Mark L. Siegal

Waddington's canalization revisited: Developmental stability and evolution

Most species maintain abundant genetic variation and experience a range of environmental conditions, yet phenotypic variation is low. That is, development is robust to changes in genotype and environment. It has been claimed that this robustness, termed canalization, evolves because of long-term natural selection for optimal phenotypes. We show that the developmental process, here modeled as a network of interacting transcriptional regulators, constrains the genetic system to produce canalization, even without selection toward an optimum. The extent of canalization, measured as the insensitivity to mutation of a networks equilibrium state, depends on the complexity of the network, such that more highly connected networks evolve to be more canalized. We argue that canalization may be an inevitable consequence of complex developmental–genetic processes and thus requires no explanation in terms of evolution to suppress phenotypic variation.

Evolutionary capacitance as a general feature of complex gene networks

An evolutionary capacitor buffers genotypic variation under normal conditions, thereby promoting the accumulation of hidden polymorphism. But it occasionally fails, thereby revealing this variation phenotypically. The principal example of an evolutionary capacitor is Hsp90, a molecular chaperone that targets an important set of signal transduction proteins. Experiments in Drosophila and Arabidopsis have demonstrated three key properties of Hsp90: (1) it suppresses phenotypic variation under normal conditions and releases this variation when functionally compromised; (2) its function is overwhelmed by environmental stress; and (3) it exerts pleiotropic effects on key developmental processes. But whether these properties necessarily make Hsp90 a significant and unique facilitator of adaptation is unclear. Here we use numerical simulations of complex gene networks, as well as genome-scale expression data from yeast single-gene deletion strains, to present a mechanism that extends the scope of evolutionary capacitance beyond the action of Hsp90 alone. We illustrate that most, and perhaps all, genes reveal phenotypic variation when functionally compromised, and that the availability of loss-of-function mutations accelerates adaptation to a new optimum phenotype. However, this effect does not require the mutations to be conditional on the environment. Thus, there might exist a large class of evolutionary capacitors whose effects on phenotypic variation complement the systemic, environment-induced effects of Hsp90.

Robustness: mechanisms and consequences

Biological systems are robust to perturbation by mutations and environmental fluctuations. New data are shedding light on the biochemical and network-level mechanisms responsible for robustness. Robustness to mutation might have evolved as an adaptation to reduce the effect of mutations, as a congruent byproduct of adaptive robustness to environmental variation, or as an intrinsic property of biological systems selected for their primary functions. Whatever its mechanism or origin, robustness to mutation results in the accumulation of phenotypically cryptic genetic variation. Partial robustness can lead to pre-adaptation, and thereby might contribute to evolvability. The identification and characterization of phenotypic capacitors - which act as switches of the degree of robustness - are critical to understanding the mechanisms and consequences of robustness.

Network hubs buffer environmental variation in Saccharomyces cerevisiae.

Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation. A gene product that normally contributes to this robustness is termed a phenotypic capacitor. When a phenotypic capacitor fails, for example when challenged by a harsh environment or mutation, the system becomes less robust and thus produces greater phenotypic variation. A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate, whereas its failure provides a mechanism by which evolutionary change might be promoted. The primary example to date of a phenotypic capacitor is Hsp90, a molecular chaperone that targets a large set of signal transduction proteins. In both Drosophila and Arabidopsis, compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype. For some traits, Hsp90 also appears to buffer stochastic variation, yet the relationship between environmental and genetic buffering remains an important unresolved question. We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors. To test this prediction, we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae. We find more than 300 gene products that, when absent, increase morphological variation. Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity, RNA elongation, protein modification, cell cycle, and response to stimuli such as stress. Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate. Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome. Capacitors with a duplicate are highly connected in the protein–protein interaction network and show considerable divergence in expression from their paralogs. In contrast, capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness. These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture.

Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant

A new experimental approach reveals a bet-hedging strategy in unstressed, clonal yeast cells, whereby they adopt a range of growth states that correlate with expression of a trehalose-synthesis regulator and predict resistance to future stress.

The evolution of dosage-compensation mechanisms†

Dosage compensation is the process by which the expression levels of sex‐linked genes are altered in one sex to offset a difference in sex‐chromosome number between females and males of a heterogametic species. Degeneration of a sex‐limited chromosome to produce heterogamety is a common, perhaps unavoidable, feature of sex‐chromosome evolution. Selective pressure to equalize sex‐linked gene expression in the two sexes accompanies degeneration, thereby driving the evolution of dosage‐compensation mechanisms. Studies of model species indicate that what appear to be very different mechanisms have evolved in different lineages: the male X chromosome is hypertranscribed in drosophilid flies, both hermaphrodite X chromosomes are downregulated in the nematode Caenorhabditis elegans, and one X is inactivated in mammalian females. Moreover, comparative genomic studies demonstrate that the trans‐acting factors (proteins and non‐coding RNAs) that have been shown to mediate dosage compensation are unrelated among the three lineages. Some tantalizing similarities in the fly and mammalian mechanisms, however, remain to be explained. BioEssays 22:1106–1114, 2000.

Precise estimates of mutation rate and spectrum in yeast.

Significance Spontaneous mutations are rare and difficult to observe in large numbers experimentally. By sequencing the genomes of 145 diploid mutation accumulation (MA) lines of the budding yeast Saccharomyces cerevisiae, we identified nearly 1,000 mutations, a larger number than in any prior eukaryotic MA experiment as far as we are aware. For the first time, to our knowledge, in MA data, we were able to estimate rates of context-dependent single-nucleotide mutations. We were also able to observe mutational classes not seen in earlier yeast MA experiments and infer the rate of strongly deleterious mutations from patterns of missing mutations in each mutational class. Our findings both answer outstanding questions in the field, as well as highlight the need for more studies of spontaneous mutation. Mutation is the ultimate source of genetic variation. The most direct and unbiased method of studying spontaneous mutations is via mutation accumulation (MA) lines. Until recently, MA experiments were limited by the cost of sequencing and thus provided us with small numbers of mutational events and therefore imprecise estimates of rates and patterns of mutation. We used whole-genome sequencing to identify nearly 1,000 spontaneous mutation events accumulated over ∼311,000 generations in 145 diploid MA lines of the budding yeast Saccharomyces cerevisiae. MA experiments are usually assumed to have negligible levels of selection, but even mild selection will remove strongly deleterious events. We take advantage of such patterns of selection and show that mutation classes such as indels and aneuploidies (especially monosomies) are proportionately much more likely to contribute mutations of large effect. We also provide conservative estimates of indel, aneuploidy, environment-dependent dominant lethal, and recessive lethal mutation rates. To our knowledge, for the first time in yeast MA data, we identified a sufficiently large number of single-nucleotide mutations to measure context-dependent mutation rates and were able to (i) confirm strong AT bias of mutation in yeast driven by high rate of mutations from C/G to T/A and (ii) detect a higher rate of mutation at C/G nucleotides in two specific contexts consistent with cytosine methylation in S. cerevisiae.

Functional and evolutionary inference in gene networks: does topology matter?

The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are ‘scale free.’ That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein–protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that ‘top-down’ inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene’s location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene’s connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.

A genomic analysis of Drosophila somatic sexual differentiation and its regulation.

In virtually all animals, males and females are morphologically, physiologically and behaviorally distinct. Using cDNA microarrays representing one-third of Drosophila genes to identify genes expressed sex-differentially in somatic tissues, we performed an expression analysis on adult males and females that: (1) were wild type; (2) lacked a germline; or (3) were mutant for sex-determination regulatory genes. Statistical analysis identified 63 genes sex-differentially expressed in the soma, 20 of which have been confirmed by RNA blots thus far. In situ hybridization experiments with 11 of these genes showed they were sex-differentially expressed only in internal genital organs. The nature of the products these genes encode provides insight into the molecular physiology of these reproductive tissues. Analysis of the regulation of these genes revealed that their adult expression patterns are specified by the sex hierarchy during development, and that doublesex probably functions in diverse ways to set their activities.

The details in the distributions: why and how to study phenotypic variability

Phenotypic variability is present even when genetic and environmental differences between cells are reduced to the greatest possible extent. For example, genetically identical bacteria display differing levels of resistance to antibiotics, clonal yeast populations demonstrate morphological and growth-rate heterogeneity, and mouse blastomeres from the same embryo have stochastic differences in gene expression. However, the distributions of phenotypes present among isogenic organisms are often overlooked; instead, many studies focus on population aggregates such as the mean. The details of these distributions are relevant to major questions in diverse fields, including the evolution of antimicrobial-drug and chemotherapy resistance. We review emerging experimental and statistical techniques that allow rigorous analysis of phenotypic variability and thereby may lead to advances across the biological sciences.