Work Description

Date: 13 September, 2023

Dataset Title: Discovery and characterization of a novel telomerase alternative splicing isoform that protects lung cancer cells from chemotherapy induced cell death.

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

Dataset Contact: Andrew Ludlow [email protected]

Funding:

Key Points:
- We identified 32 novel transcript variants of TERT using a single molecule long-read RNA sequencing.
- Among the novel transcript variants, we identified functions of TERT Delta 2-4 based on knockdown and overexpression experiments.
- TERT Delta 2-4 does not affect telomerase activity or cell growth. Instead, it enhances clonogenicity and resistance to cisplatin treatment.

Research Overview:
Single molecule long read RNA/cDNA sequencing of TERT revealed 45 TERT mRNA variants including 13 known and 32 novel variants. Among the variants, TERT Delta 2-4, which lacks exons 2-4 but retains the original open reading frame, was selected for further study. Induced pluripotent stem cells and cancer cells express higher levels of TERT Delta 2-4 compared to primary human bronchial epithelial cells. Overexpression of TERT Delta 2-4 enhanced clonogenicity and resistance to cisplatin-induced apoptosis. Knockdown of endogenous TERT Delta 2-4 in Calu-6 cells reduced clonogenicity and resistance to cisplatin. Our results suggest that TERT Delta 2-4 enhances cancer cells’ resistance to intrinsic apoptosis. RNA sequencing following knockdown of Delta 2-4 TERT indicates that translation is downregulated and that mitochondrial related proteins are upregulated compared to controls.

Methodology:
Detailed methodology can be found in Supplementary material.pdf file in deep blue. Brief, figure by figure methods are listed below for brevity.

FIGURE 1 methods:
MinION TERT Sequencing Library Preparation and Sequencing
In this study, we discovered novel TERT transcript variants with long-read sequencing (MinION) using TERT specific primers targeting Exon 1 and Exon 16.
Bioinformatics Analyses of TERT Sequencing Library: After sequencing, minimap2, transcriptClean, TALON, and Swan was used to visualize TERT transcript variants (shown in 01.SwanReport-lower_TERT.pdf and 01.SwanReport-upper_TERT.pdf). All transcripts were manually binned based on the appearance of their transcript model (02. TERT transcript from SwanReport classification.xlsx) and characterized (03. TERT transcript variants detail.xlsx). Sequences of each transcript variants were imported and confirmed (04. Lower_TERT_raw_sequence.txt, 04. Upper_TERT_raw_sequence.txt, 05. All TERT transcript sequences.zip).
Among these novel variants, we focused on determining the function of TERT Delta 2-4 . We isolated RNA from nuclear and cytoplasmic fractions to determine localization and hint at protein coding capacity of delta 2-4. ddPCR was used to quantify TERT transcripts and gel-based RT-PCR was used for MALAT1 and GAPDH.

FIGURE 2 methods:
Calu-6 cells were treated with scramble non-targeting siRNA controls or an siRNA targeting Delta 2-4 TERT for 48 hours. Telomerase activity was measured by ddTRAP. Overexpression of Delta 2-4 was achieved with lentiviral vectors stably integrated into Calu-6 and U2-Os cells. Western blotting was used to confirm overexpression. Telomerase activity was measured by ddTRAP. Telomere length was measured by terminal restriction fragment length in cells with Delta 2-4 overexpression.

FIGURE 3 methods:
For clonogenic assay, 250, 500 and 1000 cells were plated and quantified by imageJ (ColonyCountJ). Resistant to cisplatin was measured by Alamar Blue assay. All raw files to generate figures are included in 06. Raw data for figures.zip.

FIGURE 4 methods:
We also studied the impact of TERT Delta 2-4 knockdown on global gene expression by RNA-Sequencing revealing that TERT delta 2-4 may be involved in translational and mitochondrial pathways. R codes to perform bioinformatics analyses are included in 07. R codes for bioinformatics analyses.zip.

Files contained here:
This dataset contains 6 files and 3 compressed folders related to the research. The 6 files and one compressed folder (05. All TERT transcript sequences.zip) are in an order of how we discovered novel TERT transcript variants. In the next compressed folder called "06. Raw data for figures.zip", all files are raw data that we used to create figures and the tile of files corresponds to the figures. The last compressed folder (07. R codes for bioinformatics analyses.zip) contains R code that we used to perform bioinformatics analyses.
Formats of files in the dataset are following:
.pdf: PDF file.
.xlsx and .xls: Excel file.
.txt: text file.
.dna: DNA sequence file. We used Snapgene to create the files.
.pzfx and .prism: We used GraphPad Prism 9 to create the files.
.jpg, .tif: image file.
.R: R code.
.zip: Compressed folders.

List of files:
'01. SwanReport-lower_TERT.pdf' - SwanReport showing TERT transcript variants from lower TERT band.
'01. SwanReport-upper_TERT.pdf' - SwanReport showing TERT transcript variants from upper TERT band.
'02. TERT transcript from SwanReport classification.xlsx' - Classified TERT transcript variants.
'03. TERT transcript variants detail.xlsx' - Details of classified transcript variants including ORF, Figure, novelty and premature termination codon.
'04. Lower_TERT_raw_sequence.txt' - Raw sequences of transcripts that were used to create .dna files in "05. All TERT transcript sequences.zip".
'04. Upper_TERT_raw_sequence.txt'- Raw sequences of transcripts that were used to create .dna files in "05. All TERT transcript sequences.zip".
'05. All TERT transcript sequences.zip' - Sequence files created based on "04. Lower_TERT_raw_sequence.txt" and "04. Upper_TERT_raw_sequence.txt".
'06. Raw data for figures.zip' - excel, prism, and image files contain raw data to create main and supplementary figures. File titles are matched to the figures.
'07. R codes for bioinformatics analyses.zip' - R scripts that were used to perform bioinformatics analyses and create figures. File names corresponds to figures.

Related publication(s):

Use and Access:

To Cite Data:
Kim, J.J., Ahn, A., Ying, J.Y., & Ludlow, A.T. (2024). Discovery and characterization of a novel telomerase alternative splicing isoform that protects lung cancer cells from chemotherapy induced cell death. [Data set]. University of Michigan - Deep Blue. https://doi.org/has_to_be_modified