NOTE: See the Table of Materials for details related to all the materials, software, reagents, instruments, and cells used in this protocol.
1. Gene-specific in vitro DMS-MaP
<ol>
	<li>RNA in vitro transcription
	<ol>
		<li>Obtain the sequence of the RNA of interest as double-stranded (ds)DNA (e.g., as DNA fragments, plasmids, or PCR from pre-existing/genomic DNA). If the DNA sequence contains a polymerase promotor, jump to step 3.</li>
		<li>Perform overlap PCR to attach an RNA polymerase promotor upstream of the desired DNA fragment (forward primer for T7 polymerase: 5&#39; TAATACGACTCACTATAGG + first bases of target sequence 3&#39;).</li>
		<li>In vitro&#160;transcribe the DNA fragment into RNA. Always keep the RNA on ice.</li>
		<li>Digest the DNA using a DNase.</li>
		<li>Isolate the RNA using a column-based approach (step 2.4) or by ethanol precipitation (step 2.5). Elute in an appropriate volume, expecting a yield of ~50 &#181;g.</li>
		<li>Ensure the RNA integrity by running it on an agarose gel; denature the RNA for 2-3 min at 70 &#176;C before running. 
		NOTE: The buffer and agarose can contain RNases that degrade RNA and might contaminate the RNA sample. Precast agarose gels have previously been used in this lab; the results (especially with RNA) have been ambiguous at times. The best results were obtained with agarose or PAGE gels.</li>
		<li>Directly use of store&#160;the RNA at &#8722;80 &#176;C for several months unless degradation is visible after thawing.</li>
	</ol>
	</li>
	<li>In vitro DMS modification (at 105 mM DMS)
	<ol>
		<li>Prepare a sufficient amount of refolding buffer (0.4 M sodium cacodylate, pH 7.2, containing 6 mM MgCl2). 
		NOTE: For each reaction (final volume of 100 &#181;L), add 89 &#181;L of refolding buffer.</li>
		<li>For each reaction, transfer 89 &#181;L of refolding buffer to a designated 1.5 mL tube, and prewarm at 37 &#176;C in a thermoshaker placed underneath a chemical hood. 
		NOTE: DMS is highly toxic and must always be kept underneath a chemical hood until quenched by a reducing agent.</li>
		<li>Elute 1-10 pmol of RNA in 10 &#181;L of nuclease-free water (NF H2O); transfer to a PCR tube.</li>
		<li>Incubate in a thermocycler at 95 &#176;C for 1min to denature the RNA.</li>
		<li>Place on an ice block immediately to avoid misfolding.</li>
		<li>Add the RNA sample to the designated tube with refolding buffer at 37 &#176;C, mix well, and incubate for 10-20 min to refold the RNA. 
		NOTE: Most RNAs will fold in the order of milliseconds to seconds, although exceptions exist16.</li>
		<li>Add 1 &#181;L of 100% (10.5 M) DMS to the RNA sample, and incubate for 5 min while shaking at 800-1,400 rotations per minute (rpm). 
		NOTE: Shaking (or other means of mixing) at this step is crucial as DMS is hydrophobic and may not fully dissolve in the refolding buffer. Deviations in reaction times might affect the reproducibility of the DMS reactivities. To minimize pipetting error, DMS can be dissolved in 100% ethanol prior to adding it to the sample if a final concentration of 1% (105 mM) DMS is maintained. For an untreated control, DMS can be substituted by dimethyl sulfoxide (DMSO) or water.</li>
		<li>After 5 min of reaction time, quench with 60 &#181;L of 100% &#946;-mercaptoethanol (BME), mix well, and immediately place the RNA on ice. 
		NOTE: The RNA can be safely removed from the hood after quenching the reaction with BME to clean it up. However, direct exposure of BME to the surroundings should still be avoided due to its strong smell and irritating properties.</li>
		<li>Clean up the RNA by sodium acetate-ethanol precipitation (see step 2.5) or a column-based approach (see step 2.6), and elute in 10 &#181;L of water.</li>
		<li>Quantify the RNA using a spectrophotometer.</li>
		<li>Directly use of store&#160;the&#160;modified RNA at &#8722;80 &#176;C. 
		NOTE: Long-term storage should be avoided, as RNA is less stable after DMS treatment.</li>
	</ol>
	</li>
	<li>Gene-specific RT-PCR of modified RNA 
	&#8203;NOTE: See Figure 1 for the RT-PCR setup of the DMS-treated fragments.
	<ol>
		<li>Elute 100 ng of modified RNA in 10 &#181;L of nuclease-free (NF) H2O. Transfer to a PCR tube.</li>
		<li>To the tube, add 4 &#181;L of 5x first strand buffer (FSB), 1 &#181;L of dNTP mix (10 mM each), 1 &#181;L of 0.1 M dithiothreitol (DTT) (avoid freeze-thaw cycles), 1 &#181;L of RNase inhibitor, 1 &#181;L of 10 &#181;M reverse primer (single primer or a pool of primers), and 1 &#181;L of TGIRT III. 
		NOTE: For a pool of primers, do not add 1 &#181;L of 10 &#181;M of each primer directly to the RT; instead, mix the primers first, and add 1 &#181;L from the mix (at 10 &#181;M total primer concentration).</li>
		<li>Incubate at 57 &#176;C for 30 min to 1.5 h (typically, 30 min is sufficient to make a 500 nt product) in a thermocycler.</li>
		<li>Add 1 &#181;L of 4 M NaOH, mix by pipetting, and incubate at 95 &#176;C for 3 min to degrade the RNA. 
		NOTE: This step is crucial as it releases TGIRT from the cDNA by degrading the RNA. If skipped, the downstream PCR might be affected.</li>
		<li>Clean up using a column-based approach (see step 2.6) that sufficiently removes the primers, and elute in 10 &#181;L of NF H2O.</li>
		<li>PCR-amplify the cDNA by using 1 &#181;L of the reverse transcription product per 25 &#181;L of the reaction with a PCR kit designed to balance yield and fidelity. 
		NOTE: The primers should have a melting temperature of ~60 &#176;C.</li>
		<li>Run 2 &#181;L of the PCR product on an agarose gel or a precast agarose gel to verify the PCR success.</li>
		<li>Ideally, only one band should show after the PCR. If so, clean up the reaction using a column-based approach. If alternative bands are present, use the remaining PCR reaction to excise the correct band from the gel. Elute in a sufficiently small volume (e.g., 10 &#181;L).</li>
		<li>Quantify the extracted fragments using a spectrophotometer.</li>
		<li>Index the dsDNA fragments for sequencing using an approach suited to the desired sequencing platform.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64820/64820fig01.jpg" /> 
Figure 1: Experimental setup for the RT-PCR of large DMS-treated fragments. When performing reverse transcription on a modified RNA, the modifications on the sequence that the primer anneals to will not be recorded. Thus, when the fragments exceed 400-500 bp in length, fragments overlapping in the primer regions need to be designed, as exemplified here. The length of the fragments depends on the sequencing needs. When using paired-end 150 cycle sequencing, the fragments should not exceed 300 bp. Abbreviations: RT-PCR = reverse transcription polymerase chain reaction; DMS = dimethyl sulfate. <a href="https://www.jove.com/files/ftp_upload/64820/64820fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Whole-genome DMS-MaP using virus-infected cells
NOTE: In cells, DMS treatment can also be combined with the gene-specific amplification approach described above. The whole-genome library requires enormous sequencing depth to achieve full coverage on a single gene. However, if viral RNAs make up a significant fraction of the ribodepleted RNA after extraction, whole-genome sequencing would be appropriate. Furthermore, other enrichment methods can be combined with the whole-genome library generation method. 
NOTE: Uninfected cells were used for demonstration purposes in the video protocol.
<ol>
	<li>DMS treatment
	<ol>
		<li>Grow cells infected with the virus until the desired stage of infection.</li>
		<li>Transfer the cell container into a dedicated fume hood that is appropriate for handling both viruses at the required biosafety level and the chemical fumes generated by agents such as DMS.</li>
		<li>Add a 2.5% volume of DMS to the culture medium, and seal the container (typically a 10 cm plate) with parafilm. 
		NOTE: It is easy to under-modify and over-modify with DMS. When adding DMS directly to cells, it is very important to mix well. Alternatively, prewarm the new medium in a 50 mL conical tube at 37 &#176;C, and add the DMS directly shaking vigorously. Decant the spent medium on the cells, and slowly pipette in the DMS-containing medium.</li>
		<li>Transfer to a 37 &#176;C incubator for 5 min. 
		NOTE: Depending on the amount of time it takes to handle the DMS outside the incubator, it is possible that 5 min will lead to over-modification. Keep the time from adding the DMS to incubating to &#8804;1 min. If performing the experiment for the first time, it is recommended to do a DMS titration and vary the incubation time (between 3 min and 10 min) to find the optimal modification rate and ensure that the results are robust across a window of concentrations.</li>
		<li>Carefully pipette out the DMS-containing medium (into appropriate chemical waste) and gently add 10 mL of stop buffer (PBS with 30% BME [e.g., 3 mL of BME and 7 mL of PBS]). 
		NOTE: The addition of DMS and BME can lift the cells from the plate if the cells are not strongly adherent. If the cells are lifting, they can be treated as suspension cells-instead of removing the DMS-containing medium, add the stop buffer directly, and scrape out the cells with DMS and BME into a 50 mL conical tube. Pellet the cells by centrifuging for 3 min at 3,000 &#215; g; make sure to get rid of any residual DMS, which can pellet under the cells in large droplets. An extra wash step in 30% BME is recommended if the DMS medium cannot be removed initially.</li>
		<li>Scrape the cells, and transfer them to a 15 mL conical tube.</li>
		<li>Pellet by centrifugation at 3,000 &#215; g for 3 min.</li>
		<li>Remove the supernatant and wash 2x with 10 mL of PBS.</li>
		<li>Carefully remove as much residual PBS as possible.</li>
		<li>Dissolve the pellet in an appropriate amount of the RNA isolation reagent (e.g., 3 mL for a T75 culture flask, 1 mL for a 10 cm plate). 
		NOTE: Insufficient amounts of the reagent might impact the RNA yield.</li>
	</ol>
	</li>
	<li>RNA extraction and ribosomal RNA (rRNA) depletion
	<ol>
		<li>To 1 mL of homogenized cells in the RNA isolation reagent, add 200 &#181;L of chloroform, vortex for 15-20 s until bright pink, and then incubate for up to 3 min until phase separation is visible. 
		NOTE: The pink lipid phase should settle at the bottom. If this is not the case, the vortexing time was likely insufficient.</li>
		<li>Spin at maximum speed (~ 20,000 &#215; g) for 15 min at 4 &#176;C.</li>
		<li>Transfer the upper aqueous phase to a new tube.</li>
		<li>Clean up the RNA by sodium acetate-ethanol precipitation (see step 2.5) or a column-based approach (see step 2.6), and elute in a sufficient volume of NF H2O.</li>
		<li>Check the RNA integrity on an agarose gel. Look for two bands corresponding to the two ribosomal subunits.</li>
		<li>Deplete the rRNAs using the preferred approach, and elute in an adequate volume (typically 20-50 &#181;L) of NF H2O. 
		&#8203;NOTE: For downstream applications, ~500 ng of total RNA is suggested in a volume of 8 &#181;L. Non-ribosomal RNAs typically make up only 5%-10% of the total RNA.</li>
		<li>Quantify using a spectrophotometer.</li>
	</ol>
	</li>
	<li>Library generation
	<ol>
		<li>Use gene-specific RT-PCR or other approaches to generate libraries15. If using random hexamers for priming, add an incubation step at a low Tm (37-42 &#176;C) to allow for hexamer annealing. 
		NOTE: Standard library generation kits can also be used by replacing the RT enzyme with TGIRT and changing the RT temperature to 57 &#176;C.</li>
	</ol>
	</li>
	<li>Column-based RNA cleanup using the RNA Clean &#38; Concentrator columns 
	&#8203;NOTE: All steps should be conducted at room temperature.
	<ol>
		<li>Add NF H2O to the sample tube to bring it to a volume of 50 &#181;L.</li>
		<li>Add 100 &#181;L of binding buffer and 150 &#181;L of 100% ethanol to the sample.</li>
		<li>Mix and transfer to a spin column.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>Add 400 &#181;L of RNA prep buffer.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>Add 700 &#181;L of RNA wash buffer.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>Add 400 &#181;L of RNA wash buffer.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>(Optional) Transfer the column to a new collection tube, and spin at 10,000-16,000 &#215; g for 2 min.</li>
		<li>Transfer the column to a clean RNAse-free tube and add an appropriate amount of NF H2O.</li>
		<li>Spin at 10,000-16,000 &#215; g for 1 min.</li>
	</ol>
	</li>
	<li>Acid phenol-chloroform RNA extraction.
	<ol>
		<li>Add an equal volume of acid phenol:chloroform:isoamyl alcohol.</li>
		<li>Vortex thoroughly, and centrifuge at 14,000 &#215; g for 5 min.</li>
		<li>If there is no phase separation, add 20 &#181;L of 2 M NaCl, and repeat the centrifugation.</li>
		<li>Transfer the aqueous phase into a new tube.</li>
		<li>Add 500 &#181;L of isopropanol and 2 &#181;L of co-precipitant.</li>
		<li>Mix and incubate at RT for 3 min; then, incubate at &#8722;80 &#176;C overnight.</li>
		<li>Pellet the RNA by centrifugation at maximum speed (~ 20,000 &#215; g) for 30 min at 4 &#176;C.</li>
		<li>Wash the pellet with 200 &#181;L of ice-cold 70% ethanol.</li>
		<li>Spin at maximum speed (~ 20,000 &#215; g) for 5 min; discard the flowthrough.</li>
		<li>Resuspend the pellet in the appropriate amount of NF H2O.</li>
	</ol>
	</li>
	<li>Column-based cDNA cleanup using the Oligo Clean and Concentrator columns 
	NOTE: All the steps should be conducted at room temperature.
	<ol>
		<li>Add NF H2O to the sample tube to bring it to a volume of 50 &#181;L.</li>
		<li>Add 100 &#181;L of binding buffer and 400 &#181;L of 100% ethanol.</li>
		<li>Mix and transfer to a spin column.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>Add 750 &#181;L of DNA wash buffer.</li>
		<li>Spin at 10,000-16,000 &#215; g for 30 s; discard the flowthrough.</li>
		<li>(Optional) Transfer the column to a new collection tube and spin at 10,000-16,000 &#215; g for 2 min.</li>
		<li>Transfer the column to a clean RNAse-free tube, and add an appropriate amount of NF H2O.</li>
		<li>Spin at 10,000-16,000 &#215; g for 1 min.</li>
	</ol>
	</li>
</ol>
3. Analysis of the sequencing data
NOTE: To create RNA secondary structure models from the DMS-MaP sequencing data, the resulting .fastq files must be processed by several different steps. These steps can be automatically performed using the
<ol>
	<li>Trim the adaptor sequences with TrimGalore or Cutadapt.</li>
	<li>Map the reads to the reference sequences (.fasta format) using Bowtie2.</li>
	<li>Count the reads with specialized RNA structure software (e.g., DREEM14, RNA-Framework17, or similar), and create reactivity profiles.</li>
	<li>(Optional) Cluster the reads to find alternative RNA conformations using DREEM14, DRACO17, DANCE-MaP18, or similar.</li>
	<li>Predict the minimum free energy structure based on the reactivity profiles using RNAStructure12, ViennaRNA, or similar.</li>
	<li>Visualize the RNA11 structure using VARNA (https://varna.lri.fr/) or similar. 
	NOTE: For practicality, software such as DREEM (www.rnadreem.org) and RNA-Framework19 vastly incorporate steps 1&#8211;-5 in their pipelines, which streamlines the analysis process. However, any structure prediction should be handled with care (e.g., by verifying the structure&#39;s agreement with the data20.</li>
</ol>

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</ol>

The authors have no conflicts of interest to declare.

The protocol here describes how to probe RNA in vitro and in cells using DMS mutational profiling experiments. Furthermore, it gives instructions on how to prepare libraries for Illumina sequencing to generate gene-specific data and analyze the obtained .fastq files. Additionally, genome-wide library approaches can be used. However, gene-specific RT-PCR produces the highest quality and most robust data. Therefore, if comparing between samples, it is important to ensure that they are prepared with identical sequencing strategies, as the library generation causes some bias. The reproducibility should always be measured by using replicates.
Several precautions 
RNA is an unstable molecule that is sensitive to degradation both through elevated temperatures and by RNases. Therefore, special measures &#8212; the use of personal protective equipment (PPE), RNAse-free material, and RNAse inhibitors &#8212;&#160;is recommended. Most importantly, RNA should be kept on ice whenever possible. This especially applies to methylated RNA, which is even more sensitive to high temperatures.
It is important to confirm that the RNA structure of interest is not sensitive to the DMS concentration and buffer conditions. Buffers such as 100 mM Tris, 100 mM MOPS, and 100 mM HEPES at pH 7-7.5 give a high signal but may not be sufficient to maintain the pH during the reaction21. As DMS hydrolyzes in water, which decreases the pH, a strong buffer is critical to maintain a neutral pH during the modification reaction. The addition of bicine has been shown to help maintain the pH as slightly basic21 but results in low DMS modification on Gs and Us, which could be informative but should be analyzed separately due to the production of a much lower signal than As and Cs and is not discussed further in this protocol.
In gene-specific RT-PCR, the modified RNA is reverse-transcribed into the DNA and amplified in fragments by PCR. While the size of the RNA can theoretically be unlimited, these PCR fragments should not exceed a length of 400-500 base pairs (bp) to prevent bias during the reverse transcription reaction. Ideally, the fragments should be within the scope of the sequencing run (i.e., if sequencing is conducted using a 150 x 150 cycle paired-end sequencing program, a single fragment should not exceed 300 bp). When using sequencing programs with fewer cycles, the PCR products can be fragmented using a dsDNase. Furthermore, as sequences within the primer sequences do not hold any structural information, the fragments must overlap when the probed RNA comprises &#62;1 fragment. RT reactions can contain multiple RT primers for different fragments (up to 10 different RT primers). Depending on the sequences, pooling the RT primers can make the reverse transcription less efficient but typically works well. Each PCR reaction should be conducted separately.
When probing RNA with DMS, the experimental conditions play an additional role, as many RNAs are thermodynamically unstable and change their conformation based on environmental factors such as temperature. To avoid irregularities, the experimental conditions should be kept as constant as possible, also with respect to reaction times. The buffer conditions seem to be exchangeable to a certain degree17,20,22,23&#160;when the basic conditions are maintained &#8212;&#160;the buffering capacity and presence of monovalent (Na) and divalent ions (Mg) &#8212;&#160;to ensure proper folding of RNA24.
With respect to the library preparation of modified RNAs, several aspects must be taken into consideration. First, as mentioned before, modified RNAs are less stable than their unmodified counterparts, meaning they might require the optimization of the fragmentation times for optimal fragment size distribution. Furthermore, certain RNA library preparation kits, as well as many other RNAseq approaches, use random primers in the reverse transcription kit. This might lead to lower coverage of the reference, especially in the 3&#39; of a gene, and, ultimately, to insufficient coverage depth. If the coverage of a certain region is too low, it might be necessary to remove those bases from the structure prediction. Apart from RT-PCR and whole-genome RNAseq kits, other library preparation approaches can be used. Protocols that include the ligation of 3&#39; and/or 5&#39; adaptors to the RNA are advantageous when using small fragments of RNA or when the loss of probing information in the primer regions must be avoided.
Lastly, the analysis of the chemical probing experiments must always be interpreted carefully. Currently, there is no software that predicts the RNA structure of any RNA from the sequence alone with high accuracy. Although chemical probing constraints greatly improve the accuracy, generating good models for long RNAs (&#62;500 nt) is still challenging. These models should be further tested by other approaches and/or mutagenesis. RNA prediction software optimizes for the maximum number of base pairs, thus significantly penalizing open conformations, which may not accurately represent RNA folding5. Thus, the obtained structure model should be tested by quantifying the prediction agreement with the underlying chemical probing data (e.g., by AUROC) and between replicates (e.g., by mFMI), as exemplified by Lan et al.20.
Ideally, several experiments in different systems to challenge the obtained structure model should be used to strengthen one&#39;s hypothesis. These can include the usage of in vitro and in-cell approaches, compensatory mutations, and different cell lines and species. Moreover, raw reactivities are often just as or even more informative than structure predictions, as they record the &#34;ground truth&#34; snapshot of the RNA folding ensemble. As such, raw reactivities are very suitable and informative for comparing structure changes between different conditions. Importantly, the lowest free energy structures calculated using chemical probing constraints with computational prediction should only be used as a starting hypothesis toward a complete structure model.

Since the discovery that RNA has both structural1,2 and catalytic3 properties, the importance of RNA and its regulatory function in a plethora of biological processes have been gradually uncovered. Indeed, the effect of RNA structure on gene regulation has gained increasing attention4. Like proteins, RNA has primary, secondary, and tertiary structures, referring to the sequence of nucleotides, the 2D mapping of base-pairing interactions, and the 3D folding of these base-paired structures, respectively. While determining the tertiary structure is key to understanding the exact mechanisms behind RNA-dependent processes, the secondary structure is also highly informative regarding RNA function and is the basis for further 3D folding5.
However, determining the RNA structure has been intrinsically challenging with conventional approaches. While for proteins, crystallography, nuclear magnetic resonance (NMR), and cryogenic electron microscopy (cryo-EM) have made it possible to determine the diversity of structural motifs, allowing for structure prediction from the sequence alone6, these approaches are not widely applicable to RNAs. Indeed, RNAs are flexible molecules with building blocks (nucleotides) that have much more conformational and rotational freedom in comparison to their amino acid counterparts. Furthermore, the interactions through base-pairing are more dynamic and versatile than those of amino acid residues. As a result, classical approaches have been successful only for relatively small RNAs with well-defined, highly compact structures7.
Another approach to determine the RNA structure is through chemical probing combined with next-generation sequencing (NGS). This strategy generates information about the binding status of each base in an RNA sequence (i.e., its secondary structure). In brief, the bases in an RNA molecule that are not engaging in base-pairing are differentially modified by small chemical compounds. Reverse-transcribing these RNAs with specialized reverse transcriptases (RTs) incorporates the modifications into complementary deoxyribonucleic acid (cDNA) as mutations. These cDNA molecules are then amplified by the polymerase chain reaction (PCR) and sequenced. To obtain information about their &#34;status&#34; as bound or unbound, the mutation frequencies at each base in an RNA of interest are calculated and entered into structure prediction software as constraints8. Based on nearest neighbor rules9 and minimum free energy calculations10, this software generates structure models that best fit the obtained experimental data11,12.
DMS-MaPseq uses DMS, which methylates the N1 nitrogen in adenosines and &#160;N3 nitrogen in&#160;cytosines at their Watson-Crick face in a highly specific manner13. Using thermostable group II intron reverse transcriptase (TGIRT-III) in reverse transcription creates mutational profiles with unprecedented signal-to-noise ratios, even allowing for the deconvolution of overlapping profiles generated by two or more alternative conformations14,15. Furthermore, DMS can penetrate cell membranes and whole tissues, making probing within physiological contexts possible. However, the generation of good-quality data is challenging, as variations in the handling procedure can impact the results. Therefore, we provide a detailed protocol for both in vitro and in-cell DMS-MaPseq to reduce bias and guide newcomers to the method through the difficulties they may encounter. Especially in light of the recent SARS-CoV2 pandemic, high-quality data on RNA viruses is an important tool for studying gene expression and finding possible therapeutics.

<table><tbody><tr><td>1 Kb Plus DNA Ladder</td><td>10787018</td><td>Thermo</td><td></td></tr><tr><td>2-mercaptoethanol</td><td>M6250-250ML</td><td>Sigma</td><td></td></tr><tr><td>Acid-Phenol:Chloroform, pH 4.5</td><td>AM9720</td><td>Thermo</td><td></td></tr><tr><td>Advantage PCR</td><td>639206</td><td>Takara</td><td></td></tr><tr><td>CloneAmp HiFi PCR Premix</td><td>639298</td><td>Takara</td><td></td></tr><tr><td>DMS</td><td>D186309&lt;br/&gt; &amp;nbsp;</td><td>Sigma</td><td></td></tr><tr><td>dNTPs 10 mM each</td><td>U151B</td><td>Promega</td><td></td></tr><tr><td>E-Gel EX Agarose Gels, 2%</td><td>G402022</td><td>Thermo</td><td>precast agarose gels</td></tr><tr><td>Ethanol (200 proof)</td><td>E7023-4X4L</td><td>Sigma</td><td></td></tr><tr><td>Falcon tubes, 15 mL, 50 mL</td><td></td><td></td><td></td></tr><tr><td>GlycoBlue</td><td></td><td></td><td>co-precipitant</td></tr><tr><td>HCT-8 cells&amp;nbsp;</td><td>ATCC #CCL-244</td><td></td><td></td></tr><tr><td>Invitrogen&amp;nbsp;MgCl&lt;sub&gt;2&lt;/sub&gt;&amp;nbsp;(1 M)</td><td>AM9530G</td><td>fisherscientific</td><td></td></tr><tr><td>Isopropanol</td><td>278475</td><td>Sigma</td><td></td></tr><tr><td>Megascript T7 transcription</td><td>AM1334</td><td>Thermo</td><td></td></tr><tr><td>NanoDrop spectrophotometer</td><td></td><td></td><td></td></tr><tr><td>Novex TBE Gels, 8%, 10 well</td><td>EC6215BOX</td><td>Thermo</td><td></td></tr><tr><td>OC43&amp;nbsp;</td><td>ATCC #VR-1558</td><td></td><td></td></tr><tr><td>RiboRuler Low Range RNA Ladder</td><td>SM1831</td><td>Thermo</td><td></td></tr><tr><td>RNAse H</td><td>M0297L</td><td>NEB</td><td></td></tr><tr><td>Sodium Cacodylate, 0.4 M, pH 7.2</td><td>102090-964</td><td>VWR</td><td></td></tr><tr><td>Sodium hydroxide solution</td><td>S8263-150ML</td><td>Sigma</td><td></td></tr><tr><td>SuperScript II Reverse Transcriptase for FSB and DTT</td><td>18064014</td><td>Thermo</td><td></td></tr><tr><td>TGIRT-III Enzyme</td><td>TGIRT50</td><td>Ingex</td><td></td></tr><tr><td>The Oligo Clean &amp;amp; Concentrator</td><td>D4060</td><td>Genesee</td><td></td></tr><tr><td>The RNA Clean &amp;amp; Concentrator kits are RNA clean up kits</td><td>R1016</td><td>Genesee</td><td></td></tr><tr><td>TRIzol Reagents</td><td>15596018</td><td>Thermo</td><td>RNA isolation reagent</td></tr><tr><td>Water, (For RNA Work) (DEPC-Treated, DNASE, RNASE free/Mol. Biol.)</td><td>BP561-1</td><td>fisherscientific</td><td></td></tr><tr><td>xGen Broad-range RNA Library Prep 16rxn</td><td>10009865</td><td>IDT</td><td></td></tr><tr><td>Zymo RNA clean and concentrator columns</td><td></td><td></td><td></td></tr></tbody></table>

probing rna structure with dimethyl sulfate mutational profiling with sequencing in vitro and in cells

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical approaches to solving RNA structure, such as RNA crystallography or cryo-EM, have failed to keep up with the rapidly evolving field and the need for high-throughput solutions. Mutational profiling with sequencing using dimethyl sulfate (DMS) MaPseq is a sequencing-based approach to infer the RNA structure from a base&#39;s reactivity with DMS. DMS methylates the N1 nitrogen in adenosines and the N3 in cytosines at their Watson-Crick face when the base is unpaired. Reverse-transcribing the modified RNA with the thermostable group II intron reverse transcriptase (TGIRT-III) leads to the methylated bases being incorporated as mutations into the cDNA. When sequencing the resulting cDNA and mapping it back to a reference transcript, the relative mutation rates for each base are indicative of the base&#39;s &#34;status&#34; as paired or unpaired. Even though DMS reactivities have a high signal-to-noise ratio both in vitro and in cells, this method is sensitive to bias in the handling procedures. To reduce this bias, this paper provides a protocol for RNA treatment with DMS in cells and with in vitro transcribed RNA.

Gene-specific in vitro DMS-MaP 
To study the 5&#39;UTR of SARS2, the virus&#39; first 300 bp were ordered as a gBlock sequence, alongside three primers. Those included two primers to propagate the fragment (&#34;FW&#34; &#38; &#34;RV&#34;) via PCR, as well as one to attach the T7 promotor (&#34;FW-T7&#34;). These sequences can be seen in Table 1.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td>Sequence (5&#8217;-&#62;3&#8217;)</td>
		</tr>
		<tr>
			<td>FW</td>
			<td>ATTAAAGGTTTATACCTTCCCAGGTAAC</td>
		</tr>
		<tr>
			<td>RV</td>
			<td>GCAAACTGAGTTGGACGTGT</td>
		</tr>
		<tr>
			<td>FW-T7</td>
			<td>TAATACGACTCACTATAGG ATTAAAGGTTTATACCTTCCCAGGTAAC</td>
		</tr>
	</tbody>
</table>
Table 1: Primer sequence for DMS-MaP RT-PCR of SARS-CoV2 5&#39;UTR. Here, FW-T7 and RV are needed to generate a DNA template for in vitro transcription, the RV is used in the reverse transcription and the FW-RV primer-pair is used in the subsequent PCR amplification of the cDNA. The primers anneal to the very beginning of the SARS-CoV2 genome (FW) and the sequence right downstream of the region of interest. Abbreviations: DMS-MaP = Mutational profiling with sequencing using dimethyl sulfate; RT-PCR = reverse transcription polymerase chain reaction; SARS-CoV2 = severe acute respiratory syndrome-coronavirus 2; UTR = untranslated region; RV = reverse primer; FW = forward primer.
To generate RNA from the gBlock fragment, the sequence of the T7 polymerase promotor was attached using overlap PCR using the PCR premix according to the scheme seen in Figure 2A. From the elongated fragment, RNA was generated using the T7 Transcription Kit. The DNA template was subsequently digested using the DNase and RNA isolated using RNA Clean &#38; Concentrator columns.
Quality control of the in vitro transcription was done by running the RNA product on a 1% agarose gel alongside an ssRNA ladder. As there was only one band visible, in vitro DMS probing and RT-PCR were performed (see Figure 2B).
To verify the success of the PCR reaction, the sample was run on a 2% agarose gel using a dsDNA ladder. After indexing, the band should run ~150 bp higher on the same gel, accounting for the size of the indexing primers.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64820/64820fig2v2.jpg" /> 
Figure 2: In vitro transcription of the DNA template. (A)To in vitro transcribe a DNA template that does not yet have an intrinsic RNA polymerase promotor, the template must be attached by overlap PCR first. This is done by using a forward primer, which includes the sequence TAATACGACTCACTATAGG (in the case of the T7 RNA polymerase) upstream of the first bases overlapping with the desired fragment. The underlined base here symbolizes the transcription start site of the polymerase. Once the promoter has attached to the dsDNA fragment, it can be transcribed by the T7 polymerase. Importantly, the polymerase uses the strand opposed to the mentioned promoter sequence as the template (blue), effectively creating RNA identical to the sequence immediately downstream of the indicated promoter sequence (red). (B) A 1% agarose gel with an ssRNA Ladder (lane 1) and the in vitro transcribed RNA product at 300 nt (lane 2). (C) A 2% agarose gel with GeneRuler 1 kb plus Ladder (lane 1), the PCR product after RT-PCR running at 300 bp (lane 2), and the indexed fragment after library preparation running at 470 bp (lane 3). Abbreviations: RT-PCR = reverse transcription polymerase chain reaction; DMS = dimethyl sulfate; nt = nucleotides; dsDNA = double-stranded DNA; ssRNA = single-stranded RNA. <a href="https://www.jove.com/files/ftp_upload/64820/64820fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Whole-genome in vivo DMS-MaP using virus-infected cells 
Prior to the DMS treatment, the HCT-8 cells were infected with OC43. When a cytopathic effect (CPE) was observed 4 days post infection (dpi) (as seen in Figure 3A), these cells were treated, and the RNA was extracted and ribodepleted. When running the total RNA on an agarose gel, two bright bands were visible, accounting for the 40S and 60S subunits of the ribosome, which make up approximately 95% of total RNA mass (see Figure 3B). When RNA extraction was unsuccessful or was degraded (e.g., by multiple freeze-thaw cycles), the RNA degradation products were visible on the bottom of the gel (see Figure 3C, second lane). Furthermore, after rRNA depletion, the two bright bands disappeared, leaving a smear in the lane (see Figure 3C, third lane). Finally, after library preparation, the samples had varying size distributions and were shown as a smear on the final PAGE gel. The band was excised between 200 nucleotides (nt) and 500 nt, in agreement with the 150 x 150 paired-end sequencing run planned to analyze these libraries. Most importantly, the adaptor dimers running at ~150 nt were separated out (see Figure 3D).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64820/64820fig3v2.jpg" /> 
Figure 3: Checkpoints of in vivo DMS-MaP with virus-infected cells. (A) Light microscopy image of virus-infected HCT-8 cells, 4 days dpi. To obtain the highest possible yield of viral RNA from the total RNA while minimizing the adverse effects due to cell death, DMS should be added when CPE starts or even before that, as seen in the image. (B) A 1% agarose gel with six samples of 1 &#181;g of total RNA. In each lane, two bright bands, accounting for the 40S and 60S subunits, are visible, as ribosomal RNA makes up ~95% of total RNA. Note: In-cell DMS treatment causes some RNA fragmentation and smearing, but the two rRNA bands should still be visible. Mild fragmentation post modification is tolerated because the information containing the methylation mark is generated and reports on the RNA structure during the DMS incubation while the cells are still alive. (C) A 1% Agarose gel of GeneRuler 1 kb plus ladder DNA marker (lane 1) total RNA previously stored at &#8722;80 &#176;C for 6 months (lane 2) and ribodepleted RNA (lane 3). When storing RNA for a long time with several freeze-thaw cycles, the RNA starts degrading and possibly should not be used for probing experiments. Furthermore, after ribodepleting the total RNA, the two bright bands, accounting for the 40S and 60S subunits of the ribosome, fade, and a smear of the residual RNAs starts to show. (D) A PAGE gel of GeneRuler 1 kb plus ladder DNA marker (lane 1) and a library sample of whole-genome prepared RNA. The gel should be excised based on the sequencing needs. For a paired-end sequencing run spanning 150 cycles from both sides, the gel should be excised between 300 bp and 500 bp. Adaptor dimers (running at 170 bp) should be separated out. Abbreviations: DMS-MaP = Mutational profiling with sequencing using dimethyl sulfate; dpi = days post infection; CPE = cytopathic effect. <a href="https://www.jove.com/files/ftp_upload/64820/64820fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
After sequencing, the .fastq files were analyzed by submitting a job to the DREEM webserver (http://rnadreem.org/), together with a .fasta reference file. The output generated by the server includes quality control files generated by fastqc (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and TrimGalore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), as well as other output files containing the population average mutation frequencies. Apart from the diagram showing the mutation frequencies with an interactive .html (see&#160;Figure 4A) format and a .csv file with the raw reactivites per base and a struct_constraint.txt file, readable by several RNA structure prediction software, this also includes a bitvector.txt file reporting on the by-read mutations. From these, the population average structures were calculated by submitting the .fasta and struct_constraint.txt files to the RNAfold webserver (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi). This uses the ViennaRNA software to generate structure predictions based on the minimum free energy, which can be viewed online or downloaded in ct or Vienna format. To generate RNA structure models, these downloadable files were submitted to VARNA (https://varna.lri.fr/, see&#160;Figure 4B). Lastly, bitvector.txt files can be used by the stable version of DREEM (https://codeocean.com/capsule/6175523/tree/v1) to search for alternative RNA conformations. To obtain good structure models using DREEM, a coverage of 10,000 reads per base should be achieved; for clustering, up to 100,000 reads per base might be required. An overview of the whole workflow can be found in&#160;Figure 4C.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64820/64820fig4v2.jpg" /> 
Figure 4: Exemplary data obtained from chemical probing experiments of the SARS-CoV2 5&#39;UTR. (A) Reactivity profile of the first 300 bases of the SARS-CoV2 genome colored by base (A: red, C: blue, U: green, G: yellow). The raw reactivities are calculated as the absolute mutation frequency divided by the coverage. Bases with open conformation have high reactivity values; bases engaging in base-pairing have low reactivity values. U and G are not modified by DMS and have low reactivity values, originating from polymerase infidelity. The predictions were made with the DREEM webserver. (B) Structure model of the SARS-CoV2 5&#39;UTR predicted from reactivity values made with VARNA. Bases with high reactivity values are colored in red; bases with low reactivity values are colored in white. (C) Workflow of the DMS-MaP analysis starting with the .fastq files obtained from sequencing. These can be quality-controlled using fastqc; the adaptor sequences are trimmed using TrimGalore and then mapped back to a reference sequence using Bowtie2. From the obtained .bam files, DREEM counts the mutations in each read, creating a mutation map or .bitvector.txt file. These report the mutations of each read in a position-dependent way, based on which the population average reactivity profiles can be created. Alternatively, bitvectors can be clustered using DREEM to search for alternative RNA conformations. Lastly, the obtained structure models are visualized using software (e.g., VARNA). Abbreviations: DMS-MaP = Mutational profiling with sequencing using dimethyl sulfate; SARS-CoV2 = severe acute respiratory syndrome-coronavirus 2. <a href="https://www.jove.com/files/ftp_upload/64820/64820fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells at JoVE.com

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The protocol provides instruction for modifying RNA with dimethyl sulfate for mutational profiling experiments. It includes in vitro and in vivo probing with two alternative library preparation methods.

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical ...

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4.1K Views.  Harvard Medical School. The protocol provides instruction for modifying RNA with dimethyl sulfate for mutational profiling experiments. It includes in vitro and in vivo probing with two alternative library preparation methods.

Video: Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA interactions such as microRNA-mRNA interactions have been shown to regulate gene expression, the full extent to which RNA interactions occur in the cell is still unknown. Previous methods to study RNA interactions have primarily focused on subsets of RNAs that are interacting with a particular protein or RNA species. Here, we detail a method named Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) that allows genome-wide capture of RNA interactions in vivo in an unbiased manner. SPLASH utilizes in vivo crosslinking, proximity ligation, and high throughput sequencing to identify intramolecular and intermolecular RNA base-pairing partners globally. SPLASH can be applied to different organisms including bacteria, yeast and human cells, as well as diverse cellular conditions to facilitate the understanding of the dynamics of RNA organization under diverse cellular contexts. The entire experimental SPLASH protocol takes about 5 days to complete and the computational workflow takes about 7 days to complete.

Here, we detail the method of Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), which enables genome-wide mapping of intramolecular and intermolecular RNA-RNA interactions in vivo. SPLASH can be applied to study RNA interactomes of organisms including yeast, bacteria and humans.

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA ...

Genetics

A Novel Saturation Mutagenesis Approach: Single Step Characterization of Regulatory Protein Binding Sites in RNA Using Phosphorothioates

Gene regulation plays an important role in development. Numerous DNA- and RNA-binding proteins bind their target sequences with high specificity to control gene expression. These regulatory proteins control gene expression either at the level of DNA (transcription) or at the level of RNA (pre-mRNA splicing, polyadenylation, mRNA transport, decay, and translation). Identification of regulatory sequences helps understand not only how a gene is switched on or off, but also which downstream genes are regulated by a particular regulatory protein. Here, we describe a one-step approach that allows saturation mutagenesis of a protein binding site in RNA. It involves doping DNA template with non-wild-type nucleotides within the binding site, synthesis of separate RNAs with each phosphorothioate nucleotide, and isolation of the bound fraction following incubation with protein. Interference from non-wild-type nucleotides results in their preferential exclusion from the protein-bound fraction. This is monitored by gel electrophoresis following selective chemical cleavage with iodine of phosphodiester bonds containing phosphorothioates (phosphorothioate mutagenesis or PTM). This single-step saturation mutagenesis approach is applicable to the characterization of any protein binding site in RNA.

Proteins that bind specific RNA sequences play critical roles in gene expression. Detailed characterization of these binding sites is crucial for our understanding of gene regulation. Here, a single-step approach for saturation mutagenesis of protein-binding sites in RNA is described. This approach is relevant for all protein-binding sites in RNA.

Gene regulation plays an important role in development. Numerous DNA- and RNA-binding proteins bind their target sequences with high specificity to ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

An Assay for Quantifying Protein-RNA Binding in Bacteria

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding of an RNA binding protein (RBP) to the initiation region of the mRNA, which interferes with ribosome binding. In the presented method, we utilize this blocking phenomenon to quantify the binding affinity of RBPs to their cognate and non-cognate binding sites. To do this, we insert a test binding site in the initiation region of a reporter mRNA and induce the expression of the test RBP. In the case of RBP-RNA binding, we observed a sigmoidal repression of the reporter expression as a function of RBP concentration. In the case of no-affinity or very low affinity between binding site and RBP, no significant repression was observed. The method is carried out in live bacterial cells, and does not require expensive or sophisticated machinery. It is useful for quantifying and comparing between the binding affinities of different RBPs that are functional in bacteria to a set of designed binding sites. This method may be inappropriate for binding sites with high structural complexity. This is due to the possibility of repression of ribosomal initiation by complex mRNA structure in the absence of RBP, which would result in lower basal reporter gene expression, and thus less-observable reporter repression upon RBP binding.

In this method, we quantify the binding affinity of RNA binding proteins (RBPs) to cognate and non-cognate binding sites using a simple, live, reporter assay in bacterial cells. The assay is based on repression of a reporter gene.

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding ...

Site-Directed Mutagenesis for In Vitro and In Vivo Experiments Exemplified with RNA Interactions in Escherichia Coli

Site-directed mutagenesis is a technique used to introduce specific mutations in DNA to investigate the interaction between small non-coding ribonucleic acid (sRNA) molecules and target messenger RNAs (mRNAs). In addition, site-directed mutagenesis is used to map specific protein binding sites to RNA. A 2-step and 3-step PCR based introduction of mutations is described. The approach is relevant to all protein-RNA and RNA-RNA interaction studies. In short, the technique relies on designing primers with the desired mutation(s), and through 2 or 3 steps of PCR synthesizing a PCR product with the mutation. The PCR product is then used for cloning. Here, we describe how to perform site-directed mutagenesis with both the 2- and 3-step approach to introduce mutations to the sRNA, McaS, and the mRNA, csgD, to investigate RNA-RNA and RNA-protein interactions. We apply this technique to investigate RNA interactions; however, the technique is applicable to all mutagenesis studies (e.g., DNA-protein interactions, amino-acid substitution/deletion/addition). It is possible to introduce any kind of mutation except for non-natural bases but the technique is only applicable if a PCR product can be used for downstream application (e.g., cloning and template for further PCR).

Site-Directed Mutagenesis for In Vitro and In Vivo Experiments Exemplified with RNA Interactions in Escherichia Coli

Site-directed mutagenesis is a technique used to introduce specific mutations in deoxyribonucleic acid (DNA). This protocol describes how to do site-directed mutagenesis with a 2-step and 3-step polymerase chain reaction (PCR) based approach, which is applicable to any DNA fragment of interest.

Site-directed mutagenesis is a technique used to introduce specific mutations in DNA to investigate the interaction between small non-coding ribonucleic ...

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Secondary base modifications on RNA, such as m5C, affect the structure and function of the modified RNA molecules. Methylated RNA Immunoprecipitation and sequencing (MeRIP-seq) is a method that aims to enrich for methylated RNA and ultimately identify modified transcripts. Briefly, sonicated RNA is incubated with an antibody for 5-methylated cytosines and precipitated with the assistance of protein G beads. The enriched fragments are then sequenced and the potential methylation sites are mapped based on the distribution of the reads and peak detection. MeRIP can be applied to any organism, as it does not require any prior sequence or modifying enzyme knowledge. In addition, besides fragmentation, RNA is not subjected to any other chemical or temperature treatment. However, MeRIP-seq does not provide single-nucleotide prediction of the methylation site as other methods do, although the methylated area can be narrowed down to a few nucleotides. The use of different modification-specific antibodies allows MeRIP to be adjusted for the different base modifications present on RNA, expanding the possible applications of this method.

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

The methylated RNA immunoprecipitation assay is an antibody-based method used to enrich for methylated RNA fragments. Coupled with deep sequencing, it leads to the identification of transcripts carrying the m5C modification.

Secondary base modifications on RNA, such as m5C, affect the structure and function of the modified RNA molecules. Methylated RNA Immunoprecipitation and ...

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Mass spectrometry (MS)-based sequencing approaches have been shown to be useful in&#160;direct&#160;sequencing&#160;RNA without the need for a complementary DNA (cDNA) intermediate. However, such approaches are rarely applied as a de novo RNA sequencing method, but used mainly as a tool that can assist in quality assurance for confirming known sequences of purified single-stranded RNA samples. Recently, we developed&#160;a direct RNA sequencing method by integrating a 2-dimensional mass-retention time hydrophobic end-labeling strategy into MS-based sequencing (2D-HELS MS Seq). This method is capable of accurately sequencing single RNA sequences as well as mixtures containing up to 12 distinct RNA sequences. In addition to the four canonical ribonucleotides (A, C, G, and U), the method has the capacity to sequence RNA oligonucleotides containing modified nucleotides. This is possible because the modified nucleobase either has an intrinsically unique mass that can help in its identification and its location in the RNA sequence, or can be converted into a product with a unique mass. In this study, we have used RNA, incorporating two representative modified nucleotides&#160;(pseudouridine (&#936;) and 5-methylcytosine (m5C)), to illustrate the application of the method for the de novo sequencing of a single RNA oligonucleotide&#160;as well as a mixture of RNA oligonucleotides, each with a different sequence and/or modified nucleotides. The procedures and protocols described here to sequence these model RNAs will be applicable to other short RNA samples (&#60;35 nt) when using a standard high-resolution LC-MS system, and can also be used for&#160;sequence verification of modified therapeutic RNA oligonucleotides.&#160;In the future, with the development of more robust algorithms and with better instruments, this method could allow sequencing of more complex biological samples.&#160;

Here, we describe a detailed protocol for an LC-MS-based sequencing method that can be used as a direct method to sequence short RNA (&#60;35 nt per run) without a cDNA intermediate, and as a general method to sequence different nucleotide modifications in a single study at single-base precision.

Mass spectrometry (MS)-based sequencing approaches have been shown to be useful in&#160;direct&#160;sequencing&#160;RNA without the need for a ...

Mapping R-Loops and RNA:DNA Hybrids with S9.6-Based Immunoprecipitation Methods

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological favorability. In recent years, R-loops have been implicated in a variety of adaptive and maladaptive roles and have been linked to genomic instability in the context of human disorders. As a consequence, the accurate mapping of these structures in genomes is of high interest to many investigators. DRIP-seq (DNA:RNA Immunoprecipitation followed by high throughput sequencing) is described here. It is a robust and reproducible technique that permits accurate and semi-quantitative mapping of R-loops. A recent iteration of the method is also described in which fragmentation is accomplished using sonication (sDRIP-seq), which allows strand-specific and high-resolution mapping of R-loops. sDRIP-seq thus addresses some of the common limitations of the DRIP-seq method in terms of resolution and strandedness, making it a method of choice for R-loop mapping.

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological ...

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene expression. Along with a better understanding of the involved molecular mechanisms, precise measurements of the underlying kinetics have become increasingly important. Here, these powerful methodologies face major limitations due to intrinsic properties of the template samples they study, i.e. total cellular RNA. In many cases changes in total cellular RNA occur either too slowly or too quickly to represent the underlying molecular events and their kinetics with sufficient resolution. In addition, the contribution of alterations in RNA synthesis, processing, and decay are not readily differentiated.
We recently developed high-resolution gene expression profiling to overcome these limitations. Our approach is based on metabolic labeling of newly transcribed RNA with 4-thiouridine (thus also referred to as 4sU-tagging) followed by rigorous purification of newly transcribed RNA using thiol-specific biotinylation and streptavidin-coated magnetic beads. It is applicable to a broad range of organisms including vertebrates, Drosophila, and yeast. We successfully applied 4sU-tagging to study real-time kinetics of transcription factor activities, provide precise measurements of RNA half-lives, and obtain novel insights into the kinetics of RNA processing. Finally, computational modeling can be employed to generate an integrated, comprehensive analysis of the underlying molecular mechanisms.

Total cellular RNA provides a poor template for studying short-term changes in RNA synthesis and decay as well as the kinetics of RNA processing. Here, we describe metabolic labeling of newly transcribed RNA with 4-thiouridine followed by thiol-specific biotinylation and purification of newly transcribed RNA allowing to overcome these limitations.

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene ...

Biology

RNA Secondary Structure Prediction Using High-throughput SHAPE

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Summary

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