1
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Liu J, Bitsue HK, Yang Z. Skin colour: A window into human phenotypic evolution and environmental adaptation. Mol Ecol 2024:e17369. [PMID: 38713101 DOI: 10.1111/mec.17369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/13/2024] [Accepted: 04/17/2024] [Indexed: 05/08/2024]
Abstract
As modern humans ventured out of Africa and dispersed around the world, they faced novel environmental challenges that led to geographic adaptations including skin colour. Over the long history of human evolution, skin colour has changed dramatically, showing tremendous diversity across different geographical regions, for example, the majority of individuals from the expansive lands of Africa have darker skin, whereas the majority of people from Eurasia exhibit lighter skin. What adaptations did lighter skin confer upon modern humans as they migrated from Africa to Eurasia? What genetic mechanisms underlie the diversity of skin colour observed in different populations? In recent years, scientists have gradually gained a deeper understanding of the interactions between pigmentation gene and skin colour through population-based genomic studies of different groups around the world, particularly in East Asia and Africa. In this review, we summarize our current understanding of 26 skin colour-related pigmentation genes and 48 SNPs that influence skin colour. Important pigmentation genes across three major populations are described in detail: MFSD12, SLC24A5, PDPK1 and DDB1/CYB561A3/TMEM138 influence skin colour in African populations; OCA2, KITLG, SLC24A2, GNPAT and PAH are key to the evolution of skin pigmentation in East Asian populations; and SLC24A5, SLC45A2, TYR, TYRP1, ASIP, MC1R and IRF4 significantly contribute to the lightening of skin colour in European populations. We summarized recent findings in genomic studies of skin colour in populations that implicate diverse geographic environments, local adaptation among populations, gene flow and multi-gene interactions as factors influencing skin colour diversity.
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Affiliation(s)
- Jiuming Liu
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Habtom K Bitsue
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhaohui Yang
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
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2
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Modenini G, Abondio P, Sazzini M, Boattini A. Polymorphic transposable elements provide new insights on high-altitude adaptation in the Tibetan Plateau. Genomics 2024; 116:110854. [PMID: 38701989 DOI: 10.1016/j.ygeno.2024.110854] [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: 01/16/2024] [Revised: 03/23/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
Several studies demonstrated that populations living in the Tibetan plateau are genetically and physiologically adapted to high-altitude conditions, showing genomic signatures ascribable to the action of natural selection. However, so far most of them relied solely on inferences drawn from the analysis of coding variants and point mutations. To fill this gap, we focused on the possible role of polymorphic transposable elements in influencing the adaptation of Tibetan and Sherpa highlanders. To do so, we compared high-altitude and middle/low-lander individuals of East Asian ancestry by performing in silico analyses and differentiation tests on 118 modern and ancient samples. We detected several transposable elements associated with high altitude, which map genes involved in cardiovascular, hematological, chem-dependent and respiratory conditions, suggesting that metabolic and signaling pathways taking part in these functions are disproportionately impacted by the effect of environmental stressors in high-altitude individuals. To our knowledge, our study is the first hinting to a possible role of transposable elements in the adaptation of Tibetan and Sherpa highlanders.
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Affiliation(s)
- Giorgia Modenini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy.
| | - Paolo Abondio
- IRCCS Istituto Delle Scienze Neurologiche Di Bologna, Bologna, Italy
| | - Marco Sazzini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy; Interdepartmental Centre - Alma Mater Research Institute on Global Changes and Climate Change, University of Bologna, Italy
| | - Alessio Boattini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
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3
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Zhao ML, Lu ZJ, Yang L, Ding S, Gao F, Liu YZ, Yang XL, Li X, He SY. The cardiovascular system at high altitude: A bibliometric and visualization analysis. World J Cardiol 2024; 16:199-214. [PMID: 38690218 PMCID: PMC11056872 DOI: 10.4330/wjc.v16.i4.199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/14/2024] [Accepted: 04/01/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND When exposed to high-altitude environments, the cardiovascular system undergoes various changes, the performance and mechanisms of which remain controversial. AIM To summarize the latest research advancements and hot research points in the cardiovascular system at high altitude by conducting a bibliometric and visualization analysis. METHODS The literature was systematically retrieved and filtered using the Web of Science Core Collection of Science Citation Index Expanded. A visualization analysis of the identified publications was conducted employing CiteSpace and VOSviewer. RESULTS A total of 1674 publications were included in the study, with an observed annual increase in the number of publications spanning from 1990 to 2022. The United States of America emerged as the predominant contributor, while Universidad Peruana Cayetano Heredia stood out as the institution with the highest publication output. Notably, Jean-Paul Richalet demonstrated the highest productivity among researchers focusing on the cardiovascular system at high altitude. Furthermore, Peter Bärtsch emerged as the author with the highest number of cited articles. Keyword analysis identified hypoxia, exercise, acclimatization, acute and chronic mountain sickness, pulmonary hypertension, metabolism, and echocardiography as the primary research hot research points and emerging directions in the study of the cardiovascular system at high altitude. CONCLUSION Over the past 32 years, research on the cardiovascular system in high-altitude regions has been steadily increasing. Future research in this field may focus on areas such as hypoxia adaptation, metabolism, and cardiopulmonary exercise. Strengthening interdisciplinary and multi-team collaborations will facilitate further exploration of the pathophysiological mechanisms underlying cardiovascular changes in high-altitude environments and provide a theoretical basis for standardized disease diagnosis and treatment.
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Affiliation(s)
- Mao-Lin Zhao
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Zhong-Jie Lu
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Li Yang
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Sheng Ding
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Feng Gao
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Yuan-Zhang Liu
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Xue-Lin Yang
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Xia Li
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, College of Medicine, Southwest Jiaotong University, Chengdu 610083, Sichuan Province, China
| | - Si-Yi He
- Department of Cardiovascular Surgery, The General Hospital of Western Theater Command, Chengdu 610083, Sichuan Province, China.
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4
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Lee FS. Hypoxia Inducible Factor pathway proteins in high-altitude mammals. Trends Biochem Sci 2024; 49:79-92. [PMID: 38036336 PMCID: PMC10841901 DOI: 10.1016/j.tibs.2023.11.002] [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: 09/05/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
Humans and other mammals inhabit hypoxic high-altitude locales. In many of these species, genes under positive selection include ones in the Hypoxia Inducible Factor (HIF) pathway. One is PHD2 (EGLN1), which encodes for a key oxygen sensor. Another is HIF2A (EPAS1), which encodes for a PHD2-regulated transcription factor. Recent studies have provided insights into mechanisms for these high-altitude alleles. These studies have (i) shown that selection can occur on nonconserved, unstructured regions of proteins, (ii) revealed that high altitude-associated amino acid substitutions can have differential effects on protein-protein interactions, (iii) provided evidence for convergent evolution by different molecular mechanisms, and (iv) suggested that mutations in different genes can complement one another to produce a set of adaptive phenotypes.
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Affiliation(s)
- Frank S Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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5
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Palacios C, Wang P, Wang N, Brown MA, Capatosto L, Du J, Jiang J, Zhang Q, Dahal N, Lamichhaney S. Genomic Variation, Population History, and Long-Term Genetic Adaptation to High Altitudes in Tibetan Partridge (Perdix hodgsoniae). Mol Biol Evol 2023; 40:msad214. [PMID: 37768198 PMCID: PMC10583571 DOI: 10.1093/molbev/msad214] [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: 01/24/2023] [Revised: 09/09/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
Species residing across elevational gradients display adaptations in response to environmental changes such as oxygen availability, ultraviolet radiation, and temperature. Here, we study genomic variation, gene expression, and long-term adaptation in Tibetan Partridge (Perdix hodgsoniae) populations residing across the elevational gradient of the Tibetan Plateau. We generated a high-quality draft genome and used it to carry out downstream population genomic and transcriptomic analysis. The P. hodgsoniae populations residing across various elevations were genetically distinct, and their phylogenetic clustering was consistent with their geographic distribution. We identified possible evidence of gene flow between populations residing in <3,000 and >4,200 m elevation that is consistent with known habitat expansion of high-altitude populations of P. hodgsoniae to a lower elevation. We identified a 60 kb haplotype encompassing the Estrogen Receptor 1 (ESR1) gene, showing strong genetic divergence between populations of P. hodgsoniae. We identified six single nucleotide polymorphisms within the ESR1 gene fixed for derived alleles in high-altitude populations that are strongly conserved across vertebrates. We also compared blood transcriptome profiles and identified differentially expressed genes (such as GAPDH, LDHA, and ALDOC) that correlated with differences in altitude among populations of P. hodgsoniae. These candidate genes from population genomics and transcriptomics analysis were enriched for neutrophil degranulation and glycolysis pathways, which are known to respond to hypoxia and hence may contribute to long-term adaptation to high altitudes in P. hodgsoniae. Our results highlight Tibetan Partridges as a useful model to study molecular mechanisms underlying long-term adaptation to high altitudes.
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Affiliation(s)
- Catalina Palacios
- Department of Biological Sciences, Kent State University, Kent, OH 44242, USA
| | - Pengcheng Wang
- Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Nan Wang
- School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, P. R. China
| | - Megan A Brown
- Department of Biological Sciences, Kent State University, Kent, OH 44242, USA
| | - Lukas Capatosto
- Department of Biological Sciences, Kent State University, Kent, OH 44242, USA
| | - Juan Du
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, P. R. China
| | - Jiahu Jiang
- School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, P. R. China
| | - Qingze Zhang
- School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, P. R. China
| | - Nishma Dahal
- Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, HP 176061, India
| | - Sangeet Lamichhaney
- Department of Biological Sciences, Kent State University, Kent, OH 44242, USA
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6
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Jorgensen K, Song D, Weinstein J, Garcia OA, Pearson LN, Inclán M, Rivera-Chira M, León-Velarde F, Kiyamu M, Brutsaert TD, Bigham AW, Lee FS. High-Altitude Andean H194R HIF2A Allele Is a Hypomorphic Allele. Mol Biol Evol 2023; 40:msad162. [PMID: 37463421 PMCID: PMC10370452 DOI: 10.1093/molbev/msad162] [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: 03/09/2023] [Revised: 06/15/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
For over 10,000 years, Andeans have resided at high altitude where the partial pressure of oxygen challenges human survival. Recent studies have provided evidence for positive selection acting in Andeans on the HIF2A (also known as EPAS1) locus, which encodes for a central transcription factor of the hypoxia-inducible factor pathway. However, the precise mechanism by which this allele might lead to altitude-adaptive phenotypes, if any, is unknown. By analyzing whole genome sequencing data from 46 high-coverage Peruvian Andean genomes, we confirm evidence for positive selection acting on HIF2A and a unique pattern of variation surrounding the Andean-specific single nucleotide variant (SNV), rs570553380, which encodes for an H194R amino acid substitution in HIF-2α. Genotyping the Andean-associated SNV rs570553380 in a group of 299 Peruvian Andeans from Cerro de Pasco, Peru (4,338 m), reveals a positive association with increased fraction of exhaled nitric oxide, a marker of nitric oxide biosynthesis. In vitro assays show that the H194R mutation impairs binding of HIF-2α to its heterodimeric partner, aryl hydrocarbon receptor nuclear translocator. A knockin mouse model bearing the H194R mutation in the Hif2a gene displays decreased levels of hypoxia-induced pulmonary Endothelin-1 transcripts and protection against hypoxia-induced pulmonary hypertension. We conclude the Andean H194R HIF2A allele is a hypomorphic (partial loss of function) allele.
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Affiliation(s)
- Kelsey Jorgensen
- Department of Anthropology, University of California, Los Angeles, CA, USA
| | - Daisheng Song
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julien Weinstein
- Department of Anthropology, The University of Michigan, Ann Arbor, MI, USA
| | - Obed A Garcia
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Laurel N Pearson
- Department of Anthropology, The Pennsylvania State University, State College, PA, USA
| | - María Inclán
- División de. Estudios Políticos, Centro de Investigación y Docencia Económicas, Mexico City, CDMX, Mexico
| | - Maria Rivera-Chira
- Departamento de Ciencias Biológicas y Fisiológicas, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Lima, Peru
| | - Fabiola León-Velarde
- Departamento de Ciencias Biológicas y Fisiológicas, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Lima, Peru
| | - Melisa Kiyamu
- Departamento de Ciencias Biológicas y Fisiológicas, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Lima, Peru
| | - Tom D Brutsaert
- Department of Exercise Science, Syracuse University, Syracuse, NY, USA
| | - Abigail W Bigham
- Department of Anthropology, University of California, Los Angeles, CA, USA
| | - Frank S Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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7
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Salyha N, Oliynyk I. Hypoxia modeling techniques: A review. Heliyon 2023; 9:e13238. [PMID: 36718422 PMCID: PMC9877323 DOI: 10.1016/j.heliyon.2023.e13238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/08/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Hypoxia is the main cause and effect of a large number of diseases, including the most recent one facing the world, the coronavirus disease (COVID-19). Hypoxia is divided into short-term, long-term, and periodic, it can be the result of diseases, climate change, or living and traveling in the high mountain regions of the world. Since each type of hypoxia can be a cause and a consequence of various physiological changes, the methods for modeling these hypoxias are also different. There are many techniques for modeling hypoxia under experimental conditions. The most common animal for modeling hypoxia is a rat. Hypoxia models (hypoxia simulations) in rats are a tool to study the effect of various conditions on the oxygen supply of the body. These models can provide a necessary information to understand hypoxia and also provide effective treatment, highlighting the importance of various reactions of the body to hypoxia. The main parameters when choosing a model should be reproducibility and the goal that the scientist wants to achieve. Hypoxia in rats can be reproduced both ways exogenously and endogenously. The reason for writing this review was the aim to systematize the models of rats available in the literature in order to facilitate their selection by scientists. The relative strengths and limitations of each model need to be identified and understood in order to evaluate the information obtained from these models and extrapolate these results to humans to develop the necessary generalizations. Despite these problems, animal models have been and remain vital to understanding the mechanisms involved in the development and progression of hypoxia. The eligibility criteria for the selected studies was a comprehensive review of the methods and results obtained from the studies. This made it possible to make generalizations and give recommendations on the application of these methods. The review will assist scientists in choosing an appropriate hypoxia simulation method, as well as assist in interpreting the results obtained with these methods.
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Affiliation(s)
- Nataliya Salyha
- Institute of Animal Biology NAAS, Lviv, Ukraine,Corresponding author
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8
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Zhu K, Du P, Li J, Zhang J, Hu X, Meng H, Chen L, Zhou B, Yang X, Xiong J, Allen E, Ren X, Ding Y, Xu Y, Chang X, Yu Y, Han S, Dong G, Wang CC, Wen S. Cultural and demic co-diffusion of Tubo Empire on Tibetan Plateau. iScience 2022; 25:105636. [PMID: 36582485 PMCID: PMC9792914 DOI: 10.1016/j.isci.2022.105636] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/15/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022] Open
Abstract
A high point of Tibetan Plateau (TP) civilization, the expansive Tubo Empire (618-842 AD) wielded great influence across ancient western China. However, whether the Tubo expansion was cultural or demic remains unclear due to sparse ancient DNA sampling. Here, we reported ten ancient genomes at 0.017- to 0.867-fold coverages from the Dulan site with typical Tubo archaeological culture dating to 1308-1130 BP. Nine individuals from three different grave types have close relationship with previously reported ancient highlanders from the southwestern Himalayas and modern core-Tibetan populations. A Dulan-related Tubo ancestry contributed overwhelmingly (95%-100%) to the formation of modern Tibetans. A genetic outlier with dominant Eurasian steppe-related ancestry suggesting a potential population movement into the Tubo-controlled regions from Central Asia. Together with archeological evidence from burial styles and customs, our study suggested the impact of the Tubo empire on the northeast edge of the TP involved both cultural and demic diffusion.
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Affiliation(s)
- Kongyang Zhu
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China,State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Panxin Du
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, and Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Jiyuan Li
- Qinghai Provincial Cultural Relics and Archaeology Institute, Xining 810007, China
| | | | - Xiaojun Hu
- Qinghai Provincial Cultural Relics and Archaeology Institute, Xining 810007, China
| | - Hailiang Meng
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Liang Chen
- China-Central Asia Human Environmental “the Belt and Road” Joint Laboratory, Northwest University, Xi’an 710127, China
| | - Boyan Zhou
- Division of Biostatistics, Department of Population Health, School of Medicine, New York University, New York, NY 10016, USA
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China
| | - Jianxue Xiong
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Edward Allen
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Xiaoying Ren
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Yi Ding
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Yiran Xu
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Xin Chang
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Yao Yu
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Sheng Han
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Guanghui Dong
- Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China,Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China,Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China,State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China,Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China,Corresponding author
| | - Shaoqing Wen
- Institute of Archaeological Science, Fudan University, Shanghai 200433, China,Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China,MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai 200433, China,Center for the Belt and Road Archaeology and Ancient Civilizations, Shanghai 200433, China,Corresponding author
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9
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Song D, Peng K, Palmer BE, Lee FS. The ribosomal chaperone NACA recruits PHD2 to cotranslationally modify HIF-α. EMBO J 2022; 41:e112059. [PMID: 36219563 PMCID: PMC9670199 DOI: 10.15252/embj.2022112059] [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: 07/07/2022] [Revised: 08/23/2022] [Accepted: 09/23/2022] [Indexed: 01/13/2023] Open
Abstract
Prolyl hydroxylase domain protein 2 (PHD2)-catalyzed modification of hypoxia-inducible factor (HIF)-α is a key event in oxygen sensing. We previously showed that the zinc finger of PHD2 binds to a Pro-Xaa-Leu-Glu (PXLE) motif. Here, we show that the zinc finger binds to this motif in the ribosomal chaperone nascent polypeptide complex-α (NACA). This recruits PHD2 to the translation machinery to cotranslationally modify HIF-α. Importantly, this cotranslational modification is enhanced by a translational pause sequence in HIF-α. Mice with a knock-in Naca gene mutation that abolishes the PXLE motif display erythrocytosis, a reflection of HIF pathway dysregulation. In addition, human erythrocytosis-associated mutations in the zinc finger of PHD2 ablate interaction with NACA. Tibetans, who have adapted to the hypoxia of high altitude, harbor a PHD2 variant that we previously showed displays a defect in zinc finger binding to p23, a PXLE-containing HSP90 cochaperone. We show here that Tibetan PHD2 maintains interaction with NACA, thereby showing differential interactions with PXLE-containing proteins and providing an explanation for why Tibetans are not predisposed to erythrocytosis.
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Affiliation(s)
- Daisheng Song
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Kai Peng
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
- Present address:
Chime BiologicsWuhanChina
| | - Bradleigh E Palmer
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
- Present address:
Department of BiologyJohns Hopkins UniversityBaltimoreMDUSA
| | - Frank S Lee
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
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10
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Fabries P, Drogou C, Sauvet F, Nespoulous O, Erkel MC, Marchandot V, Bouaziz W, Lepetit B, Hamm-Hornez AP, Malgoyre A, Koulmann N, Gomez-Merino D, Chennaoui M. The HMOX2 polymorphism contributes to the carotid body chemoreflex in European sea-level residents by regulating hypoxic ventilatory responses. Front Med (Lausanne) 2022; 9:1000786. [PMID: 36405624 PMCID: PMC9669423 DOI: 10.3389/fmed.2022.1000786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/17/2022] [Indexed: 10/18/2023] Open
Abstract
This study investigates whether a functional single nucleotide polymorphism of HMOX2 (heme oxygenase-2) (rs4786504 T>C) is involved in individual chemosensitivity to acute hypoxia, as assessed by ventilatory responses, in European individuals. These responses were obtained at rest and during submaximal exercise, using a standardized and validated protocol for exposure to acute normobaric hypoxia. Carriers of the ancestral T allele (n = 44) have significantly lower resting and exercise hypoxic ventilatory responses than C/C homozygous carriers (n = 40). In the literature, a hypoxic ventilatory response threshold to exercise has been identified as an independent predictor of severe high altitude-illness (SHAI). Our study shows that carriers of the T allele have a higher risk of SHAI than carriers of the mutated C/C genotype. Secondarily, we were also interested in COMT (rs4680 G > A) polymorphism, which may be indirectly involved in the chemoreflex response through modulation of autonomic nervous system activity. Significant differences are present between COMT genotypes for oxygen saturation and ventilatory responses to hypoxia at rest. In conclusion, this study adds information on genetic factors involved in individual vulnerability to acute hypoxia and supports the critical role of the ≪ O2 sensor ≫ - heme oxygenase-2 - in the chemosensitivity of carotid bodies in Humans.
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Affiliation(s)
- Pierre Fabries
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- French Military Health Academy - Ecole du Val-de-Grâce, Paris, France
- Laboratoire de Biologie de l'Exercice pour la Performance et la Santé – LBEPS – UMR, Université Paris-Saclay, IRBA, Evry-Courcouronnes, France
| | - Catherine Drogou
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Vigilance Fatigue Sommeil et Santé Publique – VIFASOM – UPR 7330, Université de Paris Cité, Paris, France
| | - Fabien Sauvet
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- French Military Health Academy - Ecole du Val-de-Grâce, Paris, France
- Vigilance Fatigue Sommeil et Santé Publique – VIFASOM – UPR 7330, Université de Paris Cité, Paris, France
| | - Olivier Nespoulous
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
| | - Marie-Claire Erkel
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Vigilance Fatigue Sommeil et Santé Publique – VIFASOM – UPR 7330, Université de Paris Cité, Paris, France
| | | | - Walid Bouaziz
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
| | - Benoît Lepetit
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Laboratoire de Biologie de l'Exercice pour la Performance et la Santé – LBEPS – UMR, Université Paris-Saclay, IRBA, Evry-Courcouronnes, France
| | | | - Alexandra Malgoyre
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Laboratoire de Biologie de l'Exercice pour la Performance et la Santé – LBEPS – UMR, Université Paris-Saclay, IRBA, Evry-Courcouronnes, France
| | - Nathalie Koulmann
- French Military Health Academy - Ecole du Val-de-Grâce, Paris, France
- Laboratoire de Biologie de l'Exercice pour la Performance et la Santé – LBEPS – UMR, Université Paris-Saclay, IRBA, Evry-Courcouronnes, France
| | - Danielle Gomez-Merino
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Vigilance Fatigue Sommeil et Santé Publique – VIFASOM – UPR 7330, Université de Paris Cité, Paris, France
| | - Mounir Chennaoui
- French Armed Forces Biomedical Research Institute – IRBA, Brétigny-sur-Orge, France
- Vigilance Fatigue Sommeil et Santé Publique – VIFASOM – UPR 7330, Université de Paris Cité, Paris, France
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11
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Abstract
Strong ultraviolet (UV) radiation at high altitude imposes a serious selective pressure, which may induce skin pigmentation adaptation of indigenous populations. We conducted skin pigmentation phenotyping and genome-wide analysis of Tibetans in order to understand the underlying mechanism of adaptation to UV radiation. We observe that Tibetans have darker baseline skin color compared with lowland Han Chinese, as well as an improved tanning ability, suggesting a two-level adaptation to boost their melanin production. A genome-wide search for the responsible genes identifies GNPAT showing strong signals of positive selection in Tibetans. An enhancer mutation (rs75356281) located in GNPAT intron 2 is enriched in Tibetans (58%) but rare in other world populations (0 to 18%). The adaptive allele of rs75356281 is associated with darker skin in Tibetans and, under UVB treatment, it displays higher enhancer activities compared with the wild-type allele in in vitro luciferase assays. Transcriptome analyses of gene-edited cells clearly show that with UVB treatment, the adaptive variant of GNPAT promotes melanin synthesis, likely through the interactions of CAT and ACAA1 in peroxisomes with other pigmentation genes, and they act synergistically, leading to an improved tanning ability in Tibetans for UV protection.
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12
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Sharma K, Mishra A, Singh H, Thinlas T, Pasha MAQ. Differential methylation in EGLN1 associates with blood oxygen saturation and plasma protein levels in high-altitude pulmonary edema. Clin Epigenetics 2022; 14:123. [PMID: 36180894 PMCID: PMC9526282 DOI: 10.1186/s13148-022-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/13/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High-altitude (HA, 2500 m) hypoxic exposure evokes a multitude of physiological processes. The hypoxia-sensing genes though influence transcriptional output in disease susceptibility; the exact regulatory mechanisms remain undetermined in high-altitude pulmonary edema (HAPE). Here, we investigated the differential DNA methylation distribution in the two genes encoding the oxygen-sensing HIF-prolyl hydroxylases, prolyl hydroxylase domain protein 2 (PHD2) and factor inhibiting HIF-1α and the consequent contributions to the HAPE pathophysiology. METHODS Deep sequencing of the sodium bisulfite converted DNA segments of the two genes, Egl nine homolog 1 (EGLN1) and Hypoxia Inducible Factor 1 Subunit Alpha Inhibitor (HIF1AN), was conducted to analyze the differential methylation distribution in three study groups, namely HAPE-patients (HAPE-p), HAPE-free sojourners (HAPE-f) and healthy HA natives (HLs). HAPE-p and HAPE-f were permanent residents of low altitude (< 200 m) of North India who traveled to Leh (3500 m), India, and were recruited through Sonam Norboo Memorial (SNM) hospital, Leh. HLs were permanent residents of altitudes at and above 3500 m. In addition to the high resolution, bisulfite converted DNA sequencing, gene expression of EGLN1 and HIF1AN and their plasma protein levels were estimated. RESULTS A significantly lower methylation distribution of CpG sites was observed in EGLN1 and higher in HIF1AN (P < 0.01) in HAPE-p compared to the two control groups, HAPE-f and HLs. Of note, differential methylation distribution of a few CpG sites, 231,556,748, 231,556,804, 231,556,881, 231,557,317 and 231,557,329, in EGLN1 were significantly associated with the risk of HAPE (OR = 4.79-10.29; P = 0.048-004). Overall, the methylation percentage in EGLN1 correlated with upregulated plasma PHD2 levels (R = - 0.36, P = 0.002) and decreased peripheral blood oxygen saturation (SpO2) levels (R = 0.34, P = 0.004). We also identified a few regulatory SNPs in the DNA methylation region of EGLN1 covering chr1:231,556,683-231,558,443 suggestive of the functional role of differential methylation distribution of these CpG sites in the regulation of the genes and consequently in the HIF-1α signaling. CONCLUSIONS Significantly lower methylation distribution in EGLN1 and the consequent physiological influences annotated its functional epigenetic relevance in the HAPE pathophysiology.
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Affiliation(s)
- Kavita Sharma
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Aastha Mishra
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Himanshu Singh
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | | | - M A Qadar Pasha
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Delhi, India. .,Institute of Hypoxia Research, Hypobaric Hypoxia Society, Delhi, New Delhi, 110067, India.
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13
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Ma L, Wang R, Feng S, Yang X, Li J, Zhang Z, Zhan H, Wang Y, Xia Z, Wang CC, Kang L. Genomic insight into the population history and biological adaptations of high-altitude Tibetan highlanders in Nagqu. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.930840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Tibetan, one of the largest indigenous populations living in the high-altitude region of the Tibetan Plateau (TP), has developed a suite of physiological adaptation strategies to cope with the extreme highland environment in TP. Here, we reported genome-wide SNP data from 48 Kham-speaking Nagqu Tibetans and analyzed it with published data from 1,067 individuals in 167 modern and ancient populations to characterize the detailed Tibetan subgroup history and population substructure. Overall, the patterns of allele sharing and haplotype sharing suggested (1) the relatively genetic homogeny between the studied Nagqu Tibetans and ancient Nepalese as well as present-day core Tibetans from Lhasa, Nagqu, and Shigatse; and (2) the close relationship between our studied Kham-speaking Nagqu Tibetans and Kham-speaking Chamdo Tibetans. The fitted qpAdm models showed that the studied Nagqu Tibetans could be fitted as having the main ancestry from late Neolithic upper Yellow River millet farmers and deeply diverged lineages from Southern East Asians (represented by Upper Paleolithic Guangxi_Longlin and Laos_Hoabinhian), and a non-neglectable western Steppe herder-related ancestry (∼3%). We further scanned the candidate genomic regions of natural selection for our newly generated Nagqu Tibetans and the published core Tibetans via FST, iHS, and XP-EHH tests. The genes overlapping with these regions were associated with essential human biological functions such as immune response, enzyme activity, signal transduction, skin development, and energy metabolism. Together, our results shed light on the admixture and evolutionary history of Nagqu Tibetan populations.
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14
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Tashi QZ, Tsering SB, Zhou NN, Zhang Y, Huang YJ, Jia J, Li TJ. A Study on the Molecular Mechanism of High Altitude Heart Disease in Children. Pharmgenomics Pers Med 2022; 15:721-731. [PMID: 35903087 PMCID: PMC9316483 DOI: 10.2147/pgpm.s356206] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 04/08/2022] [Indexed: 12/22/2022] Open
Abstract
Objective High altitude heart disease (HAHD) is a common pediatric disease in high altitude areas. It usually occurs in people who have lived for a long time or have lived for more than 2500m above sea level. Its common inducement is respiratory tract infection. The clinical differential diagnosis is difficult because the symptoms of HAHD are similar to those of congenital heart disease; Due to the limitation of medical conditions, many patients are in the state of losing follow-up or not seeking medical treatment, resulting in poor prognosis of HAHD and becoming a high-altitude disease with high mortality. Clarifying the molecular mechanism of HAHD, developing early molecular screening technology and accurate treatment methods of HAHD are the key to improve the ability of prevention and treatment of HAHD. Methods First, the literature in the PubMed and CNKI databases were screened based on keywords and abstracts. Then, the literature for the study was identified based on the fitness between the content of the literature, the research objectives, and the timeliness of the literature. Finally, a systematic molecular mechanism of HAHD was established by investigating the literature and sorting out the genetic adaptations of Tibetan populations compared with low-altitude populations that migrated to the plateau. Results With the investigation of the 48 papers screened, it was found that genes capable of enhancing the hypoxic ventilatory response and resistance to pulmonary hypertension were all correlated with the hypoxia-inducible factor (HIF) pathway, consisting mainly of three pathways, HIF-1α, HIF-2α, and NO. Conclusion The low prevalence of HAHD in Tibetan aboriginal children was mainly due to the genetic adaptation of the Tibetan population to the high altitude environment, which coordinated the cellular response to hypoxia by regulating the downstream hypoxia control genes in the HIF pathway.
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Affiliation(s)
- Qu-Zhen Tashi
- Department of Pediatrics, Shigatse Peopel's Hospital, Shigatse, Tibet, 857000, People’s Republic of China
| | - Sang-Bu Tsering
- Department of Pediatrics, Shigatse Peopel's Hospital, Shigatse, Tibet, 857000, People’s Republic of China
| | - Na-Ni Zhou
- Fujungenetics Technologies Inc. Shanghai, Shanghai, 200333, People’s Republic of China
| | - Yi Zhang
- Fujungenetics Technologies Inc. Shanghai, Shanghai, 200333, People’s Republic of China
| | - Yu-Juan Huang
- Department of Emergency, Children’s Hospital of Shanghai, Shanghai, 200062, People’s Republic of China
| | - Jia Jia
- Fujungenetics Technologies Inc. Shanghai, Shanghai, 200333, People’s Republic of China
- Jia Jia, Fulgent Technologies Inc, No. 70 of Tongchuan Road, Putuo District, Shanghai, 200333, People’s Republic of China, Tel +86 18658176000, Email
| | - Ting-Jun Li
- Department of Emergency, Children’s Hospital of Shanghai, Shanghai, 200062, People’s Republic of China
- Correspondence: Ting-Jun Li, Department of Emergency, Children’s Hospital of Shanghai, No. 355 of Huding Road, Putuo District, Shanghai, 200062, People’s Republic of China, Tel +86 18930590701, Email
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15
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Liu CC, Witonsky D, Gosling A, Lee JH, Ringbauer H, Hagan R, Patel N, Stahl R, Novembre J, Aldenderfer M, Warinner C, Di Rienzo A, Jeong C. Ancient genomes from the Himalayas illuminate the genetic history of Tibetans and their Tibeto-Burman speaking neighbors. Nat Commun 2022; 13:1203. [PMID: 35260549 PMCID: PMC8904508 DOI: 10.1038/s41467-022-28827-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
Present-day Tibetans have adapted both genetically and culturally to the high altitude environment of the Tibetan Plateau, but fundamental questions about their origins remain unanswered. Recent archaeological and genetic research suggests the presence of an early population on the Plateau within the past 40 thousand years, followed by the arrival of subsequent groups within the past 10 thousand years. Here, we obtain new genome-wide data for 33 ancient individuals from high elevation sites on the southern fringe of the Tibetan Plateau in Nepal, who we show are most closely related to present-day Tibetans. They derive most of their ancestry from groups related to Late Neolithic populations at the northeastern edge of the Tibetan Plateau but also harbor a minor genetic component from a distinct and deep Paleolithic Eurasian ancestry. In contrast to their Tibetan neighbors, present-day non-Tibetan Tibeto-Burman speakers living at mid-elevations along the southern and eastern margins of the Plateau form a genetic cline that reflects a distinct genetic history. Finally, a comparison between ancient and present-day highlanders confirms ongoing positive selection of high altitude adaptive alleles.
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Affiliation(s)
- Chi-Chun Liu
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - David Witonsky
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Anna Gosling
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.,Department of Anatomy, University of Otago, Dunedin, 9054, New Zealand
| | - Ju Hyeon Lee
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Harald Ringbauer
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.,Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Richard Hagan
- Department of Anthropology, University of Oklahoma, Norman, OK, 73019, USA.,Department of Archaeology, University of York, York, YO10 5DD, UK
| | - Nisha Patel
- Department of Plant and Microbiology, University of Oklahoma, Norman, OK, 73019, USA.,Kintai Therapeutics, Cambridge, MA, 02139, USA
| | - Raphaela Stahl
- Max Planck Institute for Evolutionary Anthropology, 04103, Leipzig, Germany
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Mark Aldenderfer
- Department of Anthropology and Heritage Studies, University of California, Merced, CA, 95343, USA.
| | - Christina Warinner
- Max Planck Institute for Evolutionary Anthropology, 04103, Leipzig, Germany. .,Department of Anthropology, Harvard University, Cambridge, MA, 02138, USA.
| | - Anna Di Rienzo
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
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16
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Ma J, Zhang T, Wang W, Chen Y, Cai W, Zhu B, Xu L, Gao H, Zhang L, Li J, Gao X. Comparative Transcriptome Analyses of Gayal (Bos frontalis), Yak (Bos grunniens), and Cattle (Bos taurus) Reveal the High-Altitude Adaptation. Front Genet 2022; 12:778788. [PMID: 35087567 PMCID: PMC8789257 DOI: 10.3389/fgene.2021.778788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
Gayal and yak are well adapted to their local high-altitude environments, yet the transcriptional regulation difference of the plateau environment among them remains obscure. Herein, cross-tissue and cross-species comparative transcriptome analyses were performed for the six hypoxia-sensitive tissues from gayal, yak, and cattle. Gene expression profiles for all single-copy orthologous genes showed tissue-specific expression patterns. By differential expression analysis, we identified 3,020 and 1,995 differentially expressed genes (DEGs) in at least one tissue of gayal vs. cattle and yak vs. cattle, respectively. Notably, we found that the adaptability of the gayal to the alpine canyon environment is highly similar to the yak living in the Qinghai-Tibet Plateau, such as promoting red blood cell development, angiogenesis, reducing blood coagulation, immune system activation, and energy metabolism shifts from fatty acid β-oxidation to glycolysis. By further analyzing the common and unique DEGs in the six tissues, we also found that numerous expressed regulatory genes related to these functions are unique in the gayal and yak, which may play important roles in adapting to the corresponding high-altitude environment. Combined with WGCNA analysis, we found that UQCRC1 and COX5A are the shared differentially expressed hub genes related to the energy supply of myocardial contraction in the heart-related modules of gayal and yak, and CAPS is a shared differential hub gene among the hub genes of the lung-related module, which is related to pulmonary artery smooth muscle contraction. Additionally, EDN3 is the unique differentially expressed hub gene related to the tracheal epithelium and pulmonary vasoconstriction in the lung of gayal. CHRM2 is a unique differentially expressed hub gene that was identified in the heart of yak, which has an important role in the autonomous regulation of the heart. These results provide a basis for further understanding the complex transcriptome expression pattern and the regulatory mechanism of high-altitude domestication of gayal and yak.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Junya Li
- *Correspondence: Junya Li, ; Xue Gao,
| | - Xue Gao
- *Correspondence: Junya Li, ; Xue Gao,
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17
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Li F, Qiao Z, Duan Q, Nevo E. Adaptation of mammals to hypoxia. Animal Model Exp Med 2021; 4:311-318. [PMID: 34977482 PMCID: PMC8690989 DOI: 10.1002/ame2.12189] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 12/19/2022] Open
Abstract
Oxygen plays a pivotal role in the metabolism and activities of mammals. However, oxygen is restricted in some environments-subterranean burrow systems or habitats at high altitude or deep in the ocean-and this could exert hypoxic stresses such as oxidative damage on organisms living in these environments. In order to cope with these stresses, organisms have evolved specific strategies to adapt to hypoxia, including changes in physiology, gene expression regulation, and genetic mutations. Here, we review how mammals have adapted to the three high-altitude plateaus of the world, the limited oxygen dissolved in deep water habitats, and underground tunnels, with the aim of better understanding the adaptation of mammals to hypoxia.
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Affiliation(s)
- Fang Li
- College of Life Sciences and TechnologyMudanjiang Normal UniversityMudanjiangChina
| | - Zhenglei Qiao
- College of Life Sciences and TechnologyMudanjiang Normal UniversityMudanjiangChina
| | - Qijiao Duan
- College of Natural Resources and EnvironmentSouth China Agriculture UniversityGuangzhouChina
| | - Eviatar Nevo
- Institute of EvolutionUniversity of HaifaHaifaIsrael
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18
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Ge Q, Guo Y, Zheng W, Cai Y, Qi X, Zhao S. A comparative analysis of differentially expressed mRNAs, miRNAs and circRNAs provides insights into the key genes involved in the high-altitude adaptation of yaks. BMC Genomics 2021; 22:744. [PMID: 34654374 PMCID: PMC8518315 DOI: 10.1186/s12864-021-08044-9] [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: 01/02/2021] [Accepted: 09/29/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Yaks that inhabit the Tibetan Plateau exhibit striking phenotypic and physiological differences from cattle and have adapted well to the extreme conditions on the plateau. However, the mechanisms used by these animals for the regulation of gene expression at high altitude are not fully understood. RESULTS Here, we sequenced nine lung transcriptomes of yaks at altitudes of 3400, 4200 and 5000 m, and low-altitude Zaosheng cattle, which is a closely related species, served as controls. The analysis identified 21,764 mRNAs, 1377 circRNAs and 1209 miRNAs. By comparing yaks and cattle, 4975 mRNAs, 252 circRNAs and 75 miRNAs were identified differentially expressed. By comparing yaks at different altitudes, we identified 756 mRNAs, 64 circRNAs and 83 miRNAs that were differentially expressed (fold change ≥2 and P-value < 0.05). The pathways enriched in the mRNAs, circRNAs and miRNAs identified from the comparison of yaks and cattle were mainly associated with metabolism, including 'glycosaminoglycan degradation', 'pentose and glucuronate interconversions' and 'flavone and flavonol biosynthesis', and the mRNAs, circRNAs and miRNAs identified from the comparison of yaks at different altitude gradients were significantly enriched in metabolic pathways and immune and genetic information processing pathways. The core RNAs were identified from the mRNA-miRNA-circRNA networks constructed using the predominant differentially expressed RNAs. The core genes specific to the difference between yaks and cattle were associated with the endoplasmic reticulum and fat deposition, but those identified from the comparison among yaks at different altitude gradients were associated with maintenance of the normal biological functions of cells. CONCLUSIONS This study enhances our understanding of the molecular mechanisms involved in hypoxic adaptation in yaks and might contribute to improvements in the understanding and prevention of hypoxia-related diseases.
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Affiliation(s)
- Qianyun Ge
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Yongbo Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Wangshan Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Yuan Cai
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Xuebin Qi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Shengguo Zhao
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
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19
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Zhang T, Chen J, Zhang J, Guo YT, Zhou X, Li MW, Zheng ZZ, Zhang TZ, Murphy RW, Nevo E, Shi P. Phenotypic and genomic adaptations to the extremely high elevation in plateau zokor (Myospalax baileyi). Mol Ecol 2021; 30:5765-5779. [PMID: 34510615 DOI: 10.1111/mec.16174] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/31/2022]
Abstract
The evolutionary outcomes of high elevation adaptation have been extensively described. However, whether widely distributed high elevation endemic animals adopt uniform mechanisms during adaptation to different elevational environments remains unknown, especially with respect to extreme high elevation environments. To explore this, we analysed the phenotypic and genomic data of seven populations of plateau zokor (Myospalax baileyi) along elevations ranging from 2,700 to 4,300 m. Based on whole-genome sequencing data and demographic reconstruction of the evolutionary history, we show that two populations of plateau zokor living at elevations exceeding 3,700 m diverged from other populations nearly 10,000 years ago. Further, phenotypic comparisons reveal stress-dependent adaptation, as two populations living at elevations exceeding 3,700 m have elevated ratios of heart mass to body mass relative to other populations, and the highest population (4,300 m) displays alterations in erythrocytes. Correspondingly, genomic analysis of selective sweeps indicates that positive selection might contribute to the observed phenotypic alterations in these two extremely high elevation populations, with the adaptive cardiovascular phenotypes of both populations possibly evolving under the functional constrains of their common ancestral population. Taken together, phenotypic and genomic evidence demonstrates that heterogeneous stressors impact adaptations to extreme elevations and reveals stress-dependent and genetically constrained adaptation to hypoxia, collectively providing new insights into the high elevation adaptation.
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Affiliation(s)
- Tao Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Jie Chen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Jia Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Yuan-Ting Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Xin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Meng-Wen Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhi-Zhong Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Tong-Zuo Zhang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
| | - Robert W Murphy
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Centre for Biodiversity and Conservation Biology, Royal Ontario Museum, Toronto, ON, Canada
| | - Eviatar Nevo
- Institute of Evolution, University of Haifa, Haifa, Israel
| | - Peng Shi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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20
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Zeng L, Liu HQ, Tu XL, Ji CM, Gou X, Esmailizadeh A, Wang S, Wang MS, Wang MC, Li XL, Charati H, Adeola AC, Moshood Adedokun RA, Oladipo O, Olaogun SC, Sanke OJ, Godwin F M, Cecily Ommeh S, Agwanda B, Kasiiti Lichoti J, Han JL, Zheng HK, Wang CF, Zhang YP, Frantz LAF, Wu DD. Genomes reveal selective sweeps in kiang and donkey for high-altitude adaptation. Zool Res 2021; 42:450-460. [PMID: 34156172 PMCID: PMC8317180 DOI: 10.24272/j.issn.2095-8137.2021.095] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Over the last several hundred years, donkeys have adapted to high-altitude conditions on the Tibetan Plateau. Interestingly, the kiang, a closely related equid species, also inhabits this region. Previous reports have demonstrated the importance of specific genes and adaptive introgression in divergent lineages for adaptation to hypoxic conditions on the Tibetan Plateau. Here, we assessed whether donkeys and kiangs adapted to the Tibetan Plateau via the same or different biological pathways and whether adaptive introgression has occurred. We assembled a de novo genome from a kiang individual and analyzed the genomes of five kiangs and 93 donkeys (including 24 from the Tibetan Plateau). Our analyses suggested the existence of a strong hard selective sweep at the EPAS1 locus in kiangs. In Tibetan donkeys, however, another gene, i.e., EGLN1, was likely involved in their adaptation to high altitude. In addition, admixture analysis found no evidence for interspecific gene flow between kiangs and Tibetan donkeys. Our findings indicate that despite the short evolutionary time scale since the arrival of donkeys on the Tibetan Plateau, as well as the existence of a closely related species already adapted to hypoxia, Tibetan donkeys did not acquire adaptation via admixture but instead evolved adaptations via a different biological pathway.
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Affiliation(s)
- Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - He-Qun Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Xiao-Long Tu
- Annoroad Gene Tech. (Beijing) Co., Ltd., Beijing 100176, China
| | - Chang-Mian Ji
- Hainan Key Laboratory for Biosafety Monitoring and Molecular Breeding in Off-Season Reproduction Regions, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan 571101, China.,Biomarker Technologies Corporation, Beijing 101300, China
| | - Xiao Gou
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan 650201, China
| | - Ali Esmailizadeh
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, PB 76169-133, Iran
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | | | - Xiao-Long Li
- Biomarker Technologies Corporation, Beijing 101300, China
| | - Hadi Charati
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Adeniyi C Adeola
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Germplasm Bank of Wild Species, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Sino-Africa Joint Research Center, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | | | - Olatunbosun Oladipo
- Federal College of Animal Health and Production Technology, Moor-Plantation, Ibadan, Nigeria
| | | | - Oscar J Sanke
- Taraba State Ministry of Agriculture and Natural Resources, Jalingo 660221, Nigeria
| | | | - Sheila Cecily Ommeh
- Institute For Biotechnology Research Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000-00200, Kenya.,Department of Zoology, National Museums of Kenya, Nairobi 40658-00100, Kenya
| | - Bernard Agwanda
- Department of Zoology, National Museums of Kenya, Nairobi 40658-00100, Kenya
| | - Jacqueline Kasiiti Lichoti
- State Department of Livestock, Ministry of Agriculture, Livestock, Fisheries and Irrigation, Nairobi, Kenya
| | - 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
| | - Hong-Kun Zheng
- Biomarker Technologies Corporation, Beijing 101300, China
| | - Chang-Fa Wang
- Equus Laboratory, Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Ji'nan, Shandong 250131, China.,Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng, Shandong 252059, China. E-mail:
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China. E-mail:
| | - Laurent A F Frantz
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK. E-mail:
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.,Institute of Three-River-Source National Park, Chinese Academy of Sciences, Qinghai 810008, China. E-mail:
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21
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Graham AM, Peters JL, Wilson RE, Muñoz-Fuentes V, Green AJ, Dorfsman DA, Valqui TH, Winker K, McCracken KG. Adaptive introgression of the beta-globin cluster in two Andean waterfowl. Heredity (Edinb) 2021; 127:107-123. [PMID: 33903741 PMCID: PMC8249413 DOI: 10.1038/s41437-021-00437-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 04/04/2021] [Accepted: 04/08/2021] [Indexed: 11/09/2022] Open
Abstract
Introgression of beneficial alleles has emerged as an important avenue for genetic adaptation in both plant and animal populations. In vertebrates, adaptation to hypoxic high-altitude environments involves the coordination of multiple molecular and cellular mechanisms, including selection on the hypoxia-inducible factor (HIF) pathway and the blood-O2 transport protein hemoglobin (Hb). In two Andean duck species, a striking DNA sequence similarity reflecting identity by descent is present across the ~20 kb β-globin cluster including both embryonic (HBE) and adult (HBB) paralogs, though it was yet untested whether this is due to independent parallel evolution or adaptive introgression. In this study, we find that identical amino acid substitutions in the β-globin cluster that increase Hb-O2 affinity have likely resulted from historical interbreeding between high-altitude populations of two different distantly-related species. We examined the direction of introgression and discovered that the species with a deeper mtDNA divergence that colonized high altitude earlier in history (Anas flavirostris) transferred adaptive genetic variation to the species with a shallower divergence (A. georgica) that likely colonized high altitude more recently possibly following a range shift into a novel environment. As a consequence, the species that received these β-globin variants through hybridization might have adapted to hypoxic conditions in the high-altitude environment more quickly through acquiring beneficial alleles from the standing, hybrid-origin variation, leading to faster evolution.
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Affiliation(s)
- Allie M Graham
- Eccles Institute for Human Genetics, University of Utah, Salt Lake City, UT, USA.
| | - Jeffrey L Peters
- Department of Biological Sciences, Wright State University, Dayton, OH, USA
| | - Robert E Wilson
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Violeta Muñoz-Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Andy J Green
- Department of Wetland Ecology, Estación Biológica de Doñana, EBD-CSIC, Sevilla, Spain
| | - Daniel A Dorfsman
- Human Genetics and Genomics, Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Thomas H Valqui
- Centro de Ornitología y Biodiversidad (CORBIDI), Surco, Lima, Perú
- Universidad Nacional Agraria, La Molina, Perú
| | - Kevin Winker
- University of Alaska Museum and Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
| | - Kevin G McCracken
- Human Genetics and Genomics, Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.
- Centro de Ornitología y Biodiversidad (CORBIDI), Surco, Lima, Perú.
- University of Alaska Museum and Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA.
- Department of Biology, University of Miami, Coral Gables, FL, USA.
- Department of Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL, USA.
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22
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Bhattacharya S, Shrimali NM, Mohammad G, Koul PA, Prchal JT, Guchhait P. Gain-of-function Tibetan PHD2 D4E;C127S variant suppresses monocyte function: A lesson in inflammatory response to inspired hypoxia. EBioMedicine 2021; 68:103418. [PMID: 34102396 PMCID: PMC8190441 DOI: 10.1016/j.ebiom.2021.103418] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/01/2021] [Accepted: 05/14/2021] [Indexed: 12/30/2022] Open
Abstract
Background We have previously described an evolutionarily selected Tibetan prolyl hydroxylase-2 (PHD2D4E;C127S) variant that degrades the hypoxia-inducible factor (HIFα) more efficiently and protects these highlanders from hypoxia-triggered elevation in haemoglobin concentration. High altitude is known to cause acute mountain sickness (AMS) and high-altitude pulmonary edema (HAPE) in a section of rapidly ascending non-acclimatised lowlanders. These morbidities are often accompanied by inflammatory response and exposure to hypobaric hypoxia is presumed to be the principal causative agent. We have investigated whether PHD2D4E;C127S variant is associated with prevention of hypoxia-mediated inflammatory milieu in Tibetan highlanders and therefore identify a potential target to regulate inflammation. Methods We genotyped the Tibetans using DNA isolated from whole blood. Thereafter immunophenotying was performed on PBMCs from homozygous PHD2D4E;C127S and PHD2WT individuals using flow cytometry. RNA isolated from these individuals was used to evaluate the peripheral level of important transcripts associated with immune as well as hypoxia response employing the nCounter technology. The ex-vivo findings were validated by generating monocytic cell lines (U937 cell line) expressing PHD2D4E;C127S and PHD2WT variants post depletion of endogenous PHD2. We had also collected whole blood samples from healthy travellers and travellers afflicted with AMS and HAPE to evaluate the significance of our ex-vivo and in vitro findings. Hereafter, we also attempted to resolve hypoxia-triggered inflammation in vitro as well as in vivo by augmenting the function of PHD2 using alpha-ketoglutarate (αKG), a co-factor of PHD2. Findings We report that homozygous PHD2D4E;C127S highlanders harbour less inflammatory and patrolling monocytes in circulation as compared to Tibetan PHD2WT highlanders. In response to in vitro hypoxia, secretion of IL6 and IL1β from PHD2D4E;C127S monocytes, and their chemotactic response compared to the PHD2WT are compromised, corresponding to the down-modulated expression of related signalling molecules RELA, JUN, STAT1, ATF2 and CXCR4. We verified these functional outcomes in monocytic U937 cell line engineered to express PHD2D4E;C127S and confirmed the down-modulation of the signalling molecules at protein level under hypoxia. In contrast, non-Tibetan sojourners with AMS and HAPE at high altitude (3,600 m above sea level) displayed significant increase in these inflammatory parameters. Our data henceforth underline the role of gain-of-function of PHD2 as the rate limiting factor to harness hyper-activation of monocytes in hypoxic environment. Therefore upon pre-treatment with αKG, we observed diminished inflammatory response of monocytes in vitro and reduction in leukocyte infiltration to the lungs in mice exposed to normobaric hypoxia. Interpretation Our report suggests that gain-of-function PHD2 D4E;C127S variant can therefore protect against inflammation elicited by hypobaric hypoxia. Augmentation of PHD2 activity therefore may be an important method to alleviate inflammatory response to inspired hypoxia. Funding This study is supported by the Department of Biotechnology, Government of India.
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Affiliation(s)
- Sulagna Bhattacharya
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India; School of Biotechnology, Kalinga Institute of Industrial Technology, Orissa, India
| | - Nishith M Shrimali
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India
| | | | - Parvaiz A Koul
- Department of Internal and Pulmonary Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar, India
| | - Josef T Prchal
- Department of Medicine, University of Utah School of Medicine & Huntsman Cancer Center and George E. Wahlin Veteran's Administration Medical Center, Salt Lake City, UT, USA
| | - Prasenjit Guchhait
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India.
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23
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Quan C, Li Y, Liu X, Wang Y, Ping J, Lu Y, Zhou G. Characterization of structural variation in Tibetans reveals new evidence of high-altitude adaptation and introgression. Genome Biol 2021; 22:159. [PMID: 34034800 PMCID: PMC8146648 DOI: 10.1186/s13059-021-02382-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/14/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Structural variation (SV) acts as an essential mutational force shaping the evolution and function of the human genome. However, few studies have examined the role of SVs in high-altitude adaptation and little is known of adaptive introgressed SVs in Tibetans so far. RESULTS Here, we generate a comprehensive catalog of SVs in a Chinese Tibetan (n = 15) and Han (n = 10) population using nanopore sequencing technology. Among a total of 38,216 unique SVs in the catalog, 27% are sequence-resolved for the first time. We systematically assess the distribution of these SVs across repeat sequences and functional genomic regions. Through genotyping in additional 276 genomes, we identify 69 Tibetan-Han stratified SVs and 80 candidate adaptive genes. We also discover a few adaptive introgressed SV candidates and provide evidence for a deletion of 335 base pairs at 1p36.32. CONCLUSIONS Overall, our results highlight the important role of SVs in the evolutionary processes of Tibetans' adaptation to the Qinghai-Tibet Plateau and provide a valuable resource for future high-altitude adaptation studies.
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Affiliation(s)
- Cheng Quan
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yuanfeng Li
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Xinyi Liu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yahui Wang
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Jie Ping
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yiming Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
- Hebei University, Baoding, Hebei Province 071002 People’s Republic of China
| | - Gangqiao Zhou
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
- Hebei University, Baoding, Hebei Province 071002 People’s Republic of China
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province 211166 People’s Republic of China
- Medical College of Guizhou University, Guiyang, Guizhou Province 550025 People’s Republic of China
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24
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Feriel J, Tchipeva D, Depasse F. Effects of circadian variation, lifestyle and environment on hematological parameters: A narrative review. Int J Lab Hematol 2021; 43:917-926. [PMID: 34019728 DOI: 10.1111/ijlh.13590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/06/2021] [Accepted: 04/27/2021] [Indexed: 12/28/2022]
Abstract
The complete blood count (CBC) is the most widely prescribed laboratory test. It plays a key role in screening, diagnosing, and monitoring a variety of medical disorders. Preanalytical and analytical variables are responsible for more than 50% of laboratory errors that may lead to spurious CBC results. The effects of blood sampling, transport, storage, and analytical errors on hematological parameters have been well described. Circadian variation and changes in lifestyle and environment can also affect blood cells. It has been extensively studied in the past, but highly variable methodology and the presence of confounding factors have provided scattered and inconsistent results. We have investigated the literature to define the impact of circadian variation, modification of the sleep-wake cycle, acute and chronic exercise, eating habits, alcohol, tobacco, drugs of abuse, high-altitude, heat/cold exposure, and air pollution on CBC results. The affected cell type along with the intensity and duration of changes are detailed for each condition. We aim at providing a comprehensive overview of which situations may induce clinically significant changes and have to be taken into account by healthcare professionals before considering a hematological parameter as pathological and requesting complementary tests.
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25
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Wang T, Guo Y, Liu S, Zhang C, Cui T, Ding K, Wang P, Wang X, Wang Z. KLF4, a Key Regulator of a Transitive Triplet, Acts on the TGF-β Signaling Pathway and Contributes to High-Altitude Adaptation of Tibetan Pigs. Front Genet 2021; 12:628192. [PMID: 33936161 PMCID: PMC8082500 DOI: 10.3389/fgene.2021.628192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/10/2021] [Indexed: 11/13/2022] Open
Abstract
Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified KLF4, BCL6B, EGR1, EPAS1, SMAD6, SMAD7, KDR, ATOH8, and CCN1 genes as potential regulators of hypoxia adaption. We found that KLF4 and EGR1 genes might simultaneously regulate the BCL6B gene, forming a KLF4-EGR1-BCL6B complex. This complex, dominated by KLF4, may enhance the hypoxia tolerance of Tibetan pigs by mediating the TGF-β signaling pathway. The complex may also affect the PI3K-Akt signaling pathway, which plays an important role in angiogenesis caused by hypoxia. Therefore, we postulate that the KLF4-EGR1-BCL6B complex may be beneficial for Tibetan pigs to survive better in the hypoxia environments. Although further molecular experiments and independent large-scale studies are needed to verify our findings, these findings may provide new details of the regulatory architecture of hypoxia-adaptive genes and are valuable for understanding the genetic mechanism of hypoxic adaptation in mammals.
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Affiliation(s)
- Tao Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Yuanyuan Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Shengwei Liu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Chaoxin Zhang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Tongyan Cui
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Kun Ding
- College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot, China
| | - Peng Wang
- HeiLongJiang Provincial Husbandry Department, Harbin, China
| | - Xibiao Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Zhipeng Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
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26
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Storz JF. High-Altitude Adaptation: Mechanistic Insights from Integrated Genomics and Physiology. Mol Biol Evol 2021; 38:2677-2691. [PMID: 33751123 PMCID: PMC8233491 DOI: 10.1093/molbev/msab064] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Population genomic analyses of high-altitude humans and other vertebrates have identified numerous candidate genes for hypoxia adaptation, and the physiological pathways implicated by such analyses suggest testable hypotheses about underlying mechanisms. Studies of highland natives that integrate genomic data with experimental measures of physiological performance capacities and subordinate traits are revealing associations between genotypes (e.g., hypoxia-inducible factor gene variants) and hypoxia-responsive phenotypes. The subsequent search for causal mechanisms is complicated by the fact that observed genotypic associations with hypoxia-induced phenotypes may reflect second-order consequences of selection-mediated changes in other (unmeasured) traits that are coupled with the focal trait via feedback regulation. Manipulative experiments to decipher circuits of feedback control and patterns of phenotypic integration can help identify causal relationships that underlie observed genotype–phenotype associations. Such experiments are critical for correct inferences about phenotypic targets of selection and mechanisms of adaptation.
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Affiliation(s)
- Jay F Storz
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
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27
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Abstract
Population genomic studies of humans and other animals at high altitude have generated many hypotheses about the genes and pathways that may have contributed to hypoxia adaptation. Future advances require experimental tests of such hypotheses to identify causal mechanisms. Studies to date illustrate the challenge of moving from lists of candidate genes to the identification of phenotypic targets of selection, as it can be difficult to determine whether observed genotype-phenotype associations reflect causal effects or secondary consequences of changes in other traits that are linked via homeostatic regulation. Recent work on high-altitude models such as deer mice has revealed both plastic and evolved changes in respiratory, cardiovascular, and metabolic traits that contribute to aerobic performance capacity in hypoxia, and analyses of tissue-specific transcriptomes have identified changes in regulatory networks that mediate adaptive changes in physiological phenotype. Here we synthesize recent results and discuss lessons learned from studies of high-altitude adaptation that lie at the intersection of genomics and physiology.
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Affiliation(s)
- Jay F Storz
- School of Biological Sciences, University of Nebraska, Lincoln, Nebraska 68588, USA;
| | - Zachary A Cheviron
- Division of Biological Sciences, University of Montana, Missoula, Montana 59812, USA;
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28
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Roles of HIF and 2-Oxoglutarate-Dependent Dioxygenases in Controlling Gene Expression in Hypoxia. Cancers (Basel) 2021; 13:cancers13020350. [PMID: 33477877 PMCID: PMC7832865 DOI: 10.3390/cancers13020350] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Hypoxia—reduction in oxygen availability—plays key roles in both physiological and pathological processes. Given the importance of oxygen for cell and organism viability, mechanisms to sense and respond to hypoxia are in place. A variety of enzymes utilise molecular oxygen, but of particular importance to oxygen sensing are the 2-oxoglutarate (2-OG) dependent dioxygenases (2-OGDs). Of these, Prolyl-hydroxylases have long been recognised to control the levels and function of Hypoxia Inducible Factor (HIF), a master transcriptional regulator in hypoxia, via their hydroxylase activity. However, recent studies are revealing that such dioxygenases are involved in almost all aspects of gene regulation, including chromatin organisation, transcription and translation. Abstract Hypoxia—reduction in oxygen availability—plays key roles in both physiological and pathological processes. Given the importance of oxygen for cell and organism viability, mechanisms to sense and respond to hypoxia are in place. A variety of enzymes utilise molecular oxygen, but of particular importance to oxygen sensing are the 2-oxoglutarate (2-OG) dependent dioxygenases (2-OGDs). Of these, Prolyl-hydroxylases have long been recognised to control the levels and function of Hypoxia Inducible Factor (HIF), a master transcriptional regulator in hypoxia, via their hydroxylase activity. However, recent studies are revealing that dioxygenases are involved in almost all aspects of gene regulation, including chromatin organisation, transcription and translation. We highlight the relevance of HIF and 2-OGDs in the control of gene expression in response to hypoxia and their relevance to human biology and health.
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29
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Effect of EGLN1 Genetic Polymorphisms on Hemoglobin Concentration in Andean Highlanders. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3436581. [PMID: 33282944 PMCID: PMC7686849 DOI: 10.1155/2020/3436581] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/03/2020] [Accepted: 10/15/2020] [Indexed: 11/20/2022]
Abstract
The physiological characteristics of Andean natives living at high altitudes have been investigated extensively, with many studies reporting that Andean highlanders have a higher hemoglobin (Hb) concentration than other highlander populations. It has previously been reported that positive natural selection has acted independently on the egl-9 family hypoxia inducible factor 1 (EGLN1) gene in Tibetan and Andean highlanders and is related to Hb concentration in Tibetans. However, no study has yet revealed the genetic determinants of Hb concentration in Andeans even though several single-nucleotide polymorphisms (SNPs) in EGLN1 have previously been examined. Therefore, we explored the relationship between hematological measurements and tag SNPs designed to cover the whole EGLN1 genomic region in Andean highlanders living in Bolivia. Our findings indicated that haplotype frequencies estimated from the EGLN1 SNPs were significantly correlated with Hb concentration in the Bolivian highlanders. Moreover, we found that an Andean-dominant haplotype related to high Hb level may have expanded rapidly in ancestral Andean highlander populations. Analysis of genotype data in an ~436.3 kb genomic region containing EGLN1 using public databases indicated that the population structure based on EGLN1 genetic markers in Andean highlanders was largely different from that in other human populations. This finding may be related to an intrinsic or adaptive physiological characteristic of Andean highlanders. In conclusion, the high Hb concentrations in Andean highlanders can be partly characterized by EGLN1 genetic variants.
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30
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Kling L, Schreiber A, Eckardt KU, Kettritz R. Hypoxia-inducible factors not only regulate but also are myeloid-cell treatment targets. J Leukoc Biol 2020; 110:61-75. [PMID: 33070368 DOI: 10.1002/jlb.4ri0820-535r] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/30/2020] [Accepted: 09/30/2020] [Indexed: 12/19/2022] Open
Abstract
Hypoxia describes limited oxygen availability at the cellular level. Myeloid cells are exposed to hypoxia at various bodily sites and even contribute to hypoxia by consuming large amounts of oxygen during respiratory burst. Hypoxia-inducible factors (HIFs) are ubiquitously expressed heterodimeric transcription factors, composed of an oxygen-dependent α and a constitutive β subunit. The stability of HIF-1α and HIF-2α is regulated by oxygen-sensing prolyl-hydroxylases (PHD). HIF-1α and HIF-2α modify the innate immune response and are context dependent. We provide a historic perspective of HIF discovery, discuss the molecular components of the HIF pathway, and how HIF-dependent mechanisms modify myeloid cell functions. HIFs enable myeloid-cell adaptation to hypoxia by up-regulating anaerobic glycolysis. In addition to effects on metabolism, HIFs control chemotaxis, phagocytosis, degranulation, oxidative burst, and apoptosis. HIF-1α enables efficient infection defense by myeloid cells. HIF-2α delays inflammation resolution and decreases antitumor effects by promoting tumor-associated myeloid-cell hibernation. PHDs not only control HIF degradation, but also regulate the crosstalk between innate and adaptive immune cells thereby suppressing autoimmunity. HIF-modifying pharmacologic compounds are entering clinical practice. Current indications include renal anemia and certain cancers. Beneficial and adverse effects on myeloid cells should be considered and could possibly lead to drug repurposing for inflammatory disorders.
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Affiliation(s)
- Lovis Kling
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Adrian Schreiber
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin and Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralph Kettritz
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin and Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
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31
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Singhal NS, Bai M, Lee EM, Luo S, Cook KR, Ma DK. Cytoprotection by a naturally occurring variant of ATP5G1 in Arctic ground squirrel neural progenitor cells. eLife 2020; 9:55578. [PMID: 33050999 PMCID: PMC7671683 DOI: 10.7554/elife.55578] [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] [Received: 01/29/2020] [Accepted: 10/08/2020] [Indexed: 02/06/2023] Open
Abstract
Many organisms in nature have evolved mechanisms to tolerate severe hypoxia or ischemia, including the hibernation-capable Arctic ground squirrel (AGS). Although hypoxic or ischemia tolerance in AGS involves physiological adaptations, little is known about the critical cellular mechanisms underlying intrinsic AGS cell resilience to metabolic stress. Through cell survival-based cDNA expression screens in neural progenitor cells, we identify a genetic variant of AGS Atp5g1 that confers cell resilience to metabolic stress. Atp5g1 encodes a subunit of the mitochondrial ATP synthase. Ectopic expression in mouse cells and CRISPR/Cas9 base editing of endogenous AGS loci revealed causal roles of one AGS-specific amino acid substitution in mediating cytoprotection by AGS ATP5G1. AGS ATP5G1 promotes metabolic stress resilience by modulating mitochondrial morphological change and metabolic functions. Our results identify a naturally occurring variant of ATP5G1 from a mammalian hibernator that critically contributes to intrinsic cytoprotection against metabolic stress. When animals hibernate, they lower their body temperature and metabolism to conserve the energy they need to withstand cold harsh winters. One such animal is the Arctic ground squirrel, an extreme hibernator that can drop its body temperatures to below 0°C. This hibernation ability means the cells of Arctic ground squirrels can survive severe shortages of blood and oxygen. But, it is unclear how their cells are able to endure this metabolic stress. To answer this question, Singhal, Bai et al. studied the cells of Arctic ground squirrels for unique features that might make them more durable to stress. Examining the genetic code of these resilient cells revealed that Arctic ground squirrels may have a variant form of a protein called ATP5G1. This protein is found in a cellular compartment called the mitochondria, which is responsible for supplying energy to the rest of the cell and therefore plays an important role in metabolic processes. Singhal, Bai et al. found that when this variant form of ATP5G1 was introduced into the cells of mice, their mitochondria was better at coping with stress conditions, such as low oxygen, low temperature and poisoning. Using a gene editing tool to selectively substitute some of the building blocks, also known as amino acids, that make up the ATP5G1 protein revealed that improvements to the mitochondria were caused by switching specific amino acids. However, swapping these amino acids, which presumably affects the role of ATP5G1, did not completely remove the cells’ resilience to stress. This suggests that variants of other genes and proteins may also be involved in providing protection. These findings provide the first evidence of a protein variant that is responsible for protecting cells during the metabolic stress conditions caused by hibernation. The approach taken by Singhal, Bai et al. could be used to identify and study other proteins that increase resilience to metabolic stress. These findings could help develop new treatments for diseases caused by a limited blood supply to human organs, such as a stroke or heart attack.
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Affiliation(s)
- Neel S Singhal
- Department of Neurology, University of California-San Francisco, San Francisco, United States
| | - Meirong Bai
- Cardiovascular Research Institute, University of California-San Francisco, San Francisco, United States.,Department of Physiology, University of California-San Francisco, San Francisco, United States
| | - Evan M Lee
- Cardiovascular Research Institute, University of California-San Francisco, San Francisco, United States.,Department of Physiology, University of California-San Francisco, San Francisco, United States
| | - Shuo Luo
- Cardiovascular Research Institute, University of California-San Francisco, San Francisco, United States.,Department of Physiology, University of California-San Francisco, San Francisco, United States
| | - Kayleigh R Cook
- Cardiovascular Research Institute, University of California-San Francisco, San Francisco, United States.,Department of Physiology, University of California-San Francisco, San Francisco, United States
| | - Dengke K Ma
- Cardiovascular Research Institute, University of California-San Francisco, San Francisco, United States.,Department of Physiology, University of California-San Francisco, San Francisco, United States.,Innovative Genomics Institute, Berkeley, United States
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32
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Chen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kähönen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimäki T, Lerch MM, Linneberg A, Liu Y, Lyytikäinen LP, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Trembath RC, Ghanbari M, Völker U, Völzke H, Watkins NA, Zonderman AB, Wilson PWF, Li Y, Butterworth AS, Gauchat JF, Chiang CWK, Li B, Loos RJF, Astle WJ, Evangelou E, van Heel DA, Sankaran VG, Okada Y, Soranzo N, Johnson AD, Reiner AP, Auer PL, Lettre G. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell 2020; 182:1198-1213.e14. [PMID: 32888493 PMCID: PMC7480402 DOI: 10.1016/j.cell.2020.06.045] [Citation(s) in RCA: 275] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/16/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022]
Abstract
Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.
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Affiliation(s)
- Ming-Huei Chen
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Abdou Mousas
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
| | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bhavi Trivedi
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Parsa Akbari
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK
| | | | - Erik L Bao
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Xue Zhong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Regina Manansala
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
| | - Véronique Laplante
- Département de Pharmacologie et Physiologie, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Ken Sin Lo
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
| | - Huijun Qian
- Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Caleb A Lareau
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | | | - Karen A Hunt
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8581, Japan
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98101, USA
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Andrew Beswick
- Translational Health Sciences, Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol BS10 5NB, UK
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Hasso-Plattner-Institut, Universität Potsdam, Potsdam 14469, Germany
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands
| | - Kumaraswamynaidu Chitrala
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA; Department of Medicine, Division on Aging, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge CB2 0QQ, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge CB2 0QQ, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Niki Dimou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon 69008, France; Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Jingzhong Ding
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London SW7 2AZ, UK; Medical Research Council-Public Health England Centre for Environment, Imperial College London, London SW7 2AZ, UK; UK Dementia Research Institute, Imperial College London, London SW7 2AZ, UK; Health Data Research UK - London, London SW7 2AZ, UK
| | - Tõnu Esko
- Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | - James S Floyd
- Department of Medicine, University of Washington, Seattle, WA 98101, USA; Department of Epidemiology, University of Washington, Seattle, WA 98101, USA
| | - Linda Broer
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Michael H Guo
- Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andreas Greinacher
- Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Jeff Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Joanna M M Howson
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Oxford OX3 7FZ, UK
| | - Qin Qin Huang
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute (SITI), Shanghai 201203, China
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Tim Kacprowski
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany; Chair of Experimental Bioinformatics, Research Group Computational Systems Medicine, Technical University of Munich, Freising-Weihenstephan 85354, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland; Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Savita Karthikeyan
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Fotis Koskeridis
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Markus M Lerch
- Department of Internal Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg 2000, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Yongmei Liu
- Duke Molecular Physiology Institute, Department of Medicine, Division of Cardiology, Duke University Medical Center, Durham, NC 27701, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Hilary C Martin
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Yoshinori Murakami
- Division of Molecular Pathology, The Institute of Medical Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthias Nauck
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany; Institue of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland; Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Willem H Ouwehand
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK; Department of Hematology, University of Cambridge, Cambridge CB2 0PT, UK; National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA 98101, USA; Department of Epidemiology, University of Washington, Seattle, WA 98101, USA; Department of Health Services, University of Washington, Seattle, WA 98101, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku 20521, Finland; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20521, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland
| | - David J Roberts
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; Department of Haematology, Churchill Hospital, Oxford OX3 7LE, UK; NHS Blood and Transplant - Oxford Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Benjamin A T Rodriguez
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Jonathan D Rosen
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01002, USA
| | - Praveen Surendran
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Richard C Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King's College London, London SE1 1UL, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands; Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad 9177948564, Iran
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Nicholas A Watkins
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | | | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Jean-François Gauchat
- Département de Pharmacologie et Physiologie, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; Quantitative and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - William J Astle
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Strangeways Laboratory, Cambridge CB1 8RN, UK
| | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece; Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK
| | - David A van Heel
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan; Laboratory of Statistical Immunology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Nicole Soranzo
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; Department of Hematology, University of Cambridge, Cambridge CB2 0PT, UK
| | - Andrew D Johnson
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98109, USA
| | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA.
| | - Guillaume Lettre
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada.
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33
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Gassmann M, Cowburn A, Gu H, Li J, Rodriguez M, Babicheva A, Jain PP, Xiong M, Gassmann NN, Yuan JXJ, Wilkins MR, Zhao L. Hypoxia-induced pulmonary hypertension-Utilizing experiments of nature. Br J Pharmacol 2020; 178:121-131. [PMID: 32464698 DOI: 10.1111/bph.15144] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/26/2020] [Accepted: 04/30/2020] [Indexed: 12/19/2022] Open
Abstract
An increase in pulmonary artery pressure is a common observation in adult mammals exposed to global alveolar hypoxia. It is considered a maladaptive response that places an increased workload on the right ventricle. The mechanisms initiating and maintaining the elevated pressure are of considerable interest in understanding pulmonary vascular homeostasis. There is an expectation that identifying the key molecules in the integrated vascular response to hypoxia will inform potential drug targets. One strategy is to take advantage of experiments of nature, specifically, to understand the genetic basis for the inter-individual variation in the pulmonary vascular response to acute and chronic hypoxia. To date, detailed phenotyping of highlanders has focused on haematocrit and oxygen saturation rather than cardiovascular phenotypes. This review explores what we can learn from those studies with respect to the pulmonary circulation. LINKED ARTICLES: This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.1/issuetoc.
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Affiliation(s)
- Max Gassmann
- Institute of Veterinary Physiology, Vetsuisse Faculty, and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,University Peruana Cayetano Heredia (UPCH), Lima, Peru
| | - Andrew Cowburn
- National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London, UK
| | - Hong Gu
- Department of Pediatric Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Jia Li
- Clinical Physiology Laboratory, Institute of Pediatrics, Heart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Marisela Rodriguez
- Section of Physiology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Aleksandra Babicheva
- Section of Physiology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Pritesh P Jain
- Section of Physiology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Mingmei Xiong
- Section of Physiology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Norina N Gassmann
- Institute of Veterinary Physiology, Vetsuisse Faculty, and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland
| | - Jason X-J Yuan
- Section of Physiology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Martin R Wilkins
- National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London, UK
| | - Lan Zhao
- National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London, UK
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Kim JH, Ajani P, Murray SA, Kim JH, Lim HC, Teng ST, Lim PT, Han MS, Park BS. Sexual reproduction and genetic polymorphism within the cosmopolitan marine diatom Pseudo-nitzschia pungens. Sci Rep 2020; 10:10653. [PMID: 32606343 PMCID: PMC7326933 DOI: 10.1038/s41598-020-67547-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/10/2020] [Indexed: 01/10/2023] Open
Abstract
Different clades belonging to the cosmopolitan marine diatom Pseudo-nitzschia pungens appear to be present in different oceanic environments, however, a ‘hybrid zone’, where populations of different clades interbreed, has also been reported. Many studies have investigated the sexual reproduction of P. pungens, focused on morphology and life cycle, rather than the role of sexual reproduction in mixing the genomes of their parents. We carried out crossing experiments to determine the sexual compatibility/incompatibility between different clades of P. pungens, and examined the genetic polymorphism in the ITS2 region. Sexual reproduction did not occur only between clades II and III under any of experimental temperature conditions. Four offspring strains were established between clade I and III successfully. Strains established from offspring were found interbreed with other offspring strains as well as viable with their parental strains. We confirmed the hybrid sequence patterns between clades I and III and found novel sequence types including polymorphic single nucleotide polymorphisms (SNPs) in the offspring strains. Our results implicate that gene exchange and mixing between different clades are still possible, and that sexual reproduction is a significant ecological strategy to maintain the genetic diversity within this diatom species.
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Affiliation(s)
- Jin Ho Kim
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea.,Risk Assessment Research Center, Korea Institute of Ocean Science and Technology, Geoje, 53201, Republic of Korea.,DNA Analysis Division, National Forensic Service, Seoul, 158-707, Republic of Korea
| | - Penelope Ajani
- Climate Change Cluster, University of Technology, Sydney, 2007, Australia
| | - Shauna A Murray
- Climate Change Cluster, University of Technology, Sydney, 2007, Australia
| | - Joo-Hwan Kim
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | | | - Sing Tung Teng
- Faculty of Research Science and Technology, University Malaysia Sarawak, 94300, Kota Samarahan, Malaysia
| | - Po Teen Lim
- Bachok Marine Research Station, Institute of Ocean and Earth Sciences, University of Malaya, 16020, Bachok, Kelantan, Malaysia
| | - Myung-Soo Han
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea.
| | - Bum Soo Park
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea. .,Marine Ecosystem Research Center, Korea Institute of Ocean Science and Technology, Busan, 49111, Republic of Korea.
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35
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Liu H, Tang F, Su J, Ma J, Qin Y, Ji L, Geng H, Wang S, Zhang P, Liu J, Cui S, Ge RL, Li Z. EPAS1 regulates proliferation of erythroblasts in chronic mountain sickness. Blood Cells Mol Dis 2020; 84:102446. [PMID: 32470757 DOI: 10.1016/j.bcmd.2020.102446] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 01/13/2023]
Abstract
Excessive erythrocytosis (EE) is a characteristic of chronic mountain sickness (CMS). Currently, the pathogenesis of CMS remains unclear. This study was intended to investigate the role of EPAS1 in the proliferation of erythroblasts in CMS. Changes of HIF-1α and EPAS1/HIF-2α in the bone marrow erythroblasts of 21 patients with CMS and 14 control subjects residing at the same altitudes were determined by RT-qPCR and western blotting. We also developed a lentiviral vector, Lv-EPAS1/sh-EPAS1, to over-express/silence EPAS1 in K562 cells. Cells cycle and proliferation were detected by flow cytometry. Transcriptome analyses were carried out on Illumina. CMS patients showed a higher expression of EPAS1/HIF-2α in the bone marrow erythroblasts than those of controls. Variations in EPAS1 expression in CMS patients were positively correlated with RBC levels, and negatively correlated with SaO2. Over-expressing of EPAS1 in K562 cells accelerated the erythroid cells cycle progression and promoted the erythroid cells proliferation-and vice versa. Transcriptome data indicated that proliferation-related DEGs were significantly enriched in EPAS1 overexpression/silencing K562 cells. Our results suggest that EPAS1 might participate in the pathogenesis of EE by regulating the proliferation of erythroblasts.
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Affiliation(s)
- Huihui Liu
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China; Department of Rheumatology, Affiliated Hospital of Qinghai University, Xining, China
| | - Feng Tang
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China
| | - Juan Su
- Department of Rheumatology, Affiliated Hospital of Qinghai University, Xining, China
| | - Jie Ma
- Department of Hematology, Affiliated Hospital of Qinghai University, Xining, China
| | - Yajing Qin
- Department of Rheumatology, Affiliated Hospital of Qinghai University, Xining, China
| | - Linhua Ji
- Department of Hematology, Affiliated Hospital of Qinghai University, Xining, China
| | - Hui Geng
- Department of Rheumatology, Affiliated Hospital of Qinghai University, Xining, China
| | - Shengyan Wang
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China
| | - Peili Zhang
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China
| | - Junli Liu
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China
| | - Sen Cui
- Department of Hematology, Affiliated Hospital of Qinghai University, Xining, China
| | - Ri-Li Ge
- Research Center for High Altitude Medicine, Qinghai University, Xining, China; Qinghai Key Laboratory of Science and Technology for High Altitude Medicine, Xining, China; Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Xining, China
| | - Zhanquan Li
- Department of Rheumatology, Affiliated Hospital of Qinghai University, Xining, China.
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36
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Abstract
Tibetans have adapted to the chronic hypoxia of high altitude and display a distinctive suite of physiologic adaptations, including augmented hypoxic ventilatory response and resistance to pulmonary hypertension. Genome-wide studies have consistently identified compelling genetic signatures of natural selection in two genes of the Hypoxia Inducible Factor pathway, PHD2 and HIF2A The product of the former induces the degradation of the product of the latter. Key issues regarding Tibetan PHD2 are whether it is a gain-of-function or loss-of-function allele, and how it might contribute to high-altitude adaptation. Tibetan PHD2 possesses two amino acid changes, D4E and C127S. We previously showed that in vitro, Tibetan PHD2 is defective in its interaction with p23, a cochaperone of the HSP90 pathway, and we proposed that Tibetan PHD2 is a loss-of-function allele. Here, we report that additional PHD2 mutations at or near Asp-4 or Cys-127 impair interaction with p23 in vitro. We find that mice with the Tibetan Phd2 allele display augmented hypoxic ventilatory response, supporting this loss-of-function proposal. This is phenocopied by mice with a mutation in p23 that abrogates the PHD2:p23 interaction. Hif2a haploinsufficiency, but not the Tibetan Phd2 allele, ameliorates hypoxia-induced increases in right ventricular systolic pressure. The Tibetan Phd2 allele is not associated with hemoglobin levels in mice. We propose that Tibetans possess genetic alterations that both activate and inhibit selective outputs of the HIF pathway to facilitate successful adaptation to the chronic hypoxia of high altitude.
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37
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Guo Z, Fan C, Li T, Gesang L, Yin W, Wang N, Weng X, Gong Q, Zhang J, Wang J. Neural network correlates of high-altitude adaptive genetic variants in Tibetans: A pilot, exploratory study. Hum Brain Mapp 2020; 41:2406-2430. [PMID: 32128935 PMCID: PMC7267913 DOI: 10.1002/hbm.24954] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/16/2020] [Accepted: 02/09/2020] [Indexed: 02/05/2023] Open
Abstract
Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases.
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Affiliation(s)
- Zhiyue Guo
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Cunxiu Fan
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China.,Department of Neurology, Shanghai Changhai Hospital, Navy Medical University, Shanghai, China
| | - Ting Li
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Luobu Gesang
- Institute of High Altitude Medicine, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Wu Yin
- Department of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Ningkai Wang
- Department of Psychology, Hangzhou Normal University, Hangzhou, China
| | - Xuchu Weng
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaxing Zhang
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
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38
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Zhang Y, Hu Y, Wang X, Jiang Q, Zhao H, Wang J, Ju Z, Yang L, Gao Y, Wei X, Bai J, Zhou Y, Huang J. Population Structure, and Selection Signatures Underlying High-Altitude Adaptation Inferred From Genome-Wide Copy Number Variations in Chinese Indigenous Cattle. Front Genet 2020; 10:1404. [PMID: 32117428 PMCID: PMC7033542 DOI: 10.3389/fgene.2019.01404] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Accepted: 12/23/2019] [Indexed: 12/12/2022] Open
Abstract
Copy number variations (CNVs) have been demonstrated as crucial substrates for evolution, adaptation and breed formation. Chinese indigenous cattle breeds exhibit a broad geographical distribution and diverse environmental adaptability. Here, we analyzed the population structure and adaptation to high altitude of Chinese indigenous cattle based on genome-wide CNVs derived from the high-density BovineHD SNP array. We successfully detected the genome-wide CNVs of 318 individuals from 24 Chinese indigenous cattle breeds and 37 yaks as outgroups. A total of 5,818 autosomal CNV regions (683 bp-4,477,860 bp in size), covering ~14.34% of the bovine genome (UMD3.1), were identified, showing abundant CNV resources. Neighbor-joining clustering, principal component analysis (PCA), and population admixture analysis based on these CNVs support that most Chinese cattle breeds are hybrids of Bos taurus taurus (hereinafter to be referred as Bos taurus) and Bos taurus indicus (Bos indicus). The distribution patterns of the CNVs could to some extent be related to the geographical backgrounds of the habitat of the breeds, and admixture among cattle breeds from different districts. We analyzed the selective signatures of CNVs positively involved in high-altitude adaptation using pairwise Fst analysis within breeds with a strong Bos taurus background (taurine-type breeds) and within Bos taurus×Bos indicus hybrids, respectively. CNV-overlapping genes with strong selection signatures (at top 0.5% of Fst value), including LETM1 (Fst = 0.490), TXNRD2 (Fst = 0.440), and STUB1 (Fst = 0.420) within taurine-type breeds, and NOXA1 (Fst = 0.233), RUVBL1 (Fst = 0.222), and SLC4A3 (Fst=0.154) within hybrids, were potentially involved in the adaptation to hypoxia. Thus, we provide a new profile of population structure from the CNV aspects of Chinese indigenous cattle and new insights into high-altitude adaptation in cattle.
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Affiliation(s)
- Yaran Zhang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yan Hu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xiuge Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Qiang Jiang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Han Zhao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Jinpeng Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Zhihua Ju
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Liguo Yang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yaping Gao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Xiaochao Wei
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Jiachen Bai
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jinming Huang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, China.,Engineering Center of Animal Breeding and Reproduction, Jinan, China
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39
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Association of EGLN1 gene with high aerobic capacity of Peruvian Quechua at high altitude. Proc Natl Acad Sci U S A 2019; 116:24006-24011. [PMID: 31712437 DOI: 10.1073/pnas.1906171116] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Highland native Andeans have resided at altitude for millennia. They display high aerobic capacity (VO2max) at altitude, which may be a reflection of genetic adaptation to hypoxia. Previous genomewide (GW) scans for natural selection have nominated Egl-9 homolog 1 gene (EGLN1) as a candidate gene. The encoded protein, EGLN1/PHD2, is an O2 sensor that controls levels of the Hypoxia Inducible Factor-α (HIF-α), which regulates the cellular response to hypoxia. From GW association and analysis of covariance performed on a total sample of 429 Peruvian Quechua and 94 US lowland referents, we identified 5 EGLN1 SNPs associated with higher VO2max (L⋅min-1 and mL⋅min-1⋅kg-1) in hypoxia (rs1769793, rs2064766, rs2437150, rs2491403, rs479200). For 4 of these SNPs, Quechua had the highest frequency of the advantageous (high VO2max) allele compared with 25 diverse lowland comparison populations from the 1000 Genomes Project. Genotype effects were substantial, with high versus low VO2max genotype categories differing by ∼11% (e.g., for rs1769793 SNP genotype TT = 34.2 mL⋅min-1⋅kg-1 vs. CC = 30.5 mL⋅min-1⋅kg-1). To guard against spurious association, we controlled for population stratification. Findings were replicated for EGLN1 SNP rs1769793 in an independent Andean sample collected in 2002. These findings contextualize previous reports of natural selection at EGLN1 in Andeans, and support the hypothesis that natural selection has increased the frequency of an EGLN1 causal variant that enhances O2 delivery or use during exercise at altitude in Peruvian Quechua.
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40
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Wang M, Wang Z, He G, Wang S, Zou X, Liu J, Wang F, Ye Z, Hou Y. Whole mitochondrial genome analysis of highland Tibetan ethnicity using massively parallel sequencing. Forensic Sci Int Genet 2019; 44:102197. [PMID: 31756629 DOI: 10.1016/j.fsigen.2019.102197] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 01/12/2023]
Abstract
Mitochondrial DNA (mtDNA) is a key player in numerous multifaceted and intricate biological processes and plays a pivotal role in dissecting the peopling of different populations, due to its maternally inherited property and comparatively high mutation rate. In this study, 119 Tibetan individuals from the Muli Tibetan Autonomous County of China (average altitude above 3,000 m) were employed in mitochondrial genome (mitogenome) sequencing by massively parallel sequencing (MPS) techniques using the Precision ID mtDNA Whole Genome Panel on an Ion S5XL system. The dataset presented 88 distinct haplotypes, resulting in the haplotype diversity of 0.9909. The majority of haplotypes were assigned to East Asian lineages and the distribution of haplogroups of Muli Tibetan significantly differed from reference Tibetan populations. The maximum parsimony phylogeny reconstructed by 119 newly generated mitogenomes revealed 12 major Muli Tibetan lineages. Intriguingly, a Sherpa-specific sub-haplogroup A15c1 with the lack of mutations at 4216 and 15,924 was discerned in our dataset, which suggested that the maternal gene pool of Sherpas may derive from Tibetan populations. The shared haplogroups between Muli Tibetan and lowland Han Chinese hinted that these lineages may derive from non-Tibetans and have already differentiated before their arrival on the Tibetan Plateau. Furthermore, extensive pairwise population comparisons displayed that Muli Tibetan had a closer genetic relationship with ethnically or linguistically close Nyingtri Tibetan, Nyingtri Lhoba and Chamdo Tibetan populations. Genetic affinity was also observed between the Muli Tibetan and North Han Chinese. Collectively, the results generated in this study enriched the existing forensic mtDNA database and raised additional interest in the application of whole mitogenome sequencing in forensic investigations.
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Affiliation(s)
- Mengge Wang
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Guanglin He
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Shouyu Wang
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Fei Wang
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Ziwei Ye
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China.
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41
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Deng L, Zhang C, Yuan K, Gao Y, Pan Y, Ge X, He Y, Yuan Y, Lu Y, Zhang X, Chen H, Lou H, Wang X, Lu D, Liu J, Tian L, Feng Q, Khan A, Yang Y, Jin ZB, Yang J, Lu F, Qu J, Kang L, Su B, Xu S. Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders. Natl Sci Rev 2019; 6:1201-1222. [PMID: 34691999 PMCID: PMC8291452 DOI: 10.1093/nsr/nwz108] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/17/2019] [Accepted: 06/18/2019] [Indexed: 12/13/2022] Open
Abstract
Human genetic adaptation to high altitudes (>2500 m) has been extensively studied over the last few years, but few functional adaptive genetic variants have been identified, largely owing to the lack of deep-genome sequencing data available to previous studies. Here, we build a list of putative adaptive variants, including 63 missense, 7 loss-of-function, 1,298 evolutionarily conserved variants and 509 expression quantitative traits loci. Notably, the top signal of selection is located in TMEM247, a transmembrane protein-coding gene. The Tibetan version of TMEM247 harbors one high-frequency (76.3%) missense variant, rs116983452 (c.248C > T; p.Ala83Val), with the T allele derived from archaic ancestry and carried by >94% of Tibetans but absent or in low frequencies (<3%) in non-Tibetan populations. The rs116983452-T is strongly and positively correlated with altitude and significantly associated with reduced hemoglobin concentration (p = 5.78 × 10-5), red blood cell count (p = 5.72 × 10-7) and hematocrit (p = 2.57 × 10-6). In particular, TMEM247-rs116983452 shows greater effect size and better predicts the phenotypic outcome than any EPAS1 variants in association with adaptive traits in Tibetans. Modeling the interaction between TMEM247-rs116983452 and EPAS1 variants indicates weak but statistically significant epistatic effects. Our results support that multiple variants may jointly deliver the fitness of the Tibetans on the plateau, where a complex model is needed to elucidate the adaptive evolution mechanism.
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Affiliation(s)
- Lian Deng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chao Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yang Gao
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xueling Ge
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Yuan Yuan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Hao Chen
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haiyi Lou
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoji Wang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiaojiao Liu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Lei Tian
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qidi Feng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Asifullah Khan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Zi-Bing Jin
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Jian Yang
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
- Institute for Molecular Bioscience, Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Fan Lu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Jia Qu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China
| | - Bing Su
- 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
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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He Y, Lou H, Cui C, Deng L, Gao Y, Zheng W, Guo Y, Wang X, Ning Z, Li J, Li B, Bai C, Liu S, Wu T, Xu S, Qi X, Su B. De novo assembly of a Tibetan genome and identification of novel structural variants associated with high-altitude adaptation. Natl Sci Rev 2019; 7:391-402. [PMID: 34692055 PMCID: PMC8288928 DOI: 10.1093/nsr/nwz160] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/11/2019] [Accepted: 10/15/2019] [Indexed: 01/06/2023] Open
Abstract
Structural variants (SVs) may play important roles in human adaptation to extreme environments such as high altitude but have been under-investigated. Here, combining long-read sequencing with multiple scaffolding techniques, we assembled a high-quality Tibetan genome (ZF1), with a contig N50 length of 24.57 mega-base pairs (Mb) and a scaffold N50 length of 58.80 Mb. The ZF1 assembly filled 80 remaining N-gaps (0.25 Mb in total length) in the reference human genome (GRCh38). Markedly, we detected 17 900 SVs, among which the ZF1-specific SVs are enriched in GTPase activity that is required for activation of the hypoxic pathway. Further population analysis uncovered a 163-bp intronic deletion in the MKL1 gene showing large divergence between highland Tibetans and lowland Han Chinese. This deletion is significantly associated with lower systolic pulmonary arterial pressure, one of the key adaptive physiological traits in Tibetans. Moreover, with the use of the high-quality de novo assembly, we observed a much higher rate of genome-wide archaic hominid (Altai Neanderthal and Denisovan) shared non-reference sequences in ZF1 (1.32%–1.53%) compared to other East Asian genomes (0.70%–0.98%), reflecting a unique genomic composition of Tibetans. One such archaic hominid shared sequence—a 662-bp intronic insertion in the SCUBE2 gene—is enriched and associated with better lung function (the FEV1/FVC ratio) in Tibetans. Collectively, we generated the first high-resolution Tibetan reference genome, and the identified SVs may serve as valuable resources for future evolutionary and medical studies.
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Affiliation(s)
- Ouzhuluobu
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa 850000, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Haiyi Lou
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Chaoying Cui
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Lian Deng
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yang Gao
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Wangshan Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Yongbo Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoji Wang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhilin Ning
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Jun Li
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa 850000, China
| | - Bin Li
- Center for Disease Control, Tibet Autonomous Region, Lhasa 850000, China
| | - Caijuan Bai
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Baimakangzhuo
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Gonggalanzi
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Dejiquzong
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Bianba
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Duojizhuoma
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa 850000, China
| | - Shiming Liu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining 810012, China
| | - Tianyi Wu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining 810012, China
| | - Shuhua Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
- Corresponding author. E-mail:
| | - Xuebin Qi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa 850000, China
- Corresponding author. E-mail:
| | - Bing Su
- 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
- Corresponding author. E-mail:
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Zhang C, Gao Y, Ning Z, Lu Y, Zhang X, Liu J, Xie B, Xue Z, Wang X, Yuan K, Ge X, Pan Y, Liu C, Tian L, Wang Y, Lu D, Hoh BP, Xu S. PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations. Genome Biol 2019; 20:215. [PMID: 31640808 PMCID: PMC6805450 DOI: 10.1186/s13059-019-1838-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 09/26/2019] [Indexed: 12/23/2022] Open
Abstract
Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV ( https://www.pggsnv.org ), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- Present Address: Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yang Gao
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Zhilin Ning
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xiaoxi Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Bo Xie
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Zhe Xue
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xiaoji Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Kai Yuan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xueling Ge
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yuwen Pan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Chang Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yuchen Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Dongsheng Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Boon-Peng Hoh
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, Cheras, 56000, Kuala Lumpur, Malaysia
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China.
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Bhandari S, Cavalleri GL. Population History and Altitude-Related Adaptation in the Sherpa. Front Physiol 2019; 10:1116. [PMID: 31555147 PMCID: PMC6722185 DOI: 10.3389/fphys.2019.01116] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 08/12/2019] [Indexed: 12/29/2022] Open
Abstract
The first ascent of Mount Everest by Tenzing Norgay and Sir Edmund Hillary in 1953 brought global attention to the Sherpa people and human performance at altitude. The Sherpa inhabit the Khumbu Valley of Nepal, and are descendants of a population that has resided continuously on the Tibetan plateau for the past ∼25,000 to 40,000 years. The long exposure of the Sherpa to an inhospitable environment has driven genetic selection and produced distinct adaptive phenotypes. This review summarizes the population history of the Sherpa and their physiological and genetic adaptation to hypoxia. Genomic studies have identified robust signals of positive selection across EPAS1, EGLN1, and PPARA, that are associated with hemoglobin levels, which likely protect the Sherpa from altitude sickness. However, the biological underpinnings of other adaptive phenotypes such as birth weight and the increased reproductive success of Sherpa women are unknown. Further studies are required to identify additional signatures of selection and refine existing Sherpa-specific adaptive phenotypes to understand how genetic factors have underpinned adaptation in this population. By correlating known and emerging signals of genetic selection with adaptive phenotypes, we can further reveal hypoxia-related biological mechanisms of adaptation. Ultimately this work could provide valuable information regarding treatments of hypoxia-related illnesses including stroke, heart failure, lung disease and cancer.
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Affiliation(s)
- Sushil Bhandari
- Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Gianpiero L Cavalleri
- Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
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Zhang Y, Gou W, Zhang Y, Zhang H, Wu C. Insights into hypoxic adaptation in Tibetan chicken embryos from comparative proteomics. COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS 2019; 31:100602. [PMID: 31212116 DOI: 10.1016/j.cbd.2019.100602] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/08/2019] [Accepted: 06/09/2019] [Indexed: 01/23/2023]
Abstract
Tibetan chicken, an indigenous breed, is highly adapted to the extreme environment of the Qinghai-Tibet Plateau. It serves as a model organism to identify genetic differences between hypoxia-adapted and lowland breeds. However, the mechanisms underlying hypoxia adaptation are yet unclear. This study aimed to identify differently abundant proteins (DAPs) and elucidate the mechanisms involved in hypoxic adaptation in the Tibetan chicken. In this study, we obtained proteome data for the embryonic heart tissues of Tibetan and Chahua chickens incubated under hypoxia (TCH and CHH) and normoxia (TCN and CHN) using isobaric tags for relative and absolute quantitation (iTRAQ) technology. We identified 4210 proteins from 53,352 unique peptides in the heart tissue of chicken embryos. Pairwise TCH vs. CHH, TCH vs. TCN, CHH vs. CHN, and TCN vs. CHN comparisons revealed 118, 176, 103, and 162 differently abundant proteins, respectively. Several key proteins (EGLN1, MAP2K2, MYLK, QARS, NOTCH2, and MYH7) and pathways (glutathione metabolism, PPAR signaling pathway, and vascular smooth muscle contraction) were identified and considered important candidates for high-altitude adaptation in Tibetan chicken. This study provides novel insights into the chicken embryonic heart tissue and furthers the current understanding of the mechanisms of survival among animals in high-altitude environments.
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Affiliation(s)
- Yawen Zhang
- National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Wenyu Gou
- National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Ying Zhang
- National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hao Zhang
- National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Changxin Wu
- National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Musunuru K, Bernstein D, Cole FS, Khokha MK, Lee FS, Lin S, McDonald TV, Moskowitz IP, Quertermous T, Sankaran VG, Schwartz DA, Silverman EK, Zhou X, Hasan AAK, Luo XZJ. Functional Assays to Screen and Dissect Genomic Hits: Doubling Down on the National Investment in Genomic Research. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2019; 11:e002178. [PMID: 29654098 DOI: 10.1161/circgen.118.002178] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The National Institutes of Health have made substantial investments in genomic studies and technologies to identify DNA sequence variants associated with human disease phenotypes. The National Heart, Lung, and Blood Institute has been at the forefront of these commitments to ascertain genetic variation associated with heart, lung, blood, and sleep diseases and related clinical traits. Genome-wide association studies, exome- and genome-sequencing studies, and exome-genotyping studies of the National Heart, Lung, and Blood Institute-funded epidemiological and clinical case-control studies are identifying large numbers of genetic variants associated with heart, lung, blood, and sleep phenotypes. However, investigators face challenges in identification of genomic variants that are functionally disruptive among the myriad of computationally implicated variants. Studies to define mechanisms of genetic disruption encoded by computationally identified genomic variants require reproducible, adaptable, and inexpensive methods to screen candidate variant and gene function. High-throughput strategies will permit a tiered variant discovery and genetic mechanism approach that begins with rapid functional screening of a large number of computationally implicated variants and genes for discovery of those that merit mechanistic investigation. As such, improved variant-to-gene and gene-to-function screens-and adequate support for such studies-are critical to accelerating the translation of genomic findings. In this White Paper, we outline the variety of novel technologies, assays, and model systems that are making such screens faster, cheaper, and more accurate, referencing published work and ongoing work supported by the National Heart, Lung, and Blood Institute's R21/R33 Functional Assays to Screen Genomic Hits program. We discuss priorities that can accelerate the impressive but incomplete progress represented by big data genomic research.
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Affiliation(s)
- Kiran Musunuru
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.).
| | - Daniel Bernstein
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - F Sessions Cole
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Mustafa K Khokha
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Frank S Lee
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Shin Lin
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Thomas V McDonald
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Ivan P Moskowitz
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Thomas Quertermous
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Vijay G Sankaran
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - David A Schwartz
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Edwin K Silverman
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Xiaobo Zhou
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Ahmed A K Hasan
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
| | - Xiao-Zhong James Luo
- Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.)
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47
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He G, Wang Z, Su Y, Zou X, Wang M, Chen X, Gao B, Liu J, Wang S, Hou Y. Genetic structure and forensic characteristics of Tibeto-Burman-speaking Ü-Tsang and Kham Tibetan Highlanders revealed by 27 Y-chromosomal STRs. Sci Rep 2019; 9:7739. [PMID: 31123281 PMCID: PMC6533295 DOI: 10.1038/s41598-019-44230-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 05/08/2019] [Indexed: 01/04/2023] Open
Abstract
Culturally diverse Tibetans (Ü-Tsang, Kham and Ando) harboring a unique molecular mechanism that allows them to successfully adapt to hypoxic environments in the Qinghai-Tibet Plateau have been a subject of great interest in medical genetics, linguistics, archeology and forensic science. However, forensic characteristics and genetic variations of the Y-chromosomal 27-marker haplotype included in the Yfiler Plus system in the Ü-Tsang and Kham Tibeto-Burman-speaking Tibetans remain unexplored. Thus, we genotyped 27 Y-STRs in 230 Shigatse Ü-Tsang Tibetans (SUT) and 172 Chamdo Kham Tibetans (CKT) to investigate the forensic characterization and genetic affinity of Chinese Tibetan Highlanders. The haplotype diversities were 0.999962028 in SUT and 0.999796002 in CKT. Forensic diversity measures indicated that this 27-Y-STR amplification system is appropriate for routine forensic applications, such as identifying and separating unrelated males in deficiency paternity cases, male disaster victims and missing person identification and determining male components in sexual assault cases. Moreover, the genetic relationships among 63 worldwide populations (16,282 individuals), 16 Asian populations, and 21 Chinese populations were analyzed and reconstructed using principal component analysis, multidimensional scaling plots and a phylogenetic tree. Considerable genetic differences were observed between Tibetan populations and other geographically/ethnically diverse populations (Han Chinese). Our studied SUT and CKT have a genetically closer relationship with Gansu Ando Tibetans than with other Asians. In total, our analyses indicated that subpopulation structures exist among Asian and Chinese populations, and population-specific reference databases should be established for forensic applications.
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Affiliation(s)
- Guanglin He
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yongdong Su
- Forensic Identification Center, Public Security Bureau of Tibet Tibetan Autonomous Region, Lhasa, Tibet Tibetan Autonomous Region, 850000, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Mengge Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Xu Chen
- Department of Clinical Laboratory, the First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, Sichuan, 615000, China
| | - Bo Gao
- Yili Public Security Bureau, Yili, Xinjiang Uygur Autonomous Region, 418000, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Shouyu Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, China.
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48
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Young JM, Williams DR, Thompson AAR. Thin Air, Thick Vessels: Historical and Current Perspectives on Hypoxic Pulmonary Hypertension. Front Med (Lausanne) 2019; 6:93. [PMID: 31119132 PMCID: PMC6504829 DOI: 10.3389/fmed.2019.00093] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 04/16/2019] [Indexed: 12/21/2022] Open
Abstract
The association between pulmonary hypertension (PH) and hypoxia is well-established, with two key mechanistic processes, hypoxic pulmonary vasoconstriction and hypoxia-induced vascular remodeling, driving changes in pulmonary arterial pressure. In contrast to other forms of pulmonary hypertension, the vascular changes induced by hypoxia are reversible, both in humans returning to sea-level from high altitude and in animal models. This raises the intriguing possibility that the molecular drivers of these hypoxic processes could be targeted to modify pulmonary vascular remodeling in other contexts. In this review, we outline the history of research into PH and hypoxia, before discussing recent advances in our understanding of this relationship at the molecular level, focussing on the role of the oxygen-sensing transcription factors, hypoxia inducible factors (HIFs). Emerging links between HIF and vascular remodeling highlight the potential utility in inhibiting this pathway in pulmonary hypertension and raise possible risks of activating this pathway using HIF-stabilizing medications.
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Affiliation(s)
- Jason M Young
- Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.,Apex (Altitude Physiology Expeditions), Edinburgh, United Kingdom
| | - David R Williams
- Apex (Altitude Physiology Expeditions), Edinburgh, United Kingdom
| | - A A Roger Thompson
- Apex (Altitude Physiology Expeditions), Edinburgh, United Kingdom.,Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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49
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Nelson TC, Jones MR, Velotta JP, Dhawanjewar AS, Schweizer RM. UNVEILing connections between genotype, phenotype, and fitness in natural populations. Mol Ecol 2019; 28:1866-1876. [PMID: 30830713 PMCID: PMC6525050 DOI: 10.1111/mec.15067] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/12/2019] [Accepted: 02/27/2019] [Indexed: 12/29/2022]
Abstract
Understanding the links between genetic variation and fitness in natural populations is a central goal of evolutionary genetics. This monumental task spans the fields of classical and molecular genetics, population genetics, biochemistry, physiology, developmental biology, and ecology. Advances to our molecular and developmental toolkits are facilitating integrative approaches across these traditionally separate fields, providing a more complete picture of the genotype-phenotype map in natural and non-model systems. Here, we summarize research presented at the first annual symposium of the UNVEIL Network, an NSF-funded collaboration between the University of Montana and the University of Nebraska, Lincoln, which took place from the 1st to the 3rd of June, 2018. We discuss how this body of work advances basic evolutionary science, what it implies for our ability to predict evolutionary change, and how it might inform novel conservation strategies.
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Affiliation(s)
- Thomas C Nelson
- Division of Biological Sciences, University of Montana, 32 Campus Dr HS 104, Missoula, MT, 59812
| | - Matthew R Jones
- Division of Biological Sciences, University of Montana, 32 Campus Dr HS 104, Missoula, MT, 59812
| | - Jonathan P Velotta
- Division of Biological Sciences, University of Montana, 32 Campus Dr HS 104, Missoula, MT, 59812
| | | | - Rena M Schweizer
- Division of Biological Sciences, University of Montana, 32 Campus Dr HS 104, Missoula, MT, 59812
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50
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Kong X, Dong X, Yang S, Qian J, Yang J, Jiang Q, Li X, Wang B, Yan D, Lu S, Zhu L, Li G, Li M, Yi S, Deng M, Sun L, Zhou X, Mao H, Gou X. Natural selection on TMPRSS6 associated with the blunted erythropoiesis and improved blood viscosity in Tibetan pigs. Comp Biochem Physiol B Biochem Mol Biol 2019; 233:11-22. [PMID: 30885835 DOI: 10.1016/j.cbpb.2019.03.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/08/2019] [Accepted: 03/08/2019] [Indexed: 02/04/2023]
Abstract
Tibetan pigs, indigenous to Tibetan plateau, are well adapted to hypoxia. So far, there have been not any definitively described genes and functional sites responsible for hypoxia adaptation for the Tibetan pig. The whole genome-wide association studies in human suggested that genetic variations in TMPRSS6 was associated with hemoglobin concentration (HGB) and red cell counts (RBC). Here we conducted resequencing of the nearly entire genomic region (40.1 kb) of the candidate gene TMPRSS6 in 40 domestic pigs and 40 wild boars along continuous altitudes and identified 708 SNPs, in addition to an indel (CGTG/----) in the intron 10. We conduct the CGTG indel in 838 domestic pigs, both the CGTG deletion frequency and the pairwise r2 linkage disequilibrium showed an increase with elevated altitudes, suggesting that TMPRSS6 has been under Darwinian positive selection. As the conserved core sequence of hypoxia-response elements (HREs), the deletion of CGTG in Tibetan pigs decreased the expression levels of TMPRSS6 mRNA and protein in the liver revealed by real-time quantitative PCR and western blot, respectively. We compared domestic pigs and Tibetan pigs living continuous altitudes, found that the blood-related traits with the increase of altitude, however, the HGB did not increase with the elevation in Tibetan pigs. Genotype association analysis results dissected a genetic effect on reducing HGB by 13.25 g/L in Gongbo'gyamda Tibetan pigs, decreasing mean corpuscular volume (MCV) by 4.79 fl in Diqing Tibetan pigs. In conclusion, the CGTG deletion of TMPRSS6 resulted in lower HGB and smaller MCV, which could reflect a blunting erythropoiesis and improving blood viscosity as well as erythrocyte deformability. It remains to be determined whether a blunting of erythropoiesis for TMPRSS6 or others genetic effects are the physiological adaptations among Tibetan pigs.
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Affiliation(s)
- Xiaoyan Kong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Shuli Yang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Jinhua Qian
- Department of Animal Science, Yuxi Agriculture Vocational-Technical College, Yuxi, Yunnan, China
| | - Jianfa Yang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Qiang Jiang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Science, Jinan, Shandong, China
| | - Xingrun Li
- Department of Animal Science, Dali Vocational and Technical College of Agriculture and Forestry, Dali, Yunnan, China
| | - Bo Wang
- Research Experimental Center, Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Shaoxiong Lu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Li Zhu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Gen Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Minjuan Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Shengnan Yi
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Mingyue Deng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Liyuan Sun
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Xiaoxia Zhou
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China
| | - Huaming Mao
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China.
| | - Xiao Gou
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China.
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