Tag Archives: cryptococcus

Some links


I’ve been too busy to post much these last few days, but here are a few links to some papers I found interesting in my recent browsing.

Schmitt, I., Partida-Martinez, L.P., Winkler, R., Voigt, K., Einax, E., Dölz, F., Telle, S., Wöstemeyer, J., Hertweck, C. (2008). Evolution of host resistance in a toxin-producing bacterial–fungal alliance. The ISME Journal DOI: 10.1038/ismej.2008.19

LEVASSEUR, A. (2008). FOLy: an integrated database for the classification and functional annotation of fungal oxidoreductases potentially involved in the degradation of lignin and related aromatic compounds. Fungal Genetics and Biology DOI: 10.1016/j.fgb.2008.01.004

Shivaji, S., Bhadra, B., Rao, R.S., Pradhan, S. (2008). Rhodotorula himalayensis sp. nov., a novel psychrophilic yeast isolated from Roopkund Lake of the Himalayan mountain ranges, India. Extremophiles DOI: 10.1007/s00792-008-0144-z

Cryptococcus species deliniation

ResearchBlogging.org What delineates species boundaries in fungi? Much work has been done on biological and phylogenetic species concepts in fungi. Some concepts are reviewed in Taylor et al 2006 and in Taylor et al 2000, and applications can be seen in several pathogens such as Paraccocidiodies, Coccidioides, and the model filamentous (non-pathogenic) fungus Neurospora.

A paper in Fungal Genetics and Biology on species definitions in Cryptococcus neoformans from multi-locus sequencing seeks to provide additional treatment of the observed diversity. A large study of 117 Cryptococcus isolates were examined through multi-locus sequencing (6 loci) and identified two monophyletic lineages within C. neoformans varieties that correspond to var. neoformans and var. grubii. However within the C. gattii samples they identified four monophyletic groups consistent with deep divergences observed from whole genome trees for two strains of C. gattii, MLST, and AFLP studies. By first defining species, we can now test whether any of the species groups have different traits including prevalence in clinical settings and in nature.

BOVERS, M., HAGEN, F., KURAMAE, E., BOEKHOUT, T. (2007). Six monophyletic lineages identified within Cryptococcus neoformans and Cryptococcus gattii by multi-locus sequence typing. Fungal Genetics and Biology DOI: 10.1016/j.fgb.2007.12.004

When Microorganisms attack: Protect your historical heritage!

ResearchBlogging.orgAn article in Applied Environmental Biology describes work characterizing microorganisms that degrade materials used to preserve cultural heritage objects. These are some heavy duty synthetic compounds which are commonly used to preserve or treat wood, cover objects to protect them from moisture, light, and avoid direct attack by microbes. This article describes some interesting findings of the types of organisms that attack these preservation materials. Table 1 lists fungi like Aureobasidium pullulans which can degrade Polyvinyl chloride, Chaetomium globosum which has enzymes (someone make sure and describe all of these in the genome sequence) to dissolve Polyurethane, several wood degrading fungi that break down Nylon (Phanerochaete can break down diesel fuel), and melanin producing fungi (like Cryptococcus?) that destroy acrylics.

Continue reading When Microorganisms attack: Protect your historical heritage!

Haunted Woods

Eucalyptus is an utilitarian tree, so it’s no surprise that several organizations are interested in genetically engineering it. Indeed, its genome sequence is slated for release, which should facilitate a GE market for the species. One company in particular – ArborGen (they have a very interesting mission statement) – is using genetic engineering, cloning and classic hybridization techniques to make a cold tolerant variety. ArborGen’s grove of 355 hybrids is located in southern Alabama. While a cold tolerant genotype would enable harvest of the tree across North America, this project has been met with particular public resistance, given the species’ invasive abilities.

There may be another reason for the public to resist ArborGen’s new project: Cryptococcus gattii.   Known to associate with eucalyptus, C. gattii is a yeast-like fungus that can infect and kill mammals, including humans, that inhale its spores. Recently, a rare C. gattii genotype was the subject of an outbreak in British Columbia. Scientists and environmentalists are concerned that standing groves of eucalyptus that may be inncoulated with C. gattii could result in a subsequent health hazard for anyone living nearby. This particular risk, it should be noted, is independent of genetic engineering, but rather results from increased reliance on Eucalyptus as an industrial wood (remember, it’s not native to North America). The concerned parties have raised the issue with the US Deptarment of Agriculture and the EPA, so hopefully Cryptococcus ecologists will be afforded the opportunity to determine if the pathogen lives in ArborGen’s grove.

Final note: a special thanks to Kabir Peay, a fungal ecologist, who brought this to my attention.

Genomes on the horizon at JGI

Several more fungi are on the docket for sequencing at JGI through their community sequencing program. This includes

This complements an ever growing list of fungal genome sequences which is probably topping 80+ now not including the several dozen strains of Saccharomyces that are being sequenced at Sanger Centre and a separately funded NIH project to be sequenced at WashU.

Clusters of genomes

As announced at the Fungal Genetics meeting, the FGI at the Broad Institute is focusing on clusters of genomes rather than single ones. Some of genome projects are already grouped.

  • Coccidioides has 3 strains already plus the outgroup Uncinocarpus and conceivable one could include Histoplasma in there. This resources will grow to 14 strains (which comprise two species) of Coccidioides contributed by FGI and one from TIGR.
  • Aspergillus currently has 8 species sequenced with several in pipeline at Broad and TIGR.
  • Fusarium group has 3 species including recently released F. oxysporium.
  • The Candida clade also have several different already sequenced genomes and of course there is the already well studied (and well utilized genome resources I’ll add) for the Saccharomyces clade.
  • There are 4 genomes (well 5 but JEC21 and B-3501 are nearly identical) of Cryptococcus.

All in all a very exciting time for comparative genomics and I’m particularly intrigued to see how people will begin to use the resources.

This work to consolidate the clusters of genomes will, I hope, be very powerful. However, I still feel we are not doing a good job translating and centralizing information from different related species into a more centralized resource. Lots of money is spent on sequencing but I don’t know that we have realized the dream of having the comparative techniques illuminate the new genomes to the point that we are learning huge new things.

It seems to me, initially there is the lure of gathering low-hanging fruit from a genome analysis (which drives the first genome(s) paper), but not always the financial support of the longer term needs of the community to feed the experimental and functional work back into the genome annotation and interpretation.  The cycle works really well for Saccharomyces cerevisiae because the curators who work with the community to insure information is deposited and that literature is gleaned to link genomic and functional information. But this is expensive in terms of funding many curators for many different projects.

It seems as we add more genomes there isn’t a very centralized effort for this type of curatorial information and so we lack the gems of high-quality annotation that is only seen in a few “model” systems.  At some point a better meta-database that builds bridges between resource and literature rich “model system” communities may help, but maybe something new will have to be created? I like thinking about this as a user-driven content via a wiki which also dynamic (and versioned!) content from automated intelligent systems to map the straight-forward things.  Tools like SCI-PHY already exist that can do this and generate robust orthology groups (or Books as the PhyloFact database organizes them) for futher analysis. The SGD wiki for yeast is a start at this, but is mostly an import of SGD data into a mediawiki framework – I wonder how this can be built upon in a more explictly comparative environment.

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

Evolving a new pathway

A paper* this week from the Huffnagle lab argues that even though the human pathogenic fungus Cryptococcus neoformans can produce an oxylipin similar to prostaglandin, the authors were unable to identify any homologous cyclooxygenase genes in the genome. They showed through LC-MS-MS on supernatants from C. neoformans cells grown on arachidonic acid that molecules with activity similar to prostaglandin E2 are synthesized. BLAST searches of the genome could not identify any similar genes to cyclooxygenase genes including the PPo genes from Aspergillus which contain catalytic domains similar to mammalian cyclooxygenases.

So did C. neoformans evolve a new way to synthesize this enzyme which may act as a hormone and affect the host’s immune system? My cursory searches against other basidiomycete genomes did turn up homologs to these PPo genes in Ustilago and Coprinus so perhaps the enyzmes in the pathway have changed in the Cryptococcus lineage. Perhaps searches with protein structure of cyclooxygenases could pick up functionaly similar genes which would serve as good candidates which have little sequence similarity to the cannonical protein determined in humans.

* Paid access required for 6 months.

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.