Western blotting and TERT ddPCR variant expression in Calu-6 cells with knockdown of splicing factors. Raw values and statistical analyses for Fig 6A-D and S5 Fig A-C
'S1_Fig_I-K__S4Fig_E-G.pzfx' - Quantification of splicing factors NOVA1, PTBP1, and PTBP2 in siRNA experiments in iPSCs. Raw values and statistical analyses for S1 Fig I-K and S4 Fig E-G
'S4_FigD_loading_control.pzfx' - iPSCS GAPDH and Beta actin western blot data. Raw values and statistical analyses for S4 Fig D loading control
'S4_Fig_E-G.xlsx' - Western blot data and correlations to TERT in iPSCs at different densities for NOVA1, PTBP1 and PTBP2. Raw values for S4 Fig E-G
'S5_Fig_D-F.pzfx' - iPSC to NPC comparison of NOVA1. PTBP1 and PTBP2 western blot protein levels. Raw values and statistical analyses for S5 Fig D-F
'S5_Fig_D-F.xlsx' - iPSC to NPC comparison of NOVA1. PTBP1 and PTBP2 western blot protein levels.Raw values for S5 Fig D-F
'gene_CDC40.csv' - TCGA Raw data for gene CDC40
'gene_HNRNPA1.csv' - TCGA Raw data for gene HNRNPA1
'gene_HNRNPA2B1.csv' - TCGA Raw data for gene HNRNPA2B1
'gene_HNRNPCL1.csv' - TCGA Raw data for gene HNRNPCL1
'gene_HNRNPH1.csv' - TCGA Raw data for gene HNRNPH1
'gene_HNRNPM.csv' - TCGA Raw data for gene HNRNPM
'gene_SRPK1.csv' - TCGA Raw data for gene SRPK1
'gene_U2AF2.csv' - TCGA Raw data for gene U2AF2

Related publication(s):
Kim, J.J., et al. (2023). Dynamics of TERT Regulation via Alternative Splicing in Stem Cells and Cancer Cells. PLOS ONE.

Use and Access:
This data set is made available under a Creative Commons Attribution 4.0 International (CC BY 4.0).

To Cite Data:
Kim, J.J., Sayed, M.E., Ahn, A., Slusher, A.L. Ying, J.Y., & Ludlow, A.T. (2023). Dynamics of TERT Regulation via Alternative Splicing in Stem Cells and Cancer Cells [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/8p2k-0563