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Wei H, Cao Y, Yang P, Zhou X, Liu G, Lian B, Zhong F, Zhang J. Unraveling the adaptive evolution and functional diversification of Rab GTPases in Salix matsudana under salt condition. Int J Biol Macromol 2025; 310:143615. [PMID: 40306518 DOI: 10.1016/j.ijbiomac.2025.143615] [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: 02/14/2025] [Revised: 04/21/2025] [Accepted: 04/27/2025] [Indexed: 05/02/2025]
Abstract
Soil salinity, exacerbated by human activities and climate change, is a growing threat to agricultural and forestry productivity. Willow known for their extensive root systems and rapid growth plays a crucial role in soil stabilization and water regulation. This study investigates the Rab GTPase family in S. matsudana, which is vital for intracellular vesicular trafficking and plant responses to salt stress. We identified 120 SmRabs in S. matsudana, revealing a wide range of physicochemical properties. Phylogenetic analysis categorized these into eight clades (RabA-H), with RabA being the most populous. Duplication events were discovered, with 182 syntenic SmRab pairs, suggesting whole-genome or segmental duplications. Ka/Ks ratios indicated purifying selection, with a few exceptions suggesting positive selection. Quantitative real-time PCR analysis revealed differential expression of SmRabs under salt stresses, suggesting their roles in stress response. Heterologous expression of SmRabD2g in yeast enhanced salt tolerance, indicating a protective role. Arabidopsis transformation with SmRabD2g construct showed improved salt tolerance, with transgenic plants exhibiting reduced damage under salt stress. This comprehensive study provides insights into the evolutionary dynamics and functional roles of SmRabs in S. matsudana, offering potential for genetic engineering to enhance salt tolerance in willows, contributing to sustainable production in saline soils.
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Affiliation(s)
- Hui Wei
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China
| | - Yi Cao
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China
| | - Peijian Yang
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China
| | - Xiaoxi Zhou
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China
| | - Guoyuan Liu
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China
| | - Bolin Lian
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China.
| | - Fei Zhong
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China.
| | - Jian Zhang
- Key Laboratory of Landscape Plant Genetics and Breeding, School of Life Sciences, Nantong University, Nantong, China; Key Lab of Landscape Plant Genetics and Breeding, Nantong 226000, China.
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2
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Wang X, Wang X, Cheng Y, Luo C, Xia W, Gao Z, Bu W, Jiang Y, Fei Y, Shi W, Tang J, Liu L, Zhu J, Zhao X. Construction of metal interpretable scoring system and identification of tungsten as a novel risk factor in COPD. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 283:116842. [PMID: 39106568 DOI: 10.1016/j.ecoenv.2024.116842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/24/2024] [Accepted: 08/02/2024] [Indexed: 08/09/2024]
Abstract
Numerous studies have highlighted the correlation between metal intake and deteriorated pulmonary function, emphasizing its pivotal role in the progression of Chronic Obstructive Pulmonary Disease (COPD). However, the efficacy of traditional models is often compromised due to overfitting and high bias in datasets with low-level exposure, rendering them ineffective in delineating the contemporary risk trends associated with pulmonary diseases. To address these limitations, we embarked on developing advanced, interpretable models, crucial for elucidating the intricate mechanisms of metal toxicity and enriching the domain knowledge embedded in toxicity models. In this endeavor, we scrutinized extensive, long-term metal exposure datasets from NHANES to explore the interplay between metal and pulmonary functionality. Employing a variety of machine-learning approaches, we opted for the "Mixer of Experts" model for its proficiency in identifying a myriad of toxicological trends and sensitivities. We conceptualized and illustrated the TSAP (Toxicity Score at Population-level), a metal interpretable scoring system offering performance nearly equivalent to the amalgamation of standard interpretable methods addressing the "black box" conundrum. This streamlined, bifurcated procedural analysis proved instrumental in discerning established risk factors, thereby uncovering Tungsten as a novel contributor to COPD risk. SYNOPSIS: TSAP achieved satisfied performance with transparent interpretability, suggesting tungsten intake need further action for COPD prevention.
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Affiliation(s)
- Xuehai Wang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Xiangdong Wang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yulan Cheng
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Chao Luo
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Weiyi Xia
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Zhengnan Gao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Wenxia Bu
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yichen Jiang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yue Fei
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Weiwei Shi
- Nantong Hospital to Nanjing University of Chinese Medicine, China
| | - Juan Tang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Lei Liu
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China; Department of Pathology, Affiliated Hospital of Nantong University, Nantong 226001, China.
| | - Jinfeng Zhu
- Nantong Hospital to Nanjing University of Chinese Medicine, China.
| | - Xinyuan Zhao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China.
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3
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Joseph J. Increased Positive Selection in Highly Recombining Genes Does not Necessarily Reflect an Evolutionary Advantage of Recombination. Mol Biol Evol 2024; 41:msae107. [PMID: 38829800 PMCID: PMC11173204 DOI: 10.1093/molbev/msae107] [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: 01/30/2024] [Revised: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024] Open
Abstract
It is commonly thought that the long-term advantage of meiotic recombination is to dissipate genetic linkage, allowing natural selection to act independently on different loci. It is thus theoretically expected that genes with higher recombination rates evolve under more effective selection. On the other hand, recombination is often associated with GC-biased gene conversion (gBGC), which theoretically interferes with selection by promoting the fixation of deleterious GC alleles. To test these predictions, several studies assessed whether selection was more effective in highly recombining genes (due to dissipation of genetic linkage) or less effective (due to gBGC), assuming a fixed distribution of fitness effects (DFE) for all genes. In this study, I directly derive the DFE from a gene's evolutionary history (shaped by mutation, selection, drift, and gBGC) under empirical fitness landscapes. I show that genes that have experienced high levels of gBGC are less fit and thus have more opportunities for beneficial mutations. Only a small decrease in the genome-wide intensity of gBGC leads to the fixation of these beneficial mutations, particularly in highly recombining genes. This results in increased positive selection in highly recombining genes that is not caused by more effective selection. Additionally, I show that the death of a recombination hotspot can lead to a higher dN/dS than its birth, but with substitution patterns biased towards AT, and only at selected positions. This shows that controlling for a substitution bias towards GC is therefore not sufficient to rule out the contribution of gBGC to signatures of accelerated evolution. Finally, although gBGC does not affect the fixation probability of GC-conservative mutations, I show that by altering the DFE, gBGC can also significantly affect nonsynonymous GC-conservative substitution patterns.
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Affiliation(s)
- Julien Joseph
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR 5558, Villeurbanne, France
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Rivas-Santisteban J, Yubero P, Robaina-Estévez S, González JM, Tamames J, Pedrós-Alió C. Quantifying microbial guilds. ISME COMMUNICATIONS 2024; 4:ycae042. [PMID: 38707845 PMCID: PMC11069341 DOI: 10.1093/ismeco/ycae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 05/07/2024]
Abstract
The ecological role of microorganisms is of utmost importance due to their multiple interactions with the environment. However, assessing the contribution of individual taxonomic groups has proven difficult despite the availability of high throughput data, hindering our understanding of such complex systems. Here, we propose a quantitative definition of guild that is readily applicable to metagenomic data. Our framework focuses on the functional character of protein sequences, as well as their diversifying nature. First, we discriminate functional sequences from the whole sequence space corresponding to a gene annotation to then quantify their contribution to the guild composition across environments. In addition, we identify and distinguish functional implementations, which are sequence spaces that have different ways of carrying out the function. In contrast, we found that orthology delineation did not consistently align with ecologically (or functionally) distinct implementations of the function. We demonstrate the value of our approach with two case studies: the ammonia oxidation and polyamine uptake guilds from the Malaspina circumnavigation cruise, revealing novel ecological dynamics of the latter in marine ecosystems. Thus, the quantification of guilds helps us to assess the functional role of different taxonomic groups with profound implications on the study of microbial communities.
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Affiliation(s)
- Juan Rivas-Santisteban
- Microbiome Analysis Laboratory, Centro Nacional de Biotecnología (CNB), CSIC, Calle Darwin no. 3, Madrid, 28049, Spain
| | - Pablo Yubero
- Logic of Genomic Systems Laboratory, Centro Nacional de Biotecnología (CNB), CSIC, Spain
| | | | | | - Javier Tamames
- Microbiome Analysis Laboratory, Centro Nacional de Biotecnología (CNB), CSIC, Calle Darwin no. 3, Madrid, 28049, Spain
| | - Carlos Pedrós-Alió
- Microbiome Analysis Laboratory, Centro Nacional de Biotecnología (CNB), CSIC, Calle Darwin no. 3, Madrid, 28049, Spain
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5
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Li X, Jiang Z, Zhang C, Cai K, Wang H, Pan W, Sun X, Gao Y, Xu K. Comparative genomics analysis provide insights into evolution and stress responses of Lhcb genes in Rosaceae fruit crops. BMC PLANT BIOLOGY 2023; 23:484. [PMID: 37817059 PMCID: PMC10566169 DOI: 10.1186/s12870-023-04438-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023]
Abstract
BACKGROUND Light-harvesting chlorophyll a/b b evelopment of higher plants and in response to abiotic stress. Previous works has demonstrated that that Lhcb genes were involved in the phytochrome regulation and responded to the different light and temperature conditions in Poaceae (such as maize). However, the evolution and functions of Lhcb genes remains poorly characterized in important Rosaceae species. RESULTS In this investigation, we conducted a genome-wide analysis and identified a total of 212 Lhcb genes across nine Rosaceae species. Specifically, we found 23 Lhcb genes in Fragaria vesca, 20 in Prunus armeniaca, 33 in Malus domestica 'Gala', 21 in Prunus persica, 33 in Rosa chinensis, 29 in Pyrus bretschneideri, 18 in Rubus occidentalis, 20 in Prunus mume, and 15 in Prunus salicina. Phylogenetic analysis revealed that the Lhcb gene family could be classified into seven major subfamilies, with members of each subfamily sharing similar conserved motifs. And, the functions of each subfamily was predicted based on the previous reports from other species. The Lhcb proteins were highly conserved within their respective subfamilies, suggesting similar functions. Interestingly, we observed similar peaks in Ks values (0.1-0.2) for Lhcb genes in apple and pear, indicating a recent whole genome duplication event (about 30 to 45 million years ago). Additionally, a few Lhcb genes underwent tandem duplication and were located across all chromosomes of nine species of Rosaceae. Furthermore, the analysis of the cis-acting elements in the 2000 bp promoter region upstream of the pear Lhcb gene revealed four main categories: light response correlation, stress response correlation, hormone response correlation, and plant growth. Quantitative expression analysis demonstrated that Lhcb genes exhibited tissue-specific expression patterns and responded differently to low-temperature stress in Rosaceae species. CONCLUSIONS These findings shed light on the evolution and phylogeny of Lhcb genes in Rosaceae and highlight the critical role of Lhcb in pear's response to low temperatures. The results obtained provide valuable insights for further investigations into the functions of Lhcb genes in Rosaceae, and these functional genes will be used for further fruit tree breeding and improvement to cope with the current climate changes.
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Affiliation(s)
- Xiaolong Li
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Zeyu Jiang
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Chaofan Zhang
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Kefan Cai
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Hui Wang
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Weiyi Pan
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Xuepeng Sun
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Yongbin Gao
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China.
| | - Kai Xu
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China.
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6
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Lucaci AG, Zehr JD, Enard D, Thornton JW, Kosakovsky Pond SL. Evolutionary Shortcuts via Multinucleotide Substitutions and Their Impact on Natural Selection Analyses. Mol Biol Evol 2023; 40:msad150. [PMID: 37395787 PMCID: PMC10336034 DOI: 10.1093/molbev/msad150] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/04/2023] Open
Abstract
Inference and interpretation of evolutionary processes, in particular of the types and targets of natural selection affecting coding sequences, are critically influenced by the assumptions built into statistical models and tests. If certain aspects of the substitution process (even when they are not of direct interest) are presumed absent or are modeled with too crude of a simplification, estimates of key model parameters can become biased, often systematically, and lead to poor statistical performance. Previous work established that failing to accommodate multinucleotide (or multihit, MH) substitutions strongly biases dN/dS-based inference towards false-positive inferences of diversifying episodic selection, as does failing to model variation in the rate of synonymous substitution (SRV) among sites. Here, we develop an integrated analytical framework and software tools to simultaneously incorporate these sources of evolutionary complexity into selection analyses. We found that both MH and SRV are ubiquitous in empirical alignments, and incorporating them has a strong effect on whether or not positive selection is detected (1.4-fold reduction) and on the distributions of inferred evolutionary rates. With simulation studies, we show that this effect is not attributable to reduced statistical power caused by using a more complex model. After a detailed examination of 21 benchmark alignments and a new high-resolution analysis showing which parts of the alignment provide support for positive selection, we show that MH substitutions occurring along shorter branches in the tree explain a significant fraction of discrepant results in selection detection. Our results add to the growing body of literature which examines decades-old modeling assumptions (including MH) and finds them to be problematic for comparative genomic data analysis. Because multinucleotide substitutions have a significant impact on natural selection detection even at the level of an entire gene, we recommend that selection analyses of this type consider their inclusion as a matter of routine. To facilitate this procedure, we developed, implemented, and benchmarked a simple and well-performing model testing selection detection framework able to screen an alignment for positive selection with two biologically important confounding processes: site-to-site synonymous rate variation, and multinucleotide instantaneous substitutions.
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Affiliation(s)
- Alexander G Lucaci
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
| | - Jordan D Zehr
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
| | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona
| | - Joseph W Thornton
- Department of Human Genetics, University of Chicago, Chicago, Illinois
- Department of Ecology & Evolution, University of Chicago, Chicago, Illinois
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7
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Bhuiyan SA, Xu M, Yang L, Semizoglou E, Bhatia P, Pantaleo KI, Tochitsky I, Jain A, Erdogan B, Blair S, Cat V, Mwirigi JM, Sankaranarayanan I, Tavares-Ferreira D, Green U, McIlvried LA, Copits BA, Bertels Z, Del Rosario JS, Widman AJ, Slivicki RA, Yi J, Woolf CJ, Lennerz JK, Whited JL, Price TJ, Gereau RW, Renthal W. Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.04.547740. [PMID: 37461736 PMCID: PMC10350076 DOI: 10.1101/2023.07.04.547740] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Peripheral sensory neurons in the dorsal root ganglion (DRG) and trigeminal ganglion (TG) are specialized to detect and transduce diverse environmental stimuli including touch, temperature, and pain to the central nervous system. Recent advances in single-cell RNA-sequencing (scRNA-seq) have provided new insights into the diversity of sensory ganglia cell types in rodents, non-human primates, and humans, but it remains difficult to compare transcriptomically defined cell types across studies and species. Here, we built cross-species harmonized atlases of DRG and TG cell types that describe 18 neuronal and 11 non-neuronal cell types across 6 species and 19 studies. We then demonstrate the utility of this harmonized reference atlas by using it to annotate newly profiled DRG nuclei/cells from both human and the highly regenerative axolotl. We observe that the transcriptomic profiles of sensory neuron subtypes are broadly similar across vertebrates, but the expression of functionally important neuropeptides and channels can vary notably. The new resources and data presented here can guide future studies in comparative transcriptomics, simplify cell type nomenclature differences across studies, and help prioritize targets for future pain therapy development.
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Affiliation(s)
- Shamsuddin A Bhuiyan
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Mengyi Xu
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Alan Edwards Center for Research on Pain and Department of Physiology, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Lite Yang
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Evangelia Semizoglou
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Parth Bhatia
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Katerina I Pantaleo
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Ivan Tochitsky
- F.M. Kirby Neurobiology Center and Department of Neurobiology, Boston Children's Hospital and Harvard Medical School, 3 Blackfan Cir. Boston, MA 02115
| | - Aakanksha Jain
- F.M. Kirby Neurobiology Center and Department of Neurobiology, Boston Children's Hospital and Harvard Medical School, 3 Blackfan Cir. Boston, MA 02115
| | - Burcu Erdogan
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, 02138
| | - Steven Blair
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, 02138
| | - Victor Cat
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, 02138
| | - Juliet M Mwirigi
- Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080
| | - Ishwarya Sankaranarayanan
- Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080
| | - Diana Tavares-Ferreira
- Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080
| | - Ursula Green
- Department of Pathology, Center for Integrated Diagnostics, Massachussetts General Hospital and Havard Medical School, Boston, MA 02114
| | - Lisa A McIlvried
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Bryan A Copits
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Zachariah Bertels
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - John S Del Rosario
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Allie J Widman
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Richard A Slivicki
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Jiwon Yi
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - Clifford J Woolf
- F.M. Kirby Neurobiology Center and Department of Neurobiology, Boston Children's Hospital and Harvard Medical School, 3 Blackfan Cir. Boston, MA 02115
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachussetts General Hospital and Havard Medical School, Boston, MA 02114
| | - Jessica L Whited
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, 02138
| | - Theodore J Price
- Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080
| | - Robert W Gereau
- Program in Neurosciences, Division of Biology and Biomedical Sciences, Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri 63110, USA
| | - William Renthal
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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8
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Abstract
The CODEML program in the PAML package has been widely used to analyze protein-coding gene sequences to estimate the synonymous and nonsynonymous rates (dS and dN) and to detect positive Darwinian selection driving protein evolution. For users not familiar with molecular evolutionary analysis, the program is known to have a steep learning curve. Here, we provide a step-by-step protocol to illustrate the commonly used tests available in the program, including the branch models, the site models, and the branch-site models, which can be used to detect positive selection driving adaptive protein evolution affecting particular lineages of the species phylogeny, affecting a subset of amino acid residues in the protein, and affecting a subset of sites along prespecified lineages, respectively. A data set of the myxovirus (Mx) genes from ten mammal and two bird species is used as an example. We discuss a new feature in CODEML that allows users to perform positive selection tests for multiple genes for the same set of taxa, as is common in modern genome-sequencing projects. The PAML package is distributed at https://github.com/abacus-gene/paml under the GNU license, with support provided at its discussion site (https://groups.google.com/g/pamlsoftware). Data files used in this protocol are available at https://github.com/abacus-gene/paml-tutorial.
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Affiliation(s)
- Sandra Álvarez-Carretero
- Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Paschalia Kapli
- Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Ziheng Yang
- Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
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9
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Youssef N, Susko E, Roger AJ, Bielawski JP. Evolution of amino acid propensities under stability-mediated epistasis. Mol Biol Evol 2022; 39:6522130. [PMID: 35134997 PMCID: PMC8896634 DOI: 10.1093/molbev/msac030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites - a nonadaptive phenomenon referred to as the 'evolutionary Stokes shift'. Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability-constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. Considering shifts in propensities over windows between substitutions at a focal site, we find that following ≈ 50% of substitutions the propensity for the new resident amino acid decreases over time, and both positive and negative shifts were comparable in magnitude. Preferences were often conserved via a significant negative autocorrelation in propensity changes-increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.
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Affiliation(s)
- Noor Youssef
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Edward Susko
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada
| | - Andrew J Roger
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada
| | - Joseph P Bielawski
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
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