Biology subjects

Golicz, A. A.

Publications and source records attributed to Golicz, A. A..

10 recordsLinked to original sources

bioRxiv2025

Integrative epigenomic analysis uncovers asymmetry of enhancer activity in Brassica napus

Non-coding regulatory regions are essential to the determination of gene expression and plant phenotypes. In this work, we investigated the cis-regulatory landscape of a winter type rapeseed, Express617, across multiple sample types. Combining chromatin accessibility, DNA methylation and gene expression, we annotated thousands of novel regulatory elements in the Brassica napus genome. Among those regions, we discovered and functionally characterized super-enhancers, observing an asymmetrical distribution of these regulatory elements favoring the Cn subgenome. Super-enhancer (SE) associated genes were found enriched in tissue identity and responses to stimuli related processes. We further establish and apply an in-silico validation pipeline for super-enhancers, integrating population-level expression analysis and machine learning (ML) models predicting gene expression levels. Almost 50% of the newly identified SE-associated genes had an observed expression higher than the expression levels predicted by the ML model. Moreover, structural variants disrupting super-enhancer elements correlate with a reduction of expression in the associated genes, both consistent with the positive effect of these regulatory regions. These results greatly expand the functional annotation of rapeseed and contribute to a better understanding of the link between regulatory elements and their target genes and processes, providing novel insights and targets for B. napus (epi)-genome editing strategies.

plant biology↗

bioRxiv2025

Machine learning and multi-omic analysis reveal contrasting recombination landscape of A and C subgenomes of winter oilseed rape

Meiotic recombination is essential for generating genetic diversity, driving plant evolution, and enabling crop improvement, yet its uneven distribution across genomes constrains breeding efforts. Here, we investigated the multi-omic landmarks that shape the recombination landscape in Brassica napus by integrating epigenomic, genomic and transcriptomic data with recombination maps derived from large multi-parental rapeseed populations. Predictive machine-learning accurately predicted recombination rates and hotspot location using only feature information. Recombination was generally suppressed in centromeres and other repeat-rich, methylated regions and enriched in gene-dense, transcriptionally active domains. Proxies for chromatin configuration--such as DNA methylation, transposable elements or genes-- consistently achieved the highest predictive power with the random forest algorithm. We discovered distinct recombination landscape patterns between subgenomes, with crossovers clustering near subtelomeres in the A subgenome and more evenly spread across the C subgenome. Models trained on A-subgenome data outperformed those based on the C subgenome, although combining both subgenomes improved overall accuracy.

bioinformatics↗

bioRxiv2025

A deep learning model recapitulates position specific effects of plant regulatory sequences and suggests genes under complex regulation

Deep neural networks can be trained to predict gene expression directly from genomic sequence, thereby implicitly learning regulatory sequence patterns from scratch, minimizing the bias imposed by prior assumptions. A challenging, yet promising prospect is the extraction of novel insights into gene-regulatory mechanisms, by probing and interpreting such gene expression models. Using a branched convolutional neural network architecture trained on promoter and terminator sequences we predict gene expression for allopolyploid Brassica napus and the closely related model organism Arabidopsis thaliana. We validate the model by comparing predicted and measured expression across ecotypes. We also show that deep learning models can successfully capture the positional binding preferences of some transcription factor families, without having been trained on transcription factor binding data. Furthermore, we show that our model did not only detect local sequence patterns, but was also able to determine their function based on their positional context. We also found that increased prediction error correlated with additional more distal or epigenetic regulatory input. Our results demonstrate that deep learning can be used to understand the regulatory architecture of gene expression in plants. A better understanding of gene regulation in the context of polyploid genomes is of particular economic importance, due to their prevalence among major crops. In the future, we hope that such models may facilitate the targeted engineering of gene regulation in crops.

bioinformatics↗

bioRxiv2024

Pangenomic structural variant patterns reflect evolutionary diversification in Brassica napus

BackgroundUnderstanding genetic diversity is crucial for enhancing crop productivity. This study explores species-wide genome structural variation and its role in intraspecific and ecogeographical diversification of Brassica napus, a recently evolved, globally important allopolyploid crop. ResultsWe performed whole-genome long-read DNA-sequencing and constructed reference-guided genome assemblies for 94 diverse, homozygous accessions, including winter-type, spring-type, and East Asian oilseed, along with kale forms and swedes/rutabagas. We investigated pangenomic patterns of genomic structural variants (SV) and determined pangenome-wide distributions and frequencies of inversions, gene presence-absence variants, and collective SV including insertions and deletions. Results revealed pangenome-wide patterns for insertions, deletions, inversions, and large chromosomal deletions/duplications, reflecting evolutionary diversification across morphotypes and ecotypes. Collective SV were unevenly distributed and biased toward subgenome A, with asymmetrical selection pattern favoring subgenome C. Selection signatures for inversions exhibited no subgenome asymmetry; however, selection signal strength and frequency increased in paracentric chromosome regions, highlighting their evolutionary significance. Selective sweep analysis identified regions for collective SV and inversions, harboring genes for organ formation, cell division and expansion in swede, and stress responses in East Asian oilseed rape. Large chromosomal duplications and deletions distinguished swede from oilseed rape, particularly in subgenome C, including copy-number variation in flowering-time genes BnFLC.C09 and BnATX2.C08, and cell wall development gene BnCEL2.C08. ConclusionsThese findings underscore functional and evolutionary significance of pangenomic SV formation during B. napus diversification. Information on SV patterns with putative functional relevance, provides breeding insights, particularly for developing molecular markers to optimize performance of B. napus and other Brassica crops.

genomics↗

bioRxiv2024

Graphical pangenomics-enabled characterisation of structural variant impact on gene expression in Brassica napus

Structural variants (SVs, eg. insertions and deletions) are genomic variations > 50 bp that are known to be associated with a range of crop traits, from yield to flowering behaviour and stress responses. Recently, pangenome graphs have emerged as a powerful framework for analysing genomic data by encoding population- or species-level diversity in one data structure. Pangenome graphs have the potential to serve as unbiased references for downstream applications, including SV genotyping and pan-transcriptomic analyses. In this work, we hypothesized that extensive variation affects transcript quantification and expression quantitative trait locus (eQTL) analysis when relying on a single reference, and that using pangenome graphs can mitigate reference sequence bias. We combined long and short read whole genome sequencing data with expression profiling of Brassica napus (oilseed rape) to assess the impact of SVs on gene expression regulation and explored the utility of pangenome graphs for eQTL analysis. We demonstrate that pangenome graphs provides a superior framework for eQTL analysis by eliminating single reference bias in gene expression quantification. Combined with the graph-based genotyping of SVs, we identified 240 eQTL-SVs found in close proximity of target loci. These SVs affect expression of genes related to important traits, are often not in linkage with SNPs and represent diversity unaccounted for in classical SNP-based analyses. This study highlights the multiple advantages of graph-based approaches in population-scale studies and provides novel insight into gene expression regulation in an important crop.

plant biology↗

bioRxiv2022

Benchmarking Oxford Nanopore Read Alignment-Based Structural Variant Detection Tools in Crop Plant Genomes

Structural variations (SVs) are larger polymorphisms (>50 bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5x, 10x, and 20x coverages typically used in re-sequencing studies. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.

bioinformatics↗

bioRxiv2022

The giant diploid faba genome unlocks variation in a global protein crop

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emission and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean (Vicia faba L.) has a high yield potential and is well-suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has grown to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, though with significant copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association (GWA) analysis to dissect the genetic basis of hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate improvement of sustainable protein production across Mediterranean, subtropical, and northern temperate agro-ecological zones.

plant biology↗

bioRxiv2022

Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids

The enhanced performance of hybrids though heterosis remains a key aspect in plant breeding, however the underlying mechanisms are still not fully elucidated. To investigate the potential role of transcriptomic and epigenomic patterns in early expression of hybrid vigour, we investigated gene expression, small RNA abundance and genome-wide methylation in hybrids from two distant Brassica napus ecotypes during seed and seedling developmental stages using next-generation sequencing technologies. A total of 71217, 773, 79518 and 31825 differentially expressed genes, microRNAs, small interfering RNAs and differentially methylated regions were identified, respectively. Approximately 70% of the differential expression and methylation patterns observed could be explained due to parental dominance levels. Via gene ontology enrichment and microRNA-target association analyses during seed development we found copies of reproductive, developmental and meiotic genes with transgressive and paternal dominance patterns. Interestingly, maternal dominance was more prominent in hypermethylated and downregulated features during seed formation. This contrasts to the general maternal gamete demethylation reported during gametogenesis in most plant species. Associations between methylation and gene expression allowed identification of putative epialleles with diverse pivotal biological functions during seed formation. Furthermore, most differentially methylated regions, differentially expressed siRNAs and transposable elements were found in regions flanking genes that had no differential expression. This suggests that differential expression and methylation of epigenomic features may help maintain expression of pivotal genes in a hybrid context. Differential expression and methylation patterns during seed formation in an F1 hybrid provide novel insight into genes and mechanisms with a potential role in early heterosis. Key messageTranscriptomic and epigenomic profiling of gene expression and small RNAs during seed and seedling development reveals expression and methylation dominance levels with implications on early stage heterosis in oilseed rape.

plant biology↗

bioRxiv2022

Genome-wide analysis of lncRNAs points to their roles in the modulation of developmental regulator expression during plant male germline development

LncRNAs can function in regulating of gene expression, but their roles as essential regulators of developmental processes and organismal phenotypes remain largely unclear. Especially the roles of lncRNAs in plants are largely unexplored. However, it has been proposed that plant lncRNAs act as regulators of protein-coding genes during development and that the similar roles of animal and plant lncRNAs result from convergent evolution. Since pollen development follows an established program with well-defined and characterized stages, we have used it as a model for studying plant lncRNAs and their roles in reproductive development. We investigated of lncRNA expression and function during pollen formation in field mustard (Brassica rapa). Reference-based transcriptome assembly performed to update the existing genome annotation identified novel expressed protein-coding genes and long non-coding RNAs (lncRNAs), including 4,347 long intergenic non-coding RNAs (lincRNAs, 1058 expressed) and 2,045 lncRNAs overlapping protein-coding genes on the opposite strand (lncNATs, 780 expressed). The analysis of expression profiles reveals that lncRNAs are significant and stage-specific contributors to the gene expression profile of developing pollen. Gene co-expression networks accompanied by genome location analysis identified 38 cis-acting lincRNA, 31 cis-acting lncNAT, 7 trans-acting lincRNA and 14 trans-acting lncNAT to be substantially co-expressed with target protein-coding genes involved in biological processes regulating pollen development and male lineage specification. These findings provide a foundation for future research aiming at developing strategies to employ lncRNAs as regulatory tools for gene expression control during reproductive development.

plant biology↗

bioRxiv2018

MCRiceRepGP: a framework for identification of sexual reproduction associated coding and lincRNA genes in rice.

Sexual reproduction in plants underpins global food production and evolution. It is a complex process, requiring intricate signalling pathways integrating a multitude of internal and external cues. However, key players and especially non-coding genes controlling plant sexual reproduction remain elusive. We report the development of MCRiceRepGP a novel machine learning framework, which integrates genomic, transcriptomic, homology and available phenotypic evidence and employs multi-criteria decision analysis and machine learning to predict coding and non-coding genes involved in rice sexual reproduction.\n\nThe rice genome was re-annotated using deep sequencing transcriptomic data from reproduction-associated tissues/cell types identifying novel putative protein coding genes, transcript isoforms and long intergenic non-coding RNAs (lincRNAs). MCRiceRepGP was used for genome-wide discovery of sexual reproduction associated genes in rice; 2,275 protein-coding and 748 lincRNA genes were predicted to be involved in sexual reproduction. The annotation performed and the genes identified, especially the ones for which mutant lines with phenotypes are available provide a valuable resource. The analysis of genes identified gives insights into the genetic architecture of plant sexual reproduction. MCRiceRepGP can be used in combination with other genome-wide studies, like GWAS, giving more confidence that the genes identified are associated with the biological process of interest. As more data, especially about mutant plant phenotypes will become available, the power of MCRiceRepGP with grow providing researchers with a tool to identify candidate genes for future experiments. MCRiceRepGP is available as a web application (http://mcgplannotator.com/MCRiceRepGP/)\n\nSignificance statementRice is a staple food crop plant for over half of the worlds population and sexual reproduction resulting in grain formation is a key process underpinning global food security. Despite considerable research efforts, much remains to be learned about the molecular mechanisms involved in rice sexual reproduction. We have developed MCRiceRepGP, a novel framework which allows prediction of sexual reproduction associated genes using multi-omics data, multicriteria decision analysis and machine learning. The genes identified and the methodology developed will become a significant resource for the plant research community.

plant biology↗