Identification of Potential Antimicrobial Targets of Pseudomonas aeruginosa Biofilms through a Novel Screening Approach.

Pseudomonas aeruginosa is an opportunistic pathogen of considerable medical importance, owing to its pronounced antibiotic tolerance and association with cystic fibrosis and other life-threatening diseases. The aim of this study was to highlight the genes responsible for P. aeruginosa biofilm tolerance to antibiotics and thereby identify potential new targets for the development of drugs against biofilm-related infections. By developing a novel screening approach and utilizing a public P. aeruginosa transposon insertion library, several biofilm-relevant genes were identified. The Pf phage gene (PA0720) and flagellin gene (fliC) conferred biofilm-specific tolerance to gentamicin. Compared with the reference biofilms, the biofilms formed by PA0720 and fliC mutants were completely eliminated with a 4-fold-lower gentamicin concentration. Furthermore, the mreC, pprB, coxC, and PA3785 genes were demonstrated to play major roles in enhancing biofilm tolerance to gentamicin. The analysis of biofilm-relevant genes performed in this study provides important novel insights into the understanding of P. aeruginosa antibiotic tolerance, which will facilitate the detection of antibiotic resistance and the development of antibiofilm strategies against P. aeruginosa. IMPORTANCE Pseudomonas aeruginosa is an opportunistic pathogen of high medical importance and is one of the main pathogens responsible for the mortality of patients with cystic fibrosis. In addition to inherited antibiotic resistance, P. aeruginosa can form biofilms, defined as communities of microorganisms embedded in a self-produced matrix of extracellular polymeric substances adhering to each other and/or to a surface. Biofilms protect bacteria from antibiotic treatments and represent a major reason for antibiotic failure in the treatment of chronic infections caused by cystic fibrosis. Therefore, it is crucial to develop new therapeutic strategies aimed at specifically eradicating biofilms. The aim of this study was to generalize a novel screening method for biofilm research and to identify the possible genes involved in P. aeruginosa biofilm tolerance to antibiotics, both of which could improve the understanding of biofilm-related infections and allow for the identification of relevant therapeutic targets for drug development.

Role of the flagellar hook in the structural development and antibiotic tolerance of Pseudomonas aeruginosa biofilms.

Pseudomonas aeruginosa biofilms exhibit an intrinsic resistance to antibiotics and constitute a considerable clinical threat. In cystic fibrosis, a common feature of biofilms formed by P. aeruginosa in the airway is the occurrence of mutants deficient in flagellar motility. This study investigates the impact of flagellum deletion on the structure and antibiotic tolerance of P. aeruginosa biofilms, and highlights a role for the flagellum in adaptation and cell survival during biofilm development. Mutations in the flagellar hook protein FlgE influence greatly P. aeruginosa biofilm structuring and antibiotic tolerance. Phenotypic analysis of the flgE knockout mutant compared to the wild type (WT) reveal increased fitness under planktonic conditions, reduced initial adhesion but enhanced formation of microcolony aggregates in a microfluidic environment, and decreased expression of genes involved in exopolysaccharide formation. Biofilm cells of the flgE knock-out mutant display enhanced tolerance towards multiple antibiotics, whereas its planktonic cells show similar resistance to the WT. Confocal microscopy of biofilms demonstrates that gentamicin does not affect the viability of cells located in the inner part of the flgE knock-out mutant biofilms due to reduced penetration. These findings suggest that deficiency in flagellar proteins like FlgE in biofilms and in cystic fibrosis infections represent phenotypic and evolutionary adaptations that alter the structure of P. aeruginosa biofilms conferring increased antibiotic tolerance.

Complete genome sequence and epigenetic profile of Bacillus velezensis UCMB5140 used for plant and crop protection in comparison with other plant-associated Bacillus strains.

The application of biocontrol biopesticides based on plant growth-promoting rhizobacteria (PGPR), particularly members of the genus Bacillus, is considered a promising perspective to make agricultural practices sustainable and ecologically safe. Recent advances in genome sequencing by third-generation sequencing technologies, e.g., Pacific Biosciences' Single Molecule Real-Time (PacBio SMRT) platform, have allowed researchers to gain deeper insights into the molecular and genetic mechanisms of PGPR activities, and to compare whole genome sequences and global patterns of epigenetic modifications. In the current work, this approach was used to sequence and compare four Bacillus strains that exhibited various PGPR activities including the strain UCMB5140, which is used in the commercial biopesticide Phytosubtil. Whole genome comparison and phylogenomic inference assigned the strain UCMB5140 to the species Bacillus velezensis. Strong biocontrol activities of this strain were confirmed in several bioassays. Several factors that affect the evolution of active PGPR B. velezensis strains were identified: (1) horizontal acquisition of novel non-ribosomal peptide synthetases (NRPS) and adhesion genes; (2) rearrangements of functional modules of NRPS genes leading to strain specific combinations of their encoded products; (3) gain and loss of methyltransferases that can cause global alterations in DNA methylation patterns, which eventually may affect gene expression and regulate transcription. Notably, we identified a horizontally transferred NRPS operon encoding an uncharacterized polypeptide antibiotic in B. velezensis UCMB5140. Other horizontally acquired genes comprised a possible adhesin and a methyltransferase, which may explain the strain-specific methylation pattern of the chromosomal DNA of UCMB5140. KEY POINTS: • Whole genome sequence of the active PGPR Bacillus velezensis UCMB5140. • Identification of genetic determinants responsible for PGPR activities. • Role of methyltransferases and epigenetic mechanisms in evolution of bacteria.

Snf2 controls pulcherriminic acid biosynthesis and antifungal activity of the biocontrol yeast Metschnikowia pulcherrima.

Metschnikowia pulcherrima synthesises the pigment pulcherrimin, from cyclodileucine (cyclo(Leu-Leu)) as a precursor, and exhibits strong antifungal activity against notorious plant pathogenic fungi. This yeast therefore has great potential for biocontrol applications against fungal diseases; particularly in the phyllosphere where this species is frequently found. To elucidate the molecular basis of the antifungal activity of M. pulcherrima, we compared a wild-type strain with a spontaneously occurring, pigmentless, weakly antagonistic mutant derivative. Whole genome sequencing of the wild-type and mutant strains identified a point mutation that creates a premature stop codon in the transcriptional regulator gene SNF2 in the mutant. Complementation of the mutant strain with the wild-type SNF2 gene restored pigmentation and recovered the strong antifungal activity. Mass spectrometry (UPLC HR HESI-MS) proved the presence of the pulcherrimin precursors cyclo(Leu-Leu) and pulcherriminic acid and identified new precursor and degradation products of pulcherriminic acid and/or pulcherrimin. All of these compounds were identified in the wild-type and complemented strain, but were undetectable in the pigmentless snf2 mutant strain. These results thus identify Snf2 as a regulator of antifungal activity and pulcherriminic acid biosynthesis in M. pulcherrima and provide a starting point for deciphering the molecular functions underlying the antagonistic activity of this yeast.

A Peptidomimetic Antibiotic Targets Outer Membrane Proteins and Disrupts Selectively the Outer Membrane in Escherichia coli.

Increasing antibacterial resistance presents a major challenge in antibiotic discovery. One attractive target in Gram-negative bacteria is the unique asymmetric outer membrane (OM), which acts as a permeability barrier that protects the cell from external stresses, such as the presence of antibiotics. We describe a novel ß-hairpin macrocyclic peptide JB-95 with potent antimicrobial activity against Escherichia coli. This peptide exhibits no cellular lytic activity, but electron microscopy and fluorescence studies reveal an ability to selectively disrupt the OM but not the inner membrane of E. coli. The selective targeting of the OM probably occurs through interactions of JB-95 with selected ß-barrel OM proteins, including BamA and LptD as shown by photolabeling experiments. Membrane proteomic studies reveal rapid depletion of many ß-barrel OM proteins from JB-95-treated E. coli, consistent with induction of a membrane stress response and/or direct inhibition of the Bam folding machine. The results suggest that lethal disruption of the OM by JB-95 occurs through a novel mechanism of action at key interaction sites within clusters of ß-barrel proteins in the OM. These findings open new avenues for developing antibiotics that specifically target ß-barrel proteins and the integrity of the Gram-negative OM.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. BIOLOGICAL SIGNIFICANCE: Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms.

Response of Burkholderia cenocepacia H111 to micro-oxia.

B. cenocepacia is an opportunistic human pathogen that is particularly problematic for patients suffering from cystic fibrosis (CF). In the CF lung bacteria grow to high densities within the viscous mucus that is limited in oxygen. Pseudomonas aeruginosa, the dominant pathogen in CF patients, is known to grow and survive under oxygen-limited to anaerobic conditions by using micro-oxic respiration, denitrification and fermentative pathways. In contrast, inspection of the genome sequences of available B. cenocepacia strains suggested that B. cenocepacia is an obligate aerobic and non-fermenting bacterium. In accordance with the bioinformatics analysis we observed that B. cenocepacia H111 is able to grow with as little as 0.1% O2 but not under strictly anoxic conditions. Phenotypic analyses revealed that H111 produced larger amounts of biofilm, pellicle and proteases under micro-oxic conditions (0.5%-5% O2, i.e. conditions that mimic those encountered in CF lung infection), and was more resistant to several antibiotics. RNA-Seq and shotgun proteomics analyses of cultures of B. cenocepacia H111 grown under micro-oxic and aerobic conditions showed up-regulation of genes involved in the synthesis of the exopolysaccharide (EPS) cepacian as well as several proteases, two isocitrate lyases and other genes potentially important for life in micro-oxia. DATA DEPOSITION: RNA-Seq raw data files are accessible through the GEO Series accession number GSE48585. MS data have been deposited in the ProteomeXchange database (PXD000270).

A mighty small heart: the cardiac proteome of adult Drosophila melanogaster.

Drosophila melanogaster is emerging as a powerful model system for the study of cardiac disease. Establishing peptide and protein maps of the Drosophila heart is central to implementation of protein network studies that will allow us to assess the hallmarks of Drosophila heart pathogenesis and gauge the degree of conservation with human disease mechanisms on a systems level. Using a gel-LC-MS/MS approach, we identified 1228 protein clusters from 145 dissected adult fly hearts. Contractile, cytostructural and mitochondrial proteins were most abundant consistent with electron micrographs of the Drosophila cardiac tube. Functional/Ontological enrichment analysis further showed that proteins involved in glycolysis, Ca(2+)-binding, redox, and G-protein signaling, among other processes, are also over-represented. Comparison with a mouse heart proteome revealed conservation at the level of molecular function, biological processes and cellular components. The subsisting peptidome encompassed 5169 distinct heart-associated peptides, of which 1293 (25%) had not been identified in a recent Drosophila peptide compendium. PeptideClassifier analysis was further used to map peptides to specific gene-models. 1872 peptides provide valuable information about protein isoform groups whereas a further 3112 uniquely identify specific protein isoforms and may be used as a heart-associated peptide resource for quantitative proteomic approaches based on multiple-reaction monitoring. In summary, identification of excitation-contraction protein landmarks, orthologues of proteins associated with cardiovascular defects, and conservation of protein ontologies, provides testimony to the heart-like character of the Drosophila cardiac tube and to the utility of proteomics as a complement to the power of genetics in this growing model of human heart disease.

Protein and gene model inference based on statistical modeling in k-partite graphs.

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Identification and functional characterization of N-terminally acetylated proteins in Drosophila melanogaster.

Protein modifications play a major role for most biological processes in living organisms. Amino-terminal acetylation of proteins is a common modification found throughout the tree of life: the N-terminus of a nascent polypeptide chain becomes co-translationally acetylated, often after the removal of the initiating methionine residue. While the enzymes and protein complexes involved in these processes have been extensively studied, only little is known about the biological function of such N-terminal modification events. To identify common principles of N-terminal acetylation, we analyzed the amino-terminal peptides from proteins extracted from Drosophila Kc167 cells. We detected more than 1,200 mature protein N-termini and could show that N-terminal acetylation occurs in insects with a similar frequency as in humans. As the sole true determinant for N-terminal acetylation we could extract the (X)PX rule that indicates the prevention of acetylation under all circumstances. We could show that this rule can be used to genetically engineer a protein to study the biological relevance of the presence or absence of an acetyl group, thereby generating a generic assay to probe the functional importance of N-terminal acetylation. We applied the assay by expressing mutated proteins as transgenes in cell lines and in flies. Here, we present a straightforward strategy to systematically study the functional relevance of N-terminal acetylations in cells and whole organisms. Since the (X)PX rule seems to be of general validity in lower as well as higher eukaryotes, we propose that it can be used to study the function of N-terminal acetylation in all species.

The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry.

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.

Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data.

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.