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Gu X. d N/d S-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution. J Mol Evol 2022; 90:342-351. [PMID: 35920867 DOI: 10.1007/s00239-022-10064-2] [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: 02/19/2022] [Accepted: 06/14/2022] [Indexed: 11/25/2022]
Abstract
One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (dN) to synonymous distance (dS). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the dN/dS ratio can be used to statistically detect the adaptive evolution (or purifying selection) if dN/dS > 1 (or dN/dS < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated dN/dS < 1. Consequently, the statistical power for testing dN/dS = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called dN/dS-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the dN/dS-H model predicts dN/dS = 1-H for neutral evolution, dN/dS < 1-H for nearly neutral selection, and dN/dS > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the CN/CS-H test).
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Affiliation(s)
- Xun Gu
- The Laurence H. Baker Center in Bioinformatics on Biological Statistics, Iowa State University, Ames, IA, 50011, USA. .,Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA. .,Program of Ecological and Evolutionary Biology, Iowa State University, Ames, IA, 50011, USA.
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2
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Chen Q, Wu QN, Rong YM, Wang S, Zuo Z, Bai L, Zhang B, Yuan S, Zhao Q. Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol. Brief Bioinform 2022; 23:6590438. [DOI: 10.1093/bib/bbac175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/30/2022] [Accepted: 04/18/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Multiple primary tumor (MPT) is a special and rare cancer type, defined as more than two primary tumors presenting at the diagnosis in a single patient. The molecular characteristics and tumorigenesis of MPT remain unclear due to insufficient approaches. Here, we present MPTevol, a practical computational framework for comprehensively exploring the MPT from multiregion sequencing (MRS) experiments. To verify the utility of MPTevol, we performed whole-exome MRS for 33 samples of a rare patient with triple-primary tumors and three metastatic sites and systematically investigated clonal dynamics and metastatic routines. MPTevol assists in comparing genomic profiles across samples, detecting clonal evolutionary history and metastatic routines and quantifying the metastatic history. All triple-primary tumors were independent origins and their genomic characteristics were consistent with corresponding sporadic tumors, strongly supporting their independent tumorigenesis. We further showed two independent early monoclonal seeding events for the metastases in the ovary and uterus. We revealed that two ovarian metastases were disseminated from the same subclone of the primary tumor through undergoing whole-genome doubling processes, suggesting metastases-to-metastases seeding occurred when tumors had similar microenvironments. Surprisingly, according to the metastasis timing model of MPTevol, we found that primary tumors of about 0.058–0.124 cm diameter have been disseminating to distant organs, which is much earlier than conventional clinical views. We developed MPT-specialized analysis framework MPTevol and demonstrated its utility in explicitly resolving clonal evolutionary history and metastatic seeding routines with a rare MPT case. MPTevol is implemented in R and is available at https://github.com/qingjian1991/MPTevol under the GPL v3 license.
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Affiliation(s)
- Qingjian Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Qi-Nian Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P. R. China
| | - Yu-Ming Rong
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Shixiang Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Long Bai
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Bei Zhang
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Shuqiang Yuan
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
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3
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Tang Z, Lu Z, Chen B, Zhang W, Chang HY, Hu Z, Xu J. A Genetic Bottleneck of Mitochondrial DNA During Human Lymphocyte Development. Mol Biol Evol 2022; 39:msac090. [PMID: 35482398 PMCID: PMC9113143 DOI: 10.1093/molbev/msac090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Mitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Although the elimination of pathogenic mitochondrial DNA (mtDNA) mutations in somatic cells has been observed, the mechanisms to maintain proper functions despite their mtDNA mutation load are poorly understood. In this study, we analyzed somatic mtDNA mutations in more than 30,000 single human peripheral and bone marrow mononuclear cells. We observed a significant overrepresentation of homoplasmic mtDNA mutations in B, T, and natural killer (NK) lymphocytes. Intriguingly, their overall mutational burden was lower than that in hematopoietic progenitors and myeloid cells. This characteristic mtDNA mutational landscape indicates a genetic bottleneck during lymphoid development, as confirmed with single-cell datasets from multiple platforms and individuals. We further demonstrated that mtDNA replication lags behind cell proliferation in both pro-B and pre-B progenitor cells, thus likely causing the genetic bottleneck by diluting mtDNA copies per cell. Through computational simulations and approximate Bayesian computation (ABC), we recapitulated this lymphocyte-specific mutational landscape and estimated the minimal mtDNA copies as <30 in T, B, and NK lineages. Our integrative analysis revealed a novel process of a lymphoid-specific mtDNA genetic bottleneck, thus illuminating a potential mechanism used by highly metabolically active immune cells to limit their mtDNA mutation load.
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Affiliation(s)
- Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Baizhen Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Howard Y. Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
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4
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Chen Q, Yang H, Feng X, Chen Q, Shi S, Wu CI, He Z. Two decades of suspect evidence for adaptive molecular evolution – Negative selection confounding positive selection signals. Natl Sci Rev 2021; 9:nwab217. [PMID: 35663241 PMCID: PMC9154339 DOI: 10.1093/nsr/nwab217] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/21/2021] [Indexed: 11/21/2022] Open
Abstract
There has been a large literature in the last two decades affirming adaptive DNA sequence evolution between species. The main lines of evidence are from (i) the McDonald-Kreitman (MK) test, which compares divergence and polymorphism data, and (ii) the phylogenetic analysis by maximum likelihood (PAML) test, which analyzes multispecies divergence data. Here, we apply these two tests concurrently to genomic data of Drosophila and Arabidopsis. To our surprise, the >100 genes identified by the two tests do not overlap beyond random expectation. Because the non-concordance could be due to low powers leading to high false negatives, we merge every 20–30 genes into a ‘supergene’. At the supergene level, the power of detection is large but the calls still do not overlap. We rule out methodological reasons for the non-concordance. In particular, extensive simulations fail to find scenarios whereby positive selection can only be detected by either MK or PAML, but not both. Since molecular evolution is governed by positive and negative selection concurrently, a fundamental assumption for estimating one of these (say, positive selection) is that the other is constant. However, in a broad survey of primates, birds, Drosophila and Arabidopsis, we found that negative selection rarely stays constant for long in evolution. As a consequence, the variation in negative selection is often misconstrued as a signal of positive selection. In conclusion, MK, PAML and any method that examines genomic sequence evolution has to explicitly address the variation in negative selection before estimating positive selection. In a companion study, we propose a possible path forward in two stages—first, by mapping out the changes in negative selection and then using this map to estimate positive selection. For now, the large literature on positive selection between species has to await reassessment.
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Affiliation(s)
- Qipian Chen
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Hao Yang
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Xiao Feng
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Qingjian Chen
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Suhua Shi
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
| | - Ziwen He
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, China
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Li T, Liu J, Feng J, Liu Z, Liu S, Zhang M, Zhang Y, Hou Y, Wu D, Li C, Chen Y, Chen H, Lu X. Variation in the life history strategy underlies functional diversity of tumors. Natl Sci Rev 2021; 8:nwaa124. [PMID: 34691566 PMCID: PMC8288455 DOI: 10.1093/nsr/nwaa124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 05/12/2020] [Accepted: 05/14/2020] [Indexed: 12/27/2022] Open
Abstract
Classical r- vs. K-selection theory describes the trade-offs between high reproductive output and competitiveness and guides research in evolutionary ecology. While its impact has waned in the recent past, cancer evolution may rekindle it. Herein, we impose r- or K-selection on cancer cell lines to obtain strongly proliferative r cells and highly competitive K cells to test ideas on life-history strategy evolution. RNA-seq indicates that the trade-offs are associated with distinct expression of genes involved in the cell cycle, adhesion, apoptosis, and contact inhibition. Both empirical observations and simulations based on an ecological competition model show that the trade-off between cell proliferation and competitiveness can evolve adaptively. When the r and K cells are mixed, they exhibit strikingly different spatial and temporal distributions. Due to this niche separation, the fitness of the entire tumor increases. The contrasting selective pressure may operate in a realistic ecological setting of actual tumors.
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Affiliation(s)
- Tao Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jialin Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Feng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenzhen Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sixue Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minjie Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuezheng Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yali Hou
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Dafei Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Chunyan Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering and Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing 100191, China
| | - Yongbin Chen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming 650223, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuemei Lu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Wu CI, Wen H. Heightened protein-translation activities in mammalian cells and the disease/treatment implications. Natl Sci Rev 2020; 7:1851-1855. [PMID: 34691526 PMCID: PMC8288750 DOI: 10.1093/nsr/nwaa066] [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] [Indexed: 12/31/2022] Open
Affiliation(s)
- Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, China
| | - Haijun Wen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, China
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7
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Ruan Y, Wang H, Chen B, Wen H, Wu CI. Mutations Beget More Mutations-Rapid Evolution of Mutation Rate in Response to the Risk of Runaway Accumulation. Mol Biol Evol 2020; 37:1007-1019. [PMID: 31778175 DOI: 10.1093/molbev/msz283] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The rapidity with which the mutation rate evolves could greatly impact evolutionary patterns. Nevertheless, most studies simply assume a constant rate in the time scale of interest (Kimura 1983; Drake 1991; Kumar 2005; Li 2007; Lynch 2010). In contrast, recent studies of somatic mutations suggest that the mutation rate may vary by several orders of magnitude within a lifetime (Kandoth et al. 2013; Lawrence et al. 2013). To resolve the discrepancy, we now propose a runaway model, applicable to both the germline and soma, whereby mutator mutations form a positive-feedback loop. In this loop, any mutator mutation would increase the rate of acquiring the next mutator, thus triggering a runaway escalation in mutation rate. The process can be initiated more readily if there are many weak mutators than a few strong ones. Interestingly, even a small increase in the mutation rate at birth could trigger the runaway process, resulting in unfit progeny. In slowly reproducing species, the need to minimize the risk of this uncontrolled accumulation would thus favor setting the mutation rate low. In comparison, species that starts and ends reproduction sooner do not face the risk and may set the baseline mutation rate higher. The mutation rate would evolve in response to the risk of runaway mutation, in particular, when the generation time changes. A rapidly evolving mutation rate may shed new lights on many evolutionary phenomena (Elango et al. 2006; Thomas et al. 2010, 2018; Langergraber et al. 2012; Besenbacher et al. 2019).
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Affiliation(s)
- Yongsen Ruan
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Haiyu Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Bingjie Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Haijun Wen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,Department of Ecology and Evolution, University of Chicago, Chicago, IL
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8
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Chen Q, Lan A, Shen X, Wu CI. Molecular Evolution in Small Steps under Prevailing Negative Selection: A Nearly Universal Rule of Codon Substitution. Genome Biol Evol 2020; 11:2702-2712. [PMID: 31504473 PMCID: PMC6777424 DOI: 10.1093/gbe/evz192] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2019] [Indexed: 12/16/2022] Open
Abstract
The widely accepted view that evolution proceeds in small steps is based on two premises: 1) negative selection acts strongly against large differences and 2) positive selection favors small-step changes. The two premises are not biologically connected and should be evaluated separately. We now extend a previous approach to studying codon evolution in the entire genome. Codon substitution rate is a function of the physicochemical distance between amino acids (AAs), equated with the step size of evolution. Between nine pairs of closely related species of plants, invertebrates, and vertebrates, the evolutionary rate is strongly and negatively correlated with a set of AA distances (ΔU, scaled to [0, 1]). ΔU, a composite measure of evolutionary rates across diverse taxa, is influenced by almost all of the 48 physicochemical properties used here. The new analyses reveal a crucial trend hidden from previous studies: ΔU is strongly correlated with the evolutionary rate (R2 > 0.8) only when the genes are predominantly under negative selection. Because most genes in most taxa are strongly constrained by negative selection, ΔU has indeed appeared to be a nearly universal measure of codon evolution. In conclusion, molecular evolution at the codon level generally takes small steps due to the prevailing negative selection. Whether positive selection may, or may not, follow the small-step rule is addressed in a companion study.
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Affiliation(s)
- Qingjian Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ao Lan
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xu Shen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,Department of Ecology and Evolution, University of Chicago
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Chen Q, He Z, Lan A, Shen X, Wen H, Wu CI. Molecular Evolution in Large Steps-Codon Substitutions under Positive Selection. Mol Biol Evol 2020; 36:1862-1873. [PMID: 31077325 DOI: 10.1093/molbev/msz108] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Molecular evolution is believed to proceed in small steps. The step size can be defined by a distance reflecting physico-chemical disparities between amino acid (AA) pairs that can be exchanged by single 1-bp mutations. We show that AA substitution rates are strongly and negatively correlated with this distance but only when positive selection is relatively weak. We use the McDonald and Kreitman test to separate the influences of positive and negative selection. While negative selection is indeed stronger on AA substitutions generating larger changes in chemical properties of AAs, positive selection operates by different rules. For 65 of the 75 possible pairs, positive selection is comparable in strength regardless of AA distance. However, the ten pairs under the strongest positive selection all exhibit large leaps in chemical properties. Five of the ten pairs are shared between Drosophila and Hominoids, thus hinting at a common but modest biochemical basis of adaptation across taxa. The hypothesis that adaptive changes often take large functional steps will need to be extensively tested. If validated, molecular models will need to better integrate positive and negative selection in the search for adaptive signal.
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Affiliation(s)
- Qingjian Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Ziwen He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Ao Lan
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Xu Shen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Haijun Wen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,Department of Ecology and Evolution, University of Chicago, Chicago, IL
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