Controls for Phylogeny and Robust Analysis in Pareto Task Inference

Understanding the tradeoffs faced by organisms is a major goal of evolutionary biology. One of the main approaches for identifying these tradeoffs is Pareto task inference (ParTI). Two recent papers claim that results obtained in ParTI studies are spurious due to phylogenetic dependence (Mikami T, Iwasaki W. 2021. The flipping t-ratio test: phylogenetically informed assessment of the Pareto theory for phenotypic evolution. Methods Ecol Evol. 12(4):696–706) or hypothetical p-hacking and population-structure concerns (Sun M, Zhang J. 2021. Rampant false detection of adaptive phenotypic optimization by ParTI-based Pareto front inference. Mol Biol Evol. 38(4):1653–1664). Here, we show that these claims are baseless. We present a new method to control for phylogenetic dependence, called SibSwap, and show that published ParTI inference is robust to phylogenetic dependence. We show how researchers avoided p-hacking by testing for the robustness of preprocessing choices. We also provide new methods to control for population structure and detail the experimental tests of ParTI in systems ranging from ammonites to cancer gene expression. The methods presented here may help to improve future ParTI studies.     

The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space

When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes—phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass.     

Phenomics: the next challenge

A key goal of biology is to understand phenotypic characteristics, such as health, disease and evolutionary fitness. Phenotypic variation is produced through a complex web of interactions between genotype and environment, and such a 'genotype-phenotype' map is inaccessible without the detailed phenotypic data that allow these interactions to be studied. Despite this need, our ability to characterize phenomes - the full set of phenotypes of an individual - lags behind our ability to characterize genomes. Phenomics should be recognized and pursued as an independent discipline to enable the development and adoption of high-throughput and high-dimensional phenotyping.     

Pareto optimality,economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.     

Multivariate Phenotypic Divergence Due to the Fixation of Beneficial Mutations in Experimentally Evolved Lineages of a Filamentous Fungus

The potential for evolutionary change is limited by the availability of genetic variation. Mutations are the ultimate source of new alleles, yet there have been few experimental investigations of the role of novel mutations in multivariate phenotypic evolution. Here, we evaluated the degree of multivariate phenotypic divergence observed in a long-term evolution experiment whereby replicate lineages of the filamentous fungus Aspergillus nidulans were derived from a single genotype and allowed to fix novel (beneficial) mutations while maintained at two different population sizes. We asked three fundamental questions regarding phenotypic divergence following approximately 800 generations of adaptation: (1) whether divergence was limited by mutational supply, (2) whether divergence proceeded in relatively many (few) multivariate directions, and (3) to what degree phenotypic divergence scaled with changes in fitness (i.e. adaptation). We found no evidence that mutational supply limited phenotypic divergence. Divergence also occurred in all possible phenotypic directions, implying that pleiotropy was either weak or sufficiently variable among new mutations so as not to constrain the direction of multivariate evolution. The degree of total phenotypic divergence from the common ancestor was positively correlated with the extent of adaptation. These results are discussed in the context of the evolution of complex phenotypes through the input of adaptive mutations.     

Jack of all trades,master of some? On the role of phenotypic plasticity in plant invasions

Invasion biologists often suggest that phenotypic plasticity plays an important role in successful plant invasions. Assuming that plasticity enhances ecological niche breadth and therefore confers a fitness advantage, recent studies have posed two main hypotheses: (1) invasive species are more plastic than non-invasive or native ones; (2) populations in the introduced range of an invasive species have evolved greater plasticity than populations in the native range. These two hypotheses largely reflect the disparate interests of ecologists and evolutionary biologists. Because these sciences are typically interested in different temporal and spatial scales, we describe what is required to assess phenotypic plasticity at different levels. We explore the inevitable tradeoffs of experiments conducted at the genotype vs. species level, outline components of experimental design required to identify plasticity at different levels, and review some examples from the recent literature. Moreover, we suggest that a successful invader may benefit from plasticity as either (1) a Jack-of-all-trades, better able to maintain fitness in unfavourable environments; (2) a Master-of-some, better able to increase fitness in favourable environments; or (3) a Jack-and-master that combines some level of both abilities. This new framework can be applied when testing both ecological or evolutionary oriented hypotheses, and therefore promises to bridge the gap between the two perspectives.     

Bidirectional Genetic Control of Phenotypic Heterogeneity and Its Implication for Cancer Drug Resistance

Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or “phenotypic diversifiers,” as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity as well as their distinct evolutionary roles and functional independence.     

Molecular evolution, mutation size and gene pleiotropy: a geometric reexamination

The influence of phenotypic effects of genetic mutations on molecular evolution is not well understood. Neutral and nearly neutral theories of molecular evolution predict a negative relationship between the evolutionary rate of proteins and their functional importance; nevertheless empirical studies seeking relationships between evolutionary rate and the phenotypic role of proteins have not produced conclusive results. In particular, previous studies have not found the expected negative correlation between evolutionary rate and gene pleiotropy. Here, we studied the effect of gene pleiotropy and the phenotypic size of mutations on the evolutionary rate of genes in a geometrical model, in which gene pleiotropy was characterized by n molecular phenotypes that affect organismal fitness. For a nearly neutral process, we found a negative relationship between evolutionary rate and mutation size but pleiotropy did not affect the evolutionary rate. Further, for a selection model, where most of the substitutions were fixed by natural selection in a randomly fluctuating environment, we also found a negative relationship between evolutionary rate and mutation size, but interestingly, gene pleiotropy increased the evolutionary rate as √n. These findings may explain part of the disagreement between empirical data and traditional expectations.     

The phenotypic gambit: selective pressures and ESS methodology in evolutionary game theory

The ‘phenotypic gambit,’ the assumption that we can ignore genetics and look at the fitness of phenotypes to determine the expected evolutionary dynamics of a population, is often used in evolutionary game theory. However, as this paper will show, an overlooked genotype to phenotype map can qualitatively affect evolution in ways the phenotypic approach cannot predict or explain. This gives us reason to believe that, even in the long-term, correspondences between phenotypic predictions and dynamical outcomes are not robust for all plausible assumptions regarding the underlying genetics of traits. This paper shows important ways in which the phenotypic gambit can fail and how to proceed with evolutionary game theoretic modeling when it does.     

Formal Darwinism

Two questions are raised for Grafen’s formal darwinism project of aligning evolutionary dynamics under natural selection with the optimization of phenotypes for individuals of a population. The first question concerns mean fitness maximization during frequency-dependent selection; in such selection regimes, not only is mean fitness typically not maximized but it is implausible that any parameter closely related to fitness is being maximized. The second question concerns whether natural selection on inclusive fitness differences can be regarded as individual selection or whether it leads to a departure from the central motivation that led to the formal darwinism project, viz., to show that “Darwinian” evolution through individual selection leads to “good design” or phenotypic adaptation through trait optimization.     

Can natural selection maintain long-distance dispersal? Insight from a stream salamander system

Dispersal distributions are often characterized by many individuals that stay close to their origin and large variation in the distances moved by those that leave. This variation in dispersal distance can strongly influence demographic, ecological, and evolutionary processes. However, a lack of data on the fitness and phenotype of individual dispersers has impeded research on the role of natural selection in maintaining variation in dispersal distance. Six years of spatially explicit capture-mark-recapture data showed that survival increased with dispersal distance in the stream salamander Gyrinophilus porphyriticus. To understand the evolutionary implications of this fitness response, we tested whether variation in dispersal distance has a phenotypic basis. We used photographs of marked individuals to measure head, trunk, and leg morphology. We then tested whether dispersal distances over the six-year study period were predicted by these traits. Dispersal distance was significantly related to leg morphology: individuals with relatively long forelimbs and short hindlimbs dispersed the farthest. These results support the hypothesis that positive fitness consequences maintain phenotypes enabling long-distance dispersal. More broadly, they suggest that natural selection can promote variation in dispersal distance and associated phenotypes, offering an alternative to the view that dispersal distance is driven by stochastic or landscape-specific mechanisms.     

Mechanisms of pathogenesis and the evolution of parasite virulence

When studying how much a parasite harms its host, evolutionary biologists turn to the evolutionary theory of virulence. That theory has been successful in predicting how parasite virulence evolves in response to changes in epidemiological conditions of parasite transmission or to perturbations induced by drug treatments. The evolutionary theory of virulence is, however, nearly silent about the expected differences in virulence between different species of parasite. Why, for example, is anthrax so virulent, whereas closely related bacterial species cause little harm? The evolutionary theory might address such comparisons by analysing differences in tradeoffs between parasite fitness components: transmission as a measure of parasite fecundity, clearance as a measure of parasite lifespan and virulence as another measure that delimits parasite survival within a host. However, even crude quantitative estimates of such tradeoffs remain beyond reach in all but the most controlled of experimental conditions. Here, we argue that the great recent advances in the molecular study of pathogenesis provide a way forward. In light of those mechanistic studies, we analyse the relative sensitivity of tradeoffs between components of parasite fitness. We argue that pathogenic mechanisms that manipulate host immunity or escape from host defences have particularly high sensitivity to parasite fitness and thus dominate as causes of parasite virulence. The high sensitivity of immunomodulation and immune escape arise because those mechanisms affect parasite survival within the host, the most sensitive of fitness components. In our view, relating the sensitivity of pathogenic mechanisms to fitness components will provide a way to build a much richer and more general theory of parasite virulence.     

The stationary distribution of a continuously varying strategy in a class-structured population under mutation-selection-drift balance

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.     

Testing for environmentally induced bias in phenotypic estimates of natural selection: theory and practice

Measuring natural selection has been a fundamental goal of evolutionary biology for more than a century, and techniques developed in the last 20 yr have provided relatively simple means for biologists to do so. Many of these techniques, however, share a common limitation: when applied to phenotypic data, environmentally induced covariances between traits and fitness can lead to biased estimates of selection and misleading predictions about evolutionary change. Utilizing estimates of breeding values instead of phenotypic data with these methods can eliminate environmentally induced bias, although this approach is more difficult to implement. Despite this potential limitation to phenotypic methods and the availability of a potential solution, little empirical evidence exists on the extent of environmentally induced bias in phenotypic estimates of selection. In this article, we present a method for detecting bias in phenotypic estimates of selection and demonstrate its use with three independent data sets. Nearly 25% of the phenotypic selection gradients estimated from our data are biased by environmental covariances. We find that bias caused by environmental covariances appears mainly to affect quantitative estimates of the strength of selection based on phenotypic data and that the magnitude of these biases is large. As our estimates of selection are based on data from spatially replicated field experiments, we suggest that our findings on the prevalence of bias caused by environmental covariances are likely to be conservative.     

The multiplicity of divergence mechanisms in a single evolving population

Background

Evolutionary divergence is common within bacterial species and populations, even during a single bacterial infection. We use large-scale genomic and phenotypic analysis to identify the extent of diversification in controlled experimental populations and apply these data to differentiate between several potential mechanisms of evolutionary divergence.

Results

We defined testable differences between five proposed mechanisms and used experimental evolution studies to follow eight glucose-limited Escherichia coli chemostat populations at two growth rates. Simple phenotypic tests identified 11 phenotype combinations evolving under glucose limitation. Each evolved population exhibited 3 to 5 different combinations of the 11 phenotypic clusters. Genome sequencing of a representative of each phenotypic cluster from each population identified 193 mutations in 48 isolates. Only two of the 48 strains had evolved identically. Convergent paths to the same phenotype occurred, but two pleiotropic mutations were unique to slow-growing bacteria, permitting them greater phenotypic variance. Indeed, greater diversity arose in slower-growing, more stressed cultures. Mutation accumulation, hypermutator presence and fitness mechanisms varied between and within populations, with the evolved fitness considerably more uniform with fast growth cultures. Negative frequency-dependent fitness was shown by a subset of isolates.

Conclusions

Evolutionary diversity is unlikely to be explained by any one of the available mechanisms. For a large population as used in this study, our results suggest that multiple mechanisms contribute to the mix of phenotypes and evolved fitness types in a diversifying population. Another major conclusion is that the capacity of a population to diversify is a function of growth rate.     

Social selection acts on behavior and body mass but does not contribute to the total selection differential in eastern chipmunks

Through social interactions, phenotypes of conspecifics can affect an individual's fitness, resulting in social selection. Social selection is assumed to represent a strong and dynamic evolutionary force that can act with or in opposition to natural selection. Few studies, however, have estimated social selection and its contribution to total selection in the wild. We estimated natural and social selection gradients on exploration, docility, and body mass, and their contribution to selection differentials, in a wild eastern chipmunk population (Tamias striatus). We applied trait-based multiple regression models derived from classical phenotypic selection analyses, which allowed us to include several social partners (i.e., neighbors). We detected social selection gradients on female docility and male body mass, indicating that female with docile neighbors and males with large neighbors had lower fitness. In both sexes, social selection gradients varied with the season. However, we found no phenotypic assortment or disassortment for the studied traits. Social selection gradients, therefore, did not contribute to total selection differentials, and natural selection alone could drive phenotypic changes. Evaluating the factors that drive the evolution of the covariance between interacting phenotypes is necessary to understand the role of social selection as an evolutionary force.     

Natural and Unanticipated Modifiers of RNAi Activity in Caenorhabditis elegans

Organisms used as model genomics systems are maintained as isogenic strains, yet evidence of sequence differences between independently maintained wild-type stocks has been substantiated by whole-genome resequencing data and strain-specific phenotypes. Sequence differences may arise from replication errors, transposon mobilization, meiotic gene conversion, or environmental or chemical assault on the genome. Low frequency alleles or mutations with modest effects on phenotypes can contribute to natural variation, and it has proven possible for such sequences to become fixed by adapted evolutionary enrichment and identified by resequencing. Our objective was to identify and analyze single locus genetic defects leading to RNAi resistance in isogenic strains of Caenorhabditis elegans. In so doing, we uncovered a mutation that arose de novo in an existing strain, which initially frustrated our phenotypic analysis. We also report experimental, environmental, and genetic conditions that can complicate phenotypic analysis of RNAi pathway defects. These observations highlight the potential for unanticipated mutations, coupled with genetic and environmental phenomena, to enhance or suppress the effects of known mutations and cause variation between wild-type strains.     

THE EVOLUTION OF PHENOTYPIC CORRELATIONS AND “DEVELOPMENTAL MEMORY”

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well‐understood in the context of neural networks. This helps to explain how development facilitates the evolution of high‐fitness phenotypes and how this ability changes over evolutionary time.