Category Archives: transcription


Methylation to the max!

A new paper from the Zilberman lab at UC Berkeley shows the application of high throughput sequencing to the study of DNA methylation in eukaryotes.  They generate an huge data set of whole genome methylation patterns in several plants, animals, and five fungi including early diverging Zygomycete.

The work was performed using Bisulfite sequencing (Illumina) to capture methylated DNA, RNA-Seq of mRNA. The also performed some ChIP-Seq of H2A.Z on pufferfish to look at the nucleosome positioning in that species. For aligning the reads, they used BowTie to align the bisulfite sequences (though I’d be curious how a new aligner, BRAT, designed for Bisulfite seq reads would perform) to the genome.  They also sequenced mRNA via RNA-Seq to assay gene expression for some of the species.

They find several interesting patterns in animal and fungal genomes.  I’ll highlight one in the fungi. They find an unexpected pattern in U. reesii of reduced CGs in repeats, which shows signatures of a RIP-like process, are also methylated.  This finding is also consistent with observations in Coccidioides (Sharpton et al, Genome Res 2009) that showed depleted CGs pairs in repeats.  Since the phenomenon is also found in Coccidioides genomes this methylation of some repeats is likely not unique to U. reesii but may be important in recent evolution of the Onygenales fungi or the larger Eurotiales fungi.  There are several other interesting findings with the first such study that shows methylation data for Zygomycete fungi and a basidiomycete close to my heart, Coprinopsis.  It will be interesting is to dig deeper into this data and see how the patterns of methylation compare to other genomic features and the mechanisms regulating methylation process.

Zemach, A., McDaniel, I., Silva, P., & Zilberman, D. (2010). Genome-Wide Evolutionary Analysis of Eukaryotic DNA Methylation Science DOI: 10.1126/science.1186366

Deeper and Deeper, Down the Transcriptome-hole We Fall

Your eye contains the same genetic content as your fingernail, but these two tissues look nothing alike. One significant cause of this difference is the tissue specific regulation of the genes in the genome. In some tissues in your body, a gene may be expressed (transcribed) while that same gene may be silent in another tissue type. A great deal of modern biological research explores the regulation of expression of all the genes in a genome, collectively known as the transcriptome. Such studies are, for example, aimed at understanding which genetic regulation events account for the differences between an eye and a fingernail.

However, the effectiveness of this research is predicated upon actually knowing which parts of the genome are capable of being expressed and, subsequently, regulated. Conventionally, researchers extract RNA from an organism grown in various conditions (or, as in the case of our example, various tissues from an organism) and clone and sequence the RNA to identify at least a subset of genes that are expressed (Ebbole 2004*). Such Expressed Sequence Tags (ESTs) have proven vital to our understanding of gene and gene structure annotation as they frequently provide evidence of intron splice sites. While this method has facilitated a robust understanding of gene regulation, it is expensive, time consuming, and provides a relatively low coverage of the transcriptome. If our goal is to understand everything that is expressed, then we need a superior tool.

Enter SAGE (serial analysis of gene expression) and MPSS (massively parallel signature sequencing) [Irie 2003*, Harbers 2005*]. Both methods sequence short tags of a transcript’s 3′ end. SAGE uses conventional sequencing technology while MPSS uses Solexa, Inc.’s novel bead-based hybridization technology. One of the massive advantages of these technologies is the number of sequences they provide: large EST databases are on the order of several tens of thousands, while SAGE generally provides 100,000 to 200,00 tags and MPSS can provide over a million signatures. That being said, there are still questions regarding the sensitivity of the depth of coverage of the transcriptome. It may well be that despite a lower total sequence count, ESTs provide more information about what parts of the genome are expressed.

Fortunately, Gowda et al put all three methods to work as well as an RNA microarray (which doesn’t provide sequence, but enables its inference through hybridization) in their recent study of the Magnaporthe grisea transcriptome [Gowda 2006]. M. grisea is the causative agent of rice blast, a devastating disease that results in tremendous crop yield loss. The researchers evaluated two tissues types: the non-pathogenic mycelium and the invasive, plant penetrating appressorium.

Interestingly, 40% of the MPSS tags and 55% of the SAGE tags identified represent novel genes as they had no matches in the existing M. grisea JGI EST collection. Additionally, the authors found that no one method could identify the majority of the transcripts, but that a two-way combination of array data, MPSS or SAGE could provide over 80% of the total unique transcripts all of the methods identified. One additional suprise was that roughly a quarter of the genes identified also produced an antisense RNA, possibly for siRNA regulation of the gene.

The long story short appears to be that there is, as of yet, no magic bullet of a method. To adequately cover the transcriptome, multiple techniques are required.

*These references are, unfortunately, not located in an open access journal.

Whole genome tiling arrays

A recent paper describes the discovery of 9 new introns in Saccharomyces cerevisiae by Ron Davis’s group at Stanford, using high density tiling arrays from Affymetrix. The arrays are designed for both strands allow the detection of transcripts transcribed from both strands. The arrays were also put to work by the Davis and Steinmetz labs to create a high density map of transcription in yeast and for polymorphism mapping from the Kruglyak lab.

PNAS Yeast Transcriptional map

Whole genome tiling arrays have also been employed in other fungi. For example, Anita Sil’s group at UCSF constructed a random tiling array for Histoplasma capsulatum and used it to identify genes responding to reactive nitrogen species. A similar approach was used in Cryptococcus neoformans to investigate temperature regulated genes using random sequencing clones.

As the technology has become cheaper, it may become sensible to use a tiling array to detect transcripts rather than ESTs when attempting to annotate a genome. In the Histoplasma work transcriptional units could be identified from hybridization alone. Some of the algorithms will need some work to correct incorporate this information, and the sensitivity and density of the array will influence this. These techniques can be part of a resequencing approaches or fast genotyping progeny from QTL experiments when the sequence from both parents is known (or at least enough of the polymorphims for the genetic map).

What is superior about the current Affymetrix yeast tiling array is the inclusion of both strands. This allows detection of transcripts from both strands. Several anti-sense transcripts in yeast have been discovered recently including in the IME4 locus through more classical approaches, but perhaps many more await discovery with high resolution transcriptional data from whole genome tiling arrays.