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Cross species selection scans identify components of C(4) photosynthesis in the grasses

C(4) photosynthesis is perhaps one of the best examples of convergent adaptive evolution with over 25 independent origins in the grasses (Poaceae) alone. The availability of high quality grass genome sequences presents new opportunities to explore the mechanisms underlying this complex trait using e...

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Detalles Bibliográficos
Autores principales: Huang, Pu, Studer, Anthony J., Schnable, James C., Kellogg, Elizabeth A., Brutnell, Thomas P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5429014/
https://www.ncbi.nlm.nih.gov/pubmed/27436281
http://dx.doi.org/10.1093/jxb/erw256
Descripción
Sumario:C(4) photosynthesis is perhaps one of the best examples of convergent adaptive evolution with over 25 independent origins in the grasses (Poaceae) alone. The availability of high quality grass genome sequences presents new opportunities to explore the mechanisms underlying this complex trait using evolutionary biology-based approaches. In this study, we performed genome-wide cross-species selection scans in C(4) lineages to facilitate discovery of C(4) genes. The study was enabled by the well conserved collinearity of grass genomes and the recently sequenced genome of a C(3) panicoid grass, Dichanthelium oligosanthes. This method, in contrast to previous studies, does not rely on any a priori knowledge of the genes that contribute to biochemical or anatomical innovations associated with C(4) photosynthesis. We identified a list of 88 candidate genes that include both known and potentially novel components of the C(4) pathway. This set includes the carbon shuttle enzymes pyruvate, phosphate dikinase, phosphoenolpyruvate carboxylase and NADP malic enzyme as well as several predicted transporter proteins that likely play an essential role in promoting the flux of metabolites between the bundle sheath and mesophyll cells. Importantly, this approach demonstrates the application of fundamental molecular evolution principles to dissect the genetic basis of a complex photosynthetic adaptation in plants. Furthermore, we demonstrate how the output of the selection scans can be combined with expression data to provide additional power to prioritize candidate gene lists and suggest novel opportunities for pathway engineering.