RESUMEN
Plants adapt to their local environment through complex interactions between genes, gene networks and hormones. Although the impact of gene expression on trait regulation and evolution has been recognised for many decades, its role in the evolution of adaptation is still a subject of intense exploration. We used a Multi-parent Advanced Generation Inter-Cross (MAGIC) population, which we derived from crossing multiple parents from two distinct coastal ecotypes of an Australia wildflower, Senecio lautus. We focused on studying the contrasting gravitropic behaviours of these ecotypes, which have evolved independently multiple times and show strong responses to natural selection in field experiments, emphasising the role of natural selection in their evolution. Here, we investigated how gene expression differences have contributed to the adaptive evolution of gravitropism. We studied gene expression in 60 pools at five time points (30, 60, 120, 240 and 480 min) after rotating half of the pools 90°. We found 428 genes with differential expression in response to the 90° rotation treatment. Of these, 81 genes (~19%) have predicted functions related to the plant hormones auxin and ethylene, which are crucial for the gravitropic response. By combining insights from Arabidopsis mutant studies and analysing our gene networks, we propose a preliminary model to explain the differences in gravitropism between ecotypes. This model suggests that the differences arise from changes in the transport and availability of the two hormones auxin and ethylene. Our findings indicate that the genetic basis of adaptation involves interconnected signalling pathways that work together to give rise to new ecotypes.
RESUMEN
Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.
RESUMEN
Multidisciplinary approaches can significantly advance our understanding of complex systems. For instance, gene co-expression networks align prior knowledge of biological systems with studies in graph theory, emphasising pairwise gene to gene interactions. In this paper, we extend these ideas, promoting hypergraphs as an investigative tool for studying multi-way interactions in gene expression data. Additional freedoms are achieved by representing individual genes with hyperedges, and simultaneously testing each gene against many features/vertices. Further gene/hyperedge interactions can be captured and explored using the line graph representations, a technique that reduces the complexity of dense hypergraphs. Such an approach provides access to graph centrality measures, which identifies salient features within a data set. For instance dominant or hub-like hyperedges, leading to key knowledge on gene expression. The validity of this approach is established through the study of gene expression data for the plant species Senecio lautus and results will be interpreted within this biological setting.