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Functional evolution of nodulin 26-like intrinsic proteins: from bacterial arsenic detoxification to plant nutrient transport.

Benjamin PommerrenigTill Arvid DiehnNadine BernhardtManuela Désirée BienertNamiki Mitani-UenoJacqueline FugeAnnett BieberChristoph SpitzerAndrea BräutigamJian Feng MaFrancois ChaumontGerd Patrick Bienert
Published in: The New phytologist (2019)
Nodulin 26-like intrinsic proteins (NIPs) play essential roles in transporting the nutrients silicon and boron in seed plants, but the evolutionary origin of this transport function and the co-permeability to toxic arsenic remains enigmatic. Horizontal gene transfer of a yet uncharacterised bacterial AqpN-aquaporin group was the starting-point for plant NIP evolution. We combined intense sequence, phylogenetic and genetic context analyses and a mutational approach with various transport assays in oocytes and plants to resolve the transorganismal and functional evolution of bacterial and algal and terrestrial plant NIPs and to reveal their molecular transport specificity features. We discovered that aqpN genes are prevalently located in arsenic resistance operons of various prokaryotic phyla. We provided genetic and functional evidence that these proteins contribute to the arsenic detoxification machinery. We identified NIPs with the ancestral bacterial AqpN selectivity filter composition in algae, liverworts, moss, hornworts and ferns and demonstrated that these archetype plant NIPs and their prokaryotic progenitors are almost impermeable to water and silicon but transport arsenic and boron. With a mutational approach, we demonstrated that during evolution, ancestral NIP selectivity shifted to allow subfunctionalisations. Together, our data provided evidence that evolution converted bacterial arsenic efflux channels into essential seed plant nutrient transporters.
Keyphrases
  • drinking water
  • genome wide
  • heavy metals
  • copy number
  • dna methylation
  • risk assessment
  • gene expression
  • high throughput
  • endothelial cells
  • machine learning
  • single molecule
  • plant growth
  • genome wide analysis