Tag Archives: phylogeny

Monophyly of Taphrinomycotina

A recent paper in MBE  presents evidence that the Taphrinomycota (containing S. pombe and Pneumocystis) are in fact a monophyletic group. This is considered an early branch in the Ascomycota with the Pezizomycotina (filamentous ascomycete fungi like Neurospora and Aspergillus) and Saccharomycotina (fungi mainly with yeast forms including Candida and Saccharomyces).  The monophyly of Taphrinomyoctina fungi is something that has been fairly accepted but there are a few publications reporting  conflicting evidence in some sets gene trees. This conflict is most likely due to Long Branch Attraction (LBA) and the Philippe lab has long worked on this problem of LBA working to develop tools like PhyloBayes that attempt to correct for LBA with a parameter rich model and using lots of data (like whole genomes).  These authors are employing phylogenomics in the sense that multiple genes are used to reconstruct the phylogeny.  This use is different from the J.Eisen/Sjölander sense which is to infer gene function from a phylogeny.

This paper presents evidence using proteins of 113 mitochondrial and nuclear genes and finds strong statistical support for this monophyly.  They also note that it was necessary to remove fast evolving sites from a dataset of only mitochondrial genes in order to overcome LBA artifacts that lead to Saccharomyces and S. pombe sister relationship in previous analyses.

This paper also presents work using the Pneumocystis genome sequence helps resolve its placement and eventually understanding the evolution of this pathogen.  In this tree the sister group to Pneumocystis is Schizosaccharomyces but both lineages have very long branches.  The Saitoella lineage is basal in this paper which is different from what was found with a 4 gene (AFTOL) dataset (see Figure 2). Further work sampling more genes from these Taphrina lineages will likely help resolve the intra-clade relationships.

Y. Liu, J. W. Leigh, H. Brinkmann, M. T. Cushion, N. Rodriguez-Ezpeleta, H. Philippe, B. F. Lang (2008). Phylogenomic Analyses Support the Monophyly of Taphrinomycotina, including Schizosaccharomyces Fission Yeasts Molecular Biology and Evolution, 26 (1), 27-34 DOI: 10.1093/molbev/msn221

Phytophthora work highlighted

A link to the story about Matteo Garbelotto‘s work on Phytophthora ramorum and showing that the source in California is likely from ornamentals from a nursery. The work is to appear soon in Molecular Ecology but alas is not available yet.

A recent paper on updated Phytophthora phylogeny from Jamie Blair and co-authors is also out in FGB. They used genome sequences to determine additional markers for multi-locus sequencing and then sequenced and built trees from 82 taxa. 

Fun with estimating divergence times


Estimating divergence times is notorious difficult and the field can be downright rancorous with some being accused of reading tea leaves and chicken entrails – interesting reading for personalities as much as the different scientific approaches. There are several different approaches to trying to estimate a divergence time among species, using calibration points usually anchored by fossil data. Molecular clock methods have problems sometimes producing extremely old dates that are quite hotly debated. In fungi we have a very few fossils (and their placement on the phylogeny is debated).

There are quite a few available methods for reconstructing divergence times including r8s and multidivtime which start with various types of trees and use calibration time points that are typically informed by fossil dates. The simplest approaches assume a molecular clock (rates are same across the tree) and then one only needs to calibrate the number of substitutions (or rate really) to time to determine how phylogenetic tree branch lengths map to time. The BEAST package also does phylogenetic inference and divergence time estimation (and provided the necessary analysis for exoneration of the Tripoli Six) across a sample of trees. BEAST (and MrBayes) use MCMC to sample the space of parameters and tree space to identify phylogenies and evolutionary parameters that explain the data (an alignment of sequences).

A paper from Akerborg and colleagues introduces a new approach that uses MCMC but apply a few twists, using a birth-death model that doesn’t assume a molecular clock and employing a hill-climbing algorithm instead of MCMC to find parameter optima. They use a Maximum a posterior (MAP) framework which is more computational efficient than MCMC. They couple the MAP approach with a dynamic-programming approach that separates the estimation of rates (branch length) from the estimation of times (which often require assumption of a molecular clock). While I can’t speak with much authority on the MAP approach or yet how well this compares on different datasets, it suggests a different method to tackle these problems. They authors point out one drawback with their approach is it only allows for derivation of point-estimates so statistical confidences like bootstrap support are not easily calculated through this approach. Their software, called PRIME is available here and I will be curious to see how it performs in other peoples’ hands.

Akerborg, O., Sennblad, B., Lagergren, J. (2008). Birth-death prior on phylogeny and speed dating. BMC Evolutionary Biology, 8(1), 77. DOI: 10.1186/1471-2148-8-77

Phytopathogenic Fungi: what have we learned from genome sequences?

ResearchBlogging.orgA review in Plant Cell from Darren Soanes and colleagues summarizes some of the major findings about evolution of phytopathogenic fungi gleaned from genome sequencing highlighting 12 fungi and 2 oomycetes. By mapping evolution of genes identified as virulence factors as well as genes that appear to have similar patterns of diversification, we can hope to derive some principals about how phytopathogenic fungi have evolved from saprophyte ancestors.

They infer from phylogenies we’ve published (Fitzpatrick et al, James et al) that plant pathogenic capabilities have arisen at least 5 times in the fungi and at least 7 times in the eukaryotes. In addition they use data on gene duplication and loss in the ascomycete fungi (Wapinski et al) to infer there large numbers of losses and gains of genes have occurred in fungal lineages.

Continue reading Phytopathogenic Fungi: what have we learned from genome sequences?

This fungus will trap you (if you are a Nematode)

Blogging about Peer-Reviewed ResearchFungi, like most organisms, take an active role in finding food for survival. When thinking about hostile takeovers by fungi, one probably thinks about mycelia growing towards nutrients, rotting plant matter, the ability to extract nutrients from a living host, or perhaps producing toxins or secondary metabolites that can affect the host. However, some fungi can take an even more active role and trap their animal hosts (when that animal isn’t much bigger than you). A paper from earlier this year on “Evolution of nematode-trapping cells of predatory fungi of the Orbiliaceae based on evidence from rRNA-encoding DNA and multiprotein sequences” describes the evolutionary history of a group of fungi able to trap and eat nematodes. Nematode trapping fungi have been investigated experimentally since at least the 30s (Drechsler, Mycologia. 1937, Drechsler, J Wash Acad Sci. 1933), and some more recent studies of the relationship of the groups (Rubner, Studies in Mycology. 1996).

In the recent PNAS paper, the authors used multi-locus sequencing to reconstruct a phylogeny and history of large group of carnivorous fungi and reconstruct the ancestral history the prey trapping mechanism of either through constricting rings or adhesive traps. They were able to reconstruct the likely order of the evolutionary steps needed to make the stalk and trapping cells. They found that the most common type of trap, an Adhesive Network, was the earliest evolved trap.

Some movies also demonstrate how these fungi make their living.

ISMB/ECCB 2007 recap

ISMB2007Back from ISMB/ECCB and a mountain of things left undone that somehow still need doing … including a quick entry about what was interesting at the conference.

I heard many good talks and only a few bad ones – maybe I guessed properly in darting between the multiple sessions. The meeting itsself was much better than past ones I had attended. The combination of Special Interest Groups meeting (BOSC, AFP, and Microbial Comparative Genomics being the ones I spent my time in). I got to give my talks and tutorial during the first few days and was able to just try and soak up the rest of the meeting (when my brain wasn’t melting from the heat). Particularly good was Carole Goble’s presentation on 7-deadly sins of bioinformatics software development.

Some general evolutionary talks that I found really interesting (some of these are probably biased since I count many of the presenters as friends):

I’ll write a quick post on the BoF session on open source and data sharing as well.

Todd and I took some pictures as well.

Proteins Evolve Differentially in Saccharomyces

Blogging about Peer-Reviewed ResearchPerhaps not a surprise to anyone that has dabbled in evolutionary analysis of proteins, Kawahara and Imanishi (BMC Evolutionary Biology 2007) confirm that not every protein evolves via a molecular clock in Saccharomyces sensu scricto. Using everyone’s favorite evolutionary tool, PAML, the authors identify protein lineages via a whole genome scan that evolve relatively slow or fast compared to the rest of the clade. Some changes even appear to be due to the invisible hand of natural selection and independent of the complications that may have arisen during the whole genome duplication in the ancestor of this clade.

It has been previously speculated that, either upon protein duplication or change in the selective regime of the environment, a protein may rapidly evolve at speciation and then, upon obtaining a new, important function, slow down it’s evolutionary rate to a clock-like tempo. One of the black boxes in this hypothesis is whether or not closely related proteins can rapidly diverge. While the authors are not able to identify a mechanism explaining how, their study demonstrates the plausibility of this hypothesis. However, it remains uncertain if proteins that exhibit rapid divergence will subsequently slow down their evolutionary rate later in time.

It’s good to see evolutionary analysis being applied to fungal genomes. With so many sequenced species spanning a great range of phylogenetic distance, the fungal kingdom is poised to provide great insight into the evolution of eukaryotes.

Genome resources for Candida species

The Candida clade of Hemiascomycete fungi have received much attention from funding bodies so that many genomic and experimental resources are available address questions of pathogenecity and industrial applications of these species.

The Candida genus

Traditionally, species of yeasts that were thought to be asexual were given the genus name Candida. This has lead to Candida being a sort of taxonomic rubbish bin as this system of classification breaks down when asexuality arises more than once (creating homoplasy). For example, the asexual Candida glabrata is found within the Saccharomyces clade when molecular phylogenetics is applied. The problem lies in that many of these species appear very similar visually and microscopically and so there had not been enough phylogenetically informative phenotypic characters to easily classify them further. With the use of molecular phylogenetics the classifications have been improved as shown in several studies, however we retain the historical nature of the genus and species names for these organisms for the time being even though the phylogenetic diversity of species in the “genus” is much broader than other genus-level classifications. It will be interesting to see whether taxonomic proposals like PhyloCode or traditional revisions of the species names will provide new names for the group.

The Candida Genome Database (CGD) sister to the Saccharomyces Genome Database (SGD) provides resources for phenotype and sequences related to human commensal and dimorphic fungus Candida albicans. A recent paper by Arnaud et al describes the resources that are available through their website. An essentially completed C. albicans diploid genome with curated gene models and annotations provides an essential resource for this model pathogenic system. In addition to the SC5314 strain of C. albicans the white-opaque (WO) strain can switch between different colony morphologies – white and smooth or gray and rod shaped.

6 additional species have had their genomes in the Candida clade have had their genomes sequenced including Pichia stipis, Debaryomyces hansenii, Candida lusitaniae, Candida tropicalis, Candida guilliermondii, and Lodderomyces elongisporus. These resources will hopefully shed some light on the importance and mechanisms for dimorphic switching in the pathogen C. albicans, the importance and evolution of alternative codon usage in the clade, and better usage of the industrial yeasts like P. stipitis and D. hansenii.