Showing papers by "Aalt D. J. van Dijk published in 2021"

Domestication shapes recombination patterns in tomato.

Abstract: Meiotic recombination is a biological process of key importance in breeding, to generate genetic diversity and develop novel or agronomically relevant haplotypes. In crop tomato, recombination is curtailed as manifested by linkage disequilibrium decay over a longer distance and reduced diversity compared to wild relatives. Here we compare domesticated and wild populations of tomato and find an overall conserved recombination landscape, with local changes in effective recombination rate in specific genomic regions. We also study the dynamics of recombination hotspots resulting from domestication and found that loss of such hotspots is associated with selective sweeps, most notably in the pericentromeric heterochromatin. We found footprints of genetic changes and structural variants, among them associated with transposable elements, linked with hotspot divergence during domestication, likely causing fine-scale alterations to recombination patterns and resulting in linkage drag.

Chasing breeding footprints through structural variations in Cucumis melo and wild relatives

Abstract: Cucumis melo (melon or muskmelon) is an important crop in the family of the Cucurbitaceae. Melon is cross pollinated and domesticated at several locations throughout the breeding history, resulting in highly diverse genetic structure in the germplasm. Yet, the relations among the groups and cultivars are still incomplete. We shed light on the melonbreeding history, analyzing structural variations ranging from 50 bp up to 100 kb, identified from whole genome sequences of 100 selected melon accessions and wild relatives. Phylogenetic trees based on SV types completely resolve cultivars and wild accessions into two monophyletic groups and clustering of cultivars largely correlates with their geographic origin. Taking into account morphology, we found six mis-categorized cultivars. Unique inversions are more often shared between cultivars, carrying advantageous genes and do not directly originate from wild species. Approximately 60% of the inversion breaks carry a long poly A/T motif, and following observations in other plant species, suggest that inversions in melon likely resulted from meiotic recombination events. We show that resistance genes in the linkage V region are expanded in the cultivar genomes compared to wild relatives. Furthermore, particular agronomic traits such as fruit ripening, fragrance, and stress response are specifically selected for in the melon subspecies. These results represent distinctive footprints of selective breeding that shaped today’s melon. The sequences and genomic relations between land races, wild relatives, and cultivars will serve the community to identify genetic diversity, optimize experimental designs, and enhance crop development

Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases.

Abstract: Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia.

A THP-1 Cell Line-Based Exploration of Immune Responses Toward Heat-Treated BLG

Abstract: Allergen recognition and processing by antigen presenting cells is essential for the sensitization step of food allergy. Macrophages and dendritic cells are both phagocytic antigen presenting cells and play important roles in innate immune responses and signaling between the innate and adaptive immune system. To obtain a model system with a homogeneous genetic background, we derived macrophages and dendritic cells from THP-1 monocytes. The difference between macrophages and dendritic cells was clearly shown by differences in their transcription response (microarray) and protein expression levels. Their resemblance to primary cells was analyzed by comparison to properties as described in literature. The uptake of β-lactoglobulin after wet-heating (60°C in solution) by THP-1 derived macrophages was earlier reported to be significantly increased. To analyse the subsequent immune response, we incubated THP-1 derived macrophages and dendritic cells with native and differently processed β-lactoglobulin and determined the transcription and cytokine expression levels of the cells. A stronger transcriptional response was found in macrophages than in dendritic cells, while severely structurally modified β-lactoglobulin induced a more limited transcriptional response, especially when compared to native and limitedly modified β-lactoglobulin. These results show that processing is relevant for the transcriptional response toward β-lactoglobulin of innate immune cells.

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

Abstract: Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

Fast and adaptive protein structure representations for machine learning

Abstract: The growing prevalence and popularity of protein structure data, both experimental and computationally modelled, necessitates fast tools and algorithms to enable exploratory and interpretable structure-based machine learning Alignment-free approaches have been developed for divergent proteins, but proteins sharing func-tional and structural similarity are often better understood via structural alignment, which has typically been too computationally expensive for larger datasets Here, we introduce the concept of rotation-invariant shape-mers to multiple structure alignment, creating a structure aligner that scales well with the number of proteins and allows for aligning over a thousand structures in 20 minutes We demonstrate how alignment-free shape-mer counts and aligned structural features, when used in machine learning tasks, can adapt to different levels of functional hierarchy in protein kinases, pinpointing residues and structural fragments that play a role in catalytic activity