Publications by authors named "Raul Y Wijfjes"
Efforts to improve photosynthetic performance are increasingly employing natural genetic variation. However, genetic variation in the organellar genomes (plasmotypes) is often disregarded due to the difficulty of studying the plasmotypes and the lack of evidence that this is a worthwhile investment. Here, we systematically phenotyped plasmotype diversity using as a model species.
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- Natural populations of Arabidopsis thaliana, particularly from the Netherlands, were studied to understand how plants adapt to subtle environmental changes despite a generally mild climate.
- The research identified key genetic variations, including mutations in the GA5 gene leading to semidwarf accessions with better wind tolerance and the FSD3 gene affecting iron deficiency tolerance.
- The findings suggest that genetic diversity in this localized population is comparable to broader global collections, providing insights into the genetic mechanisms behind plant adaptation to environmental factors like drought and nutrient availability.
Background: Arbuscular mycorrhizal (AM) fungi are arguably the most important symbionts of plants, offering a range of benefits to their hosts. However, the provisioning of these benefits does not appear to be uniform among AM fungal individuals, with genetic variation between fungal symbionts having a substantial impact on plant performance. Interestingly, genetic variation has also been reported within fungal individuals, which contain millions of haploid nuclei sharing a common cytoplasm.
View Article and Find Full Text PDFPhotosynthesis is a key process in sustaining plant and human life. Improving the photosynthetic capacity of agricultural crops is an attractive means to increase their yields. While the core mechanisms of photosynthesis are highly conserved in C plants, these mechanisms are very flexible, allowing considerable diversity in photosynthetic properties.
View Article and Find Full Text PDFBackground: Copy number variation (CNV) is thought to actively contribute to adaptive evolution of plant species. While many computational algorithms are available to detect copy number variation from whole genome sequencing datasets, the typical complexity of plant data likely introduces false positive calls.
Results: To enable reliable and comprehensive detection of CNV in plant genomes, we developed Hecaton, a novel computational workflow tailored to plants, that integrates calls from multiple state-of-the-art algorithms through a machine-learning approach.