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Zhang H, Sun S, Liu J, Guo Q, Meng L, Chen J, Xiang X, Zhou Y, Zhang N, Liu H, Liu Y, Yan G, Ji Q, He L, Cai S, Cai C, Huang X, Xu S, Xiao Y, Zhang Y, Wang K, Liu Y, Chen H, Yue Z, He S, Wang J, Yang H, Liu X, Seim I, Gu Y, Li Q, Zhang G, Lee SMY, Kristiansen K, Xu X, Liu S, Fan G. The amphipod genome reveals population dynamics and adaptations to hadal environment. Cell 2025; 188:1378-1392.e18. [PMID: 40054448 DOI: 10.1016/j.cell.2025.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/16/2024] [Accepted: 01/20/2025] [Indexed: 05/13/2025]
Abstract
The amphipod Hirondellea gigas is a dominant species inhabiting the deepest part of the ocean (∼6,800-11,000 m), but little is known about its genetic adaptation and population dynamics. Here, we present a chromosome-level genome of H. gigas, characterized by a large genome size of 13.92 Gb. Whole-genome sequencing of 510 individuals from the Mariana Trench indicates no population differentiation across depths, suggesting its capacity to tolerate hydrostatic pressure across wide ranges. H. gigas in the West Philippine Basin is genetically divergent from the Mariana and Yap Trenches, suggesting genetic isolation attributed to the geographic separation of hadal features. A drastic reduction in effective population size potentially reflects glacial-interglacial changes. By integrating multi-omics analysis, we propose host-symbiotic microbial interactions may be crucial in the adaptation of H. gigas to the extremely high-pressure and food-limited environment. Our findings provide clues for adaptation to the hadal zone and population genetics.
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Affiliation(s)
- Haibin Zhang
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China; Institution of Deep-sea Life Sciences, IDSSE-BGI, Hainan Deep-sea Technology Laboratory, Sanya 57200, China.
| | - Shuai Sun
- BGI Research, Qingdao 266555, China; State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Shenzhen Key Laboratory of Marine Biology Genomics, BGI Research, Shenzhen 518083, China
| | - Jun Liu
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | - Qunfei Guo
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; BGI Research, Wuhan 430074, China
| | - Liang Meng
- BGI Research, Qingdao 266555, China; BGI Research, Sanya 572025, China; Institution of Deep-sea Life Sciences, IDSSE-BGI, Hainan Deep-sea Technology Laboratory, Sanya 57200, China
| | - Jianwei Chen
- BGI Research, Qingdao 266555, China; Qingdao Key Laboratory of Marine Genomics and Qingdao-Europe Advanced Institute for Life Sciences, BGI Research, Qingdao 266555, China; Laboratory of Integrative Biomedicine, Department of Biology, University of Copenhagen, Copenhagen 2100, Denmark
| | - Xueyan Xiang
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; BGI Research, Wuhan 430074, China; Shenzhen Key Laboratory of Marine Biology Genomics, BGI Research, Shenzhen 518083, China
| | - Yang Zhou
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | - Nannan Zhang
- BGI Research, Qingdao 266555, China; BGI Research, Sanya 572025, China
| | - Helu Liu
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | | | - Guoyong Yan
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | | | - Lisheng He
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | - Shanya Cai
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | | | - Xin Huang
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | - Shiyu Xu
- BGI Research, Qingdao 266555, China
| | - Yunlu Xiao
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | | | - Kun Wang
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | | | - Haixin Chen
- BGI Research, Sanya 572025, China; Institution of Deep-sea Life Sciences, IDSSE-BGI, Hainan Deep-sea Technology Laboratory, Sanya 57200, China
| | - Zhen Yue
- BGI Research, Sanya 572025, China; Institution of Deep-sea Life Sciences, IDSSE-BGI, Hainan Deep-sea Technology Laboratory, Sanya 57200, China
| | - Shunping He
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | | | - Huanming Yang
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China
| | - Xin Liu
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; Qingdao Key Laboratory of Marine Genomics and Qingdao-Europe Advanced Institute for Life Sciences, BGI Research, Qingdao 266555, China
| | - Inge Seim
- Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
| | - Ying Gu
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China
| | - Qiye Li
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China
| | - Guojie Zhang
- Center of Evolutionary & Organismal Biology and Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 311121, China
| | - Simon Ming-Yuen Lee
- Department of Food Science and Nutrition and PolyU-BGI Joint Research Centre for Genomics and Synthetic Biology in Global Ocean Resources, The Hong Kong Polytechnic University, Hong Kong, China
| | - Karsten Kristiansen
- Qingdao Key Laboratory of Marine Genomics and Qingdao-Europe Advanced Institute for Life Sciences, BGI Research, Qingdao 266555, China; Laboratory of Integrative Biomedicine, Department of Biology, University of Copenhagen, Copenhagen 2100, Denmark
| | - Xun Xu
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; Qingdao Key Laboratory of Marine Genomics and Qingdao-Europe Advanced Institute for Life Sciences, BGI Research, Qingdao 266555, China; BGI Research, Hangzhou 310030, China.
| | - Shanshan Liu
- State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; Institution of Deep-sea Life Sciences, IDSSE-BGI, Hainan Deep-sea Technology Laboratory, Sanya 57200, China; BGI, Shenzhen 518083, China; Shenzhen Key Laboratory of Marine Biology Genomics, BGI Research, Shenzhen 518083, China.
| | - Guangyi Fan
- BGI Research, Qingdao 266555, China; State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen 518083, China; Qingdao Key Laboratory of Marine Genomics and Qingdao-Europe Advanced Institute for Life Sciences, BGI Research, Qingdao 266555, China; BGI Research, Sanya 572025, China; Department of Food Science and Nutrition and PolyU-BGI Joint Research Centre for Genomics and Synthetic Biology in Global Ocean Resources, The Hong Kong Polytechnic University, Hong Kong, China; Shenzhen Key Laboratory of Marine Biology Genomics, BGI Research, Shenzhen 518083, China.
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Braichenko S, Borges R, Kosiol C. Polymorphism-Aware Models in RevBayes: Species Trees, Disentangling Balancing Selection, and GC-Biased Gene Conversion. Mol Biol Evol 2024; 41:msae138. [PMID: 38980178 PMCID: PMC11272101 DOI: 10.1093/molbev/msae138] [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: 12/11/2023] [Revised: 04/19/2024] [Accepted: 07/06/2024] [Indexed: 07/10/2024] Open
Abstract
The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the Polymorphism-aware phylogenetic Models (PoMos) framework rooted in the Moran model, we introduce a PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, and directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analyzing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in polymorphism-aware phylogenetic models' ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited color dimorphism in Drosophila erecta.
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Affiliation(s)
- Svitlana Braichenko
- Centre for Biological Diversity, School of Biology, University of St Andrews, Fife KY16 9TH, UK
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Rui Borges
- Institut für Populationsgenetik, Vetmeduni Vienna, Wien 1210, Austria
| | - Carolin Kosiol
- Centre for Biological Diversity, School of Biology, University of St Andrews, Fife KY16 9TH, UK
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3
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Soni V, Jensen JD. Temporal challenges in detecting balancing selection from population genomic data. G3 (BETHESDA, MD.) 2024; 14:jkae069. [PMID: 38551137 DOI: 10.1093/g3journal/jkae069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 12/21/2023] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
The role of balancing selection in maintaining genetic variation remains an open question in population genetics. Recent years have seen numerous studies identifying candidate loci potentially experiencing balancing selection, most predominantly in human populations. There are however numerous alternative evolutionary processes that may leave similar patterns of variation, thereby potentially confounding inference, and the expected signatures of balancing selection additionally change in a temporal fashion. Here we use forward-in-time simulations to quantify expected statistical power to detect balancing selection using both site frequency spectrum- and linkage disequilibrium-based methods under a variety of evolutionarily realistic null models. We find that whilst site frequency spectrum-based methods have little power immediately after a balanced mutation begins segregating, power increases with time since the introduction of the balanced allele. Conversely, linkage disequilibrium-based methods have considerable power whilst the allele is young, and power dissipates rapidly as the time since introduction increases. Taken together, this suggests that site frequency spectrum-based methods are most effective at detecting long-term balancing selection (>25N generations since the introduction of the balanced allele) whilst linkage disequilibrium-based methods are effective over much shorter timescales (<1N generations), thereby leaving a large time frame over which current methods have little power to detect the action of balancing selection. Finally, we investigate the extent to which alternative evolutionary processes may mimic these patterns, and demonstrate the need for caution in attempting to distinguish the signatures of balancing selection from those of both neutral processes (e.g. population structure and admixture) as well as of alternative selective processes (e.g. partial selective sweeps).
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
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Schield DR, Perry BW, Adams RH, Holding ML, Nikolakis ZL, Gopalan SS, Smith CF, Parker JM, Meik JM, DeGiorgio M, Mackessy SP, Castoe TA. The roles of balancing selection and recombination in the evolution of rattlesnake venom. Nat Ecol Evol 2022; 6:1367-1380. [PMID: 35851850 PMCID: PMC9888523 DOI: 10.1038/s41559-022-01829-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/15/2022] [Indexed: 02/02/2023]
Abstract
The origin of snake venom involved duplication and recruitment of non-venom genes into venom systems. Several studies have predicted that directional positive selection has governed this process. Venom composition varies substantially across snake species and venom phenotypes are locally adapted to prey, leading to coevolutionary interactions between predator and prey. Venom origins and contemporary snake venom evolution may therefore be driven by fundamentally different selection regimes, yet investigations of population-level patterns of selection have been limited. Here, we use whole-genome data from 68 rattlesnakes to test hypotheses about the factors that drive genomic diversity and differentiation in major venom gene regions. We show that selection has resulted in long-term maintenance of genetic diversity within and between species in multiple venom gene families. Our findings are inconsistent with a dominant role of directional positive selection and instead support a role of long-term balancing selection in shaping venom evolution. We also detect rapid decay of linkage disequilibrium due to high recombination rates in venom regions, suggesting that venom genes have reduced selective interference with nearby loci, including other venom paralogues. Our results provide an example of long-term balancing selection that drives trans-species polymorphism and help to explain how snake venom keeps pace with prey resistance.
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Affiliation(s)
- Drew R Schield
- Department of Biology, University of Texas at Arlington, Arlington, TX, USA.
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.
| | - Blair W Perry
- Department of Biology, University of Texas at Arlington, Arlington, TX, USA
- School of Biological Sciences, Washington State University, Pullman, WA, USA
| | - Richard H Adams
- Department of Biological and Environmental Sciences, Georgia College and State University, Milledgeville, GA, USA
| | | | | | | | - Cara F Smith
- School of Biological Sciences, University of Northern Colorado, Greeley, CO, USA
| | - Joshua M Parker
- Life Science Department, Fresno City College, Fresno, CA, USA
| | - Jesse M Meik
- Department of Biological Sciences, Tarleton State University, Stephenville, TX, USA
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Stephen P Mackessy
- School of Biological Sciences, University of Northern Colorado, Greeley, CO, USA
| | - Todd A Castoe
- Department of Biology, University of Texas at Arlington, Arlington, TX, USA.
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