The goal of this study was to identify genes and miRNAs that potentially regulate telomerase induction in normal diploid fibroblast BJ cells. Real-time PCR was used to screen 1,728 siRNAs (targeting 576 genes) and 114 miRNA mimics to identify genes and miRNAs that are direct repressors or indirect regulators of hTERT mRNA transcription. Telomerase activity was then assayed in normal, diploid, telomerase-negative BJ fibroblast cells for those siRNAs and miRNA mimics that test positive in the hTERT screen using a telomerase repeat amplification protocol (TRAP).
Telomerase Pathway is Active in Cancer Cells
Telomerase plays a role in maintaining the length of chromosomal end sequences and thus, cell proliferation (see sidebar, The Role of Telomerase). Most normal human cells do not express detectable telomerase activity, because the human telomerase catalytic subunit (hTERT) gene is not expressed [1]. However, telomerase activity is expressed or induced in 90% of cancer cell types.
hTERT mRNA Induction Screen
In order to identify genes and miRNAs that regulate telomerase activity, BJ foreskin fibroblast cells (telomerase negative) were transfected with custom libraries of
Silencer® Pre-designed siRNAs (1,728 siRNAs) and
Pre-miR™ miRNA Precursors (114 miRNA mimics) using optimized delivery conditions developed in our laboratories (HeLa cells, which express hTERT, were used as a positive control). A real-time PCR assay was used to monitor hTERT mRNA levels following transfection. In brief, 72 hours post-transfection, cells were harvested for RNA isolation using the
MagMAX™-96 Total RNA Isolation Kit. The RNA was then reverse transcribed using
M-MLV Reverse Transcriptase to generate cDNA for real-time PCR. A
TaqMan® Gene Expression Assay for TERT was used for amplification (Figure 1). Of the 576 genes and 114 miRNAs targeted by the screen, 72 "hits", or "positives", were identified.
Figure 1. Real-Time PCR Amplification of hTERT. Cells (4000 cells/well) were transfected with 0.5 µL siPORT™ NeoFX™ Transfection Agent. 72 hr post-transfection, cells were harvested for RNA isolation using the MagMAX™-96 Total RNA Isolation Kit. The RNA was then reverse transcribed using M-MLV Reverse Transcriptase to make cDNA for real-time PCR. An hTERT TaqMan® Gene Expression Assay was also used to examine hTERT expression in BJ cells versus HeLa cells (positive hTERT control). While most of the samples were negative for hTERT expression, there were some positive hits for hTERT induction in comparison to the HeLa cell positive control.
Figure 1. Real-Time PCR Amplification of hTERT. Cells (4000 cells/well) were transfected with 0.5 µL siPORT™ NeoFX™ Transfection Agent. 72 hr post-transfection, cells were harvested for RNA isolation using the MagMAX™-96 Total RNA Isolation Kit. The RNA was then reverse transcribed using M-MLV Reverse Transcriptase to make cDNA for real-time PCR. An hTERT TaqMan® Gene Expression Assay was also used to examine hTERT expression in BJ cells versus HeLa cells (positive hTERT control). While most of the samples were negative for hTERT expression, there were some positive hits for hTERT induction in comparison to the HeLa cell positive control.
Telomerase Activity Assay
Since hTERT mRNA is not normally expressed in BJ cells, the assay for hTERT mRNA induction is very sensitive. Those molecules that scored as hits in the initial hTERT mRNA induction screen (i.e., amplification of hTERT was detected by the TaqMan Gene Expression Assay) were tested for their ability to induce telomerase activity using a Telomeric Repeat Amplification Protocol (TRAP). Seventy two hours after transfection, samples were pelleted and sent to Drs. Jerry Shay's and Woodring Wright's laboratory at UT Southwestern for TRAP analysis [2] (see Figure 2 for an example). Of the 72 "hits" identified by the initial screen, 16 siRNA and 3 miRNA hits were validated by this secondary screen: ACOX1, MAD1L1, GPX1, GPX7, MTCO2, MAOB, PRKCA, PRKCZ, MAPK6, AKT1, AKT2, GSK3B, FADD, MIST1, RELN, and FRK (RAK) genes and miR-26a, miR-147, miR-195 were identified as transient and modest inducers of both hTERT mRNA expression and telomerase activity. The hits fell into 2 major categories: mitochondrial associated/oxidative damage family genes and a set of genes important in signal transduction pathways and apoptosis.
In addition, hits were identified in our siRNA screen that were predicted target genes of some miRNAs that were also hits (Figure 3). For example, based on computational approaches, miR-26a is predicted to target MAPK6, and miR-147 is predicted to target PRKCA. It was therefore interesting to note that both the siRNA and miRNA targeting MAPK6 or PRKCA induced telomerase expression. On a broader level, our data suggest a link between the regulation of hTERT expression and important signaling pathways and suggest multiple levels of regulation of hTERT mRNA expression and the telomerase pathway. Additional followup is warranted to examine the exact roles of these pathways and genes in regulating telomerase activity, as well as their roles in oncogenesis.
In addition, hits were identified in our siRNA screen that were predicted target genes of some miRNAs that were also hits (Figure 3). For example, based on computational approaches, miR-26a is predicted to target MAPK6, and miR-147 is predicted to target PRKCA. It was therefore interesting to note that both the siRNA and miRNA targeting MAPK6 or PRKCA induced telomerase expression. On a broader level, our data suggest a link between the regulation of hTERT expression and important signaling pathways and suggest multiple levels of regulation of hTERT mRNA expression and the telomerase pathway. Additional followup is warranted to examine the exact roles of these pathways and genes in regulating telomerase activity, as well as their roles in oncogenesis.
Figure 3. siRNA Hits Coincide with Targets of the miRNA Mimic Hits. MicroRNA binding sites were predicted using an algorithm developed by Ambion scientists [Wang X and Wang X (2006) Nucleic Acids Res 34(5):1646–1652.]
Figure 2. TRAP Data for miRNA and siRNA Target. Based on computational predictions, PRKCA is a target of miR-147. When an siRNA targeting PRKCA and the Pre-miR™ miRNA Precursor for miR-147 were individually transfected into BJ cells, they both induced TRAP activity as denoted by the laddering of bands on the gel. The lower band represents the 36 bp internal standard that is also used as a substrate by telomerase to add telomeric repeats. The amount of laddering indicates amounts of telomerase activity in the sample. H1299 Cells = positive control; miR-147 = miR-147 transfected sample; PRKC–A = siRNA transfected sample.
In addition, hits were identified in our siRNA screen that were predicted target genes of some miRNAs that were also hits (Figure 3). For example, based on computational approaches, miR-26a is predicted to target MAPK6, and miR-147 is predicted to target PRKCA. It was therefore interesting to note that both the siRNA and miRNA targeting MAPK6 or PRKCA induced telomerase expression. On a broader level, our data suggest a link between the regulation of hTERT expression and important signaling pathways and suggest multiple levels of regulation of hTERT mRNA expression and the telomerase pathway. Additional followup is warranted to examine the exact roles of these pathways and genes in regulating telomerase activity, as well as their roles in oncogenesis.
In addition, hits were identified in our siRNA screen that were predicted target genes of some miRNAs that were also hits (Figure 3). For example, based on computational approaches, miR-26a is predicted to target MAPK6, and miR-147 is predicted to target PRKCA. It was therefore interesting to note that both the siRNA and miRNA targeting MAPK6 or PRKCA induced telomerase expression. On a broader level, our data suggest a link between the regulation of hTERT expression and important signaling pathways and suggest multiple levels of regulation of hTERT mRNA expression and the telomerase pathway. Additional followup is warranted to examine the exact roles of these pathways and genes in regulating telomerase activity, as well as their roles in oncogenesis.
Figure 3. siRNA Hits Coincide with Targets of the miRNA Mimic Hits. MicroRNA binding sites were predicted using an algorithm developed by Ambion scientists [Wang X and Wang X (2006) Nucleic Acids Res 34(5):1646–1652.]
Conclusions
These data demonstrate the power of screening with both siRNAs and miRNA mimics for pathway analysis and target identification
Scientific Contributors
Angie Cheng, Kevin Kelnar*, Dmitry Ovcharenko*, and Lance Ford • Applied Biosystems, Austin, TX
Zhenjun Lou, Christine Duncan, Hirotoshi Hoshiyama, Woody E. Wright, and Jerry W. Shay • University of Texas Southwestern Medical Center at Dallas
*currently of Asuragen, Inc.
Scientific Contributors
Angie Cheng, Kevin Kelnar*, Dmitry Ovcharenko*, and Lance Ford • Applied Biosystems, Austin, TX
Zhenjun Lou, Christine Duncan, Hirotoshi Hoshiyama, Woody E. Wright, and Jerry W. Shay • University of Texas Southwestern Medical Center at Dallas
*currently of Asuragen, Inc.