This is a detailed and user-friendly protocol for the cultivation and successful crossing of Lotus japonicus (L. japonicus) e.g. for the generation of higher order mutants, based on methods previously reported (Grant et al., 1962; Handberg and Stougaards, 1992; Jiang and Gresshoff, 1997; Pajuelo and Stougaard, 2005).

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

The smut fungus Ustilago maydis is an established model organism for elucidating how biotrophic pathogens colonize plants and how gene families contribute to virulence. Here we describe a step by step protocol for the generation of CRISPR plasmids for single and multiplexed gene editing in U. maydis. Furthermore, we describe the necessary steps required for generating edited clonal populations, losing the Cas9 containing plasmid, and for selecting the desired clones.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

In addition to synthesizing and secreting copious amounts of pectic polymers (Young et al., 2008), Arabidopsis thaliana seed coat epidermal cells produce small amounts of cellulose and hemicelluloses typical of secondary cell walls (Voiniciuc et al., 2015c). These components are intricately linked and are released as a large mucilage capsule upon hydration of mature seeds. Alterations in the structure of minor mucilage components can have dramatic effects on the architecture of this gelatinous cell wall. The immunolabeling protocol described here makes it possible to visualize the distribution of specific polysaccharides in the seed mucilage capsule.

Damage to plant organs through both biotic and abiotic injury is very common in nature. Arabidopsis thaliana 5-day-old (5-do) seedlings represent an excellent system in which to study plant responses to mechanical wounding, both at the site of the damage and in distal unharmed tissues. Seedlings of wild type, transgenic or mutant lines subjected to single or repetitive cotyledon wounding can be used to quantify morphological alterations (e.g., root length, Gasperini et al., 2015), analyze the dynamics of reporter genes in vivo (Larrieu et al., 2015; Gasperini et al., 2015), follow transcriptional changes by quantitative RT-PCR (Acosta et al., 2013; Gasperini et al., 2015) or examine additional aspects of the wound response with a plethora of downstream procedures. Here we illustrate how to rapidly and reliably wound cotyledons of young seedlings, and show the behavior of two promoters driving the expression of β-glucuronidase (GUS) in entire seedlings and in the primary root meristem, following single or repetitive cotyledon wounding respectively. We describe two procedures that can be easily adapted to specific experimental needs.

The Arabidopsis thaliana seed coat produces large amounts of cell wall polysaccharides, which swell out of the epidermal cells upon hydration of the mature dry seeds. While most mucilage polymers immediately diffuse in the surrounding solution, the remaining fraction tightly adheres to the seed, forming a dense gel-like capsule (Macquet et al., 2007). Recent evidence suggests that the adherence of mucilage is mediated by complex interactions between several cell wall components (Griffiths et al., 2014; Voiniciuc et al., 2015a). Therefore, it is important to evaluate how different cell wall mutants impact this mucilage property. This protocol facilitates the analysis of monosaccharides in sequentially extracted mucilage fractions, and quantifies the detachment of each component from seeds.

The Arabidopsis thaliana seed coat epidermis produces copious amounts of mucilage polysaccharides (Haughn and Western, 2012). Characterization of mucilage mutants has identified novel genes required for cell wall biosynthesis and modification (North et al., 2014). The biochemical analysis of seed mucilage is essential to evaluate how different mutations affect cell wall structure (Voiniciuc et al., 2015c). Here we describe a robust method to screen the monosaccharide composition of Arabidopsis seed mucilage using ion chromatography (IC). Mucilage from up to 48 samples can be extracted and prepared for IC analysis within 24 h (only 4 h hands-on). Furthermore, this protocol enables fast separation (31 min per sample), automatic detection and quantification of both neutral and acidic sugars.

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.