The continued high rates of using antibiotics in healthcare and livestock, without sufficient new compounds reaching the market, has led to a dramatic increase in antimicrobial resistance, with multidrug-resistant bacteria emerging as a major public health threat worldwide. Because the vast majority of antiinfectives are natural products or have originated from them, we assessed the predictive power of plant molecular phylogenies and species distribution modeling in the search for clades and areas which promise to provide a higher probability of delivering new antiinfective compound leads. Our approach enables taxonomically and spatially targeted bioprospecting and supports the battle against the global antimicrobial crisis.

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In nature, plants must respond to multiple stresses simultaneously, which likely demands cross-talk between stress-response pathways to minimize fitness costs. Here we provide genetic evidence that biotic and abiotic stress responses are differentially prioritized in Arabidopsis thaliana leaves of different ages to maintain growth and reproduction under combined biotic and abiotic stresses. Abiotic stresses, such as high salinity and drought, blunted immune responses in older rosette leaves through the phytohormone abscisic acid signaling, whereas this antagonistic effect was blocked in younger rosette leaves by PBS3, a signaling component of the defense phytohormone salicylic acid. Plants lacking PBS3 exhibited enhanced abiotic stress tolerance at the cost of decreased fitness under combined biotic and abiotic stresses. Together with this role, PBS3 is also indispensable for the establishment of salt stress- and leaf age-dependent phyllosphere bacterial communities. Collectively, our work reveals a mechanism that balances trade-offs upon conflicting stresses at the organism level and identifies a genetic intersection among plant immunity, leaf microbiota, and abiotic stress tolerance.

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.

Little is known how patterns of cross-over (CO) numbers and distribution during meiosis are established. Here, we reveal that cyclin-dependent kinase A;1 (CDKA;1), the homolog of human Cdk1 and Cdk2, is a major regulator of meiotic recombination in Arabidopsis. Arabidopsis plants with reduced CDKA;1 activity experienced a decrease of class I COs, especially lowering recombination rates in centromere-proximal regions. Interestingly, this reduction of type I CO did not affect CO assurance, a mechanism by which each chromosome receives at least one CO, resulting in all chromosomes exhibiting similar genetic lengths in weak loss-of-function cdka;1 mutants. Conversely, an increase of CDKA;1 activity resulted in elevated recombination frequencies. Thus, modulation of CDKA;1 kinase activity affects the number and placement of COs along the chromosome axis in a dose-dependent manner.

Heteromannan (HM) is one of the most ancient cell wall polymers in the plant kingdom, consisting of β-(1–4)-linked backbones of glucose (Glc) and mannose (Man) units. Despite the widespread distribution of HM polysaccharides, their biosynthesis remains mechanistically unclear. HM is elongated by glycosyltransferases (GTs) from the cellulose synthase-like A (CSLA) family. MANNAN-SYNTHESIS RELATED (MSR) putative GTs have also been implicated in (gluco)mannan synthesis, but their roles have been difficult to decipher in planta and in vitro. To further characterize the products of the HM synthases and accessory proteins, we chose a synthetic biology approach to synthesize plant HM in yeast. The expression of a CSLA protein in Pichia pastoris led to the abundant production of plant HM: up to 30% of glycans in the yeast cell wall. Based on sequential chemical and enzymatic extractions, followed by detailed structural analyses, the newly produced HM polymers were unbranched and could be larger than 270 kDa. Using CSLAs from different species, we programmed yeast cells to produce an HM backbone composed exclusively of Man or also incorporating Glc. We demonstrate that specific MSR cofactors were indispensable for mannan synthase activity of a coffee CSLA or modulated a functional CSLA enzyme to produce glucomannan instead of mannan. Therefore, this powerful platform yields functional insight into the molecular machinery required for HM biosynthesis in plants.

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.

The plant hormone auxin activates primary response genes by facilitating proteolytic removal of AUXIN/INDOLE-3-ACETIC ACID (AUX/IAA)-inducible repressors, which directly bind to transcriptional AUXIN RESPONSE FACTORS (ARF). Most AUX/IAA and ARF proteins share highly conserved C-termini mediating homotypic and heterotypic interactions within and between both protein families. The high-resolution NMR structure of C-terminal domains III and IV of the AUX/IAA protein PsIAA4 from pea (Pisum sativum) revealed a globular ubiquitin-like β-grasp fold with homologies to the Phox and Bem1p (PB1) domain. The PB1 domain of wild-type PsIAA4 features two distinct surface patches of oppositely charged amino acid residues, mediating front-to-back multimerization via electrostatic interactions. Mutations of conserved basic or acidic residues on either face suppressed PsIAA4 PB1 homo-oligomerization in vitro and confirmed directional interaction of full-length PsIAA4 in vivo (yeast two-hybrid system). Mixing of oppositely mutated PsIAA4 PB1 monomers enabled NMR mapping of the negatively charged interface of the reconstituted PsIAA4 PB1 homodimer variant, whose stoichiometry (1:1) and equilibrium binding constant (KD ∼6.4 μM) were determined by isothermal titration calorimetry. In silico protein–protein docking studies based on NMR and yeast interaction data derived a model of the PsIAA4 PB1 homodimer, which is comparable with other PB1 domain dimers, but indicated considerable differences between the homodimeric interfaces of AUX/IAA and ARF PB1 domains. Our study provides an impetus for elucidating the molecular determinants that confer specificity to complex protein–protein interaction circuits between members of the two central families of transcription factors important to the regulation of auxin-responsive gene expression.

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.