Studies in mushrooms can have important impact on other research areas.  They can be useful in biotechnology as protein biosynthesis factories for producing compounds or even as an edible delivery mechanism for new drugs.  What we found in the analysis of this genome include clues to mechanisms of how white rotting fungi degrade lignin through analysis of enzyme families.  We also saw evidence for extensive antisense transcription during different developmental stages suggesting some important clues as to how some gene regulation could impact or control developmental progression.  Through gene expression comparison (by MPSS) a large number of transcription factors were shown to be differentially regulated during sexual development.  A knockout out two of these (fst3 and fst4) resulting in changes in ability to form mushrooms (fst4) or smaller mushrooms (fst3).

Several more interesting findings in this work that I hope to add back to this post when there is a little more time -

Ohm, R., de Jong, J., Lugones, L., Aerts, A., Kothe, E., Stajich, J., de Vries, R., Record, E., Levasseur, A., Baker, S., Bartholomew, K., Coutinho, P., Erdmann, S., Fowler, T., Gathman, A., Lombard, V., Henrissat, B., Knabe, N., Kües, U., Lilly, W., Lindquist, E., Lucas, S., Magnuson, J., Piumi, F., Raudaskoski, M., Salamov, A., Schmutz, J., Schwarze, F., vanKuyk, P., Horton, J., Grigoriev, I., & Wösten, H. (2010). Genome sequence of the model mushroom Schizophyllum commune Nature Biotechnology DOI: 10.1038/nbt.1643

Stajich, J., Wilke, S., Ahren, D., Au, C., Birren, B., Borodovsky, M., Burns, C., Canback, B., Casselton, L., Cheng, C., Deng, J., Dietrich, F., Fargo, D., Farman, M., Gathman, A., Goldberg, J., Guigo, R., Hoegger, P., Hooker, J., Huggins, A., James, T., Kamada, T., Kilaru, S., Kodira, C., Kues, U., Kupfer, D., Kwan, H., Lomsadze, A., Li, W., Lilly, W., Ma, L., Mackey, A., Manning, G., Martin, F., Muraguchi, H., Natvig, D., Palmerini, H., Ramesh, M., Rehmeyer, C., Roe, B., Shenoy, N., Stanke, M., Ter-Hovhannisyan, V., Tunlid, A., Velagapudi, R., Vision, T., Zeng, Q., Zolan, M., & Pukkila, P. (2010). Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea (Coprinus cinereus) Proceedings of the National Academy of Sciences, 107 (26), 11889-11894 DOI: 10.1073/pnas.1003391107

• A phylogenetic approach from a Saccharomyces perspective is at PhylomeDB.
• Fungal Orthogroups is based on Synergy algorithm from I. Wapinski formerly of the Regev group at the Broad Institutue.
• Yeast gene order browser (YGOB) for Saccharomyces spp and CGOB for Candida spp.
• OrthoMCL database based on whole genomes, not a ton of fungi but useful starting set.
• Ensembl Genomes provides ortholog prediction as part of the Compara pipeline though there is a limited phylogenetic diversity in the current Ensembl Fungal genomes.
• TreeFam has Saccharomyces cerevisiae and Schizosaccharomyces pombe as the two fungi included in the curated ortholog assignments and phylogenies.
• SIMAP provides pre-computed similarities among all proteins in UniProt.
• InParanoid provides a pretty comprehensive of available 100 whole genomes and many fungal genomes which I tried to help select.
• JGI’s Mycocosm attempts to provide a fungal focused paralog/gene family look at clusters of genes based on whole genomes
• E-Fungi is also an attempt at automated clustering with some fancy webservices logic.
• Fungal Transcription Factor database focused just on families of transcription factors.

Some of these tools are better than others in terms of providing downloadable tables.  Another problem is what Identifiers are used. Many biologists are using gene names or Locus identifiers not UniProt/GenPept IDs to identify genes or proteins of interest.  So tools that just cluster UniProt data aren’t as useful as those which refer to the gene or locus names. Also, providing a way to download all the data from a comparison is important for further mining and grouping of the data or cross-referencing local datasets.  One-by-one plugging in geneids is not really a tool that respects the idea that your user wants to ask sophisticated queries.

Also – beware that some approaches are very much pairwise comparisons lists whereas others are finding orthologous groupings.  So if you want to fine the Rad59 ortholog from all fungi it may be easier or harder depending on the source.

[I may make this a static page in the future to allow for more detailed updating since I know the available resources wax and wane]

For now I’ll leave you with a picture of this cute little mushroom fruting in the lab and a link to many more at Flickr.

The work was performed using Bisulfite sequencing (Illumina) to capture methylated DNA, RNA-Seq of mRNA. The also performed some ChIP-Seq of H2A.Z on pufferfish to look at the nucleosome positioning in that species. For aligning the reads, they used BowTie to align the bisulfite sequences (though I’d be curious how a new aligner, BRAT, designed for Bisulfite seq reads would perform) to the genome.  They also sequenced mRNA via RNA-Seq to assay gene expression for some of the species.

They find several interesting patterns in animal and fungal genomes.  I’ll highlight one in the fungi. They find an unexpected pattern in U. reesii of reduced CGs in repeats, which shows signatures of a RIP-like process, are also methylated.  This finding is also consistent with observations in Coccidioides (Sharpton et al, Genome Res 2009) that showed depleted CGs pairs in repeats.  Since the phenomenon is also found in Coccidioides genomes this methylation of some repeats is likely not unique to U. reesii but may be important in recent evolution of the Onygenales fungi or the larger Eurotiales fungi.  There are several other interesting findings with the first such study that shows methylation data for Zygomycete fungi and a basidiomycete close to my heart, Coprinopsis.  It will be interesting is to dig deeper into this data and see how the patterns of methylation compare to other genomic features and the mechanisms regulating methylation process.

Zemach, A., McDaniel, I., Silva, P., & Zilberman, D. (2010). Genome-Wide Evolutionary Analysis of Eukaryotic DNA Methylation Science DOI: 10.1126/science.1186366

• Published work from Gary Foster’s lab in Applied Env Micro show progress on genetic engineering tools to express introduced genes in the basidiomycete mushroom system Clitopilus passeckerianus. C. passeckarianus produces an antibiotic, pleuromutilin, an important antibiotic. Cover photo [Press] They also showed the  5′ intron is important for efficient expression, something that has been shown several times in fungi and provides more evidence for the role of introns in promoting or regulating an aspect of gene expression or translation. Perhaps by splicing-dependent export.

• Corradi et al – the genome of the microsporidia parasite of Daphnia (water flea). It’s as big as a fungal genome at 24Mb (S.cerevisiae is about 12Mb, Neurospora crassa about 40Mb) but only has about 2,100 genes (S.cerevisiae has ~6,000, N.crassa ~ 10,000). DOI: 10.1186/gb-2009-10-10-r106

• Allomyces macrogynus (Blastocladiomycota “Chytrid”)
• Capsaspora owczarzaki (Ichthyosporea)

Already available data from

• Monosiga brevicolis (JGI)
• Batrachochytrium dendrobatidis (JGI, BI) (Chytridiomycota)

Still in progress (BI)

• Amastigomonas sp
• Amoebidium parasiticum
• Nuclearia simplex
• Salpingoeca or Codosiga sp.
• Sphaeroforma arctica
• Stephanoeca or Acanthocopis sp.
• Mortierella verticulata
• Spizellomyces punctatus

Still in progress (Other centers)

• Monosiga ovata (WashU)
• Physarum polycephalum (WashU)

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 work provides an important stepping stone in understanding and eventually controlling this pathogen which is devastating cacao plantations. An associated review describes what we have and can learn about Witches’ broom disease.

See related:

• Will you always be able to satisfy that chocolate craving?
• Theobroma cacao to be sequenced, Oompa Loompa genome to follow.

Jorge MC Mondego, Marcelo F Carazzolle, Gustavo GL Costa, Eduardo F Formighieri, Lucas P Parizzi, Johana Rincones, Carolina Cotomacci, Dirce M Carraro, Anderson F Cunha, Helaine Carrer, Ramon O Vidal, Raissa C Estrela, Odalys Garcia, Daniela PT Thomazella, Bruno V de Oliveira, Acassia BL Pires, Maria Carolina S Rio, Marcos Renato R Araujo, Marcos H de Moraes, Luis AB Castro, Karina P Gramacho, Marilda S Goncalves, Jose P Moura Neto, Aristoteles Goes Neto, Luciana V Barbosa, Mark J Guiltinan, Bryan A Bailey, Lyndel W Meinhardt, Julio CM Cascardo, Goncalo AG Pereira (2008). A genome survey of Moniliophthora perniciosa gives new insights into Witches’ Broom Disease of cacao BMC Genomics, 9 (1) DOI: 10.1186/1471-2164-9-548

• 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

The analyses in the manuscript focused on these differences between Neurospora and Podospora identifying some key differences in carbon utilization contrasting the coprophillic (Podospora) and plant saprophyte (Neurospora).  There are several observations of gene family expansions in the Podospora genome which could be interpreted as additional enzyme capacity to break down carbon sources that are present in dung.

The genome of Neurospora has be shaped by the action of the genome defense mechanisms like RIP that has been on interpretation of the reduced number of large gene families and paucity of transposons. The authors report a surprising finding that in their analysis that despite sharing orthologs of genes that are involved in several genome defense, they in fact find fewer repetitive sequences in Podospora while it still fails to have good evidence of RIP.

Overall, these data suggest that P. anserina has experienced a fairly complex history of transposition and duplications, although it has not accumulated as many repeats as N. crassa. P. anserina possesses all the orthologues of N. crassa factors necessary for gene silencing, including RIP, meiotic MSUD and also vegetative quelling, a post transcriptional gene silencing mechanism akin to RNA interference

I think this data and observations interleaves nicely with the work our group is exploring on evolution of genome of several Neurospora species which have different mating systems. The fact that the gene components that play a role in MSUD and a RIP are found in Podpospora but yet the degree of RIP and the lack of any observed meiotic silencing suggests some interesting occurrences on the Neurospora branch to be explored.  The potentially different degrees of RIP efficiency and types of mating systems (heterothallic and pseudohomothallic) among the Neurospora spp may also provide a link to understanding how RIP evolved and its role on N. crassa evolution.

Senescence in Podospora

Another aspect of Podopsora biology that isn’t touched on, is the use of the fungus as a model for senescence.  The fungus exhibits maternal senescence which involves targeted changes in the mitochondria that leads to cell death.  The evolutionary and molecular basis for this process has been of interest to many research groups and the genome sequence can provide an additional toolkit for identifying the factors involved in the apoptosis process in this filamentous fungi. Whether it will help find a real link for aging research in other eukaryotes remains to be seen, but it is a good model system for some aspects of how aging and damage to mtDNA are linked.

• Dufour et al, PNAS 2001
• Esser et al, Nature 1977
• Turker et al, MCB 1987

Espagne, E., Lespinet, O., Malagnac, F., Da Silva, C., Jaillon, O., Porcel, B.M., Couloux, A., Aury, J., et al (2008). The genome sequence of the model ascomycete fungus Podospora anserina. Genome Biology, 9(5), R77. DOI: 10.1186/gb-2008-9-5-r77

This is a recent post on the challenges of gene annotation coordination among “manual” and “automated” annotation of gene structure of groups at the same institution.  

Scale that up among multiple genomes, genome centers, quality of prediction programs and assemblies, and you can see why the fungal genome comparisons could use a little bit more help. It is great to hear what the animal genome annotation groups are doing to solve informatics challenges and data management issues and coordination. I’m big fan of more informatics+science in the open where it is feasible. 

There is also some preliminary (old) sequence from Pneumocystis (although it is hard to track down that sequence, a paper from 2006 says there is draft sequence but none shows up in GenBank).  

See also:

•  Yeast known knowns and known unknowns. 
• Approaching 100% coverage for GO assignments in S.pombe