This publication was made possible by funding from grant T32ES019851 (to C. Fritsch), R01AG054641, and an AFAR young investigator grant (to M. Vermulst).

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C., Ochman, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Spectra+of+Transcription+Insertions+and+Deletions+Reveal+That+Slippage+Depends+on+RNA:DNA+Hybrid+Complementarity.">Genome-Wide Spectra of Transcription Insertions and Deletions Reveal That Slippage Depends on RNA:DNA Hybrid Complementarity.</a> mBio. 8, (2017).</li><li>Martin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cutadapt+Removes+Adapter+Sequences+From+High-Throughput+Sequencing+Reads.">Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads.</a> EMBnet.journal. 17 (1), 10-12 (2011).</li><li>Kent, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BLAT--the+BLAST-like+alignment+tool.">BLAT--the BLAST-like alignment tool.</a> Genome Research. 12, 656-664 (2002).</li><li>Kim, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat2:+accurate+alignment+of+transcriptomes+in+the+presence+of+insertions,+deletions+and+gene+fusions.">TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.</a> Genome Biology. 14, R36 (2013).</li><li>Zhou, Y. 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The authors have nothing to disclose.

Here, we describe an optimized protocol for the preparation of C-seq libraries for the detection of transcription errors in Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol has numerous advantages over existing protocols, as well as alternative techniques.
Over the past 15 years, numerous reporter systems have been developed that rely on luciferase7,8 or Cre-Lox recombination3,4,15,21 to detect transcription errors. These reporter systems have been invaluable to researchers&#39; understanding of transcriptional fidelity because they allowed genes, alleles, and molecular mechanisms to be identified that directly regulate the fidelity of transcription. However, they only report on errors in artificially damaged templates, or within highly specific genetic contexts, which limits the scientific questions they can answer. An important advantage of the C-seq protocol is that it monitors the fidelity of transcription throughout the entire transcriptome5, greatly expanding the scientific knowledge of the accuracy with which genetic information is expressed. Secondly, because the C-seq assay utilizes RNA as its source material, it is likely that this assay can be adapted to any organism of choice, obviating the need to generate complicated reporter constructs for each transgenic model. This protocol also has advantages over existing massively parallel sequencing approaches17,22. Most notably, most of these approaches make use of heavy metals to fragment RNA libraries. However, these fragmentation methods introduce artifacts into the RNA that are indistinguishable from true transcription errors5. To solve this problem, this protocol fragments RNA enzymatically with RNase III, which has the added advantage that it leaves compatible ends for self-ligation, greatly simplifying RNA circularization and increasing the sensitivity of the assay ~10-fold.
At the same time, the C-seq assay has its own share of limitations. First, even though the assay measures transcription errors throughout the entire genome, a large component of the data tends to be derived from genes that are highly expressed, as transcripts from these genes tend to dominate most RNA libraries. Another factor that skews the analysis towards highly expressed genes is the threshold built into the bio-informatic pipeline that prevents genetic mutations and RNA editing events from confounding the final measurements. This threshold dictates that only bases that were sequenced 20 times or more can be used for the final analysis. A second limitation concerns library diversity. Like most kits used for RNA extraction, the kit used here does not efficiently purify small RNA molecules, which limits the number of reads derived from molecules synthesized by RNAP III. The diversity of the final sequencing library is further limited by the mRNA purification step employed here. Even though molecules generated by RNAP I can be efficiently sequenced, most of the remaining mitochondrial RNAs, tRNAs, and non-coding RNAs are lost during this step. To capture these molecules more frequently, a different kit that specifically purifies small RNA molecules should be used, or cell types should be targeted that are enriched for these molecules. Please note, though, that most of these problems can be solved by sequencing deeper than usual, or simultaneously sequencing the DNA from the same cells, although both of these solutions will require a larger financial investment by the investigators.
In addition, future technological developments are poised to improve the C-seq assay at a fundamental level. For example, by extending the read-length of existing sequencing technology, it will be possible to sequence longer molecules, which means that more repeats can be used to ascertain the fidelity of transcription. Such improvements will automatically result in a greater sequencing depth and an increase in the percentage of reads that can be used for downstream analysis. A similar argument can be made for any sequencing technology that allows for more molecules to be sequenced in parallel. In addition, numerous components of the C-seq assay remain to be optimized, including the efficiency of RNA circularization, the stringency of size selection, and the time-consuming nature of the assay itself. Finally, it is strongly recommended that all users gain access to a dedicated, high-sensitivity tool for nucleic acid analysis and potential troubleshooting (see Table of Materials). Without this tool, it can be extremely difficult to determine at what point potential problems arise, thereby making them impossible to fix.
Although the entire protocol is fairly unforgiving (small mistakes tend to have significant consequences), there are several steps that are absolutely critical to the success of the C-seq assay. One of the most important requirements is that the isolated RNA is treated as gently as possible. Most RNA extraction methods and downstream processing kits care little about the chemical composition of the RNA beyond basic industry standards, which is sufficient for most molecular analyses. However, the C-seq assay is tasked with identifying a single transcription error among hundreds of thousands of WT bases, greatly increasing the need to reduce molecular stress. Even stress that impacts only 1 in 10,000 bases can introduce artifacts that completely invalidate the results. For example, users must never avoid/limit expose the RNA to any temperature over 65 &#176;C. Secondly, each user must carefully optimize the time required for the RNA fragmentation with RNase III. It is absolutely crucial for the success of the assay that the final molecules are approximately 60&#8211;80 bases in length. Shorter molecules may be difficult to map to the genome, and longer molecules will not allow for 3 independent repeats to be sequenced within a 250 bp read. Finally, it is recommended not to freeze and thaw the RNA more than once during the entire protocol. Instead, isolate the RNA and synthesize the cDNA in a single day. Because of the time and effort involved with this commitment, the complexity of the protocol itself, and the number of samples that are frequently processed at once, it is recommended that two investigators work together to generate these libraries.
When successful, these experiments will make it possible to accurately detect transcription errors in any organism, under any experimental condition. For example, by comparing control and diseased individuals to each other, it may be possible to determine whether certain diseases are associated with transcription errors, which may reveal a new component of the etiology of numerous human pathologies. Other parameters that could be probed are organismal aging, nutrition, genotype, and environmental factors such as exposure to toxic chemicals. Moreover, because this assay detects transcription errors throughout the transcriptome, it will allow researchers to dissect the different mechanisms that contribute to the fidelity of RNA polymerase I, II, and III, as well as the mitochondrial RNA polymerase. Accordingly, we expect that this protocol will open up a new field of mutagenesis to widespread experimentation, characterized by the study of mutations in RNA rather than in DNA.

The genome provides a precise biological blueprint of life. To implement this blueprint correctly, it is important for the genome to be transcribed with great precision. However, transcription is unlikely to be error free. For example, RNA polymerases have long been known to be error-prone in vitro1,2, and recently it was shown that they commit errors in vivo as well3,5,6, particularly when confronted with DNA damage7,8,9,10. Taken together, these observations indicate that transcription errors occur continuously in all living cells, suggesting that they could be a potent source of mutated proteins.
This process, termed transcriptional mutagenesis, differs from classical mutagenesis in two ways. First, in contrast to genetic mutations, transcription errors affect both mitotic and post-mitotic cells, as they do not depend on DNA replication. Studying the mechanisms that impact the fidelity of transcription will, therefore, provide valuable insight into the mutation load of both mitotic and post-mitotic cells. Interestingly, transcription errors have recently been implicated in the promotion of protein aggregation11,12,13 and have been hypothesized to contribute to both carcinogenesis10 and the development of antibiotic resistance in bacteria14.
Second, in contrast to genetic mutations, transcription errors are transient in nature. Their temporary existence is particularly challenging because it makes transcription errors exceedingly difficult to detect. For example, while several labs have devised valuable reporter assays for the study of transcriptional mutagenesis, these assays are only able to measure transcription errors in a limited number of contexts and model organisms4,15. To overcome these limitations, many researchers have turned to RNA sequencing technology (RNA-seq), which theoretically allows transcription errors to be recorded throughout the transcriptome of any species. However, these studies are easily confounded by library construction artifacts, such as reverse transcription errors, PCR amplification errors, and the error-prone nature of sequencing itself. For example, reverse transcriptases commit approximately one error every ~20,000 bases, while RNA polymerases (RNAPs) are expected to make only one error every 300,000 bases5,6. Because the error rate of reverse transcription alone dwarfs the error rate of RNA polymerases inside cells, it is virtually impossible to distinguish true transcription errors from artifacts caused by the library preparation in traditional RNA-Seq data (Figure 1a).
To solve this problem, we developed an optimized version of the Circle-Sequencing (Cirseq, or C-seq henceforth) assay5,16. This assay allows the user to detect transcription errors and other rare variants in RNA throughout the transcriptome5. The circular-sequencing assay carries this name because a key step in this assay revolves around RNA circularization. Once the RNA targets are circularized, they are reverse transcribed in a rolling circle fashion, to produce linear cDNA molecules that contain numerous copies of the same RNA template. If an error was present in one of these templates, this error would also be present in every single repeat contained within the cDNA molecule. In contrast, errors introduced by reverse transcription, PCR amplification, or sequencing tend to arise randomly, and will thus be present in only one or two repeats. Thus, by generating a consensus sequence for each cDNA molecule, and distinguishing random errors from errors that occur in all repeats, library construction artifacts can effectively be separated from true transcription errors (Figure 1b).
If used properly, the C-seq assay can be used to accurately detect the rate of base substitutions, insertions, and deletions in RNA throughout the transcriptome of any species (for example, see Traverse and Ochman17). For example, we have used the C-seq assay to provide genome-wide measurements of the error rate of transcription in Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans with a single base resolution5 (unpublished observations). Originally used to accurately sequence RNA virus populations, this optimized version of the C-seq assay has been streamlined to minimize harsh conditions during the library preparation that contribute to library construction artifacts. In addition, by using a number of commercially available kits, the throughput of the assay is greatly improved, as well as its user-friendliness. If used properly, this assay can accurately detect thousands of transcription errors per replicate, thereby greatly improving on previous studies6. Overall, this method provides a powerful tool to study transcriptional mutagenesis and will allow the user to gain novel insights into the mechanisms that control the fidelity of transcription in a wide range of organisms.

<table>
	<tbody>
		<tr>
			<td>RiboPure RNA purification kit</td>
			<td>ThermoFisher</td>
			<td>AM1926</td>
			<td>Total RNA purification</td>
		</tr>
		<tr>
			<td>Genelute mRNA purification kit</td>
			<td>Sigma-Aldrich</td>
			<td>MRN70-1KT</td>
			<td>mRNA purification</td>
		</tr>
		<tr>
			<td>Nuclease-free Water</td>
			<td>Ambion</td>
			<td>AM9937</td>
			<td>Elution and dilution</td>
		</tr>
		<tr>
			<td>Ambion RNase III</td>
			<td>ThermoFisher</td>
			<td>AM2290</td>
			<td>RNA fragmentation</td>
		</tr>
		<tr>
			<td>T4 RNA Ligase 1 (ssRNA Ligase)</td>
			<td>New England Biolabs</td>
			<td>M0204S</td>
			<td>RNA circularization</td>
		</tr>
		<tr>
			<td>Ribolock</td>
			<td>ThermoFisher</td>
			<td>EO0381</td>
			<td>RNase inhibitor</td>
		</tr>
		<tr>
			<td>SuperScript III Reverse Transcriptase</td>
			<td>ThermoFisher</td>
			<td>18080044</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>10 mM dNTP mix</td>
			<td>ThermoFisher</td>
			<td>18427013</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>Random hexamers (50 ng/&#181;l)</td>
			<td>ThermoFisher</td>
			<td>N8080127</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>NEB Second Strand Synthesis Module</td>
			<td>New England Biolabs</td>
			<td>E6111S</td>
			<td>Second Strand Synthesis</td>
		</tr>
		<tr>
			<td>NEBNext Ultra DNA Library Prep Kit for Illumina</td>
			<td>New England Biolabs</td>
			<td>E7370S</td>
			<td>cDNA library preparation</td>
		</tr>
		<tr>
			<td>NEB Next index primers</td>
			<td>NEB</td>
			<td>E7335S</td>
			<td>Multiplex PCR primers</td>
		</tr>
		<tr>
			<td>Oligo Clean &#38; Concentrator</td>
			<td>Zymo Research</td>
			<td>D4061</td>
			<td>Clean up of RNA and DNA samples</td>
		</tr>
		<tr>
			<td>DynaMag-2 Magnet</td>
			<td>ThermoFisher</td>
			<td>12321D</td>
			<td>Magnetic bead purification</td>
		</tr>
		<tr>
			<td>AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td>Magnetic bead purification</td>
		</tr>
		<tr>
			<td>Eppendorf 5424 Microcentrifuge</td>
			<td>FisherScientific</td>
			<td>05-403-93</td>
			<td>centrifugation</td>
		</tr>
		<tr>
			<td>INCU-Shaker 10L</td>
			<td>Benchmark Scientific</td>
			<td>H1010</td>
			<td>Cell culture</td>
		</tr>
		<tr>
			<td>T100 Thermal Cycler</td>
			<td>BIO RAD</td>
			<td>1861096</td>
			<td>Medium to High temperature cycling conditions</td>
		</tr>
		<tr>
			<td>PTC-200 Thermal Cycler</td>
			<td>GMI</td>
			<td>8252-30-0001&#160;</td>
			<td>Low temperature cycling conditions</td>
		</tr>
		<tr>
			<td>RNase Away</td>
			<td>Molecular Bioproducts</td>
			<td>700S-11</td>
			<td>Sterilization</td>
		</tr>
		<tr>
			<td>50 ml Centrifuge Tube</td>
			<td>Corning</td>
			<td>430290</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>15 ml Centrifuge Tube</td>
			<td>Corning</td>
			<td>430052</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>Eppendorf tubes</td>
			<td>USA Scientific</td>
			<td>1615-5500</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>4200 Tapestation System</td>
			<td>Agilent</td>
			<td>G2991AA</td>
			<td>Nucleotide analysis instrument for quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>High Sensitivity RNA Screen Tape</td>
			<td>Agilent</td>
			<td>5067-5579</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>RNA ScreenTape Sample Buffer</td>
			<td>Agilent</td>
			<td>5067-5577</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>RNA ScreenTape Ladder</td>
			<td>Agilent</td>
			<td>5067-5578</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>2100 Bioanalyzer Instrument</td>
			<td>Agilent</td>
			<td>G2939BA</td>
			<td>Double stranded DNA quality control</td>
		</tr>
		<tr>
			<td>High Sensitivity DNA Kit</td>
			<td>Agilent</td>
			<td>5067-4626</td>
			<td>Quality control for double stranded cDNA samples</td>
		</tr>
		<tr>
			<td>Water Bath</td>
			<td>VWR</td>
			<td>462-0244</td>
			<td>Incubation</td>
		</tr>
		<tr>
			<td>NanoDrop 2000/2000C Spectrophotometer</td>
			<td>ThermoFisher</td>
			<td>ND-2000C</td>
			<td>Determination of RNA concentration</td>
		</tr>
	</tbody>
</table>

genome-wide surveillance of transcription errors in eukaryotic organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

Like all massively parallel sequencing approaches, each C-seq experiment produces an unwieldy, large dataset. For first-time users, it can be difficult to handle these datasets; thus, it is recommended that all users contact an experienced bio-informatician prior to the experimentation. On average, the expectation is that users will generate approximately 55&#8211;70 Giga bases (Gbases) per run on most massively parallel sequencing platforms. For this protocol, typically, 12&#8211;30 samples were multiplexed per run so that for each sample approximately 2&#8211;6 Gbases were acquired. After trimming the adaptor sequences and low-quality base calls (&#60; 20) from this dataset, ~70% of the initial data size remained.
These bases are then analyzed further to investigate the efficiency of the RNA fragmentation and circularization by determining the size of the repeats that were generated. Most repeats tend to be 45&#8211;80 bases in length (Figure 4A) and approximately 50% of the bases that were sequenced are part of these repeats (Figure 4B). Since most of these bases are present in reads that contain 3 repeats or more, the number of unique bases that are sequenced is about one-third of the total number of bases sequenced. On average, &#62;75% of these consensus sequences can be mapped back to the reference genome. Finally, approximately 25% of these bases are covered by 20 reads or more, which ultimately means that about 10% of the data can be used for error detection. An example of this analysis is given in Figure 4, which represents the sequencing information that was acquired during the set-up of this protocol for 1 replicate of a single C-seq library that was sequenced relatively shallowly and represents the lower limit of the data that users can expect to acquire.
Approximately 5,000&#8211;25,000 errors per run tend to be identified, although these numbers can vary significantly depending on the error rate itself (the higher the error rate, the more errors will be detected), the size of the transcriptome (the larger the transcriptome, the fewer bases will be covered by 20 reads, limiting the sequencing data that can be used for error detection), and the depth at which it is sequenced (deeper sequencing will make it more likely that a given base will be covered by 20 reads or more). These errors tend to be distributed across the entire genome, so that on average ~47% of the errors are located in mRNA molecules generated by RNA polymerase II (RNAP II), ~49% are located in rRNA molecules generated by RNAP I, and the remaining ~3% are located in RNAs generated by RNAP III and the mitochondrial RNA polymerase. However, these ratios can vary significantly depending on the cell type or organism under investigation (Figure 4C). For example, cell types that rely heavily on mitochondrial function, such as cardiomyocytes, contain significantly more mitochondrial RNA than other cell types, greatly increasing the number of mitochondrial RNA molecules that are sequenced, and thus the number of errors detected.
Once a list of errors has been compiled, and their locations across the genome are known, these errors can be used to identify the parameters that control the error rate of transcription in each organism. For example, the location of these transcription errors can be correlated with numerous features of the genome, such as the presence of DNA repeats, specific genetic contexts, or the expression rate, to understand how these features alter the fidelity of transcription5. The expectation is that in the future, though, users will be able to determine how countless additional features affect transcriptional fidelity, including epigenetic markers, the 3-D organization of the genome, nutrient availability, age, or exposure to toxic compounds, to elucidate the contribution of genetic and environmental factors to the fidelity of transcription.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57731/57731fig1.jpg" /> 
Figure 1: Schematic representation of RNA-seq versus C-seq. (A) Traditional RNA-seq experiments isolate RNA from a sample of interest, fragment the RNA, and reverse transcribe it prior to the final library preparation and sequencing. However, these preparation procedures introduce numerous technical artifacts into the library in the form of reverse transcription errors, PCR amplification errors, and sequencing errors. (B) This optimized C-seq assay allows for the correction of these technical artifacts by circularizing the fragmented RNA molecules prior to reverse transcription, which allows them to be reverse transcribed in a rolling circle fashion to produce linear cDNA molecules that contain several copies of the original RNA template in tandem repeat. These tandem repeats can then be used to distinguish true transcription errors from artifacts, as true transcription errors (star) will be present in all repeats at the same location, whereas artifacts such as reverse transcription errors (square) and PCR amplification errors (circle) are only present in one or two repeats of any given cDNA molecule. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57731/57731fig2.jpg" /> 
Figure 2: Representative results for library preparation. These panels show an electrophoretogram for (A) a high sensitivity RNA screen tape ladder (see Table of Materials), (B) the total RNA purified from Saccharomyces cerevisiae, (C) RNase III-fragmented RNA, and (D) rolling circle reverse-transcribed cDNA. (E) The final size-selected cDNA library runs on a high sensitivity double-stranded DNA analysis chip (see Table of Materials). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57731/57731fig3.jpg" /> 
Figure 3: Schematic of the bio-informatic pipeline used to analyze circle sequencing data. After trimming the sequencing reads, repeats are identified, and a consensus sequence is generated using the most likely base call at any given position. Then, the ligation point of the initial RNA template is identified and the consensus sequence is aligned to the reference genome so that potential transcription errors can be identified. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57731/57731fig4.jpg" /> 
Figure 4: Example of results for C-Seq pipeline. (A) This panel shows the size distribution of the consensus sequences obtained from a typical C-Seq experiment. (B) This panel shows the number of bases, reads, and percentage of reads sequenced in each step of the C-Seq bioinformatics pipeline. (C) This panel shows the number of transcription errors detected and the percentage of errors attributed to RNA polymerase I, RNA polymerase II, RNA polymerase III, and mitochondrial RNA polymerase. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms at JoVE.com

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

genome-wide-surveillance-transcription-errors-eukaryotic

Nanyang Technological University

Research

JoVE Journal

Genetics

9.4K Views.  Children's Hospital of Philadelphia. This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Video: Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

In Vitro Transcription Assays and Their Application in Drug Discovery

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process requires multi-subunit DNA-dependent RNA polymerase (RNAP) and a series of transcription factors that act to modulate the activity of RNAP during gene expression. Sequencing gel electrophoresis of radiolabeled transcripts is used to provide detailed mechanistic information on how transcription proceeds and what parameters can affect it. In this paper we describe the protocol to study how the essential elongation factor NusA regulates transcriptional pausing, as well as a method to identify an antibacterial agent targeting transcription initiation through inhibition of RNAP holoenzyme formation. These methods can be used a as platform for the development of additional approaches to explore the mechanism of action of the transcription factors which still remain unclear, as well as new antibacterial agents targeting transcription which is an underutilized drug target in antibiotic research and development.

In Vitro Transcription Assays and Their Application in Drug Discovery

In this manuscript, we describe a protocol to functionally examine transcription and the inhibitory activity of antibacterial agents targeting bacterial transcription.

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process ...

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic region is replicated at a distinct time during S phase through the simultaneous activation of multiple origins of replication. Time of replication (ToR) correlates with many genomic and epigenetic features and is linked to mutation rates and cancer. Comprehending the full genomic view of the replication program, in health and disease is a major future goal and challenge.
This article describes in detail the &#34;Copy Number Ratio of S/G1 for mapping genomic Time of Replication&#34; method (herein called: CNR-ToR), a simple approach to map the genome wide ToR of mammalian cells. The method is based on the copy number differences between S phase cells and G1 phase cells. The CNR-ToR method is performed in 6 steps: 1. Preparation of cells and staining with propidium iodide (PI); 2. Sorting G1 and S phase cells using fluorescence-activated cell sorting (FACS); 3. DNA purification; 4. Sonication; 5. Library preparation and sequencing; and 6. Bioinformatic analysis. The CNR-ToR method is a fast and easy approach that results in detailed replication maps.

We describe here a relatively fast and simple approach for mapping genome-wide mammalian replication timing, from cell isolation to the basic analysis of the sequencing results. A genomic map of a representative replication program will be provided following the protocol.

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic ...

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on (TRO) Approach

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here is a powerful experimental approach for analyzing the transcription termination defect under physiological conditions. Like the traditional TRO assay, it relies on the presence of a transcriptionally active polymerase beyond the 3&#8242; end of the gene as an indicator of a transcription termination defect1. It overcomes two major problems encountered with the traditional TRO assay. First, it can detect if the polymerase reading through the termination signal is the one that initiated transcription from the promoter-proximal region, or if it is simply representing a pervasively transcribing polymerase that initiated non-specifically from somewhere in the body or the 3&#8242; end of the gene. Secondly, it can distinguish if the transcriptionally active polymerase signal beyond the terminator region is truly the readthrough sense mRNA transcribing polymerase or a terminator-initiated non-coding anti-sense RNA signal. Briefly, the protocol involves permeabilizing the exponentially growing yeast cells, allowing the transcripts that initiated in vivo to elongate in the presence of the BrUTP nucleotide, purifying BrUTP-labelled RNA by the affinity approach, reverse transcribing the purified nascent RNA and amplifying the cDNA using strand-specific primers flanking the promoter and the terminator regions of the gene2.

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on TRO Approach

We describe a basic experimental approach for analysis of termination of transcription by RNA polymerase II in vivo using BrUTP by the strand-specific transcription run-on (TRO) approach in budding yeast. This protocol can be extended to study transcription termination by other RNA polymerases both in yeast and higher eukaryotes.

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here ...

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident processes. Formaldehyde crosslinking followed by chromatin immunoprecipitation and high-throughput sequencing (X-ChIP-seq) has been used to gain many valuable insights into genome biology. However, X-ChIP-seq has notable limitations linked to crosslinking and sonication. Native ChIP avoids these drawbacks by omitting crosslinking, but often results in poor recovery of chromatin-bound proteins. In addition, all ChIP-based methods are subject to antibody quality considerations. Enzymatic methods for mapping protein-DNA interactions, which involve fusion of a protein of interest to a DNA-modifying enzyme, have also been used to map protein-DNA interactions. We recently combined one such method, chromatin endogenous cleavage (ChEC), with high-throughput sequencing as ChEC-seq. ChEC-seq relies on fusion of a chromatin-associated protein of interest to micrococcal nuclease (MNase) to generate targeted DNA cleavage in the presence of calcium in living cells. ChEC-seq is not based on immunoprecipitation and so circumvents potential concerns with crosslinking, sonication, chromatin solubilization, and antibody quality while providing high resolution mapping with minimal background signal. We envision that ChEC-seq will be a powerful counterpart to ChIP, providing an independent means by which to both validate ChIP-seq findings and discover new insights into genomic regulation.

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

We describe chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq), a chromatin immunoprecipitation (ChIP)-orthogonal method for mapping protein binding sites genome-wide with micrococcal nuclease (MNase) fusion proteins.

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident ...

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions of chromatin. These regions represent regulatory DNA elements (e.g., promoters, enhancers, locus control regions, insulators) to which transcription factors bind. Mapping the accessible chromatin landscape is a powerful approach for uncovering active regulatory elements across the genome. This information serves as an unbiased approach for discovering the network of relevant transcription factors and mechanisms of chromatin structure that govern gene expression programs. ATAC-seq is a robust and sensitive alternative to DNase I hypersensitivity analysis coupled with next-generation sequencing (DNase-seq) and formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) for genome-wide analysis of chromatin accessibility and to the sequencing of micrococcal nuclease-sensitive sites (MNase-seq) to determine nucleosome positioning. We present a detailed ATAC-seq protocol optimized for human primary immune cells i.e. CD4+ lymphocytes (T helper 1 (Th1) and Th2 cells). This comprehensive protocol begins with cell harvest, then describes the molecular procedure of chromatin tagmentation, sample preparation for next-generation sequencing, and also includes methods and considerations for the computational analyses used to interpret the results. Moreover, to save time and money, we introduced quality control measures to assess the ATAC-seq library prior to sequencing. Importantly, the principles presented in this protocol allow its adaptation to other human immune and non-immune primary cells and cell lines. These guidelines will also be useful for laboratories which are not proficient with next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin coupled with high-throughput sequencing (ATAC-seq) is a genome-wide method to uncover accessible chromatin. This is a step-by-step ATAC-seq protocol, from molecular to the final computational analysis, optimized for human lymphocytes (Th1/Th2). This protocol can be adopted by researchers without prior experience in next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions ...

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ours, have demonstrated that quiescence is associated with widespread changes in gene expression. Some of these changes occur through changes in the level or activity of proliferation-associated transcription factors, such as E2F and MYC. We have demonstrated that mRNA decay can also contribute to changes in gene expression between proliferating and quiescent cells. In this protocol, we describe the procedure for establishing proliferating and quiescent cultures of human dermal foreskin fibroblasts. We then describe the procedures for inhibiting new transcription in proliferating and quiescent cells with Actinomycin D (ActD). ActD treatment represents a straightforward and reproducible approach to dissociating new transcription from transcript decay. A disadvantage of ActD treatment is that the time course must be limited to a short time frame because ActD affects cell viability. Transcript levels are monitored over time to determine transcript decay rates. This procedure allows for the identification of genes and isoforms that exhibit differential decay in proliferating versus quiescent fibroblasts.

We describe a protocol for generating proliferating and quiescent primary human dermal fibroblasts, monitoring transcript decay rates, and identifying differentially decaying genes.

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ...

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a protocol for performing the chromatin conformation capture technique in situ Hi-C on staged Drosophila melanogaster embryo populations. The result is a sequencing library that allows the mapping of all chromatin interactions that occur in the nucleus in a single experiment. Embryo sorting is done manually using a fluorescent stereo microscope and a transgenic fly line containing a nuclear marker. Using this technique, embryo populations from each nuclear division cycle, and with defined cell cycle status, can be obtained with very high purity. The protocol may also be adapted to sort older embryos beyond gastrulation. Sorted embryos are used as inputs for in situ Hi-C. All experiments, including sequencing library preparation, can be completed in five days. The protocol has low input requirements and works reliably using 20 blastoderm stage embryos as input material. The end result is a sequencing library for next generation sequencing. After sequencing, the data can be processed into genome-wide chromatin interaction maps that can be analyzed using a wide range of available tools to gain information about topologically associating domain (TAD) structure, chromatin loops, and chromatin compartments during Drosophila development.

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

This work describes a protocol for the generation of high resolution in situ Hi-C libraries from tightly staged pre-gastrulation Drosophila melanogaster embryos.

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a ...

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.

DNA regulatory elements, such as enhancers, control gene expression by physically contacting target gene promoters, often through long-range chromosomal interactions spanning large genomic distances. Promoter Capture Hi-C (PCHi-C) identifies significant interactions between promoters and distal regions, enabling the assignment of potential regulatory sequences to their target genes.

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the ...