Modeling the evolution of a classic genetic switch

Parallel reorganization of protein function in the spindle checkpoint pathway through evolutionary paths in the fitness landscape that appear neutral in laboratory experiments

Regulatory networks often increase in complexity during evolution through gene duplication and divergence of component proteins. Two models that explain this increase in complexity are: 1) adaptive changes after gene duplication, such as resolution of adaptive conflicts, and 2) non-adaptive processes such as duplication, degeneration and complementation. Both of these models predict complementary changes in the retained duplicates, but they can be distinguished by direct fitness measurements in organisms with short generation times. Previously, it has been observed that repeated duplication of an essential protein in the spindle checkpoint pathway has occurred multiple times over the eukaryotic tree of life, leading to convergent protein domain organization in its duplicates. Here, we replace the paralog pair in S. cerevisiae with a single-copy protein from a species that did not undergo gene duplication. Surprisingly, using quantitative fitness measurements in laboratory conditions stressful for the spindle-checkpoint pathway, we find no evidence that reorganization of protein function after gene duplication is beneficial. We then reconstruct several evolutionary intermediates from the inferred ancestral network to the extant one, and find that, at the resolution of our assay, there exist stepwise mutational paths from the single protein to the divergent pair of extant proteins with no apparent fitness defects. Parallel evolution has been taken as strong evidence for natural selection, but our results suggest that even in these cases, reorganization of protein function after gene duplication may be explained by neutral processes.

What does it take to evolve an enhancer? A simulation-based study of factors influencing the emergence of combinatorial regulation

There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression. The evolution of enhancers poses interesting unanswered questions, for example, the evolutionary time taken for a typical enhancer to emerge or the factors shaping its evolution. Existing approaches to cis-regulatory evolution have often ignored the combinatorial nature and varied biochemical mechanisms of gene regulation encoded in enhancers. We report on our investigation of enhancer evolution through the use of PEBCRES, a framework for evolutionary simulation of enhancers that employs a mechanistic and well-supported sequence-to-expression model to assign fitness to the evolving enhancer genotype. We estimated the time necessary to evolve, from genomic background, enhancers capable of driving complex gene expression patterns similar to those involved in early development in Drosophila. We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors. To our knowledge, this is the first estimate of waiting times for realistic enhancers to evolve. The in silico evolved enhancers had, with a few interesting exceptions, site compositions similar to those seen in real enhancers for the same patterns. Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.

Stochastic galactokinase expression underlies GAL gene induction in a GAL3 mutant of Saccharomyces cerevisiae

GAL1 and GAL3 are paralogous signal transducers that functionally inactivate Gal80p to activate the Gal4p-dependent transcriptional activation of GAL genes in Saccharomyces cerevisiae in response to galactose. Unlike a wild-type strain, the gal3∆ strain shows delayed growth kinetics as a result of the signaling function of GAL1. The mechanism ensuring that GAL1 is eventually expressed to turn on the GAL switch in the gal3∆ strain remains a paradox. Using galactose and histidine growth complementation assays, we demonstrate that 0.3% of the gal3∆ cell population responds to galactose. This is corroborated by flow cytometry and microscopic analysis. The galactose responders and nonresponders isolated from the galactose-adapted population attain the original bimodal state and this phenotype is found to be as hard wired as a genetic trait. Computational analysis suggests that the log-normal distribution in GAL4 synthesis can lead to bimodal expression of GAL80, resulting in the bimodal expression of GAL genes. Heterozygosity at the GAL80 but not at the GAL1, GAL2 or GAL4 locus alters the extent of bimodality of the gal3∆ cell population. We suggest that the asymmetric expression pattern between GAL1 and GAL3 results in the ability of S. cerevisiae to activate the GAL pathway by conferring nongenetic heterogeneity.

Insights into molecular evolution from yeast genomics

Enabled by comparative genomics, yeasts have increasingly developed into a powerful model system for molecular evolution. Here we survey several areas in which yeast studies have made important contributions, including regulatory evolution, gene duplication and divergence, evolution of gene order and evolution of complexity. In each area we highlight key studies and findings based on techniques ranging from statistical analysis of large datasets to direct laboratory measurements of fitness. Future work will combine traditional evolutionary genetics analysis and experimental evolution with tools from systems biology to yield mechanistic insight into complex phenotypes.

Simulations of enhancer evolution provide mechanistic insights into gene regulation

There is growing interest in models of regulatory sequence evolution. However, existing models specifically designed for regulatory sequences consider the independent evolution of individual transcription factor (TF)-binding sites, ignoring that the function and evolution of a binding site depends on its context, typically the cis-regulatory module (CRM) in which the site is located. Moreover, existing models do not account for the gene-specific roles of TF-binding sites, primarily because their roles often are not well understood. We introduce two models of regulatory sequence evolution that address some of the shortcomings of existing models and implement simulation frameworks based on them. One model simulates the evolution of an individual binding site in the context of a CRM, while the other evolves an entire CRM. Both models use a state-of-the art sequence-to-expression model to predict the effects of mutations on the regulatory output of the CRM and determine the strength of selection. We use the new framework to simulate the evolution of TF-binding sites in 37 well-studied CRMs belonging to the anterior-posterior patterning system in Drosophila embryos. We show that these simulations provide accurate fits to evolutionary data from 12 Drosophila genomes, which includes statistics of binding site conservation on relatively short evolutionary scales and site loss across larger divergence times. The new framework allows us, for the first time, to test hypotheses regarding the underlying cis-regulatory code by directly comparing the evolutionary implications of the hypothesis with the observed evolutionary dynamics of binding sites. Using this capability, we find that explicitly modeling self-cooperative DNA binding by the TF Caudal (CAD) provides significantly better fits than an otherwise identical evolutionary simulation that lacks this mechanistic aspect. This hypothesis is further supported by a statistical analysis of the distribution of intersite spacing between adjacent CAD sites. Experimental tests confirm direct homodimeric interaction between CAD molecules as well as self-cooperative DNA binding by CAD. We note that computational modeling of the D. melanogaster CRMs alone did not yield significant evidence to support CAD self-cooperativity. We thus demonstrate how specific mechanistic details encoded in CRMs can be revealed by modeling their evolution and fitting such models to multispecies data.