Detection of elevated protein modification in human cerebellum in alcoholic cerebellar degeneration

Modification of proteins by reactive ethanol metabolites has been known for some time to occur in the liver, the main site of ethanol metabolism. In more recent studies of laboratory animals, similar modifications have been detected in organs with lesser ability to metabolize ethanol, such as skeletal and cardiac muscle and brain. Such modification may alter protein function or form a neoantigen, making it a target for immune attack. We now report an analysis of protein modification derived from ethanol metabolites in human brain tissue by ELISA using adduct-specific antibodies. We obtained autopsy cerebellum samples from 10 alcoholic cerebellar degeneration cases and 10 matched controls under informed written consent from the next of kin and clearance from the UQ Human Ethics Committee. Elevated levels of protein modifications derived from acetaldehyde (unreduced-acetaldehyde and acetaldehyde-advanced glycation end-product adducts), from malondialdehyde (malondialdehyde adducts) and from combined adducts (malondialdehydeacetaldehyde (MAA) adducts) were detected in alcoholic cerebellar degeneration samples when compared to controls. Other adduct types found in liver samples, such as reduced-acetaldehyde and those derived from hydroxyethyl radicals, were not detected in brain samples. This may reflect the different routes of ethanol metabolism in the two tissues. This is the first report of elevated protein modification in alcoholic cerebellar degeneration, and suggests that such modification may play a role in the pathogenesis of brain injury. Supported by NIAAA under grant NIH AA12404 and the NHMRC (Australia) under grant #981723.

Detection of elevated protein modification in alcoholic cerebellar degeneration

Modification of proteins by reactive ethanol metabolites has been known, for some time, to occur in the liver, the main site of ethanol metabolism. More recently, similar modifi cation has been detected in organs with lesser ability to metabolise ethanol such as skeletal and cardiac muscle, and brain. Such modifi cation may alter protein function or form a neoantigen, making it a target for immune attack.

The influence of diet on protein oxidation

Protein oxidation can be perceived as essential for the control of intracellular signalling and gene expression on the one hand, but in contrast, a potentially cytotoxic hazard of aerobic life. Reduction and oxidation of thiol groups on specific cysteine residues can act as critical molecular switches, in modulating response to growth factors, apoptotic and inflammatory stimuli to name a few. Such oxidative reactions are likely to be transient and represent low levels of oxidative modification to a protein. Sustained oxidative stress conditions through absence of essential dietary antioxidant or low activity of endogenous enzyme scavengers can cause irreversible damage and loss of function. Such modifications are believed to be important in many diseases associated with ageing. Therefore, it has been postulated that diet may exert an influence on the steady state of protein oxidation and thus offer potential health benefits through preservation of normal protein function. In the present paper, the current evidence from in vivo studies on the effects of dietary antioxidants and oxidants on protein oxidation will be evaluated, and needs for future research will be highlighted.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Decomposition of Boolean Functions – Recognizing a Good Solution by Traces

The problem of sequent two-block decomposition of a Boolean function is regarded in case when a good solution does exist. The problem consists mainly in finding an appropriate weak partition on the set of arguments of the considered Boolean function, which should be decomposable at that partition. A new fast heuristic combinatorial algorithm is offered for solving this task. At first the randomized search for traces of such a partition is fulfilled. The recognized traces are represented by some "triads" - the simplest weak partitions corresponding to non-trivial decompositions. After that the whole sought-for partition is restored from the discovered trace by building a track initialized by the trace and leading to the solution. The results of computer experiments testify the high practical efficiency of the algorithm.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Prognostic significance of IGF and ECM induced signalling proteins in breast cancer patients

Breast cancer is a leading contributor to the burden of disease in Australia. Fortunately, the recent introduction of diverse therapeutic strategies have improved the survival outcome for many women. Despite this, the clinical management of breast cancer remains problematic as not all approaches are sufficiently sophisticated to take into account the heterogeneity of this disease and are unable to predict disease progression, in particular, metastasis. As such, women with good prognostic outcomes are exposed to the side effects of therapies without added benefit. Furthermore, women with aggressive disease for whom these advanced treatments would deliver benefit cannot be distinguished and opportunities for more intensive or novel treatment are lost. This study is designed to identify novel factors associated with disease progression, and the potential to inform disease prognosis. Frequently overlooked, yet common mediators of disease are the interactions that take place between the insulin-like growth factor (IGF) system and the extracellular matrix (ECM). Our laboratory has previously demonstrated that multiprotein insulin-like growth factor-I (IGF-I): insulin-like growth factor binding protein (IGFBP): vitronectin (VN) complexes stimulate migration of breast cancer cells in vitro, via the cooperative involvement of the insulin-like growth factor type I receptor (IGF-IR) and VN-binding integrins. However, the effects of IGF and ECM protein interactions on the dissemination and progression of breast cancer in vivo are unknown. It was hypothesised that interactions between proteins required for IGF induced signalling events and those within the ECM contribute to breast cancer metastasis and are prognostic and predictive indicators of patient outcome. To address this hypothesis, semiquantitative immunohistochemistry (IHC) analyses were performed to compare the extracellular and subcellular distribution of IGF and ECM induced signalling proteins between matched normal, primary cancer, and metastatic cancer among archival formalin-fixed paraffin-embedded (FFPE) breast tissue samples collected from women attending the Princess Alexandra Hospital, Brisbane. Multivariate Cox proportional hazards (PH) regression survival models in conjunction with a modified „purposeful selection of covariates. method were applied to determine the prognostic potential of these proteins. This study provides the first in-depth, compartmentalised analysis of the distribution of IGF and ECM induced signalling proteins. As protein function and protein localisation are closely correlated, these findings provide novel insights into IGF signalling and ECM protein function during breast cancer development and progression. Distinct IGF signalling and ECM protein immunoreactivity was observed in the stroma and/or in subcellular locations in normal breast, primary cancer and metastatic cancer tissues. Analysis of the presence and location of stratifin (SFN) suggested a causal relationship in ECM remodelling events during breast cancer development and progression. The results of this study have also suggested that fibronectin (FN) and ¥â1 integrin are important for the formation of invadopodia and epithelial-to-mesenchymal transition (EMT) events. Our data also highlighted the importance of the temporal and spatial distribution of IGF induced signalling proteins in breast cancer metastasis; in particular, SFN, enhancer-of-split and hairy-related protein 2 (SHARP-2), total-akt/protein kinase B 1 (Total-AKT1), phosphorylated-akt/protein kinase B (P-AKT), extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (ERK1/2) and phosphorylated-extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (P-ERK1/2). Multivariate survival models were created from the immunohistochemical data. These models were found to fit well with these data with very high statistical confidence. Numerous prognostic confounding effects and effect modifications were identified among elements of the ECM and IGF signalling cascade and corroborate the survival models. This finding provides further evidence for the prognostic potential of IGF and ECM induced signalling proteins. In addition, the adjusted measures of associations obtained in this study have strengthened the validity and utility of the resulting models. The findings from this study provide insights into the biological interactions that occur during the development of breast tissue and contribute to disease progression. Importantly, these multivariate survival models could provide important prognostic and predictive indicators that assist the clinical management of breast disease, namely in the early identification of cancers with a propensity to metastasise, and/or recur following adjuvant therapy. The outcomes of this study further inform the development of new therapeutics to aid patient recovery. The findings from this study have widespread clinical application in the diagnosis of disease and prognosis of disease progression, and inform the most appropriate clinical management of individuals with breast cancer.

Prostacyclin synthase expression and epigenetic regulation in nonsmall cell lung cancer

BACKGROUND: Prostacyclin synthase (PGIS) metabolizes prostaglandin H(2), into prostacyclin. This study aimed to determine the expression profile of PGIS in nonsmall cell lung cancer (NSCLC) and examine potential mechanisms involved in PGIS regulation. METHODS: PGIS expression was examined in human NSCLC and matched controls by reverse transcriptase polymerase chain reaction (RT-PCR), Western analysis, and immunohistochemistry. A 204-patient NSCLC tissue microarray was stained for PGIS and cyclooxygenase 2 (COX2) expression. Staining intensity was correlated with clinical parameters. Epigenetic mechanisms underpinning PGIS promoter expression were examined using RT-PCR, methylation-specific PCR, and chromatin immunoprecipitation analysis. RESULTS: PGIS expression was reduced/absent in human NSCLC protein samples (P <.0001), but not mRNA relative to matched controls. PGIS tissue expression was higher in squamous cell carcinoma (P =.004) and in male patients (P <.05). No significant correlation of PGIS or COX2 expression with overall patient survival was observed, although COX2 was prognostic for short-term (2-year) survival (P <.001). PGIS mRNA expression was regulated by DNA CpG methylation and histone acetylation in NSCLC cell lines, with chromatin remodeling taking place directly at the PGIS gene. PGIS mRNA expression was increased by both demethylation agents and histone deacetylase inhibitors. Protein levels were unaffected by demethylation agents, whereas PGIS protein stability was negatively affected by histone deacetylase inhibitors. CONCLUSIONS: PGIS protein expression is reduced in NSCLC, and does not correlate with overall patient survival. PGIS expression is regulated through epigenetic mechanisms. Differences in expression patterns between mRNA and protein levels suggest that PGIS expression and protein stability are regulated post-translationally. PGIS protein stability may have an important therapeutic role in NSCLC. © 2011 American Cancer Society.

COX-derived prostanoid pathways in gastrointestinal cancer development and progression : novel targets for prevention and intervention

Arachidonic acid metabolism through cyclooxygenase (COX) pathways leads to the generation of biologically active eicosanoids. Eicosanoid expression levels vary during development and progression of gastrointestinal (GI) malignancies. COX-2 is the major COX-isoform responsible for G.I. cancer development/progression. COX-2 expression increases during progression from a normal to cancerous state. Evidence from observational studies has demonstrated that chronic NSAID use reduces the risk of cancer development, while both incidence and risk of death due to G.I. cancers were significantly reduced by daily aspirin intake. A number of randomized controlled trials (APC trial, Prevention of Sporadic Adenomatous Polyps trial, APPROVe trial) have also shown a significant protective effect in patients receiving selective COX-2 inhibitors. However, chronic use of selective COX-2 inhibitors at high doses was associated with increased cardiovascular risk, while NSAIDs have also been associated with increased risk. More recently, downstream effectors of COX-signaling have been investigated in cancer development/progression. PGE 2, which binds to both EP and PPAR receptors, is the major prostanoid implicated in the carcinogenesis of G.I. cancers. The role of TXA 2 in G.I. cancers has also been examined, although further studies are required to uncover its role in carcinogenesis. Other prostanoids investigated include PGD 2 and its metabolite 15d-PGJ2, PGF 1α and PGI 2. Targeting these prostanoids in G.I. cancers has the promise of avoiding cardiovascular toxicity associated with chronic selective COX-2 inhibition, while maintaining anti-tumor reactivity.A progressive sequence from normal to pre-malignant to a malignant state has been identified in G.I. cancers. In this review, we will discuss the role of the COX-derived prostanoids in G.I. cancer development and progression. Targeting these downstream prostanoids for chemoprevention and/or treatment of G.I. cancers will also be discussed. Finally, we will highlight the latest pre-clinical technologies as well as avenues for future investigation in this highly topical research field. © 2011 Elsevier B.V.

The role of prostacyclin synthase and thromboxane synthase signaling in the development and progression of cancer

Prostacyclin synthase and thromboxane synthase signaling via arachidonic acid metabolism affects a number of tumor cell survival pathways such as cell proliferation, apoptosis, tumor cell invasion and metastasis, and angiogenesis. However, the effects of these respective synthases differ considerably with respect to the pathways described. While prostacyclin synthase is generally believed to be anti-tumor, a pro-carcinogenic role for thromboxane synthase has been demonstrated in a variety of cancers. The balance of oppositely-acting COX-derived prostanoids influences many processes throughout the body, such as blood pressure regulation, clotting, and inflammation. The PGI2/TXA2 ratio is of particular interest in-vivo, with the corresponding synthases shown to be differentially regulated in a variety of disease states. Pharmacological inhibition of thromboxane synthase has been shown to significantly inhibit tumor cell growth, invasion, metastasis and angiogenesis in a range of experimental models. In direct contrast, prostacyclin synthase overexpression has been shown to be chemopreventive in a murine model of the disease, suggesting that the expression and activity of this enzyme may protect against tumor development. In this review, we discuss the aberrant expression and known functions of both prostacyclin synthase and thromboxane synthase in cancer. We discuss the effects of these enzymes on a range of tumor cell survival pathways, such as tumor cell proliferation, induction of apoptosis, invasion and metastasis, and tumor cell angiogenesis. As downstream signaling pathways of these enzymes have also been implicated in cancer states, we examine the role of downstream effectors of PGIS and TXS activity in tumor growth and progression. Finally, we discuss current therapeutic strategies aimed at targeting these enzymes for the prevention/treatment of cancer. © 2010 Elsevier B.V. All rights reserved.

Histone deacetylase inhibitors target diabetes via chromatin remodeling or as chemical chaperones?

Globally, obesity and diabetes (particularly type 2 diabetes) represents a major challenge to world health. Despite decades of intense research efforts, the genetic basis involved in diabetes pathogenesis & conditions associated with obesity are still poorly understood. Recent advances have led to exciting new developments implicating epigenetics as an important mechanism underpinning diabetes and obesity related disease. One epigenetic mechanism known as the "histone code" describes the idea that specific patterns of post-translational modifications to histones act like a molecular "code" recognised and used by non-histone proteins to regulate specific chromatin functions. One modification which has received significant attention is that of histone acetylation. The enzymes which regulate this modification are described as lysine acetyltransferases or KATs and histone deacetylases or HDACs. Due to their conserved catalytic domain HDACs have been actively targeted as a therapeutic target. Some of the known inhibitors of HDACs (HDACi) have also been shown to act as "chemical chaperones" to alleviate diabetic symptoms. In this review, we discuss the available evidence concerning the roles of HDACs in regulating chaperone function and how this may have implications in the management of diabetes. © 2009 Bentham Science Publishers Ltd.

Lipoxygenase metabolism : roles in tumor progression and survival

The metabolism of arachidonic acid through lipoxygenase pathways leads to the generation of various biologically active eicosanoids. The expression of these enzymes vary throughout the progression of various cancers, and thereby they have been shown to regulate aspects of tumor development. Substantial evidence supports a functional role for lipoxygenase-catalyzed arachidonic and linoleic acid metabolism in cancer development. Pharmacologic and natural inhibitors of lipoxygenases have been shown to suppress carcinogenesis and tumor growth in a number of experimental models. Signaling of hydro[peroxy]fatty acids following arachidonic or linoleic acid metabolism potentially effect diverse biological phenomenon regulating processes such as cell growth, cell survival, angiogenesis, cell invasion, metastatic potential and immunomodulation. However, the effects of distinct LOX isoforms differ considerably with respect to their effects on both the individual mechanisms described and the tumor being examined. 5-LOX and platelet type 12-LOX are generally considered pro-carcinogenic, with the role of 15-LOX-1 remaining controversial, while 15-LOX-2 suppresses carcinogenesis. In this review, we focus on the molecular mechanisms regulated by LOX metabolism in some of the major cancers. We discuss the effects of LOXs on tumor cell proliferation, their roles in cell cycle control and cell death induction, effects on angiogenesis, migration and the immune response, as well as the signal transduction pathways involved in these processes. Understanding the molecular mechanisms underlying the anti-tumor effect of specific, or general, LOX inhibitors may lead to the design of biologically and pharmacologically targeted therapeutic strategies inhibiting LOX isoforms and/or their biologically active metabolites, that may ultimately prove useful in the treatment of cancer, either alone or in combination with conventional therapies. © 2007 Springer Science+Business Media, LLC.

Epidermal growth factor receptors and cyclooxygenase-2 in the pathogenesis of non-small cell lung cancer : potential targets for chemoprevention and systemic therapy

The epidermal growth factor receptor (EGFR) is part of a family of plasma membrane receptor tyrosine kinases that control many important cellular functions, from growth and proliferation to cell death. Cyclooxygenase (COX)-2 is an enzyme which catalyses the conversion of arachidonic acid to prostagladins and thromboxane. It is induced by various inflammatory stimuli, including the pro-inflammatory cytokines, Interleukin (IL)-1β, Tumour Necrosis Factor (TNF)-α and IL-2. Both EGFR and COX-2 are over-expressed in non-small cell lung cancer (NSCLC) and have been implicated in the early stages of tumourigenesis. This paper considers their roles in the development and progression of lung cancer, their potential interactions, and reviews the recent progress in cancer therapies that are directed toward these targets. An increasing body of evidence suggests that selective inhibitors of both EGFR and COX-2 are potential therapeutic agents for the treatment of NSCLC, in the adjuvant, metastatic and chemopreventative settings. © 2002 Elsevier Science Ireland Ltd. All rights reserved.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.