Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Summary

Herein we propose a strategy to study the effect of a transcription factor of interest on the microRNA transcriptome using publically available data, computational resources and high throughput data from microRNA arrays after transfecting cells with small hairpin (sh)RNA targeting a transcription factor of interest.

Abstract

While the transcription regulation of protein coding genes was extensively studied, little is known on how transcription factors are involved in transcription of non-coding RNAs, specifically of microRNAs. Here, we propose a strategy to study the potential role of transcription factor in regulating transcription of microRNAs using publically available data, computational resources and high throughput data. We use the H3K4me3 epigenetic signature to identify microRNA promoters and chromatin immunoprecipitation (ChIP)-sequencing data from the ENCODE project to identify microRNA promoters that are enriched with transcription factor binding sites. By transfecting cells of interest with shRNA targeting a transcription factor of interest and subjecting the cells to microRNA array, we study the effect of this transcription factor on the microRNA transcriptome. As an illustrative example we use our study on the effect of STAT3 on the microRNA transcriptome of chronic lymphocytic leukemia (CLL) cells.

Introduction

MicroRNAs are endogenous small non coding regulatory RNAs that typically function as negative regulators of mRNA expression at the posttranscriptional level. Approximately 1,000 non-coding 20 to 25 nucleotide long microRNAs are found in the human genome ^1,2. MicroRNAs regulate gene expression through canonical base pairing between the seed sequence of the microRNA and its complementary seed match sequence, which is commonly located at the 3' untranslated region (UTR) of the target mRNAs. Collectively, microRNAs regulate more than 30% of protein coding genes ³, but only little is known about the transcription from DNA of microRNAs. It has been suggested that the regulation of microRNA transcription is similar to that of mRNA ^4,5. In particular, similar to its activity in promoting transcription of protein coding genes, transcription factors are thought to activate transcription of microRNAs ⁶. Transcription factor-microRNA interplay has been reported as a modulator factor of gene expression ⁷, and may also form feed-back and feed-forward loops. For example, Yamakuchi et al. reported a feedback loop in which p53 induces the expression of microRNA34a, which in turn inhibits translation of the p53 repressor SIRT and thereby increasing p53 activity ⁸.

Whereas specific examples of transcription factor dependent expression of microRNAs have been reported, an accepted method which provides information on how a transcription factor of interest regulates the expression of the microRNA-transcriptome is lacking. The purpose of the protocol suggested herein is to provide an in-depth description of transcription factor-dependent regulation of the microRNA-transcriptome. By combining publically available data, bioinformatics tools and using microarray technology, researchers who follow this algorithm would be able to capture on a genomic scale how any transcription factor in any cell type of interest regulates the expression of the microRNA-transcriptome and to explore a putative contribution of the transcription factor-mRNA in regulating microRNA expression.

Protocol

1. Identify Transcription Factor Binding Sites in the Promoter of MicroRNA Genes Using Data Mining Approach

1. Use the University of California Santa Cruz (UCSC) genome browser to extract chromatin immunoprecipitation (ChIP) sequencing data generated as part of the Encyclopedia of DNA element (ENCODE) project.
   1. Open the table browser in the UCSC genome browser.
   2. Use the following specifications to extract the table: Clade: (Mammals), genome: (Human), Assembly: (Feb2009(GRch37/hg18)), group: (regulation), track (TxnFactorChIP), table: (weEncoderesTFbsCo7steredv3), region: (Genome), output format (all genes from selected table).
   3. Save the output from 1.1.2 as a .txt file and into a spreadsheet.
   4. Sort and filter for the transcription factor of interest (e.g., STAT2).
2. Use the list of microRNA promoters based on H3K4me3 epigenetics signature available at Baeer et al.⁹. Copy this list into a .txt file.
3. To match according to coordinates on the human genome, map the data from 1.1 and 1.2 (e.g., STAT3 binding on putative microRNA promoters) and determine the median binding affinity using the code written in C sharp as outlined in supplemental coding file 1.

2. Use shRNA to Down-regulate the Expression of a Transcription Factor of Interest

1. Plate 1.5 x 10⁶ cells from 293 human embryonic kidney cell line in 10 cm plate at approximately 50% confluence (DMEM with 10% FBS).
2. Transfect cells from 293 human embryonic kidney cell line with 5 µg of green fluorescence protein (GFP) lentivirus containing shRNA directed to the transcription factor of interest and with 5 µg of packaging vectors using transfection reagent for adherent cells according to manufacturer's protocol.
3. As a control, transfect the cells from 293 human embryonic kidney cell line with scrambled shRNA and the packaging vectors according to manufacturer's protocol.
4. Keep transfection mix on cells for 16 hr (at 37 °C, CO₂ incubator), then change media to 10 ml fresh 10% DMEM media with 10% FBS.
5. Wait 48 hr post transfection, centrifuge the cell culture (300 x g, 5 min) and collect infectious supernatant. Filter the supernatant through a 0.45 µm syringe filter (25-mm surfactant free cellulose acetate membrane) to remove any floating cells.
6. Concentrate the supernatant and collect the lentivirus using an ultracentrifugal filter device with threshold of 100 kDa. Spin at 950 x g for 30 - 60 min until the volume has been concentrate to less than 250 µl. Store the concentrated virus at -80 °C.
7. Transfect the cells with the lentivirus. Remove frozen lentivirus from -80 °C freezer and thaw to room temperature. Transfer 100 µl of viral supernatant to a fresh 1.5 ml microfuge tube.
   1. Bring up the volume in the tube to 1 ml with reduced serum medium. Add hexadimethrine bromide to 1 ml virus suspension for final concentration of 10 ng/ml, mix gently and let the mixture stand for 5 min.
8. Centrifuge 5 x 10⁶ cells for each transduction and gently resuspend the cell pellet in 0.5 ml of media containing virus. Let the cells stay in the incubator for 4 - 24 hr, then add 0.5 ml of medium with 20% FBS to a final concentration of 10% FBS.
9. Wait 48 to 72 hr and stain the cells with propidium iodide (PI) and green florescent protein (GFP) according to the manufacturer's instructions. Protect the cells from light and use a FACS sorter to measure the rates of GFP⁺ / PI^- cells. Since PI stains only dead cell, this rate is an estimate of transfection efficiency in living cells.
10. Sort the positive cell population to GFP expression (GFP+) by FACS sorter as previously described ¹⁰.
11. Use Western immune blotting as previously described ¹⁰ to determine the levels of a transcription factor of interest before and after infecting the cells of interest (for example, CLL cells) with designated shRNA.

3. Determine the Expression Level of MicroRNA Transcriptome in Cells Transfected with Transcription Factor-shRNA

1. Isolate RNA using a commercial kit according to manufacturer's protocol.
2. Label the RNA and hybridize it to microRNA microarray¹¹.
3. Determine the differentially expressed microRNA in cells transfected with transcription factor-shRNA or with empty vector controls⁵.
4. Validate the microarray results for the most differentially expressed microRNAs using real-time PCR ⁵.

4. Determine the Overlap between the Bioinformatics and the shRNA Approach in Describing the Transcription Factor Dependent Transcriptome

1. To determine the expected and observed ratios of microRNA genes that harbor transcription factor binding sites in their promoters and were downregulated in transcription factor-shRNA transfected cells, do the following:
   1. Obtain the ratio of genes that harbor the transcription factor of interest in their promoter / total microRNA from the list generated in 1.3. This list is the expected ratio (e.g., microRNA genes with STAT3 binding sites / total microRNA genes tested = 0.25).
   2. Determine the number of genes that were downregulated in transcription factor-shRNA transfected cells from the list generated in 3.3 AND has transcription factor of interest binding sites from the list generated in 1.3. This number/Total number of downregulated genes is the observed ratio (e.g., microRNA genes with STAT3 binding sites/total number of downregulated microRNAs = 0.6).
2. Use χ2 statistics to compare the observed and expected ratios that were generated above and determine whether the list of genes that were downregulated in transcription factor-shRNA transfected cells are enriched with transcription factor binding sites in their promoter.

Results

STAT3 is a transcription factor which typically induces the transcription of genes that have anti apoptotic and proliferative effects ¹² . Whether STAT3 also affect the non-coding RNA transcriptome is currently unknown. In all CLL cells STAT3 is constitutively phosphorylated on serine 707 residues ^10,13. Phosphoserine STAT3 shuttles to the nucleus, binds to DNA, and activates genes known to be activated by tyrosine pSTAT3 in other cell types ¹⁰. Because CL...

Discussion

The mechanism underlying the RNA polymerase II- dependent transcription of protein coding genes has been extensively studied. While these elements make up only 1% - 2% of the human genome, evidence from the ENCODE project suggest that over 80% of the human genome may undergo transcription ¹⁷ and what regulates the transcription of the non-coding DNA elements remains largely unknown ⁶.

Several studies, which indicated that Pol II is also responsible for the transcription o...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Lipofectamin 2000	Life Technologies	11668027
0.45 µm syringe filter	Thermo Scientific (Nalgene)	190-2545
Amicon ultracentrifugal filter device with threshold of 100 kDa	Merck Millipore
Polybrene	Merck Millipore	TR-1003-G
TRIzol reagent	Life Tachnologies (Invitrogen)	15596-026
293 Cell line human	Sigma-Aldrich	85120602