Jing Hou, Meng Liu, Kai Yang, Bao Liu, Huanhuan Liu, Jianquan Liu
{"title":"Genetic variation for adaptive evolution in response to changed environments in plants.","authors":"Jing Hou, Meng Liu, Kai Yang, Bao Liu, Huanhuan Liu, Jianquan Liu","doi":"10.1111/jipb.13961","DOIUrl":null,"url":null,"abstract":"<p><p>Plants adapt to their local environments through natural or artificial selection of optimal phenotypes. Recent advances in genomics and computational biology, which integrate phenotypic and multi-omics data, have facilitated the rapid identification of key genes and allelic variations that underlie these adaptive evolutionary processes. Understanding the underlying molecular mechanisms has significantly enhanced our knowledge of how plants respond to changed habitats, including various biotic and abiotic stresses. In this review, we highlight recent progress in elucidating the genetic basis of phenotypic variation in morphological traits and stress responses, as well as the emergence of new ecotypes, subspecies, and species during adaptive evolution across varied environments. This occurs through allelic divergences in both coding and non-coding regions in both model and non-model plants. Furthermore, the terrestrialization and early diversification of land plants involved the acquisition of additional genes, primarily through horizontal gene transfer and whole-genome duplication, which facilitated the development of complex molecular pathways to adapt to increasingly diverse environments. Finally, we discuss emerging trends and prospects for exploring and utilizing beneficial alleles for environmental adaptation, to guide crop breeding efforts in response to global climate change.</p>","PeriodicalId":195,"journal":{"name":"Journal of Integrative Plant Biology","volume":" ","pages":""},"PeriodicalIF":9.3000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Plant Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1111/jipb.13961","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Plants adapt to their local environments through natural or artificial selection of optimal phenotypes. Recent advances in genomics and computational biology, which integrate phenotypic and multi-omics data, have facilitated the rapid identification of key genes and allelic variations that underlie these adaptive evolutionary processes. Understanding the underlying molecular mechanisms has significantly enhanced our knowledge of how plants respond to changed habitats, including various biotic and abiotic stresses. In this review, we highlight recent progress in elucidating the genetic basis of phenotypic variation in morphological traits and stress responses, as well as the emergence of new ecotypes, subspecies, and species during adaptive evolution across varied environments. This occurs through allelic divergences in both coding and non-coding regions in both model and non-model plants. Furthermore, the terrestrialization and early diversification of land plants involved the acquisition of additional genes, primarily through horizontal gene transfer and whole-genome duplication, which facilitated the development of complex molecular pathways to adapt to increasingly diverse environments. Finally, we discuss emerging trends and prospects for exploring and utilizing beneficial alleles for environmental adaptation, to guide crop breeding efforts in response to global climate change.
期刊介绍:
Journal of Integrative Plant Biology is a leading academic journal reporting on the latest discoveries in plant biology.Enjoy the latest news and developments in the field, understand new and improved methods and research tools, and explore basic biological questions through reproducible experimental design, using genetic, biochemical, cell and molecular biological methods, and statistical analyses.