Pol II ChIP-Seq

Advantages of using TranscriptionPath to measure gene expression

TranscriptionPath reveals four distinct transcription patterns

TranscriptionPath is based on ChIP using an antibody that targets RNA Polymerase II (RNAPII). Four distinct patterns are observed when using ChIP-Seq to analyze the immunoprecipitated DNA. These are: 1. Paused and Transcribed – This state is defined by a significant peak at the transcription start site (TSS) followed by a lower, more even signal across the gene body. The peak at the TSS is indicative of either formation of the pre-initiation complex (PIC) or pausing following RNAPII release from the PIC. The elevated signal across the gene body is indicative of transcription of the gene. 2. Not Paused and Transcribed – This state is defined by an even signal across the gene body with no peak at the TSS. 3. Paused and Not Transcribed – This state is defined by a peak at the TSS and no signal in the gene body. These genes have assembled the PIC and are poised for transcription but the genes are not being transcribed. 4. Not Paused and Not Transcribed – This state is defined by no recruitment of RNAPII to the gene body or TSS. These genes are inactive. The different states provide information on the mechanisms of transcription of each gene.

Figure 1: TranscriptionPath™ data reveals four states of RNAPII occupancy.

TranscriptionPath-Seq was performed using chromatin from mouse neuroblastoma cells. Four patterns of RNAPII occupancy were detected and examples are shown above. A. Promoter Paused and Transcribed – Transcription is rate-limited post-initiation. B. Not Paused and Transcribed – Transcription is rate-limited by RNAPII recruitment. C. Promoter Paused and Not Transcribed – These genes are poised for activation but are not transcribed. The example above shows that the poised state is inducible. D. Not Paused and Not Transcribed – These genes are inactive.

TranscriptionPath can detect changes at early time points

Changes in gene expression occur within minutes of cell treatment but these changes are often ignored because most gene expression studies measure only much later time points. In order to truly understand a cellular response it is important to understand the primary response, that is, the changes that are happening within minutes of treatment. Measuring mRNA at these early time points has limitations as transcripts over 100 Kb will take over 1 hour to be fully transcribed, processed and exported to the cytoplasm where they can be detected. The TranscriptionPath method can detect gene expression changes that are missed by mRNA detection methods because it measures RNAPII occupancy at DNA in real time.

The examples in the figure below show that the TranscriptionPath-qPCR assay is more robust and can detect changes earlier in the time course than mRNA measurements by RT-qPCR.

Figure 2: TranscriptionPath™ is better than mRNA methods at detecting changes in gene expression at early time points.

Mouse Min6 cells were treated with forskolin to induce changes in gene expression. Cells were fixed, chromatin prepared and TranscriptionPath-Seq analysis performed using qPCR primers approximately 1500 bp 3′ of the transcription start site (TSS). Total mRNA was measured by preparing cDNA and designing primers that span splice junctions. Igf1r (A), Irs2 (B) and Egr1 (C) are all genes for which induced transcription was detected. For all of these genes the peak in transcription is detectable before the peak in mRNA and the data shows that the longer a gene is, the farther the peak in mRNA lags behind the peak in transcription (compare A to C). TranscriptionPath also enables earlier detection and more dramatic differences for the down-regulated gene Slc2a2 (D) with the most dramatic decrease in transcription observed at 30 minutes compared to 3 hours for RT-qPCR measurements.

TranscriptionPath in combination with TF ChIP

ChIP is widely used to detect transcription factor binding or histone modifications. ChIP-Seq is a combination of ChIP followed by Next-Gen sequencing that enables TF binding and modified histone occupancy to be be mapped across the entire genome. However, mapping binding sites lacks contextual information because the process of TF binding does not always lead to transcriptional activation or repression. In order to more fully understand the importance and/or function of individual TF binding events it is necessary to understand the effects of TF binding on transcription of each bound gene. This type of analysis can be achieved by integrating ChIP-Seq data with TranscriptionPath-Seq gene expression data.

Figure 3: Induced TF binding correlates with TranscriptionPath™-measured gene expression.

Performing ChIP-Seq using an antibody against RNA pol II produces a genome-wide profile of gene transcription rates. ChIP-Seq was performed using chromatin from control and estrogen-treated MCF-7 cells and antibodies against RNA Pol II and the estrogen-inducible transcription factor SRC3. Estrogen treatment induced the binding of SRC3 in the promoter and gene body of the RET gene (copper arrows in top 2 panels). Induced SRC3 binding correlates with induced transcription of the RET gene as measured by TranscriptionPath (bottom 2 panels).

TranscriptionPath can identify alternate start sites and unannotated genes

30-50% of all human genes use alternate transcription start sites. Using the TranscriptionPath method for gene expression studies has the added advantage of providing information on transcription start sites (TSSs). These alternate TSSs can occur within the existing annotation or far upstream of the annotated start site. Often the identified alternate TSS can be verified by searching additional gene databases. Additionally, TranscriptionPath has the ability to detect transcription of unannotated genes. The two examples below illustrate ways in which TranscriptionPath data is rich in additional biological information that goes beyond simply measuring transcription rates.

Figure 4: Alternate transcription start sites (TSSs) revealed by TranscriptionPath™.

TranscriptionPath-Seq was performed using chromatin from a mouse neuroblastoma cell line. The data presented above is from two genes with transcription initiation in areas of the genome that do not correspond to their respective start sites in the RefSeq gene database (purple gene annotations). A. Comparison to an alternate gene database (mgcGenes, green gene annotation) reveals a known alternative transcription start site for Fam49b that corresponds to the transcription start site detected with TranscriptionPath. B. Comparison to the RefSeq and mgcGenes databases shows the same annotations for Cbx7, but comparison to a third gene annotation database (Acembly Genes, blue gene annotation) reveals a known alternative start site for Cbx7 that corresponds to the transcription start site detected with TranscriptionPath. The red arrows show that the start sites from the alternate gene databases correspond precisely with location of paused RNAPII.

The following papers cite the use of and/or provide additional information about TranscriptionPath™ Services provided by Active Motif’s Epigenetic Services:

“Dynamic O-GlcNAc cycling at promoters of Caenorhabditis elegans genes regulating longevity, stress, and immunity” by Love et al (2010) Proc. Natl. Acad. Sci. USA 107(16):7413-7418.
“Identification of target genes in breast cancer cells directly regulated by the SRC-3/AIB1 coactivator” by Labhart et al (2005) Proc. Natl. Acad. Sci. USA 102(5):1339-1344.
“GATA4 expression is primarily regulated via a miR-26b-dependent post-transcriptional mechanism during cardiac hypertrophy” by Han et al (2012) Cardiovasc Res 93(4):645-654.
“The Transcription Factor Neural Retina Leucine Zipper (NRL) Controls Photoreceptor-specific Expression of Myocyte Enhancer Factor Mef2c from an Alternative Promoter” by Hao et al (2011) JBC 286(40):34893-902.
“Global Characterization of Transcriptional Impact of the SRC-3 Coregulator” by Lanz et al (2010) Molecular Endocrinology 24(4):859-872.