The flavonoid biosynthesis is a well-characterised model system for specialised metabolism and transcriptional regulation in plants. Flavonoids have numerous biological functions such as UV protection and pollinator attraction, but also biotechnological potential. Here, we present Knowledge-based Identification of Pathway Enzymes (KIPEs) as an automatic approach for the identification of players in the flavonoid biosynthesis. KIPEs combines comprehensive sequence similarity analyses with the inspection of functionally relevant amino acid residues and domains in subjected peptide sequences. Comprehensive sequence sets of flavonoid biosynthesis enzymes and knowledge about functionally relevant amino acids were collected. As a proof of concept, KIPEs was applied to investigate the flavonoid biosynthesis of the medicinal plant Croton tiglium on the basis of a transcriptome assembly. Enzyme candidates for all steps in the biosynthesis network were identified and matched to previous reports of corresponding metabolites in Croton species.

High-throughput sequencing technologies have rapidly developed during the past years and have become an essential tool in plant sciences. However, the analysis of genomic data remains challenging and relies mostly on the performance of automatic pipelines. Frequently applied pipelines involve the alignment of sequence reads against a reference sequence and the identification of sequence variants. Since most benchmarking studies of bioinformatics tools for this purpose have been conducted on human datasets, there is a lack of benchmarking studies in plant sciences. In this study, we evaluated the performance of 50 different variant calling pipelines, including five read mappers and ten variant callers, on six real plant datasets of the model organism Arabidopsis thaliana. Sets of variants were evaluated based on various parameters including sensitivity and specificity. We found that all investigated tools are suitable for analysis of NGS data in plant research. When looking at different performance metrics, BWA-MEM and Novoalign were the best mappers and GATK returned the best results in the variant calling step.

Flavonol synthase (FLS) is a key enzyme for the formation of flavonols, which are a subclass of the flavonoids. FLS catalyzes the conversion of dihydroflavonols to flavonols. The enzyme belongs to the 2-oxoglutarate-dependent dioxygenases (2-ODD) superfamily. We characterized the FLS gene family of Brassica napus that covers 13 genes, based on the genome sequence of the B. napus cultivar Express 617. The goal was to unravel which BnaFLS genes are relevant for seed flavonol accumulation in the amphidiploid species B. napus. Two BnaFLS1 homeologs were identified and shown to encode bifunctional enzymes. Both exhibit FLS activity as well as flavanone 3-hydroxylase (F3H) activity, which was demonstrated in vivo and in planta. BnaFLS1-1 and -2 are capable of converting flavanones into dihydroflavonols and further into flavonols. Analysis of spatio-temporal transcription patterns revealed similar expression profiles of BnaFLS1 genes. Both are mainly expressed in reproductive organs and co-expressed with the genes encoding early steps of flavonoid biosynthesis. Our results provide novel insights into flavonol biosynthesis in B. napus and contribute information for breeding targets with the aim to modify the flavonol content in rapeseed.

Combined awareness about the power and limitations of bioinformatics and molecular biology enables advanced research based on high-throughput data. Despite an increasing demand of scientists with a combined background in both fields, the education of dry and wet lab subjects are often still separated. This work describes an example of integrated education with a focus on genomics and transcriptomics. Participants learned computational and molecular biology methods in the same practical course. Peer-review was applied as a teaching method to foster cooperative learning of students with heterogeneous backgrounds. The positive evaluation results indicate that this approach was accepted by the participants and would likely be suitable for wider scale application.

Gluten-related disorders (GRDs) are a group of diseases that involve the activation of the immune system triggered by the ingestion of gluten, with a worldwide prevalence of 5%. Among them, Celiac disease (CeD) is a T-cell-mediated autoimmune disease causing a plethora of symptoms from diarrhea and malabsorption to lymphoma. Even though GRDs have been intensively studied, the environmental triggers promoting the diverse reactions to gluten proteins in susceptible individuals remain elusive. It has been proposed that pathogens could act as disease-causing environmental triggers of CeD by molecular mimicry mechanisms. Additionally, it could also be possible that unrecognized molecular, structural, and physical parallels between gluten and pathogens have a relevant role. Herein, we report sequence, structural and physical similarities of the two most relevant gluten peptides, the 33-mer and p31-43 gliadin peptides, with bacterial pathogens using bioinformatics going beyond the molecular mimicry hypothesis. First, a stringent BLASTp search using the two gliadin peptides identified high sequence similarity regions within pathogen-derived proteins, e.g., extracellular proteins from Streptococcus pneumoniae and Granulicatella sp. Second, molecular dynamics calculations of an updated α-2-gliadin model revealed close spatial localization and solvent-exposure of the 33-mer and p31-43 peptide, which was compared with the pathogen-related proteins by homology models and localization predictors. We found putative functions of the identified pathogen-derived sequence by identifying T-cell epitopes and SH3/WW-binding domains. Finally, shape and size parallels between the pathogens and the superstructures of gliadin peptides gave rise to novel hypotheses about activation of innate immunity and dysbiosis. Based on our structural findings and the similarities with the bacterial pathogens, evidence emerges that these pathologically relevant gluten-derived peptides could behave as non-replicating pathogens opening new research questions in the interface of innate immunity, microbiome, and food research.