Name: a rat methyl-seq platform to identify epigenetic changes associated with stress exposure
Uploaded: 2018-10-24T14:45:00
Duration: 9 min 6 s
Description: Watch this Scientific Journal Video about A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure at JoVE.com

This study was funded by NIH grant MH101392 (RSL) and support from the following awards and foundations: a NARSAD Young Investigator Award, Margaret Ann Price Investigator Fund, the James Wah Mood Disorders Scholar Fund via the Charles T. Bauer Foundation, Baker Foundation, and the Project Match Foundation (RSL).

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S., 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=Adaptation+of+the+CHARM+DNA+methylation+platform+for+the+rat+genome+reveals+novel+brain+region-specific+differences.">Adaptation of the CHARM DNA methylation platform for the rat genome reveals novel brain region-specific differences.</a> Epigenetics. 6 (11), 1378-1390 (2011).</li><li>Jankord, R., 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=Stress+vulnerability+during+adolescent+development+in+rats.">Stress vulnerability during adolescent development in rats.</a> Endocrinology. 152 (2), 629-638 (2011).</li><li>Pellow, S., Chopin, P., File, S. E., Briley, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+open:closed+arm+entries+in+an+elevated+plus-maze+as+a+measure+of+anxiety+in+the+rat.">Validation of open:closed arm entries in an elevated plus-maze as a measure of anxiety in the rat.</a> Journal of Neuroscience Methods. 14 (3), 149-167 (1985).</li><li>Krueger, F., Andrews, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bismark:+a+flexible+aligner+and+methylation+caller+for+Bisulfite-Seq+applications.">Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications.</a> Bioinformatics. 27 (11), 1571-1572 (2011).</li><li>Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+gapped-read+alignment+with+Bowtie+2.">Fast gapped-read alignment with Bowtie 2.</a> Nature Methods. 9 (4), 357-359 (2012).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biology. 10 (3), R25 (2009).</li><li>Hansen, K. D., Langmead, B., Irizarry, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BSmooth:+from+whole+genome+bisulfite+sequencing+reads+to+differentially+methylated+regions.">BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions.</a> Genome Biology. 13 (10), R83 (2012).</li><li>Lee, R. S., 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=Chronic+corticosterone+exposure+increases+expression+and+decreases+deoxyribonucleic+acid+methylation+of+Fkbp5+in+mice.">Chronic corticosterone exposure increases expression and decreases deoxyribonucleic acid methylation of Fkbp5 in mice.</a> Endocrinology. 151 (9), 4332-4343 (2010).</li><li>Lee, R. S., 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=A+measure+of+glucocorticoid+load+provided+by+DNA+methylation+of+Fkbp5+in+mice.">A measure of glucocorticoid load provided by DNA methylation of Fkbp5 in mice.</a> Psychopharmacology. , (2011).</li><li>Bose, M., Olivan, B., Laferrere, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+and+obesity:+the+role+of+the+hypothalamic-pituitary-adrenal+axis+in+metabolic+disease.">Stress and obesity: the role of the hypothalamic-pituitary-adrenal axis in metabolic disease.</a> Current Opinion in Endocrinology, Diabetes, and Obesity. 16 (5), 340-346 (2009).</li><li>Brydon, L., Magid, K., Steptoe, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Platelets,+coronary+heart+disease,+and+stress.">Platelets, coronary heart disease, and stress.</a> Brain, Behavior, and Immunity. 20 (2), 113-119 (2006).</li><li>McKlveen, J. M., 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=Chronic+Stress+Increases+Prefrontal+Inhibition:+A+Mechanism+for+Stress-Induced+Prefrontal+Dysfunction.">Chronic Stress Increases Prefrontal Inhibition: A Mechanism for Stress-Induced Prefrontal Dysfunction.</a> Biological Psychiatry. 80 (10), 754-764 (2016).</li><li>Akalin, A., 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=methylKit:+a+comprehensive+R+package+for+the+analysis+of+genome-wide+DNA+methylation+profiles.">methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles.</a> Genome Biology. 13 (10), R87 (2012).</li><li>Cervera-Juanes, R., Wilhelm, L. J., Park, B., Grant, K. A., Ferguson, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alcohol-dose-dependent+DNA+methylation+and+expression+in+the+nucleus+accumbens+identifies+coordinated+regulation+of+synaptic+genes.">Alcohol-dose-dependent DNA methylation and expression in the nucleus accumbens identifies coordinated regulation of synaptic genes.</a> Translational Psychiatry. 7 (1), e994 (2017).</li><li>Cervera-Juanes, R., Wilhelm, L. J., Park, B., Grant, K. 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The manuscript is part of a contest prize from Agilent Technologies.

In this study, we designed and implemented the Methyl-Seq platform for the rat genome. By demonstrating its utility with a rat model of stress, we demonstrated that the experimental and analytical pipeline can provide differentially methylated regions between two comparison groups.
To ensure a successful implementation of the platform, several critical steps need to be observed. First, initial DNA quality and quantity has a significant impact on the quality and quantity of the final Methyl-Seq library. We used a fluorometer, rather than a spectrophotometer, to ensure that our DNA measurement reflected the quantity of double-stranded DNA present. The bioanalyzer was used to measure the molecular size and quantity of DNA following shearing and after adapter ligation. Verifying the molecular size &#34;shift&#34; between these steps is crucial to confirm the presence of adapters at the ends of each DNA fragment that will undergo adapter-mediated PCR in the subsequent steps. The quantity of DNA remaining at the end of the adapter ligation step is also important, since at least 100 ng of the library product is needed at this step to ensure sufficient quantity is available after the target enrichment and bisulfite conversion steps. A final high-sensitivity measurement was performed on the constructed Methyl-Seq library so that the library can be properly diluted for subsequent clustering on the next-generation sequencer. Finally, bisulfite pyrosequencing was employed as a highly-quantitative, independent method to assess the accuracy of the analytical pipeline. The final validation using the original samples and replication using additional animals are crucial steps to ensure that the experiment can detect biologically significant changes in DNA methylation.
We also include several guidelines in the event of deviation from the protocol or if problems are encountered. First, it is possible to lose too much DNA during end-repair, adapter ligation, or magnetic bead purification steps. Alternatively, starting amounts of DNA could be small (&#60;200 ng) due to limited tissue/DNA availability or implementation of various enrichment methods such as fluorescence activated cell sorting. Increasing the cycle number during the two library amplification steps may be able to compensate for the excessive loss of DNA or low starting DNA amount throughout the library construction protocol. However, no more than an additional 2&#8211;3 cycles are recommended, as excessive template amplification is likely to lead to an increase in the number of duplicate reads being sequenced. These duplicates are excluded during the alignment step to prevent bias in percent methylation calculations. Second, if the average DNA size does not increase by more than 30 bps, check to ensure that the reagents are new, as T4 DNA polymerase, Klenow, and/or T4 ligase may be old. Commercially available replacement reagents can be used.
Additionally, it is possible that the predicted DMRs might not validate by pyrosequencing, where DNA methylation differences do not exist or are significantly less than those predicted by analysis. Poor validation of candidate regions is a problem too common for many genome-wide analyses, such as when pyrosequencing results do not confirm differential methylation or the effect size is much smaller than that predicted by the analysis. BSmooth is one analytical package that &#34;smoothes&#34; the methylation levels across a window of multiple CpGs. For the current experiment, BSmooth implicated a DMR whose methylation levels were validated by bisulfite pyrosequencing. However, there will likely be discrepancies between methylation levels predicted by BSmooth and those verified by pyrosequencing. The discrepancies arise from the smoothing function that estimates the average methylation values across all of the CpGs within a DMR, including consecutive CpGs that may differ in DNA methylation by more than 50% or CpGs whose methylation values were excluded due to sub-threshold read depth. R-packages such as MethylKit24 can be used to identify smaller windows of CpGs or even single CpGs whose methylation levels correlate strongly with those validated by pyrosequencing. Implementing different packages and testing their predicted regions or CpGs of differential methylation by pyrosequencing will ensure robustness of data. Alternatively, original Methyl-Seq libraries can be resequenced and added to the read files to increase read depth. Since determination of methylation levels are semi-quantitative and dictated by the number of reads [(# of CpGs)/(# of TpGs+CpGs)], increasing the read depth for a given CpG will increase the accuracy of its percent methylation value. In this study, we only considered CpGs whose methylation values were determined by at least ten reads and achieved an overall read coverage of 19x for each CpG.
The rat Methyl-Seq platform is not without its limitations. While it is more cost effective than whole-genome bisulfite sequencing, it is considerably more expensive than other methods. Nevertheless, most of the cost was for purchasing lanes on the sequencer and not for the capture system. Depending on the read depth necessary, with cross-tissue comparisons requiring less due to large (25&#8211;70%) differences12 in DNA methylation, the cost can be reduced by multiplexing more samples per lane and using a higher-capacity platform. Also, the sample preparation is more time-consuming than other methods. While similar to other pulldown approaches that incorporate next-generation sequencing, the added bisulfite conversion and purification steps add to the work load. Overall, the Methyl-Seq platform is a cost-effective alternative to whole-genome sequencing and provides base-pair resolution at more than 2.3 million CpGs, which is considerably more than those assayed by microarray-based platforms. To date, the commercially-available human and mouse Methyl-Seq platforms have been used to document alcohol-dependent changes in the macaque brain25,26, neurodevelopmental genes in the mouse brain9, and blood-brain targets of glucocorticoids10. Further, the ability to target specific regions regardless of sequence recognition by restriction enzymes makes it an ideal platform for cross-species comparisons. For this study, we designed the Methyl-Seq platform for the rat, for which many pharmacological, metabolic, and behavioral experiments are performed without the benefit of a genome-wide methylomic tool. Our data show that it can be used to detect DMRs in a rat model of stress and correlated other physiological parameters such as overall plasma CORT levels.
The Methyl-Seq platform is ideal for epigenetic experiments in animals with sequenced genomes that may not have enough experimental evidence documenting regulatory regions. When such regions are made available, additional regions may be custom-designed and attached to the current version. Further, the platform is ideal for comparative genomics, since the target enrichment is not constrained by restriction enzyme recognition. For instance, the promoter region of any gene of interest can be captured regardless of whether it harbors a specific restriction site. Similarly, any regulatory regions, such as those identified in mouse or humans, which are conserved in the genome of interest can be captured.

Advances in high-throughput sequencing have led to a wealth of genomic sequences for both model and non-model organisms. The availability of such sequences has facilitated research in genetics, comparative genomics, and transcriptomics. For instance, available genomic sequences are highly useful for aligning sequencing data from ChIP-Seq experiments that enrich DNA based on its association with histone modifications1, or bisulfite sequencing, which measures DNA methylation by detecting uracil formed from bisulfite conversion of unmethylated cytosines2. However, there have been delays in the implementation of epigenomic platforms that incorporate available genomic sequencing data in their design due to a lack of annotated data of species-specific regulatory sequences that can influence gene function. 
In particular, DNA methylation is one of the most widely studied epigenetic modifications on DNA that can leverage available genomic data for building a methylomic platform. One such example is an array-based platform for the human methylome3, which has been widely used in various disciplines from oncology to psychiatry4,5. Unfortunately, similar platforms for non-human animal models are scarce, as there are virtually no widely-used platforms that have taken advantage of the genomic sequence in their initial design.
A common method to assess the methylomic landscape of non-human animal models is reduced representation bisulfite sequencing (RRBS)6. This approach overcomes the cost of whole-genome bisulfite sequencing that, while providing a comprehensive methylomic landscape, provides lower read-depth coverage due to cost and limited functional information in large gene-poor areas of the genome2. RRBS involves restriction digest and size-selection of genomic DNA to enrich for highly GC-rich sequences such as CpG islands that are commonly found near gene promoters and thought to play a role in gene regulation7. While the RRBS method has been used in a number of important studies, its reliance on restriction enzymes is not without notable challenges and limitations. For instance, enrichment of GC-rich sequences in RRBS is entirely dependent on the presence of specific sequences recognized by the restriction enzyme and subsequent size selection by electrophoresis. This means that any genomic areas that do not contain these restriction sites are excluded during size selection. Also, cross-species comparisons are challenging unless the same restriction sites are present in the same loci among the different species. 
One approach to overcoming the limitations of RRBS is to use an enrichment method that takes advantage of the published genomic sequence in the design of the platform. The array-based human platform uses primer probes designed against specific CpGs for allele-specific (CG vs. TG after bisulfite conversion) target annealing and primer extension. Its design reflects not only the available human genomic sequence, but experimentally-verified regulatory regions acquired from multiple lines of inquiry, such as ENCODE and ENSEMBL8. Despite its wide use in human methylomic investigations, a similar platform does not exist for model animals. In addition, the array-based format places significant constraint on the surface area available for probe placement. In the past several years, efforts have been made to combine the target-specificity afforded by capture probe design and the high-throughput feature of next-generation sequencing. Such an endeavor has resulted in the sequencing-based target enrichment system for the mouse genome (mouse Methyl-Seq), which was used to identify brain-specific or glucocorticoid-induced differences in methylation9,10. Similar platforms for other model and non-model animals are needed to facilitate epigenomic research in these animals.
Here, we demonstrate the implementation of this novel platform to conduct methylomic analysis on the rat. The rat has served as an important animal model in pharmacology, metabolism, neuroendocrinology, and behavior. For example, there is an increasing need to understand the underlying mechanisms that give rise to drug toxicity, obesity, stress response, or drug addiction. A high-throughput platform capable of capturing methylomic changes associated with these conditions would increase our understanding of the mechanisms. Since the rat genome still lacks annotation for regulatory regions, we incorporated non-redundant promoters, CpG islands, island shores11, and previously identified GC-rich sequences into the rat Methyl-Seq platform12. 
To assess successful design and implementation of the SureSelect Target Enrichment (generically referred to as Methyl-Seq) platform for the rat genome, we employed a rat model of chronic variable stress (CVS)13 to identify differentially methylated regions between unstressed and stressed animals. Our platform design, protocol, and implementation may be useful for investigators who may want to conduct a comprehensive and unbiased epigenetic investigation on an organism whose genomic sequence is already available but remains poorly annotated.

<table>
	<tbody>
		<tr>
			<td>Radioimmuno assay (RIA)</td>
			<td>MP Biomedicals</td>
			<td>7120126</td>
			<td>Corticosterone, 125I labeled</td>
		</tr>
		<tr>
			<td>Master Pure DNA Purification Kit</td>
			<td>Epicentre/Illumina</td>
			<td>MC85200</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermal-LOK 2-Position Dry Heat Bath</td>
			<td>USA Scientific</td>
			<td>2510-1102</td>
			<td>Used with 1.5 mL tubes</td>
		</tr>
		<tr>
			<td>Vortex Genie 2</td>
			<td>Fisher</td>
			<td>12-812</td>
			<td>Vortex Mixer</td>
		</tr>
		<tr>
			<td>Ethyl alcohol, Pure</td>
			<td>Sigma-Aldrich</td>
			<td>E7023</td>
			<td>100% Ethanol, molecular grade</td>
		</tr>
		<tr>
			<td>Centrifuge 5424 R</td>
			<td>Eppendorf</td>
			<td>-</td>
			<td>Must be capable of 20000 x g</td>
		</tr>
		<tr>
			<td>Qubit 2.0</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32866</td>
			<td>Fluorometer</td>
		</tr>
		<tr>
			<td>Qubit dsDNA BR Assay Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32850</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS Assay Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32851</td>
			<td>High sensitivity DNA detection reagents</td>
		</tr>
		<tr>
			<td>Qubit Assay Tubes</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32856</td>
			<td></td>
		</tr>
		<tr>
			<td>SureSelectXT Rat Methyl-Seq Reagent Kit</td>
			<td>Agilent Technologies</td>
			<td>G9651A</td>
			<td>Reagents for preparing the Methyl-Seq library</td>
		</tr>
		<tr>
			<td>SureSelect Rat Methyl-Seq Capture Library</td>
			<td>Agilent Technologies</td>
			<td>931143</td>
			<td>RNA baits for enrichment of rat targets</td>
		</tr>
		<tr>
			<td>IDTE, pH 8.0</td>
			<td>IDT DNA</td>
			<td>11-05-01-09</td>
			<td>10 mM TE, 0.1 mM EDTA</td>
		</tr>
		<tr>
			<td>DNA LoBind Tube 1.5 mL</td>
			<td>Eppendorf</td>
			<td>22431021</td>
			<td></td>
		</tr>
		<tr>
			<td>Covaris E-series or S-series</td>
			<td>Covaris</td>
			<td>-</td>
			<td>Isothermal sonicator</td>
		</tr>
		<tr>
			<td>microTUBE AFA Fiber Pre-Slit Snap-Cap 6x16mm (25)</td>
			<td>Covaris</td>
			<td>520045</td>
			<td></td>
		</tr>
		<tr>
			<td>Water, Ultra Pure (Molecular Biology Grade)</td>
			<td>Quality Biological</td>
			<td>351-029-721</td>
			<td></td>
		</tr>
		<tr>
			<td>Veriti 96 Well-Thermal Cycler</td>
			<td>Applied Biosystems</td>
			<td>4375786</td>
			<td></td>
		</tr>
		<tr>
			<td>AMPure XP Beads</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td>DNA-Binding magnetic beads</td>
		</tr>
		<tr>
			<td>96S Super Magnet</td>
			<td>ALPAQUA</td>
			<td>A001322</td>
			<td>Magnetic plate for purification steps</td>
		</tr>
		<tr>
			<td>2200 TapeStation</td>
			<td>Agilent Technologies</td>
			<td>G2965AA</td>
			<td>Electrophresis-based bioanalyzer</td>
		</tr>
		<tr>
			<td>D1000 ScreenTape</td>
			<td>Agilent Technologies</td>
			<td>5067-5582</td>
			<td></td>
		</tr>
		<tr>
			<td>D1000 ScreenTape High Sensitivity</td>
			<td>Agilent Technologies</td>
			<td>5067-5584</td>
			<td></td>
		</tr>
		<tr>
			<td>D1000 Reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-5583</td>
			<td></td>
		</tr>
		<tr>
			<td>D1000 Reagents High Sensitivity</td>
			<td>Agilent Technologies</td>
			<td>5067-5585</td>
			<td></td>
		</tr>
		<tr>
			<td>DNA110 SpeedVac</td>
			<td>ThermoFisher Scientific</td>
			<td>-</td>
			<td>Vacuum Concentrator</td>
		</tr>
		<tr>
			<td>Dynabeads MyOne Streptavidin T1 magnetic beads</td>
			<td>Invitrogen</td>
			<td>65601</td>
			<td>Streptavidin magnetic beads</td>
		</tr>
		<tr>
			<td>Labquake Tube Rotator</td>
			<td>ThermoFisher Scientific</td>
			<td>415110Q</td>
			<td>Nutator Mixer is also acceptable</td>
		</tr>
		<tr>
			<td>EZ DNA Methylation-Gold Kit</td>
			<td>Zymo Research</td>
			<td>D5006</td>
			<td>Bisulfite conversion kit. Contains Binding, Wash, Desulphonation, and Elution buffers</td>
		</tr>
		<tr>
			<td>Illumina Hi-Seq 2500</td>
			<td>Illumina</td>
			<td>-</td>
			<td>Next-generation sequencing machine</td>
		</tr>
		<tr>
			<td>PCR and Pyrosequencing Primers</td>
			<td>IDT DNA</td>
			<td>Variable</td>
			<td></td>
		</tr>
		<tr>
			<td>Taq DNA Polymerase with ThermoPol Buffer - 2,000 units</td>
			<td>New England BioLabs</td>
			<td>M0267L</td>
			<td></td>
		</tr>
		<tr>
			<td>Deoxynucleotide (dNTP) Solution Set</td>
			<td>New England BioLabs</td>
			<td>N0446S</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyromark MD96</td>
			<td>QIAGEN</td>
			<td>-</td>
			<td>Pyrosequencing machine</td>
		</tr>
		<tr>
			<td>Ethyl Alcohol 200 Proof</td>
			<td>Pharmco-Aaper</td>
			<td>111000200</td>
			<td>70% Ethanol solution</td>
		</tr>
		<tr>
			<td>Sodium Hydroxide Pellets</td>
			<td>Sigma-Aldrich</td>
			<td>221465</td>
			<td>0.2 M NaOH denature buffer solution</td>
		</tr>
		<tr>
			<td>Tris (Base) from J.T. Baker</td>
			<td>Fisher Scientific</td>
			<td>02-004-508</td>
			<td>10 mM Tris Acetate Buffer wash buffer solution</td>
		</tr>
		<tr>
			<td>PyroMark Gold Q96 Reagents (50x96)</td>
			<td>QIAGEN</td>
			<td>972807</td>
			<td>Reagents required for pyrosequencing</td>
		</tr>
		<tr>
			<td>PyroMark Annealing Buffer</td>
			<td>QIAGEN</td>
			<td>979009</td>
			<td></td>
		</tr>
		<tr>
			<td>PyroMark Binding Buffer (200 ml)</td>
			<td>QIAGEN</td>
			<td>979006</td>
			<td></td>
		</tr>
		<tr>
			<td>Streptavidin Sepharose High Performance Beads</td>
			<td>GE Healthcare</td>
			<td>17-5113-01</td>
			<td>Streptavidin-coated sepharose beads</td>
		</tr>
		<tr>
			<td>PyroMark Q96 HS Plate</td>
			<td>QIAGEN</td>
			<td>979101</td>
			<td>Pyrosequencing assay plate</td>
		</tr>
		<tr>
			<td>Eppendorf Thermomixer R</td>
			<td>Fisher Scientific</td>
			<td>05-400-205</td>
			<td>Plate mixer. 96-well block sold separately (cat. No 05-400-207)</td>
		</tr>
		<tr>
			<td>SureDesign Website</td>
			<td>Agilent Technologies</td>
			<td>-</td>
			<td>Target capture design software (https://earray.chem.agilent.com/suredesign/)</td>
		</tr>
		<tr>
			<td>UCSC Genome Browser</td>
			<td>University of California Santa Cruz</td>
			<td>-</td>
			<td>rat Nov 2004 rn4 assembly</td>
		</tr>
		<tr>
			<td>Agilent Methyl-Seq Protocol</td>
			<td>Agilent Technologies</td>
			<td>-</td>
			<td>https://www.agilent.com/cs/library/usermanuals/public/G7530-90002.pdf</td>
		</tr>
	</tbody>
</table>

a rat methyl-seq platform to identify epigenetic changes associated with stress exposure

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these animal models. The rat is one particular model animal where an epigenetic tool can complement many pharmacological and behavioral studies to provide insightful mechanistic information. To this end, we adapted the SureSelect Target Capture System (referred to as Methyl-Seq) for the rat, which can assess DNA methylation levels across the rat genome. The rat design targeted promoters, CpG islands, island shores, and GC-rich regions from all RefSeq genes. 
To implement the platform on a rat experiment, male Sprague Dawley rats were exposed to chronic variable stress for 3 weeks, after which blood samples were collected for genomic DNA extraction. Methyl-Seq libraries were constructed from the rat DNA samples by shearing, adapter ligation, target enrichment, bisulfite conversion, and multiplexing. Libraries were sequenced on a next-generation sequencing platform and the sequenced reads were analyzed to identify DMRs between DNA of stressed and unstressed rats. Top candidate DMRs were independently validated by bisulfite pyrosequencing to confirm the robustness of the platform.
Results demonstrate that the rat Methyl-Seq platform is a useful epigenetic tool that can capture methylation changes induced by exposure to stress.

Watch this Scientific Journal Video about A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure at JoVE.com

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

Here, we describe the protocol and implementation of Methyl-Seq, an epigenomic platform, using a rat model to identify epigenetic changes associated with chronic stress exposure. Results demonstrate that the rat Methyl-Seq platform is capable of detecting methylation differences that arise from stress exposure in rats.

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these ...

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