Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.

RNA post-transcriptional modifications play a crucial role in a myriad of biological processes and cellular functions. To date, more than 160 RNA modifications have been discovered; therefore, accurate identification of RNA-modification sites is fundamental for a better understanding of RNA-mediated biological functions and mechanisms. However, due to limitations in experimental methods, systematic identification of different types of RNA-modification sites remains a major challenge. Recently, more than 20 computational methods have been developed to identify RNA-modification sites in tandem with high-throughput experimental methods, with most of these capable of predicting only single types of RNA-modification sites. These methods show high diversity in their dataset size, data quality, core algorithms, features extracted and feature selection techniques and evaluation strategies. Therefore, there is an urgent need to revisit these methods and summarize their methodologies, in order to improve and further develop computational techniques to identify and characterize RNA-modification sites from the large amounts of sequence data. With this goal in mind, first, we provide a comprehensive survey on a large collection of 27 state-of-the-art approaches for predicting N1-methyladenosine and N6-methyladenosine sites. We cover a variety of important aspects that are crucial for the development of successful predictors, including the dataset quality, operating algorithms, sequence and genomic features, feature selection, model performance evaluation and software utility. In addition, we also provide our thoughts on potential strategies to improve the model performance. Second, we propose a computational approach called DeepPromise based on deep learning techniques for simultaneous prediction of N1-methyladenosine and N6-methyladenosine. To extract the sequence context surrounding the modification sites, three feature encodings, including enhanced nucleic acid composition, one-hot encoding, and RNA embedding, were used as the input to seven consecutive layers of convolutional neural networks (CNNs), respectively. Moreover, DeepPromise further combined the prediction score of the CNN-based models and achieved around 43% higher area under receiver-operating curve (AUROC) for m1A site prediction and 2-6% higher AUROC for m6A site prediction, respectively, when compared with several existing state-of-the-art approaches on the independent test. In-depth analyses of characteristic sequence motifs identified from the convolution-layer filters indicated that nucleotide presentation at proximal positions surrounding the modification sites contributed most to the classification, whereas those at distal positions also affected classification but to different extents. To maximize user convenience, a web server was developed as an implementation of DeepPromise and made publicly available at http://DeepPromise.erc.monash.edu/, with the server accepting both RNA sequences and genomic sequences to allow prediction of two types of putative RNA-modification sites.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact.

Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs' extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites' data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS: In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION: http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

MOTIVATION: Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases' functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. RESULTS: We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. AVAILABILITY AND IMPLEMENTATION: The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation: http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. Supplementary information: Supplementary data are available at Bioinformatics online.

Powering Amyloid Beta Degrading Enzymes: A Possible Therapy for Alzheimer's Disease.

The accumulation of amyloid beta (Aß) in the brain is believed to play a central role in the development and progression of Alzheimer's disease. Revisions to the amyloid cascade hypothesis now acknowledge the dynamic equilibrium in which Aß exists and the importance of enzymes involved in the production and breakdown of Aß in maintaining healthy Aß levels. However, while a wealth of pharmacological and immunological therapies are being generated to inhibit the Aß-producing enzymes, ß-site APP cleavage enzyme 1 and γ-secretase, the therapeutic potential of stimulating Aß-degrading enzymes such as neprilysin, endothelin-converting enzyme-1 and insulin-degrading enzyme remains relatively unexplored. Recent evidence indicates that increasing Aß degradation as opposed to inhibiting synthesis is a more effective strategy to prevent Aß build-up. Therefore Aß degrading enzymes have become valuable targets of therapy. In this review, we discuss the pathway of Aß synthesis and clearance along with the opportunities they present for therapeutic intervention, the benefits of increasing the expression/activity of Aß-degrading enzymes, and the untapped therapeutic potential of enzyme activation.

Serelaxin attenuates renal inflammation and fibrosis in a mouse model of dilated cardiomyopathy.

NEW FINDINGS: What is the central question of this study? The aim was to determine the renoprotective effects of serelaxin in the setting of chronic heart failure. What are the main findings and its importance? Our data indicate that serelaxin can reduce renal fibrosis and inflammation in experimental heart failure. Currently, there are no effective treatments to rescue renal function in heart failure patients, and our data suggest that serelaxin might have the potential to reduce renal fibrosis and inflammation in heart failure. ABSTRACT: Serelaxin has been demonstrated to attenuate renal fibrosis and inflammation in cardiorenal disease. In the present study, we tested the hypothesis that serelaxin can prevent the decline in renal function in dilated cardiomyopathy (DCM) by targeting renal fibrosis and inflammation. Male transgenic mice with DCM (n = 16) and their wild-type littermates (WT; n = 20) were administered either vehicle or serelaxin (500 µg kg-1  day-1 ; subcutaneous minipumps; 8 weeks). Cardiac function was assessed via echocardiography before and during the eighth week of serelaxin treatment. Renal function and inflammation as well as cardiac and renal fibrosis were assessed at the end of the study. Serelaxin had minimal effect on cardiac function (P ≥ 0.99). Tubulointerstitial and glomerular fibrosis were ∼3-fold greater in vehicle-treated DCM mice compared with vehicle-treated WT mice (P ≤ 0.001). Renal mRNA expression of Tnfα and Il1α were ∼4- and ∼3-fold greater, respectively, in vehicle-treated DCM mice compared with vehicle-treated WT mice (P ≤ 0.05). Tubulointerstitial and glomerular fibrosis were 46 and 45% less, respectively, in serelaxin-treated DCM mice than in vehicle-treated DCM mice (P ≤ 0.01). Renal cortical mRNA expression of Tnfα and Il1α were 56 and 58% less, respectively, in the former group compared with the latter (P ≤ 0.05). The urinary albumin:creatinine ratio was ∼3-fold greater in vehicle-treated DCM mice compared with vehicle-treated WT mice (P = 0.02). The urinary albumin:creatinine ratio was not significantly different between vehicle-treated DCM mice and serelaxin-treated DCM mice (P = 0.38). These data suggest that serelaxin can attenuate renal fibrosis and inflammation and has the potential to exert renoprotective effects in DCM.

Soluble and catalytically active endothelin converting enzyme-1 is present in cerebrospinal fluid of subarachnoid hemorrhage patients.

Endothelin converting Enzyme-1 (ECE-1) is essential for the production of Endothelin-1 (ET-1), which is associated with vasospasm following subarachnoid hemorrhage (SAH). We have previously demonstrated the presence of a catalytically active soluble form of ECE-1 in the media of endothelial cells. We aimed to determine if this form of ECE-1 exists in vivo, in cerebrospinal fluid (CSF) of SAH patients. We examined CSF taken from SAH subjects for the presence of soluble ECE-1 using a bradykinin based quenched fluorescent substrate assay. We obtained further confirmation by characterizing the CSF mediated cleavage products of BigET-1 and BigET18₋34 (6 µg/ml) using mass spectrometry. The specificity of cleavage was confirmed using the ECE-1 inhibitor CGS35066 5 nmol/L. SAH CSF samples had mean ECE-1 activity of 0.127 ± 0.037 µmols of substrate cleaved/µl of CSF/24 h. The C-terminal peptides generated upon the cleavage of BigET-1 and BigET18₋34 were detected 48 h after incubation of these substrates with CSF. Cleavage of these substrates was inhibited by CGS35066. Results of Western blots also produced strong evidence for the presence of truncated soluble ECE-1 in CSF. These results strongly suggest the presence of a truncated but catalytically active form of ECE-1 in the CSF of SAH subjects. Further studies are necessary to determine the biological significance of soluble ECE-1 in CSF of SAH subjects, including an association with vasospasm after SAH.

Proteomic characterization of a natural host-pathogen interaction: repertoire of in vivo expressed bacterial and host surface-associated proteins.

Interactions between a host and a bacterial pathogen are mediated by cross-talk between molecules present on, or secreted by, pathogens and host binding-molecules. Identifying proteins involved at this interface would provide substantial insights into this interaction. Although numerous studies have examined in vitro models of infection at the level of transcriptional change and proteomic profiling, there is virtually no information available on naturally occurring host-pathogen interactions in vivo. We employed membrane shaving to identify peptide fragments cleaved from surface-expressed bacterial proteins and also detected proteins originating from the infected host. We optimized this technique for media-cultured Corynebacterium pseudotuberculosis, a sheep pathogen, revealing a set of 247 surface proteins. We then studied a natural host-pathogen interaction by performing membrane shaving on C. pseudotuberculosis harvested directly from naturally infected sheep lymph nodes. Thirty-one bacterial surface proteins were identified, including 13 not identified in culture media, suggesting that a different surface protein repertoire is expressed in this hostile environment. Forty-nine host proteins were identified, including immune mediators and antimicrobial peptides such as cathelicidin. This novel application of proteolytic shaving has documented sets of host and pathogen proteins present at the bacterial surface in an infection of the native host.

The x-ray crystal structure of mannose-binding lectin-associated serine proteinase-3 reveals the structural basis for enzyme inactivity associated with the Carnevale, Mingarelli, Malpuech, and Michels (3MC) syndrome.

The mannose-binding lectin associated-protease-3 (MASP-3) is a member of the lectin pathway of the complement system, a key component of human innate and active immunity. Mutations in MASP-3 have recently been found to be associated with Carnevale, Mingarelli, Malpuech, and Michels (3MC) syndrome, a severe developmental disorder manifested by cleft palate, intellectual disability, and skeletal abnormalities. However, the molecular basis for MASP-3 function remains to be understood. Here we characterize the substrate specificity of MASP-3 by screening against a combinatorial peptide substrate library. Through this approach, we successfully identified a peptide substrate that was 20-fold more efficiently cleaved than any other identified to date. Furthermore, we demonstrated that mutant forms of the enzyme associated with 3MC syndrome were completely inactive against this substrate. To address the structural basis for this defect, we determined the 2.6-Å structure of the zymogen form of the G666E mutant of MASP-3. These data reveal that the mutation disrupts the active site and perturbs the position of the catalytic serine residue. Together, these insights into the function of MASP-3 reveal how a mutation in this enzyme causes it to be inactive and thus contribute to the 3MC syndrome.

Production of soluble Neprilysin by endothelial cells.

A non-membrane bound form of Neprilysin (NEP) with catalytic activity has the potential to cleave substrates throughout the circulation, thus leading to systemic effects of NEP. We used the endothelial cell line Ea.hy926 to identify the possible role of exosomes and A Disintegrin and Metalloprotease 17 (ADAM-17) in the production of non-membrane bound NEP. Using a bradykinin based quenched fluorescent substrate (40 µM) assay, we determined the activity of recombinant human NEP (rhNEP; 12 ng), and NEP in the media of endothelial cells (10% v/v; after 24 h incubation with cells) to be 9.35±0.70 and 6.54±0.41 µmols of substrate cleaved over 3h, respectively. The presence of NEP in the media was also confirmed by Western blotting. At present there are no commercially available inhibitors specific for ADAM-17. We therefore synthesised two inhibitors TPI2155-14 and TPI2155-17, specific for ADAM-17 with IC50 values of 5.36 and 4.32 µM, respectively. Treatment of cells with TPI2155-14 (15 µM) and TPI2155-17 (4.3 µM) resulted in a significant decrease in NEP activity in media (62.37±1.43 and 38.30±4.70, respectively as a % of control; P<0.0001), implicating a possible role for ADAM-17 in NEP release. However, centrifuging media (100,000g for 1 h at 4 °C) removed all NEP activity from the supernatant indicating the likely role of exosomes in the release of NEP. Our data therefore indicated for the first time that NEP is released from endothelial cells via exosomes, and that this process is dependent on ADAM-17.

Nitric oxide inhibits the production of soluble endothelin converting enzyme-1.

This study examined the effect of nitric oxide on the production of soluble ECE-1. Activity of ECE-1 in media was measured using a quenched fluorescent substrate assay, and expressed as a percentage of control. Endothelial cells were incubated with the nitric oxide donor Diethylenetriamine NONOate (DETA; 250-800 µM), NOS substrate L-Arg (200-1,000 µM), a L-Arg transport inhibitor (L-Lys; 10 µM) and NOS inhibitors (L-Gln and N5-[imino(nitroamino)methyl]-L-ornithine, methyl ester, monohydrochloride (L-NAME); 10-100 µM). The effect of L-Arg (1,000 µM) was also tested in the presence of L-Lys (10 µM), L-Gln (100 µM) and L-NAME (10-100 µM). Ultracentrifugation (100,000×g, 4 °C, 1 h) completely removed ECE-1 activity from the supernatant. In addition, fractionation of concentrated media on a sucrose density gradient indicated that ECE-1 activity was localised to the mid portion of the gradient, thus suggesting the possible role of exosomes in ECE-1 release. Production of soluble ECE-1 by Ea.hy926 cells was inhibited significantly (P < 0.05, unpaired t test, n = 4) in the presence of DETA (75.31 ± 3.59; 800 µM) and L-Arg (60.97 ± 9.22; 1,000 µM). L-Arg-mediated reduction in the release of soluble ECE-1 was blocked by the inhibition of NOS using L-NAME (100 µM; 99.19 ± 0.58) and L-Gln (100 µM; 104.41 ± 0.65). In addition, the presence of L-Lys (10 µM) significantly blocked the L-Arg (1,000 µM)-induced reduction in soluble ECE-1 levels (122.38 ± 13.16). These treatments had no effect on the expression of ECE-1 on the cell surface. Our data provide evidence that NO can inhibit the production of soluble ECE-1 by endothelial cells.

The proteome browser web portal.

In 2010, the Human Proteome Organization launched the Human Proteome Project (HPP), aimed at identifying and characterizing the proteome of the human body. To support complete coverage, one arm of the project will take a gene- or chromosomal-centric strategy (C-HPP) aimed at identifying at least one protein product from each protein-coding gene. Despite multiple large international biological databases housing genomic and protein data, there is currently no single system that integrates updated pertinent information from each of these data repositories and assembles the information into a searchable format suitable for the type of global proteomics effort proposed by the C-HPP. We have undertaken the goal of producing a data integration and analysis software system and browser for the C-HPP effort and of making data collections from this resource discoverable through metadata repositories, such as Australian National Data Service's Research Data Australia. Here we present our vision and progress toward the goal of developing a comprehensive data integration and analysis software tool that provides a snapshot of currently available proteomic related knowledge around each gene product, which will ultimately assist in analyzing biological function and the study of human physiology in health and disease.

Endothelin-converting enzyme-1 inhibition and renoprotection in end-stage renal disease.

Endothelin is one of the most potent peptide vasoconstrictors thus far characterised. It is produced by the cleavage of its precursor big endothelin-1 by endothelin-converting enzyme-1 (ECE-1). The endothelin system which includes endothelin-1 (ET-1), ET receptors and ECE-1 is well characterised in the kidney and is known to play a key role in the pathogenesis of end-stage renal disease (ESRD). Therefore, inhibition of ECE-1 and antagonism of ET receptors represent potential therapeutic approaches for the treatment of ESRD. Here, we review the current literature on the localisation of ECE-1 in the normal kidney and how ECE-1 expression is altered in pathological conditions leading to ESRD. We also discuss the roles of neutral endopeptidase (NEP) and chymase in mediating the production of ET-1 in the kidney in ESRD. As such, we also discuss that complete inhibition of ET-1 production in the kidney requires the inhibition of ECE-1, NEP and chymase.

S1 pocket of a bacterially derived subtilisin-like protease underpins effective tissue destruction.

The ovine footrot pathogen, Dichelobacter nodosus, secretes three subtilisin-like proteases that play an important role in the pathogenesis of footrot through their ability to mediate tissue destruction. Virulent and benign strains of D. nodosus secrete the basic proteases BprV and BprB, respectively, with the catalytic domain of these enzymes having 96% sequence identity. At present, it is not known how sequence variation between these two putative virulence factors influences their respective biological activity. We have determined the high resolution crystal structures of BprV and BprB. These data reveal that that the S1 pocket of BprV is more hydrophobic but smaller than that of BprB. We show that BprV is more effective than BprB in degrading extracellular matrix components of the host tissue. Mutation of two residues around the S1 pocket of BprB to the equivalent residues in BprV dramatically enhanced its proteolytic activity against elastin substrates. Application of a novel approach for profiling substrate specificity, the Rapid Endopeptidase Profiling Library (REPLi) method, revealed that both enzymes prefer cleaving after hydrophobic residues (and in particular P1 leucine) but that BprV has more restricted primary substrate specificity than BprB. Furthermore, for P1 Leu-containing substrates we found that BprV is a significantly more efficient enzyme than BprB. Collectively, these data illuminate how subtle changes in D. nodosus proteases may significantly influence tissue destruction as part of the ovine footrot pathogenesis process.

Protein Kinase C recognition sites in the cytoplasmic domain of Endothelin Converting Enzyme-1c.

Endothelin Converting Enzyme-1 (ECE-1) is essential for the production of the potent vasoconstrictor Endothelin-1 (ET-1). The activation of Protein Kinase C (PKC) by phorbol 12-myristate 13-acetate (PMA) can increase ECE-1 phosphorylation, which in turn promotes ECE-1c trafficking to the cell surface where ET-1 production occurs. This study has identified the specific residues in the N-terminal cytoplasmic tail of ECE-1c isoform that are phosphorylated upon the activation of PKC. Levels of phosphorylation are expressed as a % phosphorylation in untreated CHO-K1 cells. We transfected CHO-K1 cells with wild type and mutant forms of ECE-1c (Ala(4)-ECE-1c, Ala(35)ECE-1c and Ala(4/35)ECE-1c) to confirm the involvement of Thr(4) and Ser(35) residues in PMA induced phosphorylation of ECE-1c. Phosphorylation of wild type ECE-1c increased in response to PMA treatment (150±13%, unpaired t-test, P<0.05, significantly different compared to untreated control). The two single mutants and one combined mutant significantly reduced the PMA induced phosphorylation (103-117±6-13%; unpaired t-test; n=8; P<0.05 significantly different compared to untreated control). The mutations had no effect on the basal ECE-1c phosphorylation. In addition, they had no effect on the catalytic activity as evidenced by the similar rate of substrate cleavage compared to wild type. This study is the first to confirm the residues phosphorylated upon the activation of PKC by PMA. The results complete a gap in our understanding of the mechanism(s) behind PKC induced trafficking of ECE-1.

The subtilisin-like protease AprV2 is required for virulence and uses a novel disulphide-tethered exosite to bind substrates.

Many bacterial pathogens produce extracellular proteases that degrade the extracellular matrix of the host and therefore are involved in disease pathogenesis. Dichelobacter nodosus is the causative agent of ovine footrot, a highly contagious disease that is characterized by the separation of the hoof from the underlying tissue. D. nodosus secretes three subtilisin-like proteases whose analysis forms the basis of diagnostic tests that differentiate between virulent and benign strains and have been postulated to play a role in virulence. We have constructed protease mutants of D. nodosus; their analysis in a sheep virulence model revealed that one of these enzymes, AprV2, was required for virulence. These studies challenge the previous hypothesis that the elastase activity of AprV2 is important for disease progression, since aprV2 mutants were virulent when complemented with aprB2, which encodes a variant that has impaired elastase activity. We have determined the crystal structures of both AprV2 and AprB2 and characterized the biological activity of these enzymes. These data reveal that an unusual extended disulphide-tethered loop functions as an exosite, mediating effective enzyme-substrate interactions. The disulphide bond and Tyr92, which was located at the exposed end of the loop, were functionally important. Bioinformatic analyses suggested that other pathogenic bacteria may have proteases that utilize a similar mechanism. In conclusion, we have used an integrated multidisciplinary combination of bacterial genetics, whole animal virulence trials in the original host, biochemical studies, and comprehensive analysis of crystal structures to provide the first definitive evidence that the extracellular secreted proteases produced by D. nodosus are required for virulence and to elucidate the molecular mechanism by which these proteases bind to their natural substrates. We postulate that this exosite mechanism may be used by proteases produced by other bacterial pathogens of both humans and animals.

Application of protein purification methods for the enrichment of a cytotoxin from Campylobacter jejuni.

BACKGROUND: Campylobater jejuni, a major foodborne diarrhoeal pathogen is reported to produce a number of cytotoxins of which only a cytolethal distending toxin (CDT) has been characterised so far. One or more additional cytotoxins other than CDT, including a Chinese hamster ovary (CHO) cell active, Vero cell inactive cytotoxin, may mediate inflammatory diarrhoea. Our objective was to develop a method to enrich and thus partially characterise this cytotoxin, as a pathway to the eventual identification and characterisation of the toxin. RESULTS: A number of biochemical methods including cation- and anion-exchange chromatography were evaluated to enrich the cytotoxin from a cell lysate of a known cytotoxin-producing C. jejuni, C31. The cytotoxin in crude lysate was initially prepared by size-exclusion desalting and then subjected to high pressure liquid chromatography (HPLC) ion-exchange fractionation. One pooled fraction (pool B) was cytotoxic for CHO cells equivalent to crude toxin (tissue culture infectivity dose 50 [TCID(50)] of 1-2 µg/ml). The proteins of pool B were identified by mass spectrometry (MS) after separation by SDS-PAGE and trypsin digestion. Also, pool B was directly digested with trypsin and then subjected to liquid chromatography tandem mass spectrometry (LCMS) analysis for identification of lesser abundant proteins in the fraction. A total of 41 proteins were found in the fraction, which included enzymes involved in metabolic and transport functions. Eighteen non-cytoplasmic proteins including 2 major antigenic peptide proteins (PEB2 and PEB3) and 3 proteins of unknown function were also identified in the screen. Cytotoxicity in pool B was trypsin-sensitive indicating its protein nature. The cytotoxic activity was heat-stable to 50°C, and partially inactivated at 60-70°C. The pool B fraction also induced fluid accumulation in the adult rabbit ileal loop assay with cytotoxicity for mucosa confirming the presence of the cytotoxin. CONCLUSIONS: We report the enrichment and partial purification of C. jejuni cytotoxin by HPLC ion-exchange chromatography. Further purification may be achieved using additional complementary chromatographic techniques. A short-list of six candidate cytotoxin proteins was identified using an LCMS screen of pool B. Successful isolation of the cytotoxin will initiate steps for the determination of the role of this cytotoxin in the pathogenesis of C. jejuni diarrhoea.