Günter P. Wagner

PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY

The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess “evolvability,” i.e., the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype‐phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variations, which are the actually realized differences between individuals. The genotype‐phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility, developmental dissociability, and morphological integration. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype‐phenotype map which allows improvement by mutation and selection? Is the genotype‐phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype‐phenotype map? We propose that the genotype‐phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype‐phenotype map in which there are few pleiotropic effects among characters serving different functions, with pleiotropic effects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of different functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insufficient to assess the plausibility of these models, they form the beginning of a framework for understanding the evolution of the genotype‐phenotype map.

The road to modularity.

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo–devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

PERSPECTIVE:EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS

Abstract Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness—genetic robustness—and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness; and (3) exploring the implications of genetic robustness for future evolution.

A POPULATION GENETIC THEORY OF CANALIZATION

Canalization is the suppression of phenotypic variation. Depending on the causes of phenotypic variation, one speaks either of genetic or environmental canalization. Genetic canalization describes insensitivity of a character to mutations, and the insensitivity to environmental factors is called environmental canalization. Genetic canalization is of interest because it influences the availability of heritable phenotypic variation to natural selection, and is thus potentially important in determining the pattern of phenotypic evolution. In this paper a number of population genetic models are considered of a quantitative character under stabilizing selection. The main purpose of this study is to define the population genetic conditions and constraints for the evolution of canalization. Environmental canalization is modeled as genotype specific environmental variance. It is shown that stabilizing selection favors genes that decrease environmental variance of quantitative characters. However, the theoretical limit of zero environmental variance has never been observed. Of the many ways to explain this fact, two are addressed by our model. It is shown that a “canalization limit” is reached if canalizing effects of mutations are correlated with direct effects on the same character. This canalization limit is predicted to be independent of the strength of stabilizing selection, which is inconsistent with recent experimental data (Sterns et al. 1995). The second model assumes that the canalizing genes have deleterious pleiotropic effects. If these deleterious effects are of the same magnitude as all the other mutations affecting fitness very strong stabilizing selection is required to allow the evolution of environmental canalization. Genetic canalization is modeled as an influence on the average effect of mutations at a locus of other genes. It is found that the selection for genetic canalization critically depends on the amount of genetic variation present in the population. The more genetic variation, the stronger the selection for canalizing effects. All factors that increase genetic variation favor the evolution of genetic canalization (large population size, high mutation rate, large number of genes). If genetic variation is maintained by mutation‐selection balance, strong stabilizing selection can inhibit the evolution of genetic canalization. Strong stabilizing selection eliminates genetic variation to a level where selection for canalization does not work anymore. It is predicted that the most important characters (in terms of fitness) are not necessarily the most canalized ones, if they are under very strong stabilizing selection (k > 0.2Ve). The rate of decrease of mutational variance Vm is found to be less than 10% of the initial Vm. From this result it is concluded that characters with typical mutational variances of about 10–3 Ve are in a metastable state where further evolution of genetic canalization is too slow to be of importance at a microevolutionary time scale. The implications for the explanation of macroevolutionary patterns are discussed.

Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

Measures of RNA abundance are important for many areas of biology and often obtained from high-throughput RNA sequencing methods such as Illumina sequence data. These measures need to be normalized to remove technical biases inherent in the sequencing approach, most notably the length of the RNA species and the sequencing depth of a sample. These biases are corrected in the widely used reads per kilobase per million reads (RPKM) measure. Here, we argue that the intended meaning of RPKM is a measure of relative molar RNA concentration (rmc) and show that for each set of transcripts the average rmc is a constant, namely the inverse of the number of transcripts mapped. Further, we show that RPKM does not respect this invariance property and thus cannot be an accurate measure of rmc. We propose a slight modification of RPKM that eliminates this inconsistency and call it TPM for transcripts per million. TPM respects the average invariance and eliminates statistical biases inherent in the RPKM measure.

Canalization in evolutionary genetics: a stabilizing theory?

Canalization is an elusive concept. The notion that biological systems ought to evolve to a state of higher stability against mutational and environmental perturbations seems simple enough, but has been exceedingly difficult to prove. Part of the problem has been the lack of a definition of canalization that incorporates an evolutionary genetic perspective and provides a framework for both mathematical and empirical study. After briefly reviewing the importance of canalization in studies of evolution and development, we aim, with this essay, to outline a research program that builds upon the definition of canalization as the reduction in variability of a trait, and uses molecular genetic approaches to shed light on the problems of canalization.

Mutational robustness can facilitate adaptation

Robustness seems to be the opposite of evolvability. If phenotypes are robust against mutation, we might expect that a population will have difficulty adapting to an environmental change, as several studies have suggested. However, other studies contend that robust organisms are more adaptable. A quantitative understanding of the relationship between robustness and evolvability will help resolve these conflicting reports and will clarify outstanding problems in molecular and experimental evolution, evolutionary developmental biology and protein engineering. Here we demonstrate, using a general population genetics model, that mutational robustness can either impede or facilitate adaptation, depending on the population size, the mutation rate and the structure of the fitness landscape. In particular, neutral diversity in a robust population can accelerate adaptation as long as the number of phenotypes accessible to an individual by mutation is smaller than the total number of phenotypes in the fitness landscape. These results provide a quantitative resolution to a significant ambiguity in evolutionary theory.

Methods for the Comparative Analysis of Variation Patterns

-Although comparisons of variation patterns with theoretical expectations and across species are playing an increasingly important role in systematics, there has been a lack of appropriate procedures for statistically testing the proposed hypotheses. We present a series of statistical tests for hypotheses of morphological integration and for interspecific comparison, along with examples of their application. These tests are based on various randomization and resampling procedures, such as Mantels test with its recent extensions and bootstrapping. They have the advantage of avoiding the specific and strict distributional assumptions invoked by analytically-based statistics. The statistical procedures described include one for testing the fit of observed correlation matrices to hypotheses of morphological integration and a related test for significant differences in the fit of two alternative hypotheses of morphological integration to the observed correlation structure. Tests for significant similarity in the patterns and magnitudes of variance and correlation among species are also provided. [Morphometrics; comparative analysis; morphological integration; quadratic assignment procedures; Mantels test; bootstrap.] Comparing observed patterns of morphometric variation to theories of morphological integration (Olson and Miller, 1958; Cheverud, 1982) and among species, or subspecific populations (Arnold, 1981; Riska, 1985), has been a largely ad hoc procedure. Previously, a large body of methods has been used to analyze variation patterns, including various forms of cluster analysis, factor analysis, principal components, multi-dimensional scaling, matrix correlations, and visual inspection. The results of such analyses were then discussed relative to some theory of variation patterns or compared between species or populations. These comparisons might either be verbal or quantitative, but tests of statistical significance were rarely employed. More recently, there has been an increase in statistical rigor in the field, particularly involving the use of quadratic assignment procedures (QAP; sometimes referred to as Mantels test) (Mantel, 1967; Deitz, 1983; Dow and Cheverud, 1985; Smouse et al., 1986; Dow et al., 1987a, b; Hubert, 1987) for testing the statistical significance of matrix comparisons (Cheverud and Leamy, 1985; Lofsvold, 1986; Kohn and Atchley, 1988; Cheverud, 1989a; Wagner, 1989) and the use of confirmatory factor analysis (Zelditch, 1987, 1988) for testing hypotheses concerning levels and patterns of variation. These new methods allow statistical inference for hypotheses of morphological integration and for comparisons across species. We will describe the use of several of these newer methods, especially those using randomization, for testing hypotheses of morphological integration and interspecific comparison and provide brief examples of their use. The procedures described below can be used to rigorously test hypotheses concerning the causes of morphological variation and covariation patterns. A closely related set of procedures can be directed towards comparative, cross-taxon, analyses of variation and correlation patterns. The systematic study of distinction among group means is well known and extensively represented in the literature. However, systematic studies of variation patterns (as measured by a multivariate variance/covariance or correlation matrix) have been relatively rare. This has been due, in part, to a lack of relevant theory and appropriate systematic methodology. Important theoretical advances over the

Tissue-specific splicing of disordered segments that embed binding motifs rewires protein interaction networks.

Summary Alternative inclusion of exons increases the functional diversity of proteins. Among alternatively spliced exons, tissue-specific exons play a critical role in maintaining tissue identity. This raises the question of how tissue-specific protein-coding exons influence protein function. Here we investigate the structural, functional, interaction, and evolutionary properties of constitutive, tissue-specific, and other alternative exons in human. We find that tissue-specific protein segments often contain disordered regions, are enriched in posttranslational modification sites, and frequently embed conserved binding motifs. Furthermore, genes containing tissue-specific exons tend to occupy central positions in interaction networks and display distinct interaction partners in the respective tissues, and are enriched in signaling, development, and disease genes. Based on these findings, we propose that tissue-specific inclusion of disordered segments that contain binding motifs rewires interaction networks and signaling pathways. In this way, tissue-specific splicing may contribute to functional versatility of proteins and increases the diversity of interaction networks across tissues.

Transposon-mediated rewiring of gene regulatory networks contributed to the evolution of pregnancy in mammals

A fundamental challenge in biology is explaining the origin of novel phenotypic characters such as new cell types; the molecular mechanisms that give rise to novelties are unclear. We explored the gene regulatory landscape of mammalian endometrial cells using comparative RNA-Seq and found that 1,532 genes were recruited into endometrial expression in placental mammals, indicating that the evolution of pregnancy was associated with a large-scale rewiring of the gene regulatory network. About 13% of recruited genes are within 200 kb of a Eutherian-specific transposable element (MER20). These transposons have the epigenetic signatures of enhancers, insulators and repressors, directly bind transcription factors essential for pregnancy and coordinately regulate gene expression in response to progesterone and cAMP. We conclude that the transposable element, MER20, contributed to the origin of a novel gene regulatory network dedicated to pregnancy in placental mammals, particularly by recruiting the cAMP signaling pathway into endometrial stromal cells.