There are several other rotting fungi that are nearly done at JGI (but the task of writing and coordinating the analyses for the papers are ongoing!) include Schizophyllum commune and Pleurotus ostreatus. There are also several more mycorrhizal and plant pathogenic basidiomycete fungi as well as some classic model systems that have finished genomes and are in the process of finalizing papers.  It is an exciting time that is just beginning as these genome and transcriptional data are integrated and compared for their different ecological, morphological, and metabolic capabilities.

The article is unfortunately not Open Access so I haven’t even read it from home yet, but pass along this news to you, dear reader. Will get a chance to read through more than the abstract to see what glistening gems have been extracted from this genomic endeavor.
D. Martinez, J. Challacombe, I. Morgenstern, D. Hibbett, M. Schmoll, C. P. Kubicek, P. Ferreira, F. J. Ruiz-Duenas, A. T. Martinez, P. Kersten, K. E. Hammel, A. V. Wymelenberg, J. Gaskell, E. Lindquist, G. Sabat, S. S. BonDurant, L. F. Larrondo, P. Canessa, R. Vicuna, J. Yadav, H. Doddapaneni, V. Subramanian, A. G. Pisabarro, J. L. Lavin, J. A. Oguiza, E. Master, B. Henrissat, P. M. Coutinho, P. Harris, J. K. Magnuson, S. E. Baker, K. Bruno, W. Kenealy, P. J. Hoegger, U. Kues, P. Ramaiya, S. Lucas, A. Salamov, H. Shapiro, H. Tu, C. L. Chee, M. Misra, G. Xie, S. Teter, D. Yaver, T. James, M. Mokrejs, M. Pospisek, I. V. Grigoriev, T. Brettin, D. Rokhsar, R. Berka, D. Cullen (2009). Genome, transcriptome, and secretome analysis of wood decay fungus Postia placenta supports unique mechanisms of lignocellulose conversion Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0809575106

We’re also integrating SNP data using the HapMap glyphs in which you can see one way to view this information in the Genome Browser for Coccidioides.  Working on other information including PhastCons conservation profiles and other information in our development server and hope to make this public soon.

This updated annotation is able to join and split several sets of genes and the gene count sits at just under 14k genes in this 36Mb genome. There are a couple of hiccups in the GTF and Genome contig/supercontig file naming that I am told will be fixed by early next week.  Additional work to annotate the “Kinome” by the Broad team provides some promising new insight to this genome annotation as well.

We’re using this updated genome assembly address questions about evolution of genome structure by studying syntenic conservation and aspects of crossing over points during meiosis.  The C. cinereus system has long been used as model for fungal development and morphogensis of mushrooms as it is straightforward to induce mushroom fruiting in the laboratory.  It also a model for studying meiosis due to the synchronized meiosis occurring in the cells in the cap of the mushroom.

Happy genome shrooming.

A new and improved annotation of Cryptococcus neoformans var grubii strain H99 (serotype A) has been made available in GenBank and the Broad Institute website. This update is collaboration between several groups providing data and analyses and the genome annotation team at the Broad Institute.

Some changes noted by the Broad Institute include:

“This release of gene predictions for the serotype A isolate Cryptococcus neoformans var. grubii H99 is based on a new genomic assembly provided by Dr. Fred Dietrich at the Duke Center for Genome Technology. The new assembly consists of 14 nuclear chromosomes and a single 21 KB mitochondrial chromosome, and has resulted in a reduction of the estimated genome size from 19.5 to 18.9 Mb. Improvements in the assembly and in our annotation process have resulted in a set of 6,967 predicted protein products, 335 fewer than the previous release.”

This isn’t the only lichen being sequenced. Xanthoria parietina is also in the queue at JGI, but has taken a while to get going because of some logistical problems getting the DNA (and any problems are amplified because it takes a long time to get new material since lichens grow very slow).

The transfer of the power for researchers to be able to quick exploratory whole-genome sequencing with next-gen and eventually, high quality genome sequences from next-gen sequencing is predicted to transform how this kind of science gets done. It means we’ll probably just sequence a mutant strain instead of trying to map the mutation – this is happening already in anecdotal stories in worms and in our work in mushrooms. N.B. this is done after a mutagenized strain has been cleaned up a bit to insure we’re looking for one or only a few mutations based on some crosses – but that is part of standard genetic approaches anyways.

This fast,cheap,whole-genome-sequencing is also the stuff of personal genomics, but for basic research it will also mean that a first pass exploring gene repertoire of an organism will be a multi-week instead of multi-year project. I just hope we’re training enough people who can efficiently extract the information from all this data with solid bioinformatics, computational, data-oriented programming, and statistical skills to support all the labs that will want to take this approach. You’ll need a life-vest to swim in the big data pool for a while until more tools are developed that can be deployed by non-experts.

In practice it should simplify the initial genome annotation protocols used and could really streamline the procedures. It doesn’t replace the need to gathering EST sequence data that can also be used generate a training set in an automated fashion.  EST and transcriptional evidence is still very important for identification of UTR and alternative splicing isoforms.

Hopefully these data from the predictions will integrate into the Cryptococcus and Coprinus genome annotations that are undergoing an update at the Broad.  We’ll see how well this performs on a couple of the Chytrid genome sequences we are working on as well.

• Exploration of snoRNAs finding that a large fraction are clustered in the genome and located in introns.
• Description of transcription factors and their evolutionary conservation and potential link to multicellularity.
• Duplication and diversification of the RNA processing machinery for small RNA mediated silencing.
• Gleaning additional information from Chlamy ESTs that have been over-trimmed.
• Integrating metabolomics and proteomics into better genome annotation.
• Evolution of signaling proteins found in multicellular animals and now Chlamydomonas.

The most surprising findings from the paper include the fact that there are so few members of some of the enzyme families even though this fungus is able to generate enzymes with so much cellulase activity. The authors found that there is not a significantly larger number of glucoside hydrolases which is a collection of carbohydrate degrading enzymes great for making simple sugars out of complex ones. In fact, several plant pathogens compared (Fusarium graminearum and Magnaporthe grisea) and the sake fermenting Aspergillus oryzae all have more members of this family than does.  T. reesei has almost the least (36) copies of a cellulose binding domain (CBM) of any of the filamentous ascomycete fungi.  They used the CAZyme database (carbohydrate active enzymes) database which has done a fantastic job building up profiles of different enzymes involved in carhohydrate degradation binding, and modifications.

Whether T. reesei is really the best cellulose degrading fungus is definitely an open question.  That it works well in the industrial culture that it has been utilized in is important, but there may be other species of fungi with improved cellulase activity and who may in fact have many more copies of cellulases.  So it will be good to add other fungi to the mix with quantitative information about degradation to try and glean what are the most important combination of enzymes and activities.

One technical note.  The comparison of copy number differences employed in the paper is a simple enough Chi-Squared, work that I’ve done with Matt Hahn and others include a gene family size comparison approach that also taked into account phylogenetic distances and assumes a birth-death process of gene family size change.  It would be great to apply the copy number differences through this or other approaches that just evaluate gene trees for these domains to see where the differences are significant and if they can be polarized to a particular branch of the tree.

So will this genome sequence lead to cheaper, better biofuel production? Certainly it provides an important toolkit to start systematically testing individual cellulase enzymes. It’s hard to say how fast this will make an impact, but the work of JBEI and a host of other research groups and biotech companies are going to be able to systematically test out the utility of these individual enzymes.

There is also evolutionary work by other groups on the evolution of these Hypocreales fungi trying to better define when biotrophic and heterotrophic transitions occurred to sample fungi with different lifestyles that might have different cellulase enyzmes that may not have been observed. Defining the relationships of these fungi and when and how many times transitions to lifestyles occurred to choose the most diverse fungi may be an important part of discovering novel enzymes.

Also see

• Discovery Channel Blog
  
• JGI press release

Martinez, D., Berka, R.M., Henrissat, B., Saloheimo, M., Arvas, M., Baker, S.E., Chapman, J., Chertkov, O., Coutinho, P.M., Cullen, D., Danchin, E.G., Grigoriev, I.V., Harris, P., Jackson, M., Kubicek, C.P., Han, C.S., Ho, I., Larrondo, L.F., de Leon, A.L., Magnuson, J.K., Merino, S., Misra, M., Nelson, B., Putnam, N., Robbertse, B., Salamov, A.A., Schmoll, M., Terry, A., Thayer, N., Westerholm-Parvinen, A., Schoch, C.L., Yao, J., Barbote, R., Nelson, M.A., Detter, C., Bruce, D., Kuske, C.R., Xie, G., Richardson, P., Rokhsar, D.S., Lucas, S.M., Rubin, E.M., Dunn-Coleman, N., Ward, M., Brettin, T.S. (2008). Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nature Biotechnology DOI: 10.1038/nbt1403

Update: PNAS paper available.

The authors point out several interesting things about these genes of unknown function that throw immediate skepticism out the window: most were identified in the original genome annotation (so they aren’t “new” discoveries without a chance to be analyzed), most show signs of conservation with homologs across taxa and evidence of expression, (suggesting that they are real genes), and these genes are routinely identified in large scale analyses (pulldowns, expression profiling, etc.), indicating that they are not being missed by researchers.

Finally, the authors explore the possibility that redundant function annotation classification when these genes are knocked out. While 10% of the unclassified genes appear to be paralogs, this hypothesis cannot explain away the other 90%.It may be, they go on to propose, that researchers are looking at yeast in the wrong conditions. Knock out a gene vital to warding off an amoeba attack in nature and you’ll only observe its phenotype in the presence of an attacking amoeba. Here the authors expose an avenue of yeast biology that has been largely relatively ignored: yeast ecology. If we understood more about how yeast behaves in nature, we could better design experiments to test the functions of these unknown genes in the lab. By parameterizing yeast as a bug that grows on a petri dish, we inherently limit our consideration of how yeast evolved and, hence, what it can do.

This isn’t meant to be a criticism of the yeast community. What they’ve done in the ten years since the yeast genome was released is amazing and should serve to remind us of just what can be done when a community comes together to solve large problems. Rather, I see this as a call to genetics and genomics researchers of any organism. To understand a gene, we should consider, in addition to the genetic, biochemical and cellular environment, the gene’s evolutionary trajectory and its role in the organism’s interaction with the natural environment. Lots of attention has been placed on obtaining sequence, but unless we begin to focus on ecology, there may be a large knowledge gap that prevents us from fully understanding all of the sequence data we’ve gathered. Deconstructistic thought has dominated a great deal of contemporary biology; view an organism as a bag of genes and survey each gene, one at a time, to understand the organism. This has been a largely successful approach, but can only take us so far. It would appear, based on the synopsis by Peña-Castillo and Hughes, that the wall has been hit in yeast genetics. To get over it, we may, as a community, need to adopt a more synthetic based approach to problem solving, borrowing ideas from many fields, especially ecology, to solve single problems. It wouldn’t surprise me if we see Saccharomyces cerevisiae, as a result of the proactive nature of its research community members, become a model ecological organism in the near future.

Some of the key findings

• The presence of Repeat Induced point-mutation (RIP) has likely limited the amount of repetitive and duplicated sequences in the genome
• Most of the genes unique to F. graminearum (and thus not present in 4 other Fusarium spp genomes) are found in the telomeres
• Between the sequenced strains SNP density ranged from 0 to 17.5 polymorphisms per kb.
• Some of the genes expressed uniquely during plant infection (408 total) include known virulence factors and many plant cell-wall degrading enzymes.
• The genes showing some of the highest SNP diversity tended to be unique to Fusarium and often unique to F. graminearum

I think this sentence from the abstract hits the nail on the head.

Notably,the uncharacterized gene set is highly enriched for genes whose only homologs are in other fungi. Achieving a full catalog of yeast gene functions may require a greater focus on the life of yeast outside the laboratory.

There are certainly a lot of genes that are fungal specific and have no known function. Perhaps only by working at these genes in different fungal systems will we be able to discover their function. It may be the case that yeast itsself will be a poor model for understanding these genes and it will require forward genetics in a different fungal model to understand and assign a functional role for them.

I also think a lot of important work is being done to look at the consequence of alleles of genes in different backgrounds by trying to map QTLs for complex traits like high temperature growth in S. cerevisiae obtained from immunocompromised individuals. This way we are able to discover a gene’s function without having look at it in a null background, but rather (if we can) dissect the genes underlying a QTL we might find a more subtle understanding of genotype-phenotype relationship. Perhaps only in this way by taking strains with different phenotypes under a particular condition (e.g. resistance to oxidative stress, carbon utilization efficiency), crossing those with low and high traits, and mapping the genes we might learn about genes’ functions that would never be found in a reverse genetics screen.

I’ve been reading a lot of orthology and gene tree-species tree reconcilation papers lately, some are listed in Ian Holmes’s group as well as listing some of the software on the BioPerl site. This also follows with on our Phyloinformatics hackathon work which we are trying to formalize in some more documentation for phyloinformatics pipelines to support some of the described use cases. I’m also applying some of this to a tutorial I’m teaching at ISMB2007 this summer.

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.