Category Archives: Papers Reviewed

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

New papers about developmental stochasticity, species delimitation, and functional genomic neighborhoods.

posted by Victor Hanson-Smith

ResearchBlogging.org
Here are three articles—published this week!—that might be relevant to your interests.

1. Stochasticity versus determinism in development: a false dichotomy? Magdalena Zernicka-Goetz, et al., Nature Reviews Genetics, November 2010

The developmental trajectory (from embryo to death) of all multicellular organisms involves cell fate decisions, in which pluripotent, multipotent, and bipotent cells differentiate into specific cell types.  Cell fate decisions are usually controlled by spatiotemporal variation in protein expression levels; for example, the expression levels of transcription factors CDX2 and OCT4 in inchoate mouse embryos determines if the mouse cell becomes an inner-cell mass (ICM) cell or a trophectoderm (TE) cell.  The cells fated to be ICM and TE create a seemingly random heterogenous (“salt-and-pepper”) spatial pattern, leading researchers to conclude that the earliest fate decisions occur stochastically and without regulatory bias.

In this short NRG opinion piece, the authors assert that many seemingly stochastic developmental processes could actually be completely deterministic.  In their words:

. . . a multi-step process with deterministic causation can be so complicated as to be practically unpredictable. . . The outcome then seems lawless, but may not be.

The authors ask, “to what extent is the non-deterministic ‘noisy’ component of developmental control due to true, inevitable ontological randomness and to what extent is it due to epistemological unpredictability because of missing information on the complicated history of cells?”  Although the answer remains elusive for now, this general philosophical framework should guide future investigations about molecular developmental determinants.  I would like to curate a small reading list on this topic, and I encourage you suggest research papers (in the comments field below) that demonstrate regulatory determinism within seemingly-random developmental trajectories.

Zernicka-Goetz M, & Huang S (2010). Stochasticity versus determinism in development: a false dichotomy? Nature reviews. Genetics, 11 (11), 743-4 PMID: 20877326

2. Species delimitation using dominant and codominant multilocus markers, Bernhard Hausdorf and Christian Hennig, Systematic Biology, October 2010

How do we determine the genetic boundaries between species?  The problem of species delimitation can be challenging due to admixture between incipient sister species and due to discordance between gene trees and species.  For example, species in the earliest stages of speciation will differ by only a few genes (presumably genes responsible for reproductive isolation or differential adaptation), and it can be difficult to detect speciation in these situations.

In this paper, the authors propose a method for species delimitation based on Gaussian clustering (also known as mixture modeling). They compare their method to results from the programs STRUCTURE (Pritchard 2000) and STRUCTURAMA (Huelsenbeck and Andolfatto 2007), which group individuals such that Hardy-Weinberg equilibrium is maximized within each cluster.

The authors observed the accuracy of species delimitation depends on the dominance of genetic markers.  The authors analyzed four AFLP datasets, two of which contain dominant genetic markers and two containing co-dominant markers.  When using dominant markers, Gaussian clustering was the most accurate method; when using codominant markers, STRUCTURAMA was the most accurate.  Based on these preliminary results, it seems that Gaussian clustering is a useful method, but there is no single panacea for the problem of species delimitation.

Hausdorf B, & Hennig C (2010). Species delimitation using dominant and codominant multilocus markers. Systematic biology, 59 (5), 491-503 PMID: 20693311

3. Selection upon Genome Architecture: Conservation of Functional Neighborhoods with Changing Genes, Fatima Al-Shahrour et al., PLoS Computational Biology, October 2010

The authors compared the genomes of eight Eukaryotes, and observed significant evidence supporting the “functional neighborhood” hypothesis, in which genes of related function tend to cluster together in tight genomic regions.  I think the most exciting result is this:

. . .there is a significantly higher degree of coexpression in genes belonging to a given functional class [as determined by GO terms] when they are packed within a functional neighborhood than when they are elsewhere in the genome. This result, along with the lack of a significant relative enrichment of tandem duplications. . . points to coexpression as the most plausible driving force for the existence of functional neighborhoods.

The paper includes several other interesting points of discussion, including a syntentic analysis between human and chimp genomes. I think this paper could have been stronger if the authors created syntentic maps for all pairwise combinations of the eight species, but I realize this is an ambitious (and perhaps currently impossible?) task.

The Achille’s heal of their analysis is the reliance on GO terms.  Based on these terms, the authors find functional neighborhoods that have been phylogenetically conserved.  For example, the functional neighborhood for coagulation genes has been conserved across all fish (including birds and mammals). This result is very appealing, but I wonder if anyone has investigated the accuracy and/or general usefulness of the GO terms?  If you can suggest good papers, please leave a comment down below.

Al-Shahrour F, Minguez P, Marqués-Bonet T, Gazave E, Navarro A, & Dopazo J (2010). Selection upon genome architecture: conservation of functional neighborhoods with changing genes. PLoS computational biology, 6 (10) PMID: 20949098