All posts by sharpton

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.

Social Slime Mold

Slime molds are interesting organisms that receive surprisingly little attention. Take the case of Dictyostelium discoideum, a single-celled amoeba that, when starved, will aggregate with other D. discoideum amoeba cells in the neighborhood to create a motile, multicellular structure known as a slug. Eventually the slug differentiates into a reproductive structure, with some individuals making a long stalk and others producing spores. In other words, some individuals help other reproduce but do not reproduce themselves.

D. discodium lifecycleBut why form a slug? Why would a single celled organism decide to cooperate with other, genetically different individuals, particularly when it may provide no direct passage of its genes? The evolutionary benefits of kin relationships aside, previous work has shown that slugs do provide multiple benefits to the population as a whole. Continue reading Social Slime Mold

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.

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.