ATP-independent control of autotransporter virulence protein transport via the folding properties of the secreted protein

Autotransporter (AT) proteins are the largest class of extracellular virulence proteins secreted from Gram-negative bacteria. The mechanism by which AT proteins cross the bacterial outer membrane (OM), in the absence of ATP or another external energy source, is unknown. Here we demonstrate a linear correlation between localized regions of stability (ΔG(folding)) in the mature virulence protein (the AT "passenger") and OM secretion efficiency. Destabilizing the C-terminal β-helical domain of a passenger reduced secretion efficiency. In contrast, destabilizing the globular N-terminal domain of a passenger produced a linearly correlated increase in secretion efficiency. Thus, C-terminal passenger stability facilitates OM secretion, whereas N-terminal stability hinders it. The contributions of regional passenger stability to OM secretion demonstrate a crucial role for the passenger itself in directing its secretion, suggesting a novel type of ATP-independent, folding-driven transporter.     

Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.     

iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test.     

Quantification of real-time Salmonella effector type III secretion kinetics reveals differential secretion rates for SopE2 and SptP

Gram-negative pathogenic bacteria such as Salmonella utilize the type III secretion system to inject bacterial effector proteins into a host cell. Upon entry, these effectors bind mammalian cell proteins, hijack cellular signaling pathways, and redirect cellular function, thus enabling bacterial infection. In this study we use the FlAsH/tetracysteine labeling system to fluorescently tag specific effectors in Salmonella to observe real-time secretion of these proteins into a mammalian host cell. The tetracysteine tag is genomically incorporated, thus preserving endogenous control of bacterial effectors. We demonstrate that two effectors, SopE2 and SptP, exhibit different secretion kinetics, as well as different rates of degradation within the host cell. These proteins respectively activate and suppress GTPase Cdc42, suggesting that there is a temporal hierarchy for effector delivery and persistence within the cell that is directly related to effector function.     

Detection of bacterial quorum sensing <Emphasis Type="Italic">N</Emphasis>-acyl homoserine lactones in clinical samples

Bacteria communicate among themselves using certain chemical signaling molecules. These signaling molecules generally are N-acyl homoserine lactones (AHLs) in Gram-negative bacteria and oligopeptides in Gram-positive bacteria. In addition, both Gram-positive and Gram-negative bacteria produce a family of signaling molecules known as autoinducer-2 that they employ for their communications. Bacteria coordinate their behavior by releasing and responding to the chemical signaling molecules present in proportion to their population density. This phenomenon is known as quorum sensing. The role of bacteria in the pathogenesis of several diseases, including gastrointestinal (GI) disorders, is well established. Moreover, rather recently bacterial quorum sensing has been implicated in the onset of bacterial pathogenicity. Thus, we hypothesized that the signaling molecules involved in bacterial communication may serve as potential biomarkers for the diagnosis and management of several bacteria-related diseases. For that, we previously developed a method based on genetically engineered whole-cell sensing systems for the rapid, sensitive, cost-effective and quantitative detection of AHLs in biological samples, such as saliva and stool, from both healthy and diseased individuals with GI disorders. Although various analytical methods, based on physical-chemical techniques and bacterial whole-cell biosensors, have been developed for the detection of AHLs in the supernatants of bacterial cultures, only a few of them have been applied to AHL monitoring in real samples. In this paper, we report work performed in our laboratory and review that from others that describes the detection of AHLs in biological, clinical samples, and report some of our recent experimental results.     

Membrane proteome in Escherichia coli probed by MS3 mass spectrometry: a preliminary report

Characterization of the membrane proteome is particularly intriguing since a better knowledge in this field might lead to new insights into the function of different membrane systems. Despite the biological relevance of surface proteins however, their characterization still remains a challenging task. Outer membrane proteins (OMPs) of Gram-negative bacteria are key molecules that interface the cell with the environment. Hence, surface proteins of Gram-negative bacteria contain proteins that might be good targets for drugs, antimicrobials or detection systems and they may become components of effective vaccines. In this respect, Escherichia coli has been chosen as a model organism for several structural and functional studies aimed at understanding the biophysical and biochemical organization of proteins in Gram-negative cell walls. Here we present first results for the identification of bacterial surface exposed proteins in E. coli K12 based on the use of dansyl chloride labelling coupled with bidimensional tandem mass spectrometry exploiting the advantage of precursor ion/MS3 scan modes. This procedure resulted in a promising, simple, and rapid strategy for the identification of membrane proteins in E. coli as model organism, thus avoiding time-consuming procedures based on two-dimensional liquid chromatography and electrophoresis. The proteins identified could be grouped into five major families: outer membrane (29 proteins), lipoproteins (6 proteins), transmembrane (43 proteins) families.     

Profiling the Heme-Binding Proteomes of Bacteria Using Chemical Proteomics

Heme is a cofactor with myriad roles and essential to almost all living organisms. Beyond classical gas transport and catalytic functions, heme is increasingly appreciated as a tightly controlled signalling molecule regulating protein expression. However, heme acquisition, biosynthesis and regulation is poorly understood beyond a few model organisms, and the heme-binding proteome has not been fully characterised in bacteria. Yet as heme homeostasis is critical for bacterial survival, heme-binding proteins are promising drug targets. Herein we report a chemical proteomics method for global profiling of heme-binding proteins in live cells for the first time. Employing a panel of heme-based clickable and photoaffinity probes enabled the profiling of 32–54 % of the known heme-binding proteomes in Gram-positive and Gram-negative bacteria. This simple-to-implement profiling strategy could be interchangeably applied to different cell types and systems and fuel future research into heme biology.     

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.     

Structural characterization of flexible proteins using small-angle X-ray scattering

Structural analysis of flexible macromolecular systems such as intrinsically disordered or multidomain proteins with flexible linkers is a difficult task as high-resolution techniques are barely applicable. A new approach, ensemble optimization method (EOM), is proposed to quantitatively characterize flexible proteins in solution using small-angle X-ray scattering (SAXS). The flexibility is taken into account by allowing for the coexistence of different conformations of the protein contributing to the experimental scattering pattern. These conformers are selected using a genetic algorithm from a pool containing a large number of randomly generated models covering the protein configurational space. Quantitative criteria are developed to analyze the EOM selected models and to determine the optimum number of conformers in the ensemble. Simultaneous fitting of multiple scattering patterns from deletion mutants, if available, provides yet more detailed local information about the structure. The efficiency of EOM is demonstrated in model and practical examples on completely or partially unfolded proteins and on multidomain proteins interconnected by linkers. In the latter case, EOM is able to distinguish between rigid and flexible proteins and to directly assess the interdomain contacts.     

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein’s function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.     

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.     

Site-specific incorporation of photo-cross-linker and bioorthogonal amino acids into enteric bacterial pathogens

Enteric bacterial pathogens are known to effectively pass through the extremely acidic mammalian stomachs and cause infections in the small and/or large intestine of human hosts. However, their acid-survival strategy and pathogenesis mechanisms remain elusive, largely due to the lack of tools to directly monitor and manipulate essential components (e.g., defense proteins or invasive toxins) participating in these processes. Herein, we have extended the pyrrolysine-based genetic code expansion strategy for encoding unnatural amino acids in enteric bacterial species, including enteropathogenic Escherichia coli , Shigella , and Salmonella . Using this system, a photo-cross-linking amino acid was incorporated into a Shigella acid chaperone HdeA (shHdeA), which allowed the identification of a comprehensive list of in vivo client proteins that are protected by shHdeA upon acid stress. To further demonstrate the application of our strategy, an azide-bearing amino acid was introduced into a Shigella type 3 secretion effector, OspF, without interruption of its secretion efficiency. This site-specifically installed azide handle allowed the facile detection of OspF's secretion in bacterial extracellular space. Taken together, these bioorthogonal functionalities we incorporated into enteric pathogens were shown to facilitate the investigation of unique and important proteins involved in the pathogenesis and stress-defense mechanisms of pathogenic bacteria that remain exceedingly difficult to study using conventional methodologies.     

Characterization and analysis of mycobacteria and Gram-negative bacteria and co-culture mixtures by Raman microspectroscopy, FTIR, and atomic force microscopy

The molecular composition of mycobacteria and Gram-negative bacteria cell walls is structurally different. In this work, Raman microspectroscopy was applied to discriminate mycobacteria and Gram-negative bacteria by assessing specific characteristic spectral features. Analysis of Raman spectra indicated that mycobacteria and Gram-negative bacteria exhibit different spectral patterns under our experimental conditions due to their different biochemical components. Fourier transform infrared (FTIR) spectroscopy, as a supplementary vibrational spectroscopy, was also applied to analyze the biochemical composition of the representative bacterial strains. As for co-cultured bacterial mixtures, the distribution of individual cell types was obtained by quantitative analysis of Raman and FTIR spectral images and the spectral contribution from each cell type was distinguished by direct classical least squares analysis. Coupled atomic force microscopy (AFM) and Raman microspectroscopy realized simultaneous measurements of topography and spectral images for the same sampled surface. This work demonstrated the feasibility of utilizing a combined Raman microspectroscopy, FTIR, and AFM techniques to effectively characterize spectroscopic fingerprints from bacterial Gram types and mixtures.

Contribution of new cryomethods to a better knowledge of bacterial anatomy

Electron microscopy has largely contributed to the study of bacterial anatomy. However, as varied alterations can occur during cell preparation, at the level of cell structure and at the molecular level, it is difficult to know to what extent electron micrographs correspond to the true appearance of the living state. The recent development of cryomethods which avoid some of the alterations which may occur during conventional fixation and embedding procedures, has shed new light on bacterial anatomy. These have definitively proved that mesosomes do not exist, but are artefactual structures induced by the fixative. New features of the bacterial ⪡nucleus⪢ relating to its shape and fine structure appeared in thin sections of Gram-positive and Gram-negative bacteria prepared by cryosubstitution. New information has also been obtained on the cell wall structure of different bacterial species.     

Elucidation of functional groups on gram-positive and gram-negative bacterial surfaces using infrared spectroscopy

Surface functional group chemistry of intact Gram-positive and Gram-negative bacterial cells and their isolated cell walls was examined as a function of pH, growth phase, and growth media (for intact cells only) using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Infrared spectra of aqueous model organic molecules, representatives of the common functional groups found in bacterial cell walls (i.e., hydroxyl, carboxyl, phosphoryl, and amide groups), were also examined in order to assist the interpretation of the infrared spectra of bacterial samples. The surface sensitivity of the ATR-FTIR spectroscopic technique was evaluated using diatom cells, which possess a several-nanometers-thick layer of glycoprotein on their silica shells. The ATR-FTIR spectra of bacterial surfaces exhibit carboxyl, amide, phosphate, and carbohydrate related features, and these are identical for both Gram-positive and Gram-negative cells. These results provide direct evidence to the previously held conviction that the negative charge of bacterial surfaces is derived from the deprotonation of both carboxylates and phosphates. Variation in solution pH has only a minor effect on the secondary structure of the cell wall proteins. The cell surface functional group chemistry is altered neither by the growth phase nor by the growth medium of bacteria. This study reveals the universality of the functional group chemistry of bacterial cell surfaces.     

Bacteria attachment to surfaces--AFM force spectroscopy and physicochemical analyses

Understanding bacterial adhesion to surfaces requires knowledge of the forces that govern bacterial-surface interactions. Biofilm formation on stainless steel 316 (SS316) by three bacterial species was investigated by examining surface force interaction between the cells and metal surface using atomic force microscopy (AFM). Bacterial-metal adhesion force was quantified at different surface delay time from 0 to 60s using AFM tip coated with three different bacterial species: Gram-negative Massilia timonae and Pseudomonas aeruginosa, and Gram-positive Bacillus subtilis. The results revealed that bacterial adhesion forces on SS316 surface by Gram-negative bacteria is higher (8.53±1.40 nN and 7.88±0.94 nN) when compared to Gram-positive bacteria (1.44±0.21 nN). Physicochemical analysis on bacterial surface properties also revealed that M. timonae and P. aeruginosa showed higher hydrophobicity and surface charges than B. subtilis along with the capability of producing extracellular polymeric substances (EPS). The higher hydrophobicity, surface charges, and greater propensity to form EPS by M. timonae and P. aeruginosa led to high adhesive force on the metal surface.     

Using ensemble methods to deal with imbalanced data in predicting protein–protein interactions

In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein–protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html.     

A lysozyme and magnetic bead based method of separating intact bacteria

As a response to environmental stress, bacterial cells can enter a physiological state called viable but noncultivable (VBNC). In this state, bacteria fail to grow on routine bacteriological media. Consequently, standard methods of contamination detection based on bacteria cultivation fail. Although they are not growing, the cells are still alive and are able to reactivate their metabolism. The VBNC state and low bacterial densities are big challenges for cultivation-based pathogen detection in drinking water and the food industry, for example. In this context, a new molecular-biological separation method for bacteria using point-mutated lysozymes immobilised on magnetic beads for separating bacteria is described. The immobilised mutated lysozymes on magnetic beads serve as bait for the specific capture of bacteria from complex matrices or water due to their remaining affinity for bacterial cell wall components. Beads with bacteria can be separated using magnetic racks. To avoid bacterial cell lysis by the lysozymes, the protein was mutated at amino acid position 35, leading to the exchange of the catalytic glutamate for alanine (LysE35A) and glutamine (LysE35Q). As proved by turbidity assay with reference bacteria, the muramidase activity was knocked out. The mutated constructs were expressed by the yeast Pichia pastoris and secreted into expression medium. Protein enrichment and purification were carried out by SO₃-functionalised nanoscale cationic exchanger particles. For a proof of principle, the proteins were biotinylated and immobilised on streptavidin-functionalised, fluorescence dye-labelled magnetic beads. These constructs were used for the successful capture of Syto9-marked Microccocus luteus cells from cell suspension, as visualised by fluorescence microscopy, which confirmed the success of the strategy.