Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity.
Goßmann T, Shanmugasundram A, Borno S, Duvaux L, Lemaire C, Kuhl H, Klages S, Roberts LD, Schade S, Gostner JM, Hildebrand F, et al. (2019)
Current biology : CB 29(10): 1712-1720.e7.
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Autor*in
Goßmann, ToniUniBi ;
Shanmugasundram, Achchuthan;
Borno, Stefan;
Duvaux, Ludovic;
Lemaire, Christophe;
Kuhl, Heiner;
Klages, Sven;
Roberts, Lee D;
Schade, Sophia;
Gostner, Johanna M;
Hildebrand, Falk;
Vowinckel, Jakob
Alle
Alle
Einrichtung
Abstract / Bemerkung
Some species responded successfully to prehistoric changes in climate [1, 2], while others failed to adapt and became extinct [3]. The factors that determine successful climate adaptation remain poorly understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the "ice-age" climate of the Pleistocene steppe [4, 5]. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow [6, 7]. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot's adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity. Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Erscheinungsjahr
2019
Zeitschriftentitel
Current biology : CB
Band
29
Ausgabe
10
Seite(n)
1712-1720.e7
ISSN
0960-9822
eISSN
1879-0445
Page URI
https://pub.uni-bielefeld.de/record/2935649
Zitieren
Goßmann T, Shanmugasundram A, Borno S, et al. Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity. Current biology : CB. 2019;29(10):1712-1720.e7.
Goßmann, T., Shanmugasundram, A., Borno, S., Duvaux, L., Lemaire, C., Kuhl, H., Klages, S., et al. (2019). Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity. Current biology : CB, 29(10), 1712-1720.e7. doi:10.1016/j.cub.2019.04.020
Goßmann, Toni, Shanmugasundram, Achchuthan, Borno, Stefan, Duvaux, Ludovic, Lemaire, Christophe, Kuhl, Heiner, Klages, Sven, et al. 2019. “Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity.”. Current biology : CB 29 (10): 1712-1720.e7.
Goßmann, T., Shanmugasundram, A., Borno, S., Duvaux, L., Lemaire, C., Kuhl, H., Klages, S., Roberts, L. D., Schade, S., Gostner, J. M., et al. (2019). Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity. Current biology : CB 29, 1712-1720.e7.
Goßmann, T., et al., 2019. Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity. Current biology : CB, 29(10), p 1712-1720.e7.
T. Goßmann, et al., “Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity.”, Current biology : CB, vol. 29, 2019, pp. 1712-1720.e7.
Goßmann, T., Shanmugasundram, A., Borno, S., Duvaux, L., Lemaire, C., Kuhl, H., Klages, S., Roberts, L.D., Schade, S., Gostner, J.M., Hildebrand, F., Vowinckel, J., Bichet, C., Mulleder, M., Calvani, E., Zelezniak, A., Griffin, J.L., Bork, P., Allaine, D., Cohas, A., Welch, J.J., Timmermann, B., Ralser, M.: Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity. Current biology : CB. 29, 1712-1720.e7 (2019).
Goßmann, Toni, Shanmugasundram, Achchuthan, Borno, Stefan, Duvaux, Ludovic, Lemaire, Christophe, Kuhl, Heiner, Klages, Sven, Roberts, Lee D, Schade, Sophia, Gostner, Johanna M, Hildebrand, Falk, Vowinckel, Jakob, Bichet, Coraline, Mulleder, Michael, Calvani, Enrica, Zelezniak, Aleksej, Griffin, Julian L, Bork, Peer, Allaine, Dominique, Cohas, Aurelie, Welch, John J, Timmermann, Bernd, and Ralser, Markus. “Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity.”. Current biology : CB 29.10 (2019): 1712-1720.e7.
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Stanke M, Diekhans M, Baertsch R, Haussler D., Bioinformatics 24(5), 2008
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Gotoh O., Nucleic Acids Res. 36(8), 2008
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InterProScan 5: genome-scale protein function classification.
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KEGG as a reference resource for gene and protein annotation.
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M., Nucleic Acids Res. 44(D1), 2015
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eggNOG v4.0: nested orthology inference across 3686 organisms.
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The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs.
Schattner P, Brooks AN, Lowe TM., Nucleic Acids Res. 33(Web Server issue), 2005
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The complete mitochondrial genome of Daurian ground squirrel, Spermophilus dauricus.
Jin GY, Huang HJ, Zhang MH., Mitochondrial DNA A DNA Mapp Seq Anal 27(4), 2015
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MUSCLE: multiple sequence alignment with high accuracy and high throughput.
Edgar RC., Nucleic Acids Res. 32(5), 2004
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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies.
Stamatakis A., Bioinformatics 30(9), 2014
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The effects of Cenozoic global change on squirrel phylogeny.
Mercer JM, Roth VL., Science 299(5612), 2003
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Helgen K.M., Russell F., Helgen L.E., Wilson D.E.., 2009
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Nuclear DNA phylogeny of the squirrels (Mammalia: Rodentia) and the evolution of arboreality from c-myc and RAG1.
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Accounting for alignment uncertainty in phylogenomics.
Wu M, Chatterji S, Eisen JA., PLoS ONE 7(1), 2012
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PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments.
Suyama M, Torrents D, Bork P., Nucleic Acids Res. 34(Web Server issue), 2006
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Benjamini Y., Hochberg Y.., 1995
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WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013.
Wang J, Duncan D, Shi Z, Zhang B., Nucleic Acids Res. 41(Web Server issue), 2013
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The Sequence Alignment/Map format and SAMtools.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup., Bioinformatics 25(16), 2009
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Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup., Bioinformatics 25(16), 2009
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A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.
Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM., Fly (Austin) 6(2), 2012
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STRING v10: protein-protein interaction networks, integrated over the tree of life.
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Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C., Nucleic Acids Res. 43(Database issue), 2014
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Fast and accurate short read alignment with Burrows-Wheeler transform.
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Fast gapped-read alignment with Bowtie 2.
Langmead B, Salzberg SL., Nat. Methods 9(4), 2012
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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA., Genome Res. 20(9), 2010
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BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data.
Narasimhan V, Danecek P, Scally A, Xue Y, Tyler-Smith C, Durbin R., Bioinformatics 32(11), 2016
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BEDTools: a flexible suite of utilities for comparing genomic features.
Quinlan AR, Hall IM., Bioinformatics 26(6), 2010
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Quinlan AR, Hall IM., Bioinformatics 26(6), 2010
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A 32-year demography of yellow-bellied marmots (Marmota flaviventris)
Schwartz O.A., Armitage K.B., Van D.., 1998
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A framework for variation discovery and genotyping using next-generation DNA sequencing data.
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DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ., Nat. Genet. 43(5), 2011
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From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.
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Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data.
Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD., PLoS Genet. 5(10), 2009
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Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD., PLoS Genet. 5(10), 2009
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European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation.
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EggLib: processing, analysis and simulation tools for population genetics and genomics.
De Mita S, Siol M., BMC Genet. 13(), 2012
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Genetic Diversity and the Efficacy of Purifying Selection across Plant and Animal Species.
Chen J, Glemin S, Lascoux M., Mol. Biol. Evol. 34(6), 2017
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Genetic diversity-fitness correlation revealed by microsatellite analyses in European alpine marmots (Marmota marmota)
Silva A.D., Da A., Luikart G., Yoccoz N.G., Cohas A., Allainé D.., 2005
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varver: a database of microsatellite variation in vertebrates.
Yashima AS, Innan H., Mol Ecol Resour 17(4), 2016
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Yashima AS, Innan H., Mol Ecol Resour 17(4), 2016
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The contrasting roles of PPARδ and PPARγ in regulating the metabolic switch between oxidation and storage of fats in white adipose tissue.
Roberts LD, Murray AJ, Menassa D, Ashmore T, Nicholls AW, Griffin JL., Genome Biol. 12(8), 2011
PMID: 21843327
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Facing growth in the European Nucleotide Archive.
Cochrane G, Alako B, Amid C, Bower L, Cerdeno-Tarraga A, Cleland I, Gibson R, Goodgame N, Jang M, Kay S, Leinonen R, Lin X, Lopez R, McWilliam H, Oisel A, Pakseresht N, Pallreddy S, Park Y, Plaister S, Radhakrishnan R, Riviere S, Rossello M, Senf A, Silvester N, Smirnov D, Ten Hoopen P, Toribio A, Vaughan D, Zalunin V., Nucleic Acids Res. 41(Database issue), 2012
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Cochrane G, Alako B, Amid C, Bower L, Cerdeno-Tarraga A, Cleland I, Gibson R, Goodgame N, Jang M, Kay S, Leinonen R, Lin X, Lopez R, McWilliam H, Oisel A, Pakseresht N, Pallreddy S, Park Y, Plaister S, Radhakrishnan R, Riviere S, Rossello M, Senf A, Silvester N, Smirnov D, Ten Hoopen P, Toribio A, Vaughan D, Zalunin V., Nucleic Acids Res. 41(Database issue), 2012
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The human genome browser at UCSC.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D., Genome Res. 12(6), 2002
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Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D., Genome Res. 12(6), 2002
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