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Cutter AD. Sexual conflict, heterochrony and tissue specificity as evolutionary problems of adaptive plasticity in development. Proc Biol Sci 2023; 290:20231854. [PMID: 37817601 PMCID: PMC10565415 DOI: 10.1098/rspb.2023.1854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/15/2023] [Indexed: 10/12/2023] Open
Abstract
Differential gene expression represents a fundamental cause and manifestation of phenotypic plasticity. Adaptive phenotypic plasticity in gene expression as a trait evolves when alleles that mediate gene regulation serve to increase organismal fitness by improving the alignment of variation in gene expression with variation in circumstances. Among the diverse circumstances that a gene encounters are distinct cell types, developmental stages and sexes, as well as an organism's extrinsic ecological environments. Consequently, adaptive phenotypic plasticity provides a common framework to consider diverse evolutionary problems by considering the shared implications of alleles that produce context-dependent gene expression. From this perspective, adaptive plasticity represents an evolutionary resolution to conflicts of interest that arise from any negatively pleiotropic effects of expression of a gene across ontogeny, among tissues, between the sexes, or across extrinsic environments. This view highlights shared properties within the general relation of fitness, trait expression and context that may nonetheless differ substantively in the grain of selection within and among generations to influence the likelihood of adaptive plasticity as an evolutionary response. Research programmes that historically have focused on these separate issues may use the insights from one another by recognizing their shared dependence on context-dependent gene regulatory evolution.
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Affiliation(s)
- Asher D. Cutter
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada M5S 3B2
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Salazar-Tortosa DF, Huang YF, Enard D. Assessing the Presence of Recent Adaptation in the Human Genome With Mixture Density Regression. Genome Biol Evol 2023; 15:evad170. [PMID: 37713622 PMCID: PMC10563788 DOI: 10.1093/gbe/evad170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the presence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogeneous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) which complicates the interpretation and distinction of adaptive versus neutral evolution signals. In this study, we introduce mixture density regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect the occurrence/detection of positive selection, if the latter was present in the first place to generate these associations. We find that an MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (integrated haplotype score), with one of the two distributions likely enriched in positive selection. We further find several factors associated with signals of recent adaptation, including the recombination rate, the density of regulatory elements in immune cells, GC content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support the presence of strong positive selection in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation.
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Affiliation(s)
- Diego F Salazar-Tortosa
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
- Department of Ecology, University of Granada, Granada, Spain
| | - Yi-Fei Huang
- Department of Biology, Pennsylvania State University, University Park, State College, Pennsylvania, PA 16801, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, Pennsylvania, PA 16801, USA
| | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
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LaPolice TM, Huang YF. An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data. BMC Bioinformatics 2023; 24:347. [PMID: 37723435 PMCID: PMC10506225 DOI: 10.1186/s12859-023-05481-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/13/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome. RESULTS Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods. CONCLUSION The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.
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Affiliation(s)
- Troy M LaPolice
- Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA.
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, PA, 16802, USA.
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA.
| | - Yi-Fei Huang
- Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA.
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA.
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Roberts M, Josephs EB. Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana. Genetics 2023; 224:iyad074. [PMID: 37094602 PMCID: PMC10484170 DOI: 10.1093/genetics/iyad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/06/2023] [Accepted: 04/07/2023] [Indexed: 04/26/2023] Open
Abstract
Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on Arabidopsis thaliana from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene's expression and the strength of selection on said gene in A. thaliana. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.
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Affiliation(s)
- Miles Roberts
- Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI 48824, USA
| | - Emily B Josephs
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824, USA
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Zhang X, Fang B, Huang YF. Transcription factor binding sites are frequently under accelerated evolution in primates. Nat Commun 2023; 14:783. [PMID: 36774380 PMCID: PMC9922303 DOI: 10.1038/s41467-023-36421-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 01/31/2023] [Indexed: 02/13/2023] Open
Abstract
Recent comparative genomic studies have identified many human accelerated elements (HARs) with elevated substitution rates in the human lineage. However, it remains unknown to what extent transcription factor binding sites (TFBSs) are under accelerated evolution in humans and other primates. Here, we introduce two pooling-based phylogenetic methods with dramatically enhanced sensitivity to examine accelerated evolution in TFBSs. Using these new methods, we show that more than 6000 TFBSs annotated in the human genome have experienced accelerated evolution in Hominini, apes, and Old World monkeys. Although these TFBSs individually show relatively weak signals of accelerated evolution, they collectively are more abundant than HARs. Also, we show that accelerated evolution in Pol III binding sites may be driven by lineage-specific positive selection, whereas accelerated evolution in other TFBSs might be driven by nonadaptive evolutionary forces. Finally, the accelerated TFBSs are enriched around developmental genes, suggesting that accelerated evolution in TFBSs may drive the divergence of developmental processes between primates.
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Affiliation(s)
- Xinru Zhang
- Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA. .,Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA. .,Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, PA, 16802, USA.
| | - Bohao Fang
- Department of Organismic and Evolutionary Biology and the Museum of Comparative Zoology, Harvard University, Boston, MA, 02135, USA
| | - Yi-Fei Huang
- Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA. .,Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA.
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Moutinho AF, Eyre-Walker A, Dutheil JY. Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis. PLoS Biol 2022; 20:e3001775. [PMID: 36099311 PMCID: PMC9470001 DOI: 10.1371/journal.pbio.3001775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 08/01/2022] [Indexed: 11/19/2022] Open
Abstract
Understanding the dynamics of species adaptation to their environments has long been a central focus of the study of evolution. Theories of adaptation propose that populations evolve by “walking” in a fitness landscape. This “adaptive walk” is characterised by a pattern of diminishing returns, where populations further away from their fitness optimum take larger steps than those closer to their optimal conditions. Hence, we expect young genes to evolve faster and experience mutations with stronger fitness effects than older genes because they are further away from their fitness optimum. Testing this hypothesis, however, constitutes an arduous task. Young genes are small, encode proteins with a higher degree of intrinsic disorder, are expressed at lower levels, and are involved in species-specific adaptations. Since all these factors lead to increased protein evolutionary rates, they could be masking the effect of gene age. While controlling for these factors, we used population genomic data sets of Arabidopsis and Drosophila and estimated the rate of adaptive substitutions across genes from different phylostrata. We found that a gene’s evolutionary age significantly impacts the molecular rate of adaptation. Moreover, we observed that substitutions in young genes tend to have larger physicochemical effects. Our study, therefore, provides strong evidence that molecular evolution follows an adaptive walk model across a large evolutionary timescale. This study uses population genomic datasets from Arabidopsis and Drosophila to show that young genes adapt faster and are subject to mutations of larger fitness effects, providing strong evidence that molecular evolution follows an adaptive walk model across a large evolutionary timescale.
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Affiliation(s)
- Ana Filipa Moutinho
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Plön, Germany
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
- * E-mail:
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Julien Y. Dutheil
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Unité Mixte de Recherche 5554 Institut des Sciences de l’Evolution, CNRS, IRD, EPHE, Université de Montpellier, Montpellier, France
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