Literature DB >> 20515514

Genetic variation in senescence marker protein-30 is associated with natural variation in cold tolerance in Drosophila.

Katie J Clowers1, Richard F Lyman, Trudy F C Mackay, Theodore J Morgan.   

Abstract

A comprehensive understanding of the genetic basis of phenotypic adaptation in nature requires the identification of the functional allelic variation underlying adaptive phenotypes. The manner in which organisms respond to temperature extremes is an adaptation in many species. In the current study, we investigate the role of molecular variation in senescence marker protein-30 (Smp-30) on natural phenotypic variation in cold tolerance in Drosophila melanogaster. Smp-30 encodes a product that is thought to be involved in the regulation of Ca2+ ion homeostasis and has been shown previously to be differentially expressed in response to cold stress. Thus, we sought to assess whether molecular variation in Smp-30 was associated with natural phenotypic variation in cold tolerance in a panel of naturally derived inbred lines from a population in Raleigh, North Carolina. We identified four non-coding polymorphisms that were strongly associated with natural phenotypic variation in cold tolerance. Interestingly, two polymorphisms that were in close proximity to one another (2 bp apart) exhibited opposite phenotypic effects. Consistent with the maintenance of a pair of antagonistically acting polymorphisms, tests of molecular evolution identified a significant excess of maintained variation in this region, suggesting balancing selection is acting to maintain this variation. These results suggest that multiple mutations in non-coding regions can have significant effects on phenotypic variation in adaptive traits within natural populations, and that balancing selection can maintain polymorphisms with opposite effects on phenotypic variation.

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Year:  2010        PMID: 20515514     DOI: 10.1017/S0016672310000108

Source DB:  PubMed          Journal:  Genet Res (Camb)        ISSN: 0016-6723            Impact factor:   1.588


  8 in total

1.  Genomic variation in natural populations of Drosophila melanogaster.

Authors:  Charles H Langley; Kristian Stevens; Charis Cardeno; Yuh Chwen G Lee; Daniel R Schrider; John E Pool; Sasha A Langley; Charlyn Suarez; Russell B Corbett-Detig; Bryan Kolaczkowski; Shu Fang; Phillip M Nista; Alisha K Holloway; Andrew D Kern; Colin N Dewey; Yun S Song; Matthew W Hahn; David J Begun
Journal:  Genetics       Date:  2012-06-05       Impact factor: 4.562

2.  Reestablishment of ion homeostasis during chill-coma recovery in the cricket Gryllus pennsylvanicus.

Authors:  Heath A MacMillan; Caroline M Williams; James F Staples; Brent J Sinclair
Journal:  Proc Natl Acad Sci U S A       Date:  2012-11-26       Impact factor: 11.205

3.  Natural Populations of Drosophila melanogaster Reveal Features of an Uncharacterized Circadian Property: The Lower Temperature Limit of Rhythmicity.

Authors:  Sarah E Maguire; Paul S Schmidt; Amita Sehgal
Journal:  J Biol Rhythms       Date:  2014-06-10       Impact factor: 3.182

4.  Rapid growth reduces cold resistance: evidence from latitudinal variation in growth rate, cold resistance and stress proteins.

Authors:  Robby Stoks; Marjan De Block
Journal:  PLoS One       Date:  2011-02-24       Impact factor: 3.240

5.  A comparative study of the short term cold resistance response in distantly related Drosophila species: the role of regucalcin and frost.

Authors:  Micael Reis; Cristina P Vieira; Ramiro Morales-Hojas; Bruno Aguiar; Hélder Rocha; Christian Schlötterer; Jorge Vieira
Journal:  PLoS One       Date:  2011-10-03       Impact factor: 3.240

6.  Cold acclimation wholly reorganizes the Drosophila melanogaster transcriptome and metabolome.

Authors:  Heath A MacMillan; Jose M Knee; Alice B Dennis; Hiroko Udaka; Katie E Marshall; Thomas J S Merritt; Brent J Sinclair
Journal:  Sci Rep       Date:  2016-06-30       Impact factor: 4.379

7.  Insights into DDT Resistance from the Drosophila melanogaster Genetic Reference Panel.

Authors:  Joshua M Schmidt; Paul Battlay; Rebecca S Gledhill-Smith; Robert T Good; Chris Lumb; Alexandre Fournier-Level; Charles Robin
Journal:  Genetics       Date:  2017-09-21       Impact factor: 4.562

8.  Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction.

Authors:  Réka Howard; Alicia L Carriquiry; William D Beavis
Journal:  G3 (Bethesda)       Date:  2017-09-07       Impact factor: 3.154

  8 in total

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