Prolonged lifespan with enhanced exploratory behavior in mice overexpressing the oxidized nucleoside triphosphatase hMTH1

The contribution that oxidative damage to DNA and/or RNA makes to the aging process remains undefined. In this study, we used the hMTH1‐Tg mouse model to investigate how oxidative damage to nucleic acids affects aging. hMTH1‐Tg mice express high levels of the hMTH1 hydrolase that degrades 8‐oxodGTP and 8‐oxoGTP and excludes 8‐oxoguanine from both DNA and RNA. Compared to wild‐type animals, hMTH1‐overexpressing mice have significantly lower steady‐state levels of 8‐oxoguanine in both nuclear and mitochondrial DNA of several organs, including the brain. hMTH1 overexpression prevents the age‐dependent accumulation of DNA 8‐oxoguanine that occurs in wild‐type mice. These lower levels of oxidized guanines are associated with increased longevity and hMTH1‐Tg animals live significantly longer than their wild‐type littermates. Neither lipid oxidation nor overall antioxidant status is significantly affected by hMTH1 overexpression. At the cellular level, neurospheres derived from adult hMTH1‐Tg neural progenitor cells display increased proliferative capacity and primary fibroblasts from hMTH1‐Tg embryos do not undergo overt senescence in vitro. The significantly lower levels of oxidized DNA/RNA in transgenic animals are associated with behavioral changes. These mice show reduced anxiety and enhanced investigation of environmental and social cues. Longevity conferred by overexpression of a single nucleotide hydrolase in hMTH1‐Tg animals is an example of lifespan extension associated with healthy aging. It provides a link between aging and oxidative damage to nucleic acids.     

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Background

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.

Results

We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.

Conclusions

In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

     

Integrating domain similarity to improve protein complexes identification in TAP-MS data

Background

Detecting protein complexes in protein-protein interaction (PPI) networks plays an important role in improving our understanding of the dynamic of cellular organisation. However, protein interaction data generated by high-throughput experiments such as yeast-two-hybrid (Y2H) and tandem affinity-purification/mass-spectrometry (TAP-MS) are characterised by the presence of a significant number of false positives and false negatives. In recent years there has been a growing trend to incorporate diverse domain knowledge to support large-scale analysis of PPI networks.

Methods

This paper presents a new algorithm, by incorporating Gene Ontology (GO) based semantic similarities, to detect protein complexes from PPI networks generated by TAP-MS. By taking co-complex relations in TAP-MS data into account, TAP-MS PPI networks are modelled as bipartite graph, where bait proteins consist of one set of nodes and prey proteins are on the other. Similarities between pairs of bait proteins are computed by considering both the topological features and GO-driven semantic similarities. Bait proteins are then grouped in to sets of clusters based on their pair-wise similarities to produce a set of 'seed' clusters. An expansion process is applied to each 'seed' cluster to recruit prey proteins which are significantly associated with the same set of bait proteins. Thus, completely identified protein complexes are then obtained.

Results

The proposed algorithm has been applied to real TAP-MS PPI networks. Fifteen quality measures have been employed to evaluate the quality of generated protein complexes. Experimental results show that the proposed algorithm has greatly improved the accuracy of identifying complexes and outperformed several state-of-the-art clustering algorithms. Moreover, by incorporating semantic similarity, the proposed algorithm is more robust to noises in the networks.

     

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

Background

Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.

Results

We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.

Conclusions

M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder

     

Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter

Background

Many problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles.

Methods

This paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima.

Results and conclusions

We show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface.

     

B-cell depletion in SLE: clinical and trial experience with rituximab and ocrelizumaband implications for study design

B cells are believed to be central to the disease process in systemic lupuserythematosus (SLE), making them a target for new therapeutic intervention. In recentyears there have been many publications regarding the experience in SLE of B-celldepletion utilising rituximab, an anti-CD20 mAb that temporarily depletes B cells,reporting promising results in uncontrolled open studies and in routine clinical use.However, the two large randomised controlled trials in extra-renal lupus (EXPLORERstudy) and lupus nephritis (LUNAR study) failed to achieve their primary endpoints.Based on the clinical experience with rituximab this failure was somewhat unexpectedand raised a number of questions and concerns, not only into the true level ofbenefit of B-cell depletion in a broad population but also how to test the true levelof effectiveness of an investigational agent as we seek to improve the design oftherapeutic trials in SLE. A better understanding of what went wrong in these trialsis essential to elucidate the underlying reasons for the disparate observations notedin open studies and controlled trials. In this review, we focus on various factorsthat may affect the ability to accurately and confidently establish the level oftreatment effect of the investigational agent, in this case rituximab, in the twostudies and explore hurdles faced in the randomised controlled trials investigatingthe efficacy of ocrelizumab, the humanised anti-CD20 mAb, in SLE. Further, based onthe lessons learned from the clinical trials, we make suggestions that could beimplemented in future clinical trial design to overcome the hurdles faced.     

HMPAS: Human Membrane Protein Analysis System

Background

Membrane proteins perform essential roles in diverse cellular functions and are regarded as major pharmaceutical targets. The significance of membrane proteins has led to the developing dozens of resources related with membrane proteins. However, most of these resources are built for specific well-known membrane protein groups, making it difficult to find common and specific features of various membrane protein groups.

Methods

We collected human membrane proteins from the dispersed resources and predicted novel membrane protein candidates by using ortholog information and our membrane protein classifiers. The membrane proteins were classified according to the type of interaction with the membrane, subcellular localization, and molecular function. We also made new feature dataset to characterize the membrane proteins in various aspects including membrane protein topology, domain, biological process, disease, and drug. Moreover, protein structure and ICD-10-CM based integrated disease and drug information was newly included. To analyze the comprehensive information of membrane proteins, we implemented analysis tools to identify novel sequence and functional features of the classified membrane protein groups and to extract features from protein sequences.

Results

We constructed HMPAS with 28,509 collected known membrane proteins and 8,076 newly predicted candidates. This system provides integrated information of human membrane proteins individually and in groups organized by 45 subcellular locations and 1,401 molecular functions. As a case study, we identified associations between the membrane proteins and diseases and present that membrane proteins are promising targets for diseases related with nervous system and circulatory system. A web-based interface of this system was constructed to facilitate researchers not only to retrieve organized information of individual proteins but also to use the tools to analyze the membrane proteins.

Conclusions

HMPAS provides comprehensive information about human membrane proteins including specific features of certain membrane protein groups. In this system, user can acquire the information of individual proteins and specified groups focused on their conserved sequence features, involved cellular processes, and diseases. HMPAS may contribute as a valuable resource for the inference of novel cellular mechanisms and pharmaceutical targets associated with the human membrane proteins. HMPAS is freely available at http://fcode.kaist.ac.kr/hmpas.

     

Palmitoylethanolamide and luteolin ameliorate development of arthritis caused by injection of collagen type II in mice

IntroductionN-palmitoylethanolamine (PEA) is an endogenous fatty acid amide belonging to the family of the N-acylethanolamines (NAEs). Recently, several studies demonstrated that PEA is an important analgesic, antiinflammatory, and neuroprotective mediator. The aim of this study was to investigate the effect of co-ultramicronized PEA + luteolin formulation on the modulation of the inflammatory response in mice subjected to collagen-induced arthritis (CIA).MethodsCIA was induced by an intradermally injection of 100 μl of the emulsion (containing 100 μg of bovine type II collagen (CII)) and complete Freund adjuvant (CFA) at the base of the tail. On day 21, a second injection of CII in CFA was administered. Mice subjected to CIA were administered PEA (10 mg/kg 10% ethanol, intraperitoneally (i.p.)) or co-ultramicronized PEA + luteolin (1 mg/kg, i.p.) every 24 hours, starting from day 25 to 35.ResultsMice developed erosive hind-paw arthritis when immunized with CII in CFA. Macroscopic clinical evidence of CIA first appeared as periarticular erythema and edema in the hindpaws. The incidence of CIA was 100% by day 28 in the CII-challenged mice, and the severity of CIA progressed over a 35-day period with a resorption of bone. The histopathology of CIA included erosion of the cartilage at the joint. Treatment with PEA or PEA + luteolin ameliorated the clinical signs at days 26 to 35 and improved histologic status in the joint and paw. The degree of oxidative and nitrosative damage was significantly reduced in PEA + luteolin-treated mice, as indicated by nitrotyrosine and malondialdehyde (MDA) levels. Plasma levels of the proinflammatory cytokines and chemokines were significantly reduced by PEA + luteolin treatment.ConclusionsWe demonstrated that PEA co-ultramicronized with luteolin exerts an antiinflammatory effect during chronic inflammation and ameliorates CIA.     

The use of proton pump inhibitors in treating and preventing NSAID-induced mucosal damage

NSAIDs are prescribed widely but have rare serious gastrointestinal side effects. More recently, adverse cardiovascular effects of these drugs have also been recognized, leading to the withdrawal of some agents and continuing uncertainty about the best approach for patients requiring NSAID therapy. Proton pump inhibitors (PPIs) provide potent and long-lasting inhibition of gastric acid secretion and have proven efficacy in healing NSAID-associated ulcers, including those with continued exposure to NSAIDs. PPIs have also shown efficacy in reducing the risk of ulcerations due to NSAID use compared with NSAIDs alone in randomized controlled trials (RCTs) where endoscopic ulcers are used as the primary endpoint, albeit a surrogate marker for clinical ulcers and complications. Large RCT outcome trials comparing patients exposed to NSAIDs with and without PPI co-therapy have not been performed, but adequately powered RCTs in high-risk patients demonstrate that PPI + nonselective NSAID provides similar rates of symptomatic ulcer recurrence rates as the use of a cyclooxygenase (COX)-2 selective inhibitor. A RCT in high-risk patients with previous ulcer complications supports the additive bene3 t of two risk-reducing strategies, as ulcer complication recurrence was eliminated in high-risk patients who were given a COX-2 selective agent with a PPI. Helicobacter pylori, an independent risk factor for ulcers, should be sought out and eradicated in patients at increased gastrointestinal risk, typically those with an ulcer history. Following H. pylori eradication, however, patients remain at risk and co-therapy with a PPI is recommended. NSAID medication selection should consider both the individual patients' gastrointestinal and cardiovascular risks.