Patterns and evolutionary consequences of pleiotropy

Behavioural vs. physiological adaptation: which contributes more to the evolution of complex traits in a warming climate?

Through behavioural adaptation, organisms can alter their environment and consequently, their exposure to selective pressures. In contrast, physiological traits adapt by accommodating environmental influences. Here, we examine how the coevolution of behavioural and physiological traits is shaped by their different relationships to the environment by modelling the adaptation of species with temperature-dependent sex determination to climate change. In these species, pivotal temperature and maternal nesting behaviour can evolve in response to rising temperatures that destabilize sex ratios. We used individual-based simulation modelling to ascertain the relative response to selection of these traits and determine how temperature-dependent embryonic survival and behavioural plasticity influence their coevolution. We found that pivotal temperature evolved to ameliorate sex ratio bias more readily than nesting behaviour, though behaviour played an important role in adaptation to extreme environments. Selection favoured increased behavioural evolution when embryonic survival depended on nest temperature, while plasticity reduced the adaptive potential of behaviour. We demonstrate that the capacity of behavioural traits to respond to multiple selective pressures has a substantial impact on the coevolution of behavioural and physiological traits. Our findings highlight the complex interactions that occur when species adapt to new environments and the potential for plasticity to shape the course of evolution.

Epistasis-mediated compensatory evolution in a fitness landscape with adaptational tradeoffs

The evolutionary adaptation of an organism to a stressful environment often comes at the cost of reduced fitness. For example, resistance to antimicrobial drugs frequently reduces growth rate in the drug-free environment. This cost can be compensated without loss in resistance by mutations at secondary sites when the organism evolves again in the stress-free environment. Here, we analytically and numerically study evolution on a simple model fitness landscape to show that compensatory evolution can occur even in the presence of the stress and without the need for mutations at secondary sites. Fitness in the model depends on two phenotypes-the null-fitness defined as the fitness in the absence of stress, and the resistance level to the stress. Mutations universally exhibit antagonistic pleiotropy between the two phenotypes, that is they increase resistance while decreasing the null-fitness. Initial adaptation in this model occurs in a smooth region of the landscape with a rapid accumulation of stress resistance mutations and a concurrent decrease in the null-fitness. This is followed by a second, slower phase exhibiting partial recovery of the null-fitness. The second phase occurs on the rugged part of the landscape and involves the exchange of high-cost resistance mutations for low-cost ones. This process, which we call exchange compensation, is the result of changing epistatic interactions in the genotype as evolution progresses. The model provides general lessons about the tempo and mode of evolution under universal antagonistic pleiotropy with specific implications for drug resistance evolution.

Precise Lineage Tracking Using Molecular Barcodes Demonstrates Fitness Trade-offs for Ivermectin Resistance in Nematodes

A fundamental tenet of evolutionary genetics is that the direction and strength of selection on individual loci varies with the environment. Barcoded evolutionary lineage tracking is a powerful approach for high-throughput measurement of selection within experimental evolution that to date has largely been restricted to studies within microbial systems, largely because the random integration of barcodes within animals is limited by physical and molecular protection of the germline. Here, we use the recently developed TARDIS barcoding system in Caenorhabditis elegans (Stevenson et al., 2023) to implement the first randomly inserted genomic-barcode fitness experiment within an animal model and use this system to precisely measure the influence of the concentration of the anthelmintic compound ivermectin on the strength of selection on an ivermectin resistance cassette. The combination of the trio of knockouts in neuronally expressed GluCl channels, avr-14, avr-15, and glc-1, has been previously demonstrated to provide resistance to ivermectin at high concentrations. Varying the concentration of ivermectin in liquid culture allows the strength of selection on these genes to be precisely controlled within populations of millions of individuals, with the frequency of each barcode then being measured at multiple time points via sequencing at deep coverage and used to estimate the fitness of the individual lineages in the population. The mutations display a high cost to resistance at low concentrations, rapidly losing out to wildtype genotypes, but the balance tips in their favor when the ivermectin concentration exceeds 2nM. This trade-off in resistance is likely generated by a hindered rate of development in resistant individuals. Our results demonstrate that C. elegans can be used to generate high precision estimates of fitness using a high-throughput barcoding approach to yield novel insights into evolutionarily and economically important traits.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans owing to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, we reveal a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (∼0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes owing to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potential for phenotypic innovation in young genes compared to older genes, a process under natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

Transcriptomic and proteomic effects of gene deletion are not evolutionarily conserved

Although the textbook definition of gene function is the effect for which the gene was selected and/or by which it is maintained, gene function is commonly inferred from the phenotypic effects of deleting the gene. Because some of the deletion effects are byproducts of other effects, they may not reflect the gene's selected-effect function. To evaluate the degree to which the phenotypic effects of gene deletion inform gene function, we compare the transcriptomic and proteomic effects of systematic gene deletions in budding yeast (Saccharomyces cerevisiae) with those effects in fission yeast (Schizosaccharomyces pombe). Despite evidence for functional conservation of orthologous genes, their deletions result in no more sharing of transcriptomic or proteomic effects than that from deleting nonorthologous genes. Because the wild-type mRNA and protein levels of orthologous genes are significantly correlated between the two yeasts and because transcriptomic effects of deleting the same gene strongly overlap between studies in the same S. cerevisiae strain by different laboratories, our observation cannot be explained by rapid evolution or large measurement error of gene expression. Analysis of transcriptomic and proteomic effects of gene deletions in multiple S. cerevisiae strains by the same laboratory reveals a high sensitivity of these effects to the genetic background, explaining why these effects are not evolutionarily conserved. Together, our results suggest that most transcriptomic and proteomic effects of gene deletion do not inform selected-effect function. This finding has important implications for assessing and/or understanding gene function, pleiotropy, and biological complexity.

Functional dissection of three pollen-side quantitative trait loci against multiple stylar unilateral incompatibility mechanisms in Solanum pennellii LA0716

In Solanum pennellii LA0716, three stylar UI (sui) factors and one pollen UI (pui) factor were shown to be involved in S-RNase-independent unilateral incompatibility (UI). However, additional pui factor(s) and the antagonistic relationships among pui and sui factors remain to be investigated. Quantitative trait loci (QTL) mapping, functional and genetic analysis of LA0716-based crosses, and integrated multi-omics data are used to identify pui QTLs and functionally dissect pui QTLs from various types of stylar UI. In addition to the reported pui10.1 (SpFPS2), two pui QTLs (pui6.2 and pui12.1) were identified. In LA0716 styles, the three pui loci additively attenuate stylar UI, among which pui6.2 and pui12.1 appear to antagonize the sui factor, SpHT, via independent mechanisms. Furthermore, pui12.1's function was found to be conserved in the SC styles of Solanum habrochaites LA0407 and Solanum chmielewskii LA1028. Candidate genes linked to pui6.2 and pui12.1 are identified for further analysis. This study reveals several mechanisms for three newly described types of stylar UI and the corresponding pui QTLs in LA0716, which advance our understanding of the complex genetic mechanisms underlying UI in the tomato clade.

Alternative mutational architectures producing identical M -matrices can lead to different patterns of evolutionary divergence

Explaining macroevolutionary divergence in light of population genetics requires understanding the extent to which the patterns of mutational input contribute to long-term trends. In the context of quantitative traits, mutational input is typically described by the mutational variance-covariance matrix, or the M -matrix, which summarizes phenotypic variances and covariances introduced by new mutations per generation. However, as a summary statistic, the M -matrix does not fully capture all the relevant information from the underlying mutational architecture, and there exist infinitely many possible underlying mutational architectures that give rise to the same M -matrix. Using individual-based simulations, we demonstrate mutational architectures that produce the same M -matrix can lead to different levels of constraint on evolution and result in difference in within-population genetic variance, between-population divergence, and rate of adaptation. In particular, the rate of adaptation and that of neutral evolution are both reduced when a greater proportion of loci are pleiotropic. Our results reveal that aspects of mutational input not reflected by the M -matrix can have a profound impact on long-term evolution, and suggest it is important to take them into account in order to connect patterns of long-term phenotypic evolution to underlying microevolutionary mechanisms.

Precise Lineage Tracking Using Molecular Barcodes Demonstrates Fitness Trade-offs for Ivermectin Resistance in Nematodes

A fundamental tenet of evolutionary genetics is that the direction and strength of selection on individual loci varies with the environment. Barcoded evolutionary lineage tracking is a powerful approach for high-throughput measurement of selection within experimental evolution that to date has largely been restricted to studies within microbial systems, largely because the random integration of barcodes within animals is limited by physical and molecular protection of the germline. Here, we use the recently developed TARDIS barcoding system in Caenorhabditis elegans (Stevenson et al., 2023) to implement the first randomly inserted genomic-barcode experimental evolution animal model and use this system to precisely measure the influence of the concentration of the anthelmintic compound ivermectin on the strength of selection on an ivermectin resistance cassette. The combination of the trio of knockouts in neuronally expressed GluCl channels, avr-14, avr-15, and glc-1, has been previously demonstrated to provide resistance to ivermectin at high concentrations. Varying the concentration of ivermectin in liquid culture allows the strength of selection on these genes to be precisely controlled within populations of millions of individuals, yielding the largest animal experimental evolution study to date. The frequency of each barcode was determined at multiple time points via sequencing at deep coverage and then used to estimate the fitness of the individual lineages in the population. The mutations display a high cost to resistance at low concentrations, rapidly losing out to wildtype genotypes, but the balance tips in their favor when the ivermectin concentration exceeds 2nM. This trade-off in resistance is likely generated by a hindered rate of development in resistant individuals. Our results demonstrate that C. elegans can be used to generate high precision estimates of fitness using a high-throughput barcoding approach to yield novel insights into evolutionarily and economically important traits.

Characterization of Single-Cell Cis-regulatory Elements Informs Implications for Cell Differentiation

Cis-regulatory elements govern the specific patterns and dynamics of gene expression in cells during development, which are the fundamental mechanisms behind cell differentiation. However, the genomic characteristics of single-cell cis-regulatory elements closely linked to cell differentiation during development remain unclear. To explore this, we systematically analyzed ∼250,000 putative single-cell cis-regulatory elements obtained from snATAC-seq analysis of the developing mouse cerebellum. We found that over 80% of these single-cell cis-regulatory elements show pleiotropic effects, being active in 2 or more cell types. The pleiotropic degrees of proximal and distal single-cell cis-regulatory elements are positively correlated with the density and diversity of transcription factor binding motifs and GC content. There is a negative correlation between the pleiotropic degrees of single-cell cis-regulatory elements and their distances to the nearest transcription start sites, and proximal single-cell cis-regulatory elements display higher relevance strengths than distal ones. Furthermore, both proximal and distal single-cell cis-regulatory elements related to cell differentiation exhibit enhanced sequence-level evolutionary conservation, increased density and diversity of transcription factor binding motifs, elevated GC content, and greater distances from their nearest genes. Together, our findings reveal the general genomic characteristics of putative single-cell cis-regulatory elements and provide insights into the genomic and evolutionary mechanisms by which single-cell cis-regulatory elements regulate cell differentiation during development.

The evolution of ageing: classic theories and emerging ideas

Ageing is generally regarded as a non-adaptive by-product of evolution. Based on this premise three classic evolutionary theories of ageing have been proposed. These theories have dominated the literature for several decades. Despite their individual nuances, the common thread which unites them is that they posit that ageing results from a decline in the intensity of natural selection with chronological age. Empirical evidence has been identified which supports each theory. However, a consensus remains to be fully established as to which theory best accounts for the evolution of ageing. A consequence of this uncertainty are counter arguments which advocate for alternative theoretical frameworks, such as those which propose an adaptive origin for ageing, senescence, or death. Given this backdrop, this review has several aims. Firstly, to briefly discuss the classic evolutionary theories. Secondly, to evaluate how evolutionary forces beyond a monotonic decrease in natural selection can affect the evolution of ageing. Thirdly, to examine alternatives to the classic theories. Finally, to introduce a pluralistic interpretation of the evolution of ageing. The basis of this pluralistic theoretical framework is the recognition that certain evolutionary ideas will be more appropriate depending on the organism, its ecological context, and its life history.

Understanding developmental system drift

Developmental system drift (DSD) occurs when the genetic basis for homologous traits diverges over time despite conservation of the phenotype. In this Review, we examine the key ideas, evidence and open problems arising from studies of DSD. Recent work suggests that DSD may be pervasive, having been detected across a range of different organisms and developmental processes. Although developmental research remains heavily reliant on model organisms, extrapolation of findings to non-model organisms can be error-prone if the lineages have undergone DSD. We suggest how existing data and modelling approaches may be used to detect DSD and estimate its frequency. More direct study of DSD, we propose, can inform null hypotheses for how much genetic divergence to expect on the basis of phylogenetic distance, while also contributing to principles of gene regulatory evolution.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans due to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, our research shows a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (~0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Notably, young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes due to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potentials for phenotypic innovation in young genes compared to older genes, a process that is subject to natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Genetic Foundations of Nellore Traits: A Gene Prioritization and Functional Analyses of Genome-Wide Association Study Results

The main goal of this study was to pinpoint functional candidate genes associated with multiple economically important traits in Nellore cattle. After quality control, 1830 genomic regions sourced from 52 scientific peer-reviewed publications were used in this study. From these, a total of 8569 positional candidate genes were annotated for reproduction, 11,195 for carcass, 5239 for growth, and 3483 for morphological traits, and used in an over-representation analysis. The significant genes (adjusted p-values < 0.05) identified in the over-representation analysis underwent prioritization analyses, and enrichment analysis of the prioritized over-represented candidate genes was performed. The prioritized candidate genes were GFRA4, RFWD3, SERTAD2, KIZ, REM2, and ANKRD34B for reproduction; RFWD3, TMEM120A, MIEF2, FOXRED2, DUSP29, CARHSP1, OBI1, JOSD1, NOP58, and LOXL1-AS1 for the carcass; ANKRD34B and JOSD1 for growth traits; and no genes were prioritized for morphological traits. The functional analysis pinpointed the following genes: KIZ (plays a crucial role in spindle organization, which is essential in forming a robust mitotic centrosome), DUSP29 (involved in muscle cell differentiation), and JOSD1 (involved in protein deubiquitination, thereby improving growth). The enrichment of the functional candidate genes identified in this study highlights that these genes play an important role in the expression of reproduction, carcass, and growth traits in Nellore cattle.

Pleiotropy, a feature or a bug? Toward co-ordinating plant growth, development, and environmental responses through engineering plant hormone signaling

The advent of gene editing technologies such as CRISPR has simplified co-ordinating trait development. However, identifying candidate genes remains a challenge due to complex gene networks and pathways. These networks exhibit pleiotropy, complicating the determination of specific gene and pathway functions. In this review, we explore how systems biology and single-cell sequencing technologies can aid in identifying candidate genes for co-ordinating specifics of plant growth and development within specific temporal and tissue contexts. Exploring sequence-function space of these candidate genes and pathway modules with synthetic biology allows us to test hypotheses and define genotype-phenotype relationships through reductionist approaches. Collectively, these techniques hold the potential to advance breeding and genetic engineering strategies while also addressing genetic diversity issues critical for adaptation and trait development.

The loci of environmental adaptation in a model eukaryote

While the underlying genetic changes have been uncovered in some cases of adaptive evolution, the lack of a systematic study prevents a general understanding of the genomic basis of adaptation. For example, it is unclear whether protein-coding or noncoding mutations are more important to adaptive evolution and whether adaptations to different environments are brought by genetic changes distributed in diverse genes and biological processes or concentrated in a core set. We here perform laboratory evolution of 3360 Saccharomyces cerevisiae populations in 252 environments of varying levels of stress. We find the yeast adaptations to be primarily fueled by large-effect coding mutations overrepresented in a relatively small gene set, despite prevalent antagonistic pleiotropy across environments. Populations generally adapt faster in more stressful environments, partly because of greater benefits of the same mutations in more stressful environments. These and other findings from this model eukaryote help unravel the genomic principles of environmental adaptation.

Reduced social function in experimentally evolved Dictyostelium discoideum implies selection for social conflict in nature

Many microbes interact with one another, but the difficulty of directly observing these interactions in nature makes interpreting their adaptive value complicated. The social amoeba Dictyostelium discoideum forms aggregates wherein some cells are sacrificed for the benefit of others. Within chimaeric aggregates containing multiple unrelated lineages, cheaters can gain an advantage by undercontributing, but the extent to which wild D. discoideum has adapted to cheat is not fully clear. In this study, we experimentally evolved D. discoideum in an environment where there were no selective pressures to cheat or resist cheating in chimaeras. Dictyostelium discoideum lines grown in this environment evolved reduced competitiveness within chimaeric aggregates and reduced ability to migrate during the slug stage. By contrast, we did not observe a reduction in cell number, a trait for which selection was not relaxed. The observed loss of traits that our laboratory conditions had made irrelevant suggests that these traits were adaptations driven and maintained by selective pressures D. discoideum faces in its natural environment. Our results suggest that D. discoideum faces social conflict in nature, and illustrate a general approach that could be applied to searching for social or non-social adaptations in other microbes.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are structural novelties pivotal in mammalian evolution. Their phenotypic impact on humans, however, remains elusive due to the technical and ethical complexities in functional studies. Through combining gene age dating with Mendelian disease phenotyping, our research reveals that new genes associated with disease phenotypes steadily integrate into the human genome at a rate of ~ 0.07% every million years over macroevolutionary timescales. Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures between young and old genes. Notably, young genes show significant enrichment in the male reproductive system, indicating strong sexual selection. Young genes also exhibit functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, bipedal locomotion, and color vision. Our findings further reveal increasing levels of pleiotropy over evolutionary time, which accompanies stronger selective constraints. We propose a "pleiotropy-barrier" model that delineates different potentials for phenotypic innovation between young and older genes subject to natural selection. Our study demonstrates that evolutionary new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.