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The aerial parts of plants are host to taxonomically structured bacterial communities. Members of the core phyllosphere microbiota can protect Arabidopsis thaliana against foliar pathogens. However, whether plant protection is widespread and to what extent the modes of protection differ among phyllosphere microorganisms are not clear. Here, we present a systematic analysis of plant protection capabilities of the At -LSPHERE, which is a collection of >200 bacterial isolates from A. thaliana , against the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. In total, 224 bacterial leaf isolates were individually assessed for plant protection in a gnotobiotic system. Protection against the pathogen varied, with ~10% of leaf microbiota strains providing full protection, ~10% showing intermediate levels of protection and the remaining ~80% not markedly reducing disease phenotypes upon infection. The most protective strains were distributed across different taxonomic groups. Synthetic community experiments revealed additive effects of strains but also that a single strain can confer full protection in a community context. We also identify different mechanisms that contribute to plant protection. Although pattern-triggered immunity coreceptor signalling is involved in protection by a subset of strains, other strains protected in the absence of functional plant immunity receptors BAK1 and BKK1. Using a comparative genomics approach combined with mutagenesis, we reveal that direct bacteria–pathogen interactions contribute to plant protection by Rhizobium Leaf202. This shows that a computational approach based on the data provided can be used to identify genes of the microbiota that are important for plant protection. Systematic analysis of bacterial phyllosphere isolates and their ability to protect Arabidopsis thaliana plants against infection, with the model pathogen Pseudomonas syringae pv. tomato DC3000, identified protective isolates such as Rhizobium Leaf202, whose activity involves direct interaction with the pathogen.

Plants are colonized by a variety of bacteria, most of which are not pathogenic. Currently, the plant responses to phyllosphere commensals or to pathogen infection in the presence of commensals are not well understood. Here, we examined the transcriptional response of Arabidopsis thaliana leaves to colonization by common commensal bacteria in a gnotobiotic system using RNA sequencing and conducted plant mutant assays. Arabidopsis responded differently to the model bacteria Sphingomonas melonis Fr1 (S.Fr1) and Methylobacterium extorquens PA1 (M.PA1). Whereas M.PA1 only marginally affected the expression of plant genes (< 10), S.Fr1 colonization changed the expression of almost 400 genes. For the latter, genes related to defense responses were activated and partly overlapped with those elicited by the pathogen Pseudomonas syringae DC3000 (Pst). As S.Fr1 is able to mediate plant protective activity against Pst, we tested plant immunity mutants and found that the pattern-recognition co-receptor mutant bak1/bkk1 showed attenuated S.Fr1-dependent plant protection. The experiments demonstrate that the plant responds differently to members of its natural phyllosphere microbiota. A subset of commensals trigger expression of defense-related genes and thereby may contribute to plant health upon pathogen encounter.

[...]in collaboration with one of my colleagues, we investigate mRNA and protein expression changes in different types of motor neurons responding to stress of the endoplasmatic reticulum. [...]in a different line of research, we are characterizing a new function for protein ubiquitination under oxidative stress that we recently discovered [4]. Because of the fundamental character of many of our projects and my track record in method development, my most successful grant application was indeed not when I proposed disease-related work but instead was when I requested funding for the development of a new statistical tool to analyze time-series proteomics and transcriptomics data.

Gene expression is regulated at both the mRNA and protein level through on‐off switches and fine‐tuned control. In their recent study, Edfors et al (2016) use highly accurate, targeted proteomics methods and examine to what extent the amount of protein produced per mRNA transcript varies across different tissues. They find that the bulk part of protein concentrations is set at a per‐gene level: This relationship, the protein/mRNA ratio, is constant across cell types and tissues, but varies by several orders of magnitude across genes. Graphical Abstract Gene expression is regulated at both the mRNA and protein level through on–off switches and fine‐tuned control. In their recent study, Edfors et al ( 2016 ) find that the protein/mRNA ratio for a given gene is constant across cell types, but varies by several orders of magnitude across genes.

We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli ) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about ∼5,600 (∼540 in E. coli ) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.

The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse‐like pattern and returned to values close to pre‐treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes. Synopsis The contribution of mRNA and protein level regulation in the mammalian endoplasmic reticulum stress response is deconvoluted by analyzing time‐series protein and matching mRNA concentrations with a new statistical tool. Protein and mRNA concentrations are quantified at different time points, generating a high‐confidence dataset of 1,237 genes/mRNAs. A new statistical tool quantifies the contribution of regulatory processes and shows that mRNA and protein level regulation play similarly important roles. mRNA and protein level regulation have different dynamics: mRNA concentrations spike in their change and return to pre‐perturbation levels, while protein concentrations switch in their behavior and reach a new steady‐state. Hypotheses on modes of regulation for several groups of genes are presented. Graphical Abstract The contribution of mRNA and protein level regulation in the mammalian endoplasmic reticulum stress response is deconvoluted by analyzing time‐series protein and matching mRNA concentrations with a new statistical tool.

Host-associated microbiomes harbour hundreds of bacterial species that co-occur, creating the opportunity for manifold bacteria–bacteria interactions, which in turn contribute to the overall community structure. The mechanisms that underlie this self-organization among bacteria remain largely elusive. Here, we studied bacterial interactions in the phyllosphere microbiota. We screened for microbial interactions in planta by adding 200 endogenous strains individually to a 15-member synthetic community and tracking changes in community composition upon colonization of the model plant Arabidopsis . Ninety percent of the identified interactions in planta were negative, and phylogenetically closely related strains elicited consistent effects on the synthetic community, providing support for trait conservation. Community changes could be largely explained by binary interactions; however, we also identified a higher-order interaction of more than two interacting strains. We further focused on a prominent interaction between two members of the Actinobacteria. In the presence of Aeromicrobium Leaf245, the population of Nocardioides Leaf374 was reduced by almost two orders of magnitude. We identified a potent antimicrobial peptidase in Aeromicrobium Leaf245, which resulted in Nocardioides Leaf374 lysis. A respective Leaf245 mutant strain was necessary and sufficient to restore Nocardioides colonization in planta, demonstrating that direct bacteria–bacteria interactions were responsible for the population shift originally observed. Our study highlights the power of synthetic community screening and uncovers a strong microbial interaction that occurs despite a spatially heterogeneous environment. Bacteria–bacteria interactions and resultant microbiota changes were characterized using a synthetic phyllosphere community in planta.

During the course of evolution, new proteins are produced very largely as the result of gene duplication, divergence and, in many cases, combination. This means that proteins or protein domains belong to families or, in cases where their relationships can only be recognised on the basis of structure, superfamilies whose members descended from a common ancestor. The size of superfamilies can vary greatly. Also, during the course of evolution organisms of increasing complexity have arisen. In this paper we determine the identity of those superfamilies whose relative sizes in different organisms are highly correlated to the complexity of the organisms. As a measure of the complexity of 38 uni- and multicellular eukaryotes we took the number of different cell types of which they are composed. Of 1,219 superfamilies, there are 194 whose sizes in the 38 organisms are strongly correlated with the number of cell types in the organisms. We give outline descriptions of these superfamilies. Half are involved in extracellular processes or regulation and smaller proportions in other types of activity. Half of all superfamilies have no significant correlation with complexity. We also determined whether the expansions of large superfamilies correlate with each other. We found three large clusters of correlated expansions: one involves expansions in both vertebrates and plants, one just in vertebrates, and one just in plants. Our work identifies important protein families and provides one explanation of the discrepancy between the total number of genes and the apparent physiological complexity of eukaryotic organisms.

Plants are colonized by phylogenetically diverse microorganisms that affect plant growth and health. Representative genome-sequenced culture collections of bacterial isolates from model plants, including Arabidopsis thaliana , have recently been established. These resources provide opportunities for systematic interaction screens combined with genome mining to discover uncharacterized natural products. Here, we report on the biosynthetic potential of 224 strains isolated from the A. thaliana phyllosphere. Genome mining identified more than 1,000 predicted natural product biosynthetic gene clusters (BGCs), hundreds of which are unknown compared to the MIBiG database of characterized BGCs. For functional validation, we used a high-throughput screening approach to monitor over 50,000 binary strain combinations. We observed 725 inhibitory interactions, with 26 strains contributing to the majority of these. A combination of imaging mass spectrometry and bioactivity-guided fractionation of the most potent inhibitor, the BGC-rich Brevibacillus sp. Leaf182, revealed three distinct natural product scaffolds that contribute to the observed antibiotic activity. Moreover, a genome mining-based strategy led to the isolation of a trans -acyltransferase polyketide synthase-derived antibiotic, macrobrevin, which displays an unprecedented natural product structure. Our findings demonstrate that the phyllosphere is a valuable environment for the identification of antibiotics and natural products with unusual scaffolds. Natural product biosynthesis gene clusters were identified in bacterial isolates from the Arabidopsis phyllosphere, including inhibitory products with antibiotic activity, demonstrating the potential of the phyllosphere as a resource for antibiotics.