A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.

Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.

Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information.

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.

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.

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.

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.

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.

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.

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.

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.

N-Acetylcysteine Attenuates the Development of Renal Fibrosis in Transgenic Mice with Dilated Cardiomyopathy.

Mechanisms underlying the renal pathology in cardiorenal syndrome (CRS) type 2 remain elusive. We hypothesised that renal glutathione deficiency is central to the development of CRS type 2. Glutathione precursor, N-acetylcysteine (NAC;40 mg/kg/day; 8 weeks) or saline were administered to transgenic mice with dilated cardiomyopathy (DCM) and wild-type (WT) controls. Cardiac structure, function and glutathione levels were assessed at the end of this protocol. Renal fibrosis, glutathione content, expression of inflammatory and fibrotic markers, and function were also evaluated. In both genotypes, NAC had minimal effect on cardiac glutathione, structure and function (P ≥ 0.20). In NAC treated DCM mice, loss of glomerular filtration rate (GFR), tubulointerstitial and glomerular fibrosis and renal oxidised glutathione levels were attenuated by 38%, 99%, 70% and 52% respectively, compared to saline treated DCM mice (P ≤ 0.01). Renal expression of PAI-1 was greater in saline treated DCM mice than in WT mice (P < 0.05). Renal PAI-1 expression was less in NAC treated DCM mice than in vehicle treated DCM mice (P = 0.03). Renal IL-10 expression was greater in the former cohort compared to the latter (P < 0.01). These data indicate that normalisation of renal oxidized glutathione levels attenuates PAI-1 expression and renal inflammation preventing loss of GFR in experimental DCM.

Proteomics Identification of Potential Candidates Involved in Cell Proliferation for Early Stage of Brain Regeneration in the Adult Zebrafish.

The central nervous system (CNS) of the non-mammalian vertebrates has better neuroregenerative capability as compared with the mammalian CNS. Regeneration of habenula was observed 40 days after damage in zebrafish. During the early stage of regeneration, we found a significant increase of apoptotic cells on day-1 post-damage and of proliferative cells on day-3 post-damage. To identify the molecular factor(s) involved in the early stages of neuroregeneration, differentially expressed proteins during sham, 20- and 40-h post-habenula damage were investigated by proteomic approach by using two-dimensional differential gel electrophoresis (2D-DIGE) coupled with Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight (MALDI-ToF) and tandem mass spectrometry. Protein profiles revealed 17 differentially (>1.5-fold) expressed proteins: 10 upregulated, 4 downregulated, 2 proteins were found to be downregulated at the early stage but upregulated at a later stage, and 1 protein was found to be upregulated at 2 different time points. All proteins identified can be summarized under few molecular processes involved in the early stages of neuroregeneration in zebrafish CNS: apoptosis regulation (Wnt inhibitory factor 1 [WIF1]), neuroprotection (metallothionein), cell proliferation (Spred2, ependymin, Lhx1, and Wnts), differentiation (Spred2, Lhx9, and Wnts), and morphogenesis (cytoplasmic actins and draculin). These protein profiling results suggest that drastic molecular changes occur in the neuroregenerative process during this period, which includes cell proliferation, differentiation, and protection.

Clinical and Pharmacological Investigation of Myotoxicity in Sri Lankan Russell's Viper (Daboia russelii) Envenoming.

BACKGROUND: Sri Lankan Russell's viper (Daboia russelii) envenoming is reported to cause myotoxicity and neurotoxicity, which are different to the effects of envenoming by most other populations of Russell's vipers. This study aimed to investigate evidence of myotoxicity in Russell's viper envenoming, response to antivenom and the toxins responsible for myotoxicity. METHODOLOGY AND FINDINGS: Clinical features of myotoxicity were assessed in authenticated Russell's viper bite patients admitted to a Sri Lankan teaching hospital. Toxins were isolated using high-performance liquid chromatography. In-vitro myotoxicity of the venom and toxins was investigated in chick biventer nerve-muscle preparations. Of 245 enrolled patients, 177 (72.2%) had local myalgia and 173 (70.6%) had local muscle tenderness. Generalized myalgia and muscle tenderness were present in 35 (14.2%) and 29 (11.8%) patients, respectively. Thirty-seven patients had high (>300 U/l) serum creatine kinase (CK) concentrations in samples 24h post-bite (median: 666 U/l; maximum: 1066 U/l). Peak venom and 24h CK concentrations were not associated (Spearman's correlation; p = 0.48). The 24h CK concentrations differed in patients without myotoxicity (median 58 U/l), compared to those with local (137 U/l) and generalised signs/symptoms of myotoxicity (107 U/l; p = 0.049). Venom caused concentration-dependent inhibition of direct twitches in the chick biventer cervicis nerve-muscle preparation, without completely abolishing direct twitches after 3 h even at 80 µg/ml. Indian polyvalent antivenom did not prevent in-vitro myotoxicity at recommended concentrations. Two phospholipase A2 toxins with molecular weights of 13kDa, U1-viperitoxin-Dr1a (19.2% of venom) and U1-viperitoxin-Dr1b (22.7% of venom), concentration dependently inhibited direct twitches in the chick biventer cervicis nerve-muscle preparation. At 3 µM, U1-viperitoxin-Dr1a abolished twitches, while U1-viperitoxin-Dr1b caused 70% inhibition of twitch force after 3h. Removal of both toxins from whole venom resulted in no in-vitro myotoxicity. CONCLUSION: The study shows that myotoxicity in Sri Lankan Russell's viper envenoming is mild and non-life threatening, and due to two PLA2 toxins with weak myotoxic properties.

The NEAT Domain-Containing Proteins of Clostridium perfringens Bind Heme.

The ability of a pathogenic bacterium to scavenge iron from its host is important for its growth and survival during an infection. Our studies on C. perfringens gas gangrene strain JIR325, a derivative of strain 13, showed that it is capable of utilizing both human hemoglobin and ferric chloride, but not human holo-transferrin, as an iron source for in vitro growth. Analysis of the C. perfringens strain 13 genome sequence identified a putative heme acquisition system encoded by an iron-regulated surface gene region that we have named the Cht (Clostridium perfringens heme transport) locus. This locus comprises eight genes that are co-transcribed and includes genes that encode NEAT domain-containing proteins (ChtD and ChtE) and a putative sortase (Srt). The ChtD, ChtE and Srt proteins were shown to be expressed in JIR325 cells grown under iron-limited conditions and were localized to the cell envelope. Moreover, the NEAT proteins, ChtD and ChtE, were found to bind heme. Both chtDE and srt mutants were constructed, but these mutants were not defective in hemoglobin or ferric chloride utilization. They were, however, attenuated for virulence when tested in a mouse myonecrosis model, although the virulence phenotype could not be restored via complementation and, as is common with such systems, secondary mutations were identified in these strains. In summary, this study provides evidence for the functional redundancies that occur in the heme transport pathways of this life threatening pathogen.

Stimulating the Activity of Amyloid-Beta Degrading Enzymes: A Novel Approach for the Therapeutic Manipulation of Amyloid-Beta Levels.

Alzheimer's disease is a debilitating neurological disease placing significant burden on health care budgets around the world. It is widely believed that accumulation of amyloid-beta (Aß) in the brain is a key event that initiates neurodegeneration, thus the clearance of Aß from brain could be a key therapeutic strategy. Aß exists in an equilibrium in healthy individuals, and recent research would suggest that dysfunction in the clearance pathways is the driving force behind its accumulation. One mechanism of clearance is proteolytic degradation by enzymes, and increasing the expression of these enzymes in animal models of Alzheimer's disease has indeed shown promising results. This approach could be challenging to translate into the clinic given the likely need for genetic manipulation. We hypothesize that stimulating the activity of these enzymes (as opposed to increasing expression) through pharmacological agents will enhance degradation or at least prevent amyloid deposition, and is therefore another potentially novel avenue to manipulate Aß levels for therapeutic purposes. We discuss the recent research supporting this hypothesis as well as possible drawbacks to this approach.

Canopy Venom: Proteomic Comparison among New World Arboreal Pit-Viper Venoms.

Central and South American pitvipers, belonging to the genera Bothrops and Bothriechis, have independently evolved arboreal tendencies. Little is known regarding the composition and activity of their venoms. In order to close this knowledge gap, venom proteomics and toxin activity of species of Bothriechis, and Bothrops (including Bothriopsis) were investigated through established analytical methods. A combination of proteomics and bioactivity techniques was used to demonstrate a similar diversification of venom composition between large and small species within Bothriechis and Bothriopsis. Increasing our understanding of the evolution of complex venom cocktails may facilitate future biodiscoveries.

N-acetylcysteine attenuates the development of cardiac fibrosis and remodeling in a mouse model of heart failure.

Oxidative stress plays a central role in the pathogenesis of heart failure. We aimed to determine whether the antioxidantN-acetylcysteine can attenuate cardiac fibrosis and remodeling in a mouse model of heart failure. Minipumps were implanted subcutaneously in wild-type mice (n = 20) and mice with cardiomyopathy secondary to cardiac specific overexpression of mammalian sterile 20-like kinase 1 (MST-1;n = 18) to administerN-acetylcysteine (40 mg/kg per day) or saline for a period of 8 weeks. At the end of this period, cardiac remodeling and function was assessed via echocardiography. Fibrosis, oxidative stress, and expression of collagen types I andIIIwere quantified in heart tissues. Cardiac perivascular and interstitial fibrosis were greater by 114% and 209%, respectively, inMST-1 compared to wild type (P ≤ 0.001). InMST-1 mice administeredN-acetylcysteine, perivascular and interstitial fibrosis were 40% and 57% less, respectively, compared to those treated with saline (P ≤ 0. 03). Cardiac oxidative stress was 119% greater inMST-1 than in wild type (P < 0.001) andN-acetylcysteine attenuated oxidative stress inMST-1 by 42% (P = 0.005). These data indicate thatN-acetylcysteine can blunt cardiac fibrosis and related remodeling in the setting of heart failure potentially by reducing oxidative stress. This study provides the basis to investigate the role ofN-acetylcysteine in chronic heart failure.