bioRxivPlant BiologyPreprint
Detection of orthologous genes with expression shifts linked to nickel hyperaccumulation across Eudicots
M. Gallopin, C. Drevet, V. S. Garcia de la Torre, S. Jelassi, M. Michel, C. Ducos, C. Saule, C. Majorel, V. Burtet-Sarramegna, Y. Pillon, P. Bastide, O. Lespinet, S. MERLOT
Abstract
The remarkable capacity of plants to tolerate and accumulate tremendous amount of nickel is a complex adaptative trait that appeared independently in more than 700 species distributed in about fifty families. Nickel hyperaccumulation is thus proposed as a model to investigate the evolution of complex traits in plants. However, the mechanisms involved in nickel hyperaccumulation are still poorly understood in part because comparative transcriptomic analyses struggle to identify genes linked to this trait from a wide diversity of species. In this work, we have implemented a methodology based on the quantification of the expression of orthologous groups and phylogenetic comparative methods to identify genes which expression is correlated to the nickel hyperaccumulation trait. More precisely, we performed de novo transcriptome assembly and reads quantification for each species on its own transcriptome using available RNA-Seq datasets from 15 nickel hyperaccumulator and non-accumulator species. Assembled contigs were associated to orthologous groups built using proteomes predicted from completed plant genome sequences. We then analyzed the transcription profiles of 5953 orthologous groups from distant species using a phylogenetic ANOVA. We identified 31 orthologous groups with an expression shift associated with nickel hyperaccumulation. These orthologous groups correspond to genes that have been previously implicated in nickel accumulation, and to new candidates involved in this trait. We thus believe that this method can be successfully applied to identify genes linked to other complex traits from a wide diversity of species.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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