NSA 2012 M.Gavery
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NSA 2012 M.Gavery

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Mapping DNA methylation in the oyster genome.

Mapping DNA methylation in the oyster genome.

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  • Today I am going to be talking about current effortsto map the oyster epigenome..
  • Summarize some of our previous results and then present some new data using high throughput bisulfite sequencingThen I’d like to discuss some of the trends and patterns that are emerging which provide insight into functional roles by whichDNA methylation could be involved in regulating transcription in C.gigas
  • Refers to the additon of a methyl group to a cytosine residue - in animals occurs almost exclusively at CG loci - and has important functions in gene regulation –DNA meth involved in regulating developmental processesIs also a mechanism underlying cellular differentiation between tissuesAnd DNA methylation has also been shown to be very sensistive to environmental signals- which is important in it’s regulatory roleAlthough, DNA methylation and it’s functions have been well studied in plants and mammals, there is much less know about this mechanism in invertebrates
  • only a handful of species..Here it’s primarily found in exons–where it’s regulatory role is less clear However it’s been shown that DNA methylation has imptreg function in invertebrates species such as the honey beeAnd recently we have been characterizing DNA methylation in oysters..
  • And what we’ve been able to show, through both in silico and experimental analysis is - Genes with differing regulatory requirements have different levels of DNA methylation
  • Specifically that – housekeeping genes, such as those involved in basic metabolism show a high degree of methylation
  • While inducible genes, many of which are involved in responding to the environment have the lowest methylation
  • And while these initial patterns are very interesting..The mechanism and functional role behind these differences remain unclear.
  • One way to gain insight into these mechanisms is though high resolution methylation analysisThe idea being that the distribution of methylation across genomic regions (e.g. within a gene, within exons or introns, regulatory elements) could provide insight into how DNA methylation may support the regulation of genes.. –and for this analysis we are using genomic DNA from pooled adult gill tissue and performing high-throughput bisulfite sequencingSO I’d like to walk through the approach for this analysis and show you some of the results we are getting
  • The first thing we did was to reduce the portion of the genome that we’re sequencing in order to get higher coverage,
  • We did this enriching our sample with methylated dna. Using a methyl bidning domain protein we were able to fractionate out fragments of genomic dna that were ‘enriched in methylation’ for sequencing
  • The next thing we did was to prepare a library for analysis
  • We generated an illumina library for high-throughput sequencingbut now in order to analyze methylation status - the library was bisulfite treated prior to sequencing
  • Here genomic dna is treated with sodium bisulfite which converts unmethylatedcytosines to uracil residues - however, methylated cytosines are protected from conversion and will remain as cytosinesFollowing PCR an unmethylated cytosine will appear as a thymine when mapped back to the original reference sequence, whereas any cytosines remaining in sequene will represent sites that are methylated
  • Once sequencing occurs these short reads need to be mapped back to reference sequences
  • This presents some challenges because the sequence complexity is reduced by bisufliteconversion
  • So we used a specific mapping program developed to handle high-throughput bisulfite data calledConsist of a de novo assembly of publicly available genomic DNA sequences from as well as a number of characterized genes with known genomic structureThen we needed a way to visualize these results and we could do this using Galaxy: which is a free web-based platform that allows for visualization of multiple tracks of data in a very simple format.
  • Here are example of how this looks.we are looking at the 11,000bp hexokinase gene, involved in glucose metabolismAnd the first track in purple is marking the 11 exons of this geneBelow that, the next track is marking all CG loci, and so these are are the possible sites that could be methylated
  • The next track in blue is showing where reads from an MBD enriched library mapped back to the hexokinase gene– but, we can also look at this a different way where we are evaluating the % methylation at individual loci that have at least 5x coverage
  • Again, the exons are annotatedBelow that all of the CG loci are marked
  • On the next track are all cytosines that were quantitated to have at least 50% methylation or higher, so these are the most heavily mehtyated sites
  • The track below that shows moderately methylated sites between 25 and 50%
  • And finally the last track are sites where there was less than 25% methylation. The remaining CG sites did not have enough coverage to actually quantitate % methylation, but because of the way the library was enriched we infer that these regions have low methylationThese types of visualization allow us to see that there are regions of dense methylation and then specifically here regions of low methylation
  • We can explore this even further by overlaying this data with various other high-throughput data sets.
  • And now, we can also take reads from a cDNA library generated from gill tissue (not the same animals, but the same tissue) and we can map these reads to the same reference sequences. This track is displaying a histogram of where the reads mapNot surprisingly these reads map densely to the exonic regions with little mapping occuring in the non-coding regions.
  • This next tracks shows blast hits or regions of homology with C.gigas mRNA sequences- which is interesting because it could be indicating that there are additional coding regions outside of the known exons
  • I’d like to focus briefly on this region of low methylation - and share some additional methylation data we have specifically for the CG locus 1384 bp
  • 1.We are collaborating with nanostring to use their nCounter platform to perform methylation analysis at individual loci– (unfortunately I don’t have time to go into the method but I would be happy to tlak about it later if anyone is interested)2.Basically what we are able to do is to screen multiple samples types at about 25 loci3.Interestingly, one of the these loci, bp 1384 in the hexokinase gene, shows differential methylation between tissue types 4. We tested multiple gill samples and the methylation was always low – consistent with results from the high resolution analysis5. Whereas the larval samples and both male and female gonadal tissue samples showed methylation between 30 and 60%6.To confirm this result we used targeted bisufite sequencing..
  • Here are results of percent methylation for 7 CG loci in this regionWhat we found was that overall the methylation was very low in the gill sample - consistent with the high-throughput analysis But methylation was higher between 50 and 75% for larvae and between 75 and 100 sperm in this region
  • So to summarize these results for the gill tissue – 1. the majority of the gene is methylated, but there is a region of low methylation in the gill tissue2. Which interestingly at least a small region is methylated in both larvae and sperm genomic DNA.3. Also looking at this unmethylated region and the additional data sets – it’s possible that this region includes an additional coding region or alternative exon, 4.and it would be interesting to explore the possibility of a relationship between expression and methylation in this region.
  • So without giving as much detail, I’d like to just show a few more examples of the patterns that were seeing in this high-throughput bisulfite dataHere is another example Hsc 70 – the CG are focused in exons and many are methylated, but the 1st 2 exons do not appear to be methyated
  • Here is another example leucineamino pepsidase – involved in protein turnoverMost of the CG’s are methylated – with slightly less at the 5’ 3’ ends
  • Finally, we are seeing some examples like this ..Gonadotropin releasing hormone, a neuroendocrine receptor – CG are heavy, but this shows very little methylation
  • So I’ve only shown a few examples, but really we canAlthough it is early in the analysis.. ..possibly in terms of tissue specific expression or alternative splicingI also think it’s imporatnt that we’ve shown that these high-throughput techniques can be applied in informative ways in non-model species
  • To conclude,The data are starting to suggests thatFuture work will be aimed at testing these possible mechanisms and how they will be affected by specific environmental stresses – such as the presence of endocrine disrupting compounds.

NSA 2012 M.Gavery NSA 2012 M.Gavery Presentation Transcript

  • Epigenetic mechanisms as a source ofphenotypic plasticity in the Pacific oyster Crassostrea gigas Mackenzie Gavery & Steven Roberts University of Washington School of Aquatic and Fishery Sciences
  • Outline  Background:   Epigenetics   DNA methylation  Results: characterization of DNA methylation in Pacific oysters   High-throughput bisulfite sequencing  Discussion/Future Directions
  • Epigenetics  Heritable changes in trait or phenotype, caused by a mechanism other than mutation to the DNA sequence  Most well understood epigenetic mechanism is DNA methylation   Regulates gene expression C   Development G G   Tissue-specific expression C   Environmental response Me
  • DNA methylation: invertebrates   Only a handful of species have been evaluated   Model invertebrates lack DNA methylation   Most: intermediate methylation   Primarily in exons   Important regulatory functions – honey bee (e.g. Kucharski et al., 2008; Elango et al., 2009; Lyko et al., 2010)
  • Summary of Previous Results Enrichment level in MBD library Measured degree of DNA methylation (Roberts & Gavery, 2011) CpG O/E (Gavery & Roberts, 2010) Predicted degree of DNA methylation
  • Summary of Previous Results Enrichment level in MBD library Measured degree of DNA methylation (Roberts & Gavery, 2011) CpG O/E (Gavery & Roberts, 2010) Predicted degree of DNA methylation
  • Summary of Previous Results Enrichment level in MBD library Measured degree of DNA methylation (Roberts & Gavery, 2011) CpG O/E (Gavery & Roberts, 2010) Predicted degree of DNA methylation
  • Summary of Previous Results Enrichment level in MBD library Measured degree of DNA methylation (Roberts & Gavery, 2011) CpG O/E (Gavery & Roberts, 2010) Predicted degree of DNA methylation
  • Current Research:   High-resolution methylation analysis   Pooled adult gill tissue   High-throughput bisulfite sequencing
  • Workflow:  Reduce genome  Prepare gDNA library for methylation analysis  Data Analysis
  • Workflow:  Reduce genome - methyl binding domain enrichment MBD  Prepare gDNA library for MBD methylation analysis  Data Analysis
  • Workflow:  Reduce genome  Prepare gDNA library for methylation analysis  Data Analysis
  • Workflow:  Reduce genome  Prepare gDNA library for – Illumina library methylation analysis – Bisulfite conversion  Data Analysis
  • Workflow:  Reduce genome  Prepare gDNA library for – Illumina library methylation analysis – Bisulfite conversion  Data Analysis T C T C G bisulfite T U T C G PCR T T T C G
  • Workflow:  Reduce genome  Prepare gDNA library for methylation analysis  Data Analysis T C T C G bisulfite T U T C G PCR T T T C G
  • Workflow:  Reduce genome  Prepare gDNA library for methylation analysis  Data Analysis T C T C G bisulfite T U T C G PCR T T T C G
  • Workflow:  Reduce genome  Prepare gDNA library for methylation analysis  Data Analysis T C T C G – BSMAP software – Reference: bisulfite - de novo contigs T U T C G - characterized genes PCR T T T C G
  • Visualization: hexokinase0 bp 10,960 bpCG MBD-mapped reads
  • Visualization: hexokinase0 bp 10,960 bpCG MBD-mapped reads
  • Visualization: hexokinase0 bp 10,960 bpCGmCGmid mCGlow mCG
  • Visualization: hexokinase0 bp 10,960 bpCGmCGmid mCGlow mCG
  • Visualization: hexokinase0 bp 10,960 bpCGmCGmid mCGlow mCG
  • Visualization: hexokinase0 bp 10,960 bpCGmCGmid mCGlow mCG
  • Visualization: hexokinase0 bp 10,960 bpCGmCGcDNA - gillcDNA - larvae
  • Visualization: hexokinase0 bp 10,960 bpCGmCGcDNA - gillcDNA - larvae
  • Visualization: hexokinase0 bp 10,960 bpCGmCGcDNA - gillblastn EST
  • Visualization: hexokinase 1384 bp0 bp 10,960 bpCGmCGcDNA - gillblastn EST
  • Nanostring: nCounter ®   Screening of multiple sample types, multiple loci hexokinase CG loci 1384bp 80 60% methylation 40 20 0 A B C D A B m f gill larvae gonad
  • Targeted Bisulfite Sequencing hexokinase CG loci 1,287 – 1,435 gill larvae sperm 100%% methylation 75% 50% 25% 0% 1285 1 1356 2 1369 3 1384 4 1446 5 1454 6 1458 7 position (bp)
  • Visualization: hexokinase0 bp 10,960 bpCGmCGcDNA - gillblastn EST
  • Visualization: hsc700 bp 2,552 bpCGmCGmid mCGlow mCG
  • Visualization: LAP0 bp 5,494 bpCGmCGmid mCGlow mCG
  • Visualization: gnrhr0 bp 13,058 bpCGmCGmid mCGlow mCG
  • Results: summary  Using this technique we can characterize the methylation status of individual CG loci at a genome wide scale  Patterns suggest that methylation may be involved in gene regulation  Provides a proof-of-concept in non-model species
  • Conclusions/Future Directions:  DNA methylation plays an important role in regulating gene expression in C. gigas  It will be important to understand how epigenetic mechanisms will be affected by the environment  Future work will test these possible mechanisms and how they will be affected by environmental stress
  • Acknowledgements   Samuel White (UW, SAFS)   Joth Davis (Taylor Shellfish Farms)   Robin Harper & Phillipa Webster (Nanostring) email: mgavery@uw.edu website: students.washington.edu/mgavery