Tag Archives: yeast

Genomic patterns of pleiotropy and the evolution of complexity (Wang et. al 2010)

ResearchBlogging.org

Posted by Victor Hanson-Smith, Conor O’Brien, and Bryn Gaertner.

One of the grand challenges of evo-devo is to understand how mutations of genetic sequences affect concomitant phenotypic traits.  Eighty-one years ago, Fisher (1930) proposed that every mutation may affect every trait, and the effect size of a gene on a trait is uniformly distributed: thus we should observe equal proportions of mutations causing large and small per-trait effects.  As a logical consequence of Fisher’s hypothesis, more complex organisms (that is, with more traits) should evolutionarily adapt to their environment at a slower rate than less complex organisms because the presence of more traits implies a higher density of gene-trait relationships and thus incurs a “cost of complexity” (Orr 2000).  However, it is widely accepted that organisms *do* evolve to be more complex, and populations of complex organisms successfully evolve towards fitness optima.  This implies the “cost of complexity” hypothesis is incorrect, or the cost is counteracted by some unknown force.

In contrast to a Fisherian view, contemporary evo-devo research widely accepts the general principle that genes interact in hierarchical modules to produce morphological and physiological traits.  A network-centric perspective of gene-trait interactions suggests that the effect of a particular mutation on downstream traits depends on the network location of the mutated gene: mutations in genes with high network centrality tend to be more pleiotropic because those genes affect many downstream traits, whereas mutations to peripheral genes are less pleiotropic.  However, the extent of modularity and pleiotropy across genomes is unknown.

A recently-published PNAS paper (Wang et al., 2010) repudiates the Fisher-Orr “cost of complexity” hypothesis and confirms contemporary intuition regarding genetic modularity using empirical data and an extension of an exiting model of adaptation.

Wang et al. analyzed genome-wide patterns of pleiotropy in three eukaryotes—yeast, mice, and nematodes—and observed significant modularity in the gene-trait relationship graph and generally low levels of pleiotropy for most genes.  This highly modular structure and generally low pleiotropy means that a mutation is more likely to be beneficial, as it is more likely to affect a small, related set of phenotypes in the same direction, as opposed to many phenotypes in random directions.

Moreover, the authors observed that pleiotropic mutations tend to have a larger per-gene effect than non-pleiotropic mutations.  By extending Orr’s “complexity cost” equation to allow for variable levels of pleiotropy, Wang et al. observed a small non-zero degree of pleiotropy actually increases—rather that impairs—the rate of adaptation. This is because the positive correlation between pleiotropy and effect size increases the probability of fixation and fitness gain in more complex organisms, i.e., those with greater complexity.  This result is important because it may explain the repeated evolution of complexity in many taxa.

Wang et al.’s analysis is based entirely on data mined from knock-out and RNAi experiments; their conclusions are consequently limited to the sequence space of null mutations that silence the function(s) of genes.  In contrast, a less-explored region of sequence space contains mutations that merely affect the relative activity of a gene’s protein product without entirely silencing the gene.  In non-null sequence space, the magnitude of a mutation’s effect is determined not only by the pleiotropy (a.k.a. the network centrality) of the mutated gene, but also the number of redundant pathways leading from that gene to a downstream phenotype.  It is widely accepted that pathway redundancy buffers traits from upstream changes in enzyme activity or dosage [see Kacser and Burns, “The Molecular Basis on Dominance”, Genetics 1981].  Whereas the effects of null mutations are strongly predicted by the extent of pleiotropy (as presently shown by Wang et al.), we hypothesize that the effect of a non-null mutation is largely predicted by the number of interaction pathways between the mutated gene and a downstream phenotype.  This counterhypothesis, however, has yet to be tested.

Read the paper by Wang et al., here:

Wang Z, Liao BY, & Zhang J (2010). Genomic patterns of pleiotropy and the evolution of complexity. Proceedings of the National Academy of Sciences of the United States of America, 107 (42), 18034-9 PMID: 20876104

Transcriptional Rewiring in Yeast

ResearchBlogging.org

Posted by Victor Hanson-Smith.

Consider this 2006 Nature paper from Alexander Johnson’s lab. The story here is that transcriptional regulation of S. cerevisiae (i.e. yeast) mating genes has been handed-off from activation by the MATa gene to repression by the MAT-alpha gene.  This is interesting because despite significant transcriptional rewiring, the logical output (the expression of mating genes) remained the same.

First, some background on yeast. . .

Yeast are either diploid or haploid.  Both haploid and diploid cells can reproduce by mitosis, but haploid cells can sexually reproduce.  Haploid yeast are either type “a” or type “alpha.”  Type-a haploid cells can mate with type-alpha cells, and vice versa.  Haploid mating produces diploid children, which cannot themselves mate.  However, diploid children can induce meiosis (typically in response to nutritional stress) to form four haploid spores: two type-a spores and two type-alpha spores.

Type-a and type-alpha yeast cells differ in their mating pheromones.  Type-a cells produce a-factor pheromone and respond to alpha-factor; Type-alpha cells produce alpha-factor and respond to a-factor. In response to pheromone (of the opposite type) haploid yeast grow a projection called a “shmoo” towards the source of the opposite factor.

An illustration of yeast mating

Type-a cells respond to alpha-factor by using the cell surface receptor Ste2; type-alpha cells respond to a-factor pheromones using the cell surface receptor Ste3.  The interesting difference — and the focus of Tsong et al.’s paper — is that S. cerevisiae type-a mating genes are promoted by Mcm1 transcription factor, whereas C. albicans type-a mating genes are promoted by cofactors Mcm1 and MAT-a2.  Given that S. cerevisiae and C. albicans are related species, this transcriptional difference belies a rewiring event in their shared evolutionary history.

The authors identify seven type-a specific mating genes and their corresponding regulatory sequences.  Using position-specific scoring matrices and homology modeling, the authors inferred the evolutionary events that led to the hand-off between transcriptional activation and repression.  For more details, read the publication.

This paper raises several questions:

1. Did the hand-off from activation to repression incur a fitness cost?  The authors imply a binary fitness landscape: either a yeast expresses the correct mating genes or it doesn’t.  However, it seems like a more accurate fitness story would consider the energetic cost differences between the transcriptional systems used by S. cerevisiae and C. albicans.

2. The authors use C. albicans’ transcriptional phenotype as a proxy for the ancestral state.  Is this accurate?  (The answer is yes).  The alternative hypothesis, in which S. cerevisiae is the ancestral state, requires an outrageous number of gene gains and losses with respect to MAT-a2.

3. How often do these transcriptional rewiring events occur?  This question is somewhat rhetorical, because we don’t have enough information to answer it.  A naive interpretation of this paper is that the yeast MAT-a2 story is especially novel.  As we learn more about the entire transcriptional network of organisms, however, we might learn that these architectural rearrangements occur frequently.

Tsong, A., Tuch, B., Li, H., & Johnson, A. (2006). Evolution of alternative transcriptional circuits with identical logic Nature, 443 (7110), 415-420 DOI: 10.1038/nature05099