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Hu Y, Wang X, Xu Y, Yang H, Tong Z, Tian R, Xu S, Yu L, Guo Y, Shi P, Huang S, Yang G, Shi S, Wei F. Molecular mechanisms of adaptive evolution in wild animals and plants. SCIENCE CHINA. LIFE SCIENCES 2023; 66:453-495. [PMID: 36648611 PMCID: PMC9843154 DOI: 10.1007/s11427-022-2233-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 08/30/2022] [Indexed: 01/18/2023]
Abstract
Wild animals and plants have developed a variety of adaptive traits driven by adaptive evolution, an important strategy for species survival and persistence. Uncovering the molecular mechanisms of adaptive evolution is the key to understanding species diversification, phenotypic convergence, and inter-species interaction. As the genome sequences of more and more non-model organisms are becoming available, the focus of studies on molecular mechanisms of adaptive evolution has shifted from the candidate gene method to genetic mapping based on genome-wide scanning. In this study, we reviewed the latest research advances in wild animals and plants, focusing on adaptive traits, convergent evolution, and coevolution. Firstly, we focused on the adaptive evolution of morphological, behavioral, and physiological traits. Secondly, we reviewed the phenotypic convergences of life history traits and responding to environmental pressures, and the underlying molecular convergence mechanisms. Thirdly, we summarized the advances of coevolution, including the four main types: mutualism, parasitism, predation and competition. Overall, these latest advances greatly increase our understanding of the underlying molecular mechanisms for diverse adaptive traits and species interaction, demonstrating that the development of evolutionary biology has been greatly accelerated by multi-omics technologies. Finally, we highlighted the emerging trends and future prospects around the above three aspects of adaptive evolution.
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Affiliation(s)
- Yibo Hu
- CAS Key Lab of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Xiaoping Wang
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, 650091, China
| | - Yongchao Xu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hui Yang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zeyu Tong
- Institute of Evolution and Ecology, School of Life Sciences, Central China Normal University, Wuhan, 430079, China
| | - Ran Tian
- College of Life Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Shaohua Xu
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China
| | - Li Yu
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, 650091, China.
| | - Yalong Guo
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Peng Shi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China.
| | - Shuangquan Huang
- Institute of Evolution and Ecology, School of Life Sciences, Central China Normal University, Wuhan, 430079, China.
| | - Guang Yang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
- College of Life Sciences, Nanjing Normal University, Nanjing, 210023, China.
| | - Suhua Shi
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Fuwen Wei
- CAS Key Lab of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Beijing, 100101, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
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Xu MM, Gu LH, Lv WY, Duan SC, Li LW, Du Y, Lu LZ, Zeng T, Hou ZC, Ma ZS, Chen W, Adeola AC, Han JL, Xu TS, Dong Y, Zhang YP, Peng MS. Chromosome-level genome assembly of the Muscovy duck provides insight into fatty liver susceptibility. Genomics 2022; 114:110518. [PMID: 36347326 DOI: 10.1016/j.ygeno.2022.110518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/01/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022]
Abstract
The Muscovy duck (Cairina moschata) is an economically important poultry species, which is susceptible to fatty liver. Thus, the Muscovy duck may serve as an excellent candidate animal model of non-alcoholic fatty liver disease. However, the mechanisms underlying fatty liver development in this species are poorly understood. In this study, we report a chromosome-level genome assembly of the Muscovy duck, with a contig N50 of 11.8 Mb and scaffold N50 of 83.16 Mb. The susceptibility of Muscovy duck to fatty liver was mainly attributed to weak lipid catabolism capabilities (fatty acid β-oxidation and lipolysis). Furthermore, conserved noncoding elements (CNEs) showing accelerated evolution contributed to fatty liver formation by down-regulating the expression of genes involved in hepatic lipid catabolism. We propose that the susceptibility of Muscovy duck to fatty liver is an evolutionary by-product. In conclusion, this study revealed the potential mechanisms underlying the susceptibility of Muscovy duck to fatty liver.
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Affiliation(s)
- Ming-Min Xu
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Li-Hong Gu
- Institute of Animal Science & Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571100, China
| | - Wan-Yue Lv
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China
| | | | - Lian-Wei Li
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Yuan Du
- Nowbio Biotechnology Company, Kunming 650201, China
| | - Li-Zhi Lu
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Tao Zeng
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Zhuo-Cheng Hou
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA; College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Zhanshan Sam Ma
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Wei Chen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming 650201, China; Key Laboratory for Agro-Biodiversity and Pest Control of Ministry of Education, Yunnan Agricultural University, Kunming 650201, China
| | - Adeniyi C Adeola
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China; Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi 00100, Kenya
| | - Tie-Shan Xu
- Tropical Crops Genetic Resources Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China.
| | - Yang Dong
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming 650201, China; Key Laboratory for Agro-Biodiversity and Pest Control of Ministry of Education, Yunnan Agricultural University, Kunming 650201, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.
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3
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Rosvall KA. Evolutionary endocrinology and the problem of Darwin's tangled bank. Horm Behav 2022; 146:105246. [PMID: 36029721 DOI: 10.1016/j.yhbeh.2022.105246] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/20/2022] [Accepted: 08/10/2022] [Indexed: 11/04/2022]
Abstract
Like Darwin's tangled bank of biodiversity, the endocrine mechanisms that give rise to phenotypic diversity also exhibit nearly endless forms. This tangled bank of mechanistic diversity can prove problematic as we seek general principles on the role of endocrine mechanisms in phenotypic evolution. A key unresolved question is therefore: to what degree are specific endocrine mechanisms re-used to bring about replicated phenotypic evolution? Related areas of inquiry are booming in molecular ecology, but behavioral traits are underrepresented in this literature. Here, I leverage the rich comparative tradition in evolutionary endocrinology to evaluate whether and how certain mechanisms may be repeated hotspots of behavioral evolutionary change. At one extreme, mechanisms may be parallel, such that evolution repeatedly uses the same gene or pathway to arrive at multiple independent (or, convergent) origins of a particular behavioral trait. At the other extreme, the building blocks of behavior may be unique, such that outwardly similar phenotypes are generated via lineage-specific mechanisms. This review synthesizes existing case studies, phylogenetic analyses, and experimental evolutionary research on mechanistic parallelism in animal behavior. These examples show that the endocrine building blocks of behavior have some elements of parallelism across replicated evolutionary events. However, support for parallelism is variable among studies, at least some of which relates to the level of complexity at which we consider sameness (i.e. pathway vs. gene level). Moving forward, we need continued experimentation and better testing of neutral models to understand whether, how - and critically, why - mechanism A is used in one lineage and mechanism B is used in another. We also need continued growth of large-scale comparative analyses, especially those that can evaluate which endocrine parameters are more or less likely to undergo parallel evolution alongside specific behavioral traits. These efforts will ultimately deepen understanding of how and why hormone-mediated behaviors are constructed the way that they are.
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Affiliation(s)
- Kimberly A Rosvall
- Indiana University, Bloomington, USA; Department of Biology, USA; Center for the Integrative Study of Animal Behavior, USA.
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4
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Espíndola-Hernández P, Mueller JC, Kempenaers B. Genomic signatures of the evolution of a diurnal lifestyle in Strigiformes. G3 GENES|GENOMES|GENETICS 2022; 12:6595023. [PMID: 35640557 PMCID: PMC9339318 DOI: 10.1093/g3journal/jkac135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
Understanding the targets of selection associated with changes in behavioral traits represents an important challenge of current evolutionary research. Owls (Strigiformes) are a diverse group of birds, most of which are considered nocturnal raptors. However, a few owl species independently adopted a diurnal lifestyle in their recent evolutionary history. We searched for signals of accelerated rates of evolution associated with a diurnal lifestyle using a genome-wide comparative approach. We estimated substitution rates in coding and noncoding conserved regions of the genome of seven owl species, including three diurnal species. Substitution rates of the noncoding elements were more accelerated than those of protein-coding genes. We identified new, owl-specific conserved noncoding elements as candidates of parallel evolution during the emergence of diurnality in owls. Our results shed light on the molecular basis of adaptation to a new niche and highlight the importance of regulatory elements for evolutionary changes in behavior. These elements were often involved in the neuronal development of the brain.
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Affiliation(s)
- Pamela Espíndola-Hernández
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology , 82319 Seewiesen, Germany
| | - Jakob C Mueller
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology , 82319 Seewiesen, Germany
| | - Bart Kempenaers
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology , 82319 Seewiesen, Germany
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5
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Liang N, Deme L, Kong Q, Sun L, Cao Y, Wu T, Huang X, Xu S, Yang G. Divergence of Tbx4 hindlimb enhancer HLEA underlies the hindlimb loss during cetacean evolution. Genomics 2022; 114:110292. [DOI: 10.1016/j.ygeno.2022.110292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/13/2022] [Accepted: 02/01/2022] [Indexed: 11/04/2022]
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Recurrent erosion of COA1/MITRAC15 exemplifies conditional gene dispensability in oxidative phosphorylation. Sci Rep 2021; 11:24437. [PMID: 34952909 PMCID: PMC8709867 DOI: 10.1038/s41598-021-04077-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 12/15/2021] [Indexed: 11/08/2022] Open
Abstract
Skeletal muscle fibers rely upon either oxidative phosphorylation or the glycolytic pathway with much less reliance on oxidative phosphorylation to achieve muscular contractions that power mechanical movements. Species with energy-intensive adaptive traits that require sudden bursts of energy have a greater dependency on glycolytic fibers. Glycolytic fibers have decreased reliance on OXPHOS and lower mitochondrial content compared to oxidative fibers. Hence, we hypothesized that gene loss might have occurred within the OXPHOS pathway in lineages that largely depend on glycolytic fibers. The protein encoded by the COA1/MITRAC15 gene with conserved orthologs found in budding yeast to humans promotes mitochondrial translation. We show that gene disrupting mutations have accumulated within the COA1 gene in the cheetah, several species of galliform birds, and rodents. The genomic region containing COA1 is a well-established evolutionary breakpoint region in mammals. Careful inspection of genome assemblies of closely related species of rodents and marsupials suggests two independent COA1 gene loss events co-occurring with chromosomal rearrangements. Besides recurrent gene loss events, we document changes in COA1 exon structure in primates and felids. The detailed evolutionary history presented in this study reveals the intricate link between skeletal muscle fiber composition and the occasional dispensability of the chaperone-like role of the COA1 gene.
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7
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Galván I, Schwartz TS, Garland T. Evolutionary physiology at 30+: Has the promise been fulfilled?: Advances in Evolutionary Physiology: Advances in Evolutionary Physiology. Bioessays 2021; 44:e2100167. [PMID: 34802161 DOI: 10.1002/bies.202100167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 10/20/2021] [Accepted: 10/22/2021] [Indexed: 12/19/2022]
Abstract
Three decades ago, interactions between evolutionary biology and physiology gave rise to evolutionary physiology. This caused comparative physiologists to improve their research methods by incorporating evolutionary thinking. Simultaneously, evolutionary biologists began focusing more on physiological mechanisms that may help to explain constraints on and trade-offs during microevolutionary processes, as well as macroevolutionary patterns in physiological diversity. Here we argue that evolutionary physiology has yet to reach its full potential, and propose new avenues that may lead to unexpected advances. Viewing physiological adaptations in wild animals as potential solutions to human diseases offers enormous possibilities for biomedicine. New evidence of epigenetic modifications as mechanisms of phenotypic plasticity that regulate physiological traits may also arise in coming years, which may also represent an overlooked enhancer of adaptation via natural selection to explain physiological evolution. Synergistic interactions at these intersections and other areas will lead to a novel understanding of organismal biology.
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Affiliation(s)
- Ismael Galván
- Department of Evolutionary Ecology, National Museum of Natural Sciences, CSIC, Madrid, Spain
| | - Tonia S Schwartz
- Department of Biological Sciences, Auburn University, Auburn, Alabama, USA
| | - Theodore Garland
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, California, USA
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8
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McCulloch GA, Guhlin J, Dutoit L, Harrop TWR, Dearden PK, Waters JM. Genomic signatures of parallel alpine adaptation in recently evolved flightless insects. Mol Ecol 2021; 30:6677-6686. [PMID: 34592029 DOI: 10.1111/mec.16204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 12/01/2022]
Abstract
Natural selection along elevational gradients has potential to drive predictable adaptations across distinct lineages, but the extent of such repeated evolution remains poorly studied for many widespread alpine taxa. We present parallel genomic analyses of two recently evolved flightless alpine insect lineages to test for molecular signatures of repeated alpine adaptation. Specifically, we compare low-elevation vs. alpine stonefly ecotypes from parallel stream populations in which flightless upland ecotypes have been independently derived. We map 67,922 polymorphic genetic markers, generated across 176 Zelandoperla fenestrata specimens from two independent alpine stream populations in New Zealand's Rock and Pillar Range, to a newly developed plecopteran reference genome. Genome-wide scans revealed 31 regions with outlier single nucleotide polymorphisms (SNPs) differentiating lowland vs. alpine ecotypes in Lug Creek, and 37 regions with outliers differentiating ecotypes in Six Mile Creek. Of these regions, 13% (8/60) yielded outlier SNPs across both within-stream ecotype comparisons, implying comparable genomic shifts contribute to this repeated alpine adaptation. Candidate genes closely linked to repeated outlier regions include several with documented roles in insect wing-development (e.g., dishevelled), suggesting that they may contribute to repeated alpine wing reduction. Additional candidate genes have been shown to influence insect fecundity (e.g., ovo) and lifespan (e.g., Mrp4), implying that they might contribute to life history differentiation between upland and lowland ecotypes. Additional outlier genes have potential roles in the evolution of reproductive isolation among ecotypes (hedgehog and Desaturase 1). These results demonstrate how replicated outlier tests across independent lineages can potentially contribute to the discovery of genes underpinning repeated adaptation.
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Affiliation(s)
| | - Joseph Guhlin
- Genomics Aotearoa and Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Ludovic Dutoit
- Department of Zoology, University of Otago, Dunedin, New Zealand
| | - Thomas W R Harrop
- Genomics Aotearoa and Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Peter K Dearden
- Genomics Aotearoa and Department of Biochemistry, University of Otago, Dunedin, New Zealand
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Wang MS, Zhang JJ, Guo X, Li M, Meyer R, Ashari H, Zheng ZQ, Wang S, Peng MS, Jiang Y, Thakur M, Suwannapoom C, Esmailizadeh A, Hirimuthugoda NY, Zein MSA, Kusza S, Kharrati-Koopaee H, Zeng L, Wang YM, Yin TT, Yang MM, Li ML, Lu XM, Lasagna E, Ceccobelli S, Gunwardana HGTN, Senasig TM, Feng SH, Zhang H, Bhuiyan AKFH, Khan MS, Silva GLLP, Thuy LT, Mwai OA, Ibrahim MNM, Zhang G, Qu KX, Hanotte O, Shapiro B, Bosse M, Wu DD, Han JL, Zhang YP. Large-scale genomic analysis reveals the genetic cost of chicken domestication. BMC Biol 2021; 19:118. [PMID: 34130700 PMCID: PMC8207802 DOI: 10.1186/s12915-021-01052-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/19/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Species domestication is generally characterized by the exploitation of high-impact mutations through processes that involve complex shifting demographics of domesticated species. These include not only inbreeding and artificial selection that may lead to the emergence of evolutionary bottlenecks, but also post-divergence gene flow and introgression. Although domestication potentially affects the occurrence of both desired and undesired mutations, the way wild relatives of domesticated species evolve and how expensive the genetic cost underlying domestication is remain poorly understood. Here, we investigated the demographic history and genetic load of chicken domestication. RESULTS We analyzed a dataset comprising over 800 whole genomes from both indigenous chickens and wild jungle fowls. We show that despite having a higher genetic diversity than their wild counterparts (average π, 0.00326 vs. 0.00316), the red jungle fowls, the present-day domestic chickens experienced a dramatic population size decline during their early domestication. Our analyses suggest that the concomitant bottleneck induced 2.95% more deleterious mutations across chicken genomes compared with red jungle fowls, supporting the "cost of domestication" hypothesis. Particularly, we find that 62.4% of deleterious SNPs in domestic chickens are maintained in heterozygous states and masked as recessive alleles, challenging the power of modern breeding programs to effectively eliminate these genetic loads. Finally, we suggest that positive selection decreases the incidence but increases the frequency of deleterious SNPs in domestic chicken genomes. CONCLUSION This study reveals a new landscape of demographic history and genomic changes associated with chicken domestication and provides insight into the evolutionary genomic profiles of domesticated animals managed under modern human selection.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Jin-Jin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xing Guo
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Rachel Meyer
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Hidayat Ashari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia.,CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Zhu-Qing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Mukesh Thakur
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Zoological Survey of India, New Alipore, Kolkata, West Bengal, 700053, India
| | - Chatmongkon Suwannapoom
- School of Agriculture and Natural Resources, University of Phayao, Phayao, 56000, Thailand.,Unit of Excellence on Biodiversity and Natural Resources Management, University of Phayao, Phayao, 56000, Thailand
| | - Ali Esmailizadeh
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran
| | - Nalini Yasoda Hirimuthugoda
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Faculty of Agriculture, University of Ruhuna, Matara, Sri Lanka
| | - Moch Syamsul Arifin Zein
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia
| | - Szilvia Kusza
- Institute of Animal Husbandry, Biotechnology and Nature Conservation, University of Debrecen, Debrecen, H-4032, Hungary
| | - Hamed Kharrati-Koopaee
- Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran.,Institute of Biotechnology, School of Agriculture, Shiraz University, P.O. Box 1585, Shiraz, Iran
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yun-Mei Wang
- Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
| | - Ting-Ting Yin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Min Yang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Ming-Li Li
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xue-Mei Lu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China
| | - Emiliano Lasagna
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | - Simone Ceccobelli
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | | | | | - Shao-Hong Feng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China
| | - Hao Zhang
- Laboratory of Animal Genetics, Breeding and Reproduction, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Ministry of Agriculture of China, Beijing, 100193, China
| | | | | | | | - Le Thi Thuy
- National Institute of Animal Husbandry, Hanoi, Vietnam
| | - Okeyo A Mwai
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya
| | | | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.,China National Genebank, BGI-Shenzhen, Shenzhen, 518083, China.,Centre for Social Evolution, Department of Biology, University of Copenhagen, DK-1870, Copenhagen, Denmark
| | - Kai-Xing Qu
- Yunnan Academy of Grassland and Animal Science, Kunming, 650212, China
| | - Olivier Hanotte
- Cells, Organisms and Molecular Genetics, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.,Livestock Genetics Program, International Livestock Research Institute (ILRI), P.O. Box 5689, Addis Ababa, Ethiopia
| | - Beth Shapiro
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Mirte Bosse
- Wageningen University & Research - Animal Breeding and Genomics, 6708 PB, Wageningen, The Netherlands.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China. .,Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China. .,State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, 650091, China.
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10
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Waters JM, McCulloch GA. Reinventing the wheel? Reassessing the roles of gene flow, sorting and convergence in repeated evolution. Mol Ecol 2021; 30:4162-4172. [PMID: 34133810 DOI: 10.1111/mec.16018] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 12/31/2022]
Abstract
Biologists have long been intrigued by apparently predictable and repetitive evolutionary trajectories inferred across a variety of lineages and systems. In recent years, high-throughput sequencing analyses have started to transform our understanding of such repetitive shifts. While researchers have traditionally categorized such shifts as either "convergent" or "parallel," based on relatedness of the lineages involved, emerging genomic insights provide an opportunity to better describe the actual evolutionary mechanisms at play. A synthesis of recent genomic analyses confirms that convergence is the predominant driver of repetitive evolution among species, whereas repeated sorting of standing variation is the major driver of repeated shifts within species. However, emerging data reveal numerous notable exceptions to these expectations, with recent examples of de novo mutations underpinning convergent shifts among even very closely related lineages, while repetitive sorting processes have occurred among even deeply divergent taxa, sometimes via introgression. A number of very recent analyses have found evidence for both processes occurring on different scales within taxa. We suggest that the relative importance of convergent versus sorting processes depends on the interplay between gene flow among populations, and phylogenetic relatedness of the lineages involved.
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11
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Liu Q, Deng Y, Song A, Xiang Y, Chen D, Wei L. Comparative analysis of mite genomes reveals positive selection for diet adaptation. Commun Biol 2021; 4:668. [PMID: 34083730 PMCID: PMC8175442 DOI: 10.1038/s42003-021-02173-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/30/2021] [Indexed: 02/06/2023] Open
Abstract
Diet is a powerful evolutionary force for species adaptation and diversification. Acari is one of the most abundant clades of Arachnida, exhibiting diverse dietary types, while the underlying genetic adaptive mechanisms are not fully understood. Based on comparative analyses of 15 Acari genomes, we found genetic bases for three specialized diets. Herbivores experienced stronger selection pressure than other groups; the olfactory genes and gene families involving metabolizing toxins showed strong adaptive signals. Genes and gene families related to anticoagulation, detoxification, and haemoglobin digestion were found to be under strong selection pressure or significantly expanded in the blood-feeding species. Lipid metabolism genes have a faster evolutionary rate and been subjected to greater selection pressures in fat-feeding species; one positively selected site in the fatty-acid amide hydrolases 2 gene was identified. Our research provides a new perspective for the evolution of Acari and offers potential target loci for novel pesticide development.
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Affiliation(s)
- Qiong Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yuhua Deng
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China
| | - An Song
- ShaanXi JunDa Forensic Medicine Expertise Station, The Fourth Military Medical University, Xi'an, China
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - De Chen
- MOE Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.
| | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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12
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Goh SJR, Tuomisto JEE, Purcell AW, Mifsud NA, Illing PT. The complexity of T cell-mediated penicillin hypersensitivity reactions. Allergy 2021; 76:150-167. [PMID: 32383256 DOI: 10.1111/all.14355] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/16/2020] [Accepted: 04/30/2020] [Indexed: 12/20/2022]
Abstract
Penicillin refers to a group of beta-lactam antibiotics that are the first-line treatment for a range of infections. However, they also possess the ability to form novel antigens, or neoantigens, through haptenation of proteins and can stimulate a range of immune-mediated adverse reactions-collectively known as drug hypersensitivity reactions (DHRs). IgE-mediated reactions towards these neoantigens are well studied; however, IgE-independent reactions are less well understood. These reactions usually manifest in a delayed manner as different forms of cutaneous eruptions or liver injury consistent with priming of an immune response. Ex vivo studies have confirmed the infiltration of T cells into the site of inflammation, and the subsets of T cells involved appear dependent on the nature of the reaction. Here, we review the evidence that has led to our current understanding of these immune-mediated reactions, discussing the nature of the lesional T cells, the characterization of drug-responsive T cells isolated from patient blood, and the potential mechanisms by which penicillins enter the antigen processing and presentation pathway to stimulate these deleterious responses. Thus, we highlight the need for a more comprehensive understanding of the underlying genetic and molecular basis of penicillin-induced DHRs.
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Affiliation(s)
- Shawn J. R. Goh
- Infection and Immunity Program Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University Clayton Vic. Australia
| | - Johanna E. E. Tuomisto
- Infection and Immunity Program Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University Clayton Vic. Australia
| | - Anthony W. Purcell
- Infection and Immunity Program Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University Clayton Vic. Australia
| | - Nicole A. Mifsud
- Infection and Immunity Program Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University Clayton Vic. Australia
| | - Patricia T. Illing
- Infection and Immunity Program Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University Clayton Vic. Australia
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13
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van den Berg SJ, Jansen LE. Centromeres: genetic input to calibrate an epigenetic feedback loop. EMBO J 2020; 39:e106638. [PMID: 32959893 DOI: 10.15252/embj.2020106638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Centromeres are chromatin domains maintained by a self-templating feedback loop based on nucleosomes bearing the histone H3 variant CENP-A. The underlying centromeric DNA sequence is largely dispensable, yet paradoxically, it has highly conserved features. Hoffmann et al (2020) now uncover that when the epigenetic chromatin cycle falters, a genetically hardwired mechanism offers robustness to a dynamic epigenetic feedback loop ensuring long-term centromere inheritance.
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Affiliation(s)
- Sebastiaan Jw van den Berg
- Department of Biochemistry, University of Oxford, Oxford, UK.,Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Lars Et Jansen
- Department of Biochemistry, University of Oxford, Oxford, UK
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14
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Zhu D, Wu X, Zhou J, Li X, Huang X, Li J, Wu J, Bian Q, Wang Y, Tian Y. NuRD mediates mitochondrial stress-induced longevity via chromatin remodeling in response to acetyl-CoA level. SCIENCE ADVANCES 2020; 6:eabb2529. [PMID: 32789178 PMCID: PMC7400466 DOI: 10.1126/sciadv.abb2529] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 06/16/2020] [Indexed: 05/13/2023]
Abstract
Mild mitochondrial stress experienced early in life can have beneficial effects on the life span of organisms through epigenetic regulations. Here, we report that acetyl-coenzyme A (CoA) represents a critical mitochondrial signal to regulate aging through the chromatin remodeling and histone deacetylase complex (NuRD) in Caenorhabditis elegans. Upon mitochondrial stress, the impaired tricarboxylic acid cycle results in a decreased level of citrate, which accounts for reduced production of acetyl-CoA and consequently induces nuclear accumulation of the NuRD and a homeodomain-containing transcription factor DVE-1, thereby enabling decreased histone acetylation and chromatin reorganization. The metabolic stress response is thus established during early life and propagated into adulthood to allow transcriptional regulation for life-span extension. Furthermore, adding nutrients to restore acetyl-CoA production is sufficient to counteract the chromatin changes and diminish the longevity upon mitochondrial stress. Our findings uncover the molecular mechanism of the metabolite-mediated epigenome for the regulation of organismal aging.
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Affiliation(s)
- Di Zhu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
| | - Xueying Wu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun Zhou
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
| | - Xinyu Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
| | - Xiahe Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiasheng Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
| | - Junbo Wu
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Qian Bian
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Yingchun Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
| | - Ye Tian
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100093, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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15
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He Z, Xu S, Shi S. Adaptive convergence at the genomic level-prevalent, uncommon or very rare? Natl Sci Rev 2020; 7:947-951. [PMID: 34692116 PMCID: PMC8289048 DOI: 10.1093/nsr/nwaa076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 02/17/2020] [Accepted: 04/21/2020] [Indexed: 12/31/2022] Open
Affiliation(s)
- Ziwen He
- School of Life Sciences, Sun Yat-sen University, China
| | - Shaohua Xu
- School of Life Sciences, Sun Yat-sen University, China
| | - Suhua Shi
- School of Life Sciences, Sun Yat-sen University, China
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16
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Waters J, Emerson B, Arribas P, McCulloch G. Dispersal Reduction: Causes, Genomic Mechanisms, and Evolutionary Consequences. Trends Ecol Evol 2020; 35:512-522. [DOI: 10.1016/j.tree.2020.01.012] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 01/23/2020] [Accepted: 01/29/2020] [Indexed: 12/23/2022]
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17
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Ikemoto A, Sato DX, Makino T, Kawata M. Genetic factors for short life span associated with evolution of the loss of flight ability. Ecol Evol 2020; 10:6020-6029. [PMID: 32607209 PMCID: PMC7319159 DOI: 10.1002/ece3.6342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/19/2020] [Accepted: 03/13/2020] [Indexed: 12/22/2022] Open
Abstract
Acquisition or loss of flying ability is evolutionarily linked with maximum life span (MLS) in mammals and birds. Although ecological factors, such as extrinsic mortality, may lead to either shortened or extended life spans through natural selection, MLS is influenced by complex molecular and metabolic processes, and the genetic changes associated with flying ability that have led to either a longer or shorter MLS are unknown. Here, we examine the parallel evolution of flight in mammals and birds and investigate positively selected genes at branches where either the acquisition (in little brown bats and large flying foxes) or loss (in Adélie penguins, emperor penguins, common ostriches, emus, great spotted kiwis, little spotted kiwis, okarito brown kiwis, greater rheas, lesser rheas, and cassowaries) of flight abilities occurred. Although we found no shared genes under selection among all the branches of interest, 7 genes were found to be positively selected in 2 of the branches. Among the 7 genes, only IGF2BP2 is known to affect both life span and energy expenditure. The positively selected mutations detected in IGF2BP2 likely affected the functionality of the encoded protein. IGF2BP2, which has been reported to simultaneously prolong life span and increase energy expenditure, could be responsible for the evolution of shortened MLS associated with the loss of flying ability.
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Affiliation(s)
- Atsushi Ikemoto
- Graduate School of Life SciencesTohoku UniversitySendaiJapan
| | - Daiki X. Sato
- Graduate School of Life SciencesTohoku UniversitySendaiJapan
| | - Takashi Makino
- Graduate School of Life SciencesTohoku UniversitySendaiJapan
| | - Masakado Kawata
- Graduate School of Life SciencesTohoku UniversitySendaiJapan
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