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A Method to Find Longevity-Selected Positions in the Mammalian Proteome

Overview of attention for article published in PLOS ONE, June 2012
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Title
A Method to Find Longevity-Selected Positions in the Mammalian Proteome
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038595
Pubmed ID
Authors

Jeremy Semeiks, Nick V. Grishin

Abstract

Evolutionary theory suggests that the force of natural selection decreases with age. To explore the extent to which this prediction directly affects protein structure and function, we used multiple regression to find longevity-selected positions, defined as the columns of a sequence alignment conserved in long-lived but not short-lived mammal species. We analyzed 7,590 orthologous protein families in 33 mammalian species, accounting for body mass, phylogeny, and species-specific mutation rate. Overall, we found that the number of longevity-selected positions in the mammalian proteome is much higher than would be expected by chance. Further, these positions are enriched in domains of several proteins that interact with one another in inflammation and other aging-related processes, as well as in organismal development. We present as an example the kinase domain of anti-müllerian hormone type-2 receptor (AMHR2). AMHR2 inhibits ovarian follicle recruitment and growth, and a homology model of the kinase domain shows that its longevity-selected positions cluster near a SNP associated with delayed human menopause. Distinct from its canonical role in development, this region of AMHR2 may function to regulate the protein's activity in a lifespan-specific manner.

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Geographical breakdown

Country Count As %
Mexico 1 9%
Unknown 10 91%

Demographic breakdown

Readers by professional status Count As %
Other 3 27%
Student > Ph. D. Student 3 27%
Researcher 2 18%
Student > Doctoral Student 1 9%
Professor > Associate Professor 1 9%
Other 0 0%
Unknown 1 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 36%
Biochemistry, Genetics and Molecular Biology 2 18%
Social Sciences 1 9%
Medicine and Dentistry 1 9%
Neuroscience 1 9%
Other 1 9%
Unknown 1 9%