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Rieseberg, L.

Publications and source records attributed to Rieseberg, L..

3 recordsLinked to original sources

The genomic basis of the plant island syndrome in Darwin's giant daisies

Oceanic archipelagos comprise multiple disparate environments over small geographic areas and are isolated from other biotas. These conditions have led to some of the most spectacular adaptive radiations, which have been key to our understanding of evolution, and offer a unique chance to characterise the genomic basis underlying rapid and pronounced phenotypic changes. Repeated patterns of evolutionary change in plants on oceanic archipelagos, i.e. the plant island syndrome, include changes in leaf morphology, acquisition of perennial life-style, and change of ploidy. Here, we describe the genome of the critically endangered and Galapagos endemic Scalesia atractyloides Arnot., obtaining a chromosome-resolved 3.2-Gbp assembly with 43,093 candidate gene models. Using a combination of fossil transposable elements, k-mer spectra analyses and orthologue assignment, we identify the two ancestral subgenomes and date their divergence and the polyploidization event, concluding that the ancestor of all Scalesia species on the Galapagos was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, life-growth, adaptation to salinity and changes in flowering time, thus finding compelling evidence for a genomic basis of island syndrome in Darwins giant daisy radiation. This work advances understanding of factors influencing subgenome divergence in polyploid genomes, and characterizes the quick and pronounced genomic changes in a specular and diverse radiation of an iconic island plant radiation.

evolutionary biology↗

ePlant in 2021: New Species, Viewers, Data Sets, and Widgets

ePlant was introduced in 2017 for exploring large Arabidopsis thaliana data sets from the kilometre to nanometre scales. In the past four years we have used the ePlant framework to develop ePlants for 15 agronomically-important species: maize, poplar, tomato, Camelina sativa, soybean, potato, barley, Medicago truncatula, eucalyptus, rice, willow, sunflower, Cannabis sativa, wheat and sugarcane. We also updated the interface to improve performance and accessibility, and added two new views to the Arabidopsis ePlant - the Navigator and Pathways viewers. The former shows phylogenetic relationships between homologs in other species and their expression pattern similarities, with links to view data for those genes in the respective ePlants. The latter shows Plant Reactome metabolic reactions. We also describe new Arabidopsis data sets including single cell RNA-seq data from roots, and how to embed ePlant eFP expression pictographs into any web page.

bioinformatics↗

Image processing and genome-wide association studies in sunflower identify loci associated with seed-coat characteristics

Sunflower seeds (technically achenes) are characterized by a wide spectrum of sizes, shapes, and colors. These traits are genetically correlated with the branching plant architecture loci, which were introgressed into restorer lines to facilitate efficient hybrid production. To break this genetic correlation between branching and seed traits, high resolution mapping of the genes that regulate seed traits is necessary. Recent progress in genomics permits acquisition of comprehensive genotyping data for a large diversity panel, yet a major constraint for exploring the genetic basis of important phenotypes across large diversity panels is the ability to screen and characterize them efficiently. Here, we implement a cost-effective image analysis pipeline to phenotype seed characteristics in a large sunflower diversity panel comprised of 287 individuals that represents most of the genetic variation in cultivated sunflower. A genome-wide association analysis was performed for seed-coat size and shape traits and significant signals were identified around genes regulating phytohormone activity. In addition, significant seed-coat color QTLs were identified and candidate genes that effect pigmentation were detected including a phytomelanin regulating gene on chromosome 17. Finally, QTLs associated with the seed-coat striped pattern were identified and phytohormone regulating candidate genes were detected. The implementation of image analysis phenotyping for GWAS allowed efficient screening of a large diversity panel and identification of valuable genetic factors effecting seed characteristics at the finest resolution to date.

genetics↗