Category Archives: pezizomycota

Pyrenophora tritici-repentis

The genome of Pyrenophora tritici-repentis, the fourth sequenced Dothideomycete genome, was released by the FGI at the Broad Institute this spring (March 2007). P. tiritici-repentis was sequenced for its role as the cause of tan spot on wheat and as a research model for other Pyrenophora sp. that are pathogens of several grasses.

The 6X assembly contains 37.8 Mb of sequence similar to the other Dothideomycetes such as Stagnospora nodorum (37.2 Mb), Alternaria brassicola (32 Mb), and Mycosphaerella graminicola (41.8 Mb).

More Neurospora genomes

We got word last week from the JGI that our DNA for Neurospora tetrasperma and N. discreta have passed QC and library QC and are on their way to being sequenced. The center also plans to do some EST sequencing to improve gene calling abilities.

Why more Neurospora genomes? The sequencing proposal discussed these species as a model system for evolutionary and ecological genetics. It will allow us and others to test several hypotheses about the molecular evolution of things like genome defense in Neurospora and to understand more about the evolutionary history of the model organism N. crassa.

Continue reading More Neurospora genomes

That was a lot of work

I’ve never worked with Magnaporthe grisea, the fungus responsible for rice blast, one of the most devastating crop diseases, but I do know that its life cycle is complicated and that knocking out roughly 61% of the genes in the genome and evaluating the mutant phenotype to infer gene function is not trivial. In their recent letter to Nature, Jeon et al did what many of us have dreamed of doing in our fungus of interest: manipulate every gene to find those that contribute to a phenotype of interest.

In their study, the authors looked for pathogenecity genes. Interestingly, the defects in appressorium formation and condiation had the strongest correlation with defects pathogenicity, suggesting that these two developmental stages are crucial for virulence. Ultimately, the authors identify 203 loci involved in pathogenecity, the majority of which have no homologous hits in the sequence databases and have no clear enriched GO functions. Impressively, this constitutes the largest, unbiased list of pathogenecity genes identified for a single species (though so of us, I’m sure, may have a problem with the term “unbiased”).

If you’d like to play with their data, the authors have made it available in their ATMT Database.

Fungal Genetics 2007 details

I’m including a recapping as many of the talks as I remember. There were 6 concurrent sessions each afternoon so you have to miss a lot of talks. The conference was bursting at the seams as it was- at least 140 people had to be turned away beyond the 750 who attended.

If there was any theme in the conference it was “Hey we are all using these genome sequences we’ve been talking about getting”. I only found the overview talks that solely describe the genome solely a little dry as compared to those more focused on particular questions. I guess my genome palate is becoming refined.

Continue reading Fungal Genetics 2007 details

Hello, do I know you?

Blogging about Peer-Reviewed ResearchSelf and non-self recognition is important for fungi when hyphae interact fuse if they should compartmentalize and undergo apoptosis to kill the heterokaryoton or exchange nutrients. This process is part of cell defense and to limit to the movement of mycoviruses.

A paper in PLOS ONE describes the Genesis of Fungal Non-Self Repertoire. This kind of work goes on down the hall from us as well in the Glass lab among others. This recent paper describes het genes, which contain WD40 repeats and different combinations of these help control specificity. There is of course a diverse literature on this subject especially in Neurospora, and I’m not reviewing it here, but it is an imporant process in understanding how fungi interact with their environment.

Neurospora crassa

Here is an image of Neurospora crassa I took today in my first attempt at squashes. These are from strains that Dave Jacobson grew up with his constructs so I can’t take any credit other than playing with the microscope next door. Now my first attempt came out badly, so this is actually Dave’s prep as well. And these got dry so they aren’t as nices as they could be. For much nicer images, see N.B. Raju’s.

All that said, I hope these quick images give a hint at the extremely cool structures these fungi produce. These 8-chain ascospores are the result of meoisis that took place inside the perithecia (which was squeezed gently to release the rosettes [or not too gently in my case]).

N.crassa rosetteN.crassa Histone GFP

( I was previous confused about the sample and had labeled this N. tetrasperma which has 4-chained ascospores [tetra] while this sample is crassa which has 8).

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.

Making the Revolution Work for You

In a recent Microbiology Mini-Review, Meriel Jones catalogs both the potential benefits and problems that arise from fungal genome sequencing. Using the nine genomes (being) sequenced from the Aspergillus clade, Jones addresses several issues tied to a singular theme: if we are to unlock the potential that fungal genome sequencing holds, both academically and entrepreneurially, then a more robust infrastructure that enables comparative and functional annotation of genomes must be established.

Fortunately, like any good awareness advocate, Jones points us in the direction of e-Fungi, a UK based virtual project aimed at setting up such an infrastructure. Anyone can navigate this database to either compare the stored genomic information or evaluate any fungus of interest in the light of the e-Fungi genomic data. The data appears to be precomputed, similar to IMG from JGI, so there are inherent limitations on the data that one can obtain. However, tools such as these put important data in the hands of expert mycologists that can turn the information into something biologically meaningful.

As Jones points out, this is just the beginning. If fungal genomes are to live up to their promise, they must engage more than just experts at reading genomes.

Not one, but two Aspergillus niger genome sequences

Blogging about Peer-Reviewed ResearchA.niger growing on plate (this is not the sequenced strain)The JGI has previously released A. niger strain ATCC 1015 sequence in November 2005. ATCC 1015 is used in industrial production of citric acid as it is one of the best producers of citric acid. In Nature Biotechnology a Dutch team has published the sequence of another strain, CBS 513.88 which is an early ancestor of ATCC 1015 used in industrial enzyme production.