Tag Archives: fungi

Fungi on the radio

NPR imageAn NPR story on former Taylor Lab postdoc and current Harvard professor Anne Pringle airs tonight. They followed her, Ben, and Frank around collecting Amanita phalloides in Point Reyes in December. Poor Anne’s voice is going as she had a cold, but as usual she does a great job expressing her unbridled passion for mycology and biology.

The NPR newscast right after the report also has two briefs on medicinal research with fungi.

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.

Gut check

Ever wonder what goes on in a cow’s multi-chambered stomach? Probably not. I did think about it a little more after a trip to a teaching farm during grad school where we saw a cow with a fistula. This hole provides access to the cows stomach so that samples can be drawn of the community living in the gut and understand how the bovine stomach can digest the recalcitrant cellulose of grasses.

Of course all kinds of lovely things live in the dark, anaerobic environment. In fact there is a delicately balanced community of species. When cows are fed corn instead of grass this affects the rumen acid content and allows pathogenic E. coli like O:157 to survive. So far I don’t seen any JGI proposal for sequencing of the gut communities of rumens, but maybe that should be proposed.

Rumen fungi are probably not on your keyword list, but these fungi are extremomophiles living in highly anaerobic environment. A paper in Microbiology details an analysis of the genome of the anaerobic fungus Orpinomyces.

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.

The C is for Catalog

It seems intuitive enough that the size of an organism’s genome should be related to its evolutionary complexity. As a general rule, this tends to be true. But look within a class of organisms and you’ll find a great deal of genome size – also known as a C-value – variation. A newt’s genome, for example, is ten times the size of a frog’s.

This discrepancy between genome size and evolutionary complexity is known as the C-value paradox and it has long captured the imagination of biologists. Genome sequencing and annotation have revealed that a great amount of an organism’s genome is non-coding, suggesting that a great deal of genetic content may be gained or lost without affecting the so-called “evolutionary complexity” of the organism (though whether this non-coding DNA is truly “junk” is still up for debate).

In a recent Nucleic Acids Research paper, Gregory et al introduce another toolset to aid in our understand of genome size: the genome size databases. Three separate databases catalog the genome size statistics for various Plants, Animals and Fungi respectively, collectively covering >10,000 species. While various methods of estimating genome size may produce conflicting estimates of genome size (caveat emptor!), these tools should serve to help guide analyses and experiments of genome size evolution. Specifically, by enabling comparisons of genome size across multiple phylogenetic levels, these datasets should facilitate a better understanding of where the genome size/complexity relationship falls off.

As an interesting side note, the authors mention a few particular findings in fungi. The histogram of genome size in Fungi (see the figure) tends to be tighter than in Plants and Animals, with almost all taxa within the range of 1C or 10-60 Mb of DNA. That said, a few species appear to exhibit considerable intraspecific variation. While this may be due to the aforementioned methodological errors, the authors consider that dikaryotic hybrids and heterokaryotes may contribute to this observation. It seems that we may only be scratching the surface of genome size variation in Fungi and if genome size is indeed rapidly evolving in Fungi, they may serve to as good models to study this evolutionary phenomenon.

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.

Genomes of honeybee pathogens

A.apisBlogging about Peer-Reviewed ResearchThe Baylor sequencing center has published the genome of two honey bee pathogens. Recently Baylor and collaborators published a slew of honey bee genome papers and it is great that they have also chosen to follow up on the parasites as well.

The group published the genomes of the bacteria pathogen Paenibacillus larvae and fungal pathogen Ascosphaera apis. A. apis is in the Onygenales clade which also includes the fungal human pathogens Coccidioides, Histoplasma, and Blastomyces.

Currently the genome annotation is limited to the bacterial genome where many ab initio gene prediction programs exist and no annotation is provided for A. api. We should be able to apply gene prediction parameters trained from other Onygenales fungi to get a resonable annotation. Study of this pathogenic genome may also provide insight into the evolution of this clade of fungi which contains most of the primary fungal pathogens of humans.

All hail the rusts

pucciniaThe FGI and the Broad Institute have released the 7X genome assembly of Puccinia graminis f. sp tritici in roughly 4500 contigs. This represents the first rust fungus to be sequenced and the second Urediniomycete that has been sequenced, Sporobolomyces roseus being the first. This rust fungus is “the causal agent of stem rust, has caused serious disease of small cereal grains (wheat, barley, oat, and rye) worldwide.”