Tag Archives: gene regulation

ISMB/ECCB 2007 recap

ISMB2007Back from ISMB/ECCB and a mountain of things left undone that somehow still need doing … including a quick entry about what was interesting at the conference.

I heard many good talks and only a few bad ones – maybe I guessed properly in darting between the multiple sessions. The meeting itsself was much better than past ones I had attended. The combination of Special Interest Groups meeting (BOSC, AFP, and Microbial Comparative Genomics being the ones I spent my time in). I got to give my talks and tutorial during the first few days and was able to just try and soak up the rest of the meeting (when my brain wasn’t melting from the heat). Particularly good was Carole Goble’s presentation on 7-deadly sins of bioinformatics software development.

Some general evolutionary talks that I found really interesting (some of these are probably biased since I count many of the presenters as friends):

I’ll write a quick post on the BoF session on open source and data sharing as well.

Todd and I took some pictures as well.

Exploring a global regulator of gene expression in Aspergillus

Blogging about Peer-Reviewed ResearchWhen first discovered, the gene LaeA was thought to be a master switch for silencing of several NRPS secondary metabolite gene clusters in Aspergillus. NRPS and PKS are important genes in filamentous fungi as they produce many compounds that likely help fungi compete in the ecological niche mycotoxins (e.g. aflatoxin, gliotoxin), plant hormone (e.g. Gibberellin), and a potential wealth of additional undiscovered activities.

A recent paper from Nancy Keller’s lab entitled Transcriptional Regulation of Chemical Diversity in Aspergillus fumigatus by LaeA has followed up previous studies with whole genome expression profiling of a LaeA knockout strain to explore the breadth of the genome that is regulated by this transcriptional regulator. Continue reading Exploring a global regulator of gene expression in Aspergillus

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.

Splicing machinery and introns

Splicing of pre-messenger RNA is necessary to remove introns and create well formed and translateable mRNA, but the purpose of introns still remains a mystery. One idea is they provide a role in the error checking machinery, or Nonsense Mediated Decay (NMD), by providing way-points during translation. A protein is deposited at the exon junction complex (EJC) which indicates a splicing event has occurred. During translation, if the ribosome encounters a premature stop (or termination) codon (PTC) and then sees one of these EJC way-points, it signals the corrupted message for degradation.

NMD_PTC

Several predictions come out of these models including the lack of introns in the 3′ UTR and that the average length of exons should be correlated with the window that the proofreading mechanism can operate on. These are discussed in several papers out of Mike Lynch’s lab including (Lynch and Connery 2003), (Lynch and Kewalramani, 2003), (Lynch and Richardson, 2002) and recently (Scofield et al, 2007).

Efforts to understand the splicing machinery, particularly in S. cerevisiae have led to the discovery of numerous genes that code for proteins that make up the spliceosome. Some of these include small RNAs as well as protein coding genes. The SR proteins are serine-arginine rich proteins that regulate splicing and are found in almost all eukaryotes including most fungi (even those with few introns, such as S. cerevisiae). SR proteins play a role in splicing and in nuclear export (Masuyama et al, 2004, Sanford et al, 2004) indicating that a coupling of these processes may explain why genes with introns tend to be more highly expressed. The evolution of the spliceosomal family of genes is also interesting because the families appear to diversify in some eukaryotes perhaps where there are more elaborate splicing and regulatory action (Barbosa-Morais et al, 2006).

There is some debate as to whether splicing occurs after the pre-mRNA is completely synthesized or if it happens as transcription is occurring. Work on this has shown that both spliceosomal assembly can co-occur with polymerase during transcription, as well as evidence that most splicing (in yeast) is post-transcriptional (Tardiff et al, 2006). It is argued that the two steps occur together to maximize efficiency and fidelity (Das et el, 2006, Moore et al, 2006), but perhaps affinities are species-specific and have evolved to correlate with intron densities?

[Note: This post has links to non-open access journal articles. At this point I am still referring to these even if they are not all readable by everyone, because they contain some data that is only available there. I will strive to focus more narrowly on only papers that are available as open access through pubmed central or directly through open-access journals.]