Differential expression of chemokine and matrix re-modelling genes is associated with contrasting schistosome-induced hepatopathology in murine models

The pathological outcomes of schistosomiasis are largely dependent on the molecular and cellular mechanisms of the host immune response. In this study, we investigated the contribution of variations in host gene expression to the contrasting hepatic pathology observed between two inbred mouse strains following Schistosoma japonicum infection. Whole genome microarray analysis was employed in conjunction with histological and immunohistochemical analysis to define and compare the hepatic gene expression profiles and cellular composition associated with the hepatopathology observed in S. japonicum-infected BALB/c and CBA mice. We show that the transcriptional profiles differ significantly between the two mouse strains with high statistical confidence. We identified specific genes correlating with the more severe pathology associated with CBA mice, as well as genes which may confer the milder degree of pathology associated with BALB/c mice. In BALB/c mice, neutrophil genes exhibited striking increases in expression, which coincided with the significantly greater accumulation of neutrophils at granulomatous regions seen in histological sections of hepatic tissue. In contrast, up-regulated expression of the eosinophil chemokine CCL24 in CBA mice paralleled the cellular influx of eosinophils to the hepatic granulomas. Additionally, there was greater down-regulation of genes involved in metabolic processes in CBA mice, reflecting the more pronounced hepatic damage in these mice. Profibrotic genes showed similar levels of expression in both mouse strains, as did genes associated with Th1 and Th2 responses. However, imbalances in expression of matrix metalloproteinases (e.g. MMP12, MMP13) and tissue inhibitors of metalloproteinases (TIMP1) may contribute to the contrasting pathology observed in the two strains. Overall, these results provide a more complete picture of the molecular and cellular mechanisms which govern the pathological outcome of hepatic schistosomiasis. This improved understanding of the immunopathogenesis in the murine model schistosomiasis provides the basis for a better appreciation of the complexities associated with chronic human schistosomiasis.

HIV-1 sub-type C chimaeric VLPs boost cellular immune responses in mice

Several approaches have been explored to eradicate HIV; however, a multigene vaccine appears to be the best option, given their proven potential to elicit broad, effective responses in animal models. The Pr55 Gagprotein is an excellent vaccine candidate in its own right, given that it can assemble into large, enveloped, virus-like particles (VLPs) which are highly immunogenic, and can moreover be used as a scaffold for the presentation of other large non-structural HIV antigens. In this study, we evaluated the potential of two novel chimaeric HIV-1 Pr55 Gag-based VLP constructs - C-terminal fusions with reverse transcriptase and a Tat::Nef fusion protein, designated GagRT and GagTN respectively - to enhance a cellular response in mice when used as boost components in two types of heterologous prime-boost vaccine strategies. A vaccine regimen consisting of a DNA prime and chimaeric HIV-1 VLP boosts in mice induced strong, broad cellular immune responses at an optimum dose of 100 ng VLPs. The enhanced cellular responses induced by the DNA prime-VLP boost were two- to three-fold greater than two DNA vaccinations. Moreover, a mixture of GagRT and GagTN VLPs also boosted antigen-specific CD8+ and CD4+ T-cell responses, while VLP vaccinations only induced predominantly robust Gag CD4+ T-cell responses. The results demonstrate the promising potential of these chimaeric VLPs as vaccine candidates against HIV-1. © 2010 Pillay et al; licensee BioMed Central Ltd.

Cellular model systems to study the tumor biological role of kallikrein-related peptidases in ovarian and prostate cancer

Since the identification of the gene family of kallikrein related peptidases (KLKs), their function has been robustly studied at the biochemical level. In vitro biochemical studies have shown that KLK proteases are involved in a number of extracellular processes that initiate intracellular signaling pathways by hydrolysis, as reviewed in Chapters 8, 9, and 15, Volume 1. These events have been associated with more invasive phenotypes of ovarian, prostate, and other cancers. Concomitantly, aberrant expression of KLKs has been associated with poor prognosis of patients with ovarian and prostate cancer (Borgoño and Diamandis, 2004; Clements et al., 2004; Yousef and Diamandis, 2009), with prostate-specific antigen (PSA, KLK3) being a long standing, clinically employed biomarker for prostate cancer (Lilja et al., 2008). Data generated from patient samples in clinical studies, alongwith biochemical activity, suggests that KLKs function in the development and progression of these diseases. To bridge the gap between their function at the molecular level and the clinical need for efficacious treatment and prognostic biomarkers, functional assessment at the in vitro cellular level, using various culture models, is increasing, particularly in a three-dimensional (3D) context (Abbott, 2003; Bissell and Radisky, 2001; Pampaloni et al., 2007; Yamada and Cukierman, 2007).

Eyes on 3D-Current 3D biomimetic disease concept models and potential applications in age-related macular degeneration

Three dimensional cellular models that mimic disease are being increasingly investigated and have opened an exciting new research area into understanding pathomechanisms. The advantage of 3D in vitro disease models is that they allow systematic and in-depth studies of physiological and pathophysiological processes with less costs and ethical concerns that have arisen with animal models. The purpose of the 3D approach is to allow crosstalk between cells and microenvironment, and with cues from the microenvironment, cells can assemble their niche similar to in vivo conditions. The use of 3D models for mimicking disease processes such as cancer, osteoarthritis etc., is only emerging and allows multidisciplinary teams consisting of tissue engineers, biologist biomaterial scientists and clinicians to work closely together. While in vitro systems require rigorous testing before they can be considered as replicates of the in vivo model, major steps have been made, suggesting that they will become powerful tools for studying physiological and pathophysiological processes. This paper aims to summarize some of the existing 3D models and proposes a novel 3D model of the eye structures that are involved in the most common cause of blindness in the Western World, namely age-related macular degeneration (AMD).

Rodent Models of Conditioned Fear: Behavioral Measures of Fear and Memory

Pavlovian fear conditioning is a robust technique for examining behavioral and cellular components of fear learning and memory. In fear conditioning, the subject learns to associate a previously neutral stimulus with an inherently noxious co-stimulus. The learned association is reflected in the subjects' behavior upon subsequent re-exposure to the previously neutral stimulus or the training environment. Using fear conditioning, investigators can obtain a large amount of data that describe multiple aspects of learning and memory. In a single test, researchers can evaluate functional integrity in fear circuitry, which is both well characterized and highly conserved across species. Additionally, the availability of sensitive and reliable automated scoring software makes fear conditioning amenable to high-throughput experimentation in the rodent model; thus, this model of learning and memory is particularly useful for pharmacological and toxicological screening. Due to the conserved nature of fear circuitry across species, data from Pavlovian fear conditioning are highly translatable to human models. We describe equipment and techniques needed to perform and analyze conditioned fear data. We provide two examples of fear conditioning experiments, one in rats and one in mice, and the types of data that can be collected in a single experiment. © 2012 Springer Science+Business Media, LLC.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Models of protein linear molecular motors for dynamic nanodevices

Protein molecular motors are natural nano-machines that convert the chemical energy from the hydrolysis of adenosine triphosphate into mechanical work. These efficient machines are central to many biological processes, including cellular motion, muscle contraction and cell division. The remarkable energetic efficiency of the protein molecular motors coupled with their nano-scale has prompted an increasing number of studies focusing on their integration in hybrid micro- and nanodevices, in particular using linear molecular motors. The translation of these tentative devices into technologically and economically feasible ones requires an engineering, design-orientated approach based on a structured formalism, preferably mathematical. This contribution reviews the present state of the art in the modelling of protein linear molecular motors, as relevant to the future design-orientated development of hybrid dynamic nanodevices. © 2009 The Royal Society of Chemistry.

Building cross-scale models of epigenetic mechanisms

Epigenetic changes correspond to heritable modifications of the chromosome structure, which do not involve alteration of the DNA sequence but do affect gene expression. These mechanisms play an important role in normal cell differentiation, but aberration is associated also with several diseases, including cancer and neural disorders. In consequence, despite intensive studies in recent years, the contribution of modifications remains largely unquantified due to overall system complexity and insufficient data. Computational models can provide powerful auxiliary tools to experimentation, not least as scales from the sub-cellular through cell populations (or to networks of genes) can be spanned. In this paper, the challenges to development, of realistic cross-scale models, are discussed and illustrated with respect to current work.

Numerical investigation of case hardening of plant tissue during dying and its influence on the cellular level shrinkage

Dried plant food materials are one of the major contributors to the global food industry. Widening the fundamental understanding on different mechanisms of food material alterations during drying assists the development of novel dried food products and processing techniques. In this regard, case hardening is an important phenomenon, commonly observed during the drying processes of plant food materials, which significantly influences the product quality and process performance. In this work, a recent meshfree-based numerical model of the authors is further improved and used to simulate the influence of case hardening on shrinkage characteristics of plant tissues during drying. In order to model fluid and wall mechanisms in each cell, Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method (DEM) are used. The model is fundamentally more capable of simulating large deformation of multiphase materials, when compared with conventional grid-based modelling techniques such as Finite Element Methods (FEM) or Finite Difference Methods (FDM). Case hardening is implemented by maintaining distinct moisture levels in the different cell layers of a given tissue. In order to compare and investigate different factors influencing tissue deformations under case hardening, four different plant tissue varieties (apple, potato, carrot and grape) are studied. The simulation results indicate that the inner cells of any given tissue undergo limited shrinkage and cell wall wrinkling compared to the case hardened outer cell layers of the tissues. When comparing unique deformation characteristics of the different tissues, irrespective of the normalised moisture content, the cell size, cell fluid turgor pressure and cell wall characteristics influence the tissue response to case hardening.

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Molecular and Cellular Mechanisms Behind Juvenile Neuronal Ceroid Lipofuscinosis (JNCL, Batten disease)

Neurodegenerative disorders are chronic, progressive, and often fatal disorders of the nervous system caused by dysfunction, and ultimately, death of neuronal cells. The underlying mechanisms of neurodegeneration are poorly understood, and monogenic disorders can be utilised as disease models to elucidate the pathogenesis. Juvenile neuronal ceroid-lipofuscinosis (JNCL, Batten disease) is a recessively inherited lysosomal storage disorder with progressive neurodegeneration and accumulation of autofluorescent storage material in most tissues. It is caused by mutations in the CLN3 gene; however, the exact function of the corresponding CLN3 protein, as well as the molecular mechanisms of JNCL pathogenesis have remained elusive. JNCL disease exclusively affects the central nervous system leaving other organs unaffected, and therefore it is of a particular importance to conduct studies in brain tissue and neuronal cells. The aim of this thesis project was to elucidate the molecular and cell biological mechanisms underlying JNCL. This was the first study to describe the endogenous Cln3 protein, and it was shown that Cln3 localised to neuronal cells in the mouse brain. At a subcellular level, endogenous Cln3 was localised to the presynaptic terminals and to the synaptosome compartment, but not to the synaptic vesicles. Studies with the CLN3-deficient cells demonstrated an impaired endocytic membrane trafficking, and established an interconnection between CLN3, microtubulus-binding Hook1 and Rab proteins. This novel data was not only important in characterising the roles of CLN3 in cells, but also provided significant information delineating the versatile role of the Rab proteins. To identify affected cellular pathways in JNCL, global gene expression profiling of the knock-out mouse Cln3-/- neurons was performed and systematically analysed; this revealed a slight dysfunction of the mitochondria, cytoskeletal abnormality in the microtubule plus-end, and an impaired recovery from depolarizing stimulus when specific N-type Ca2+ channels were inhibited, thus leading to a prolonged time of higher intracellular calcium. All these defective pathways are interrelated, and may together be sufficient to initiate the neurodegenerative process. Results of this thesis also suggest that in neuronal cells, CLN3 most likely functions at endocytic vesicles at the presynaptic terminal, potentially involved in the regulation of the calcium-mediated synaptic transmission.

Multi-state-choice automata model for expedient learning in random environment

A new automata model Mr,k, with a conceptually significant innovation in the form of multi-state alternatives at each instance, is proposed in this study. Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

Functional and cellular analysis of autoimmune regulator (AIRE) protein

Autoimmune diseases are a major health problem. Usually autoimmune disorders are multifactorial and their pathogenesis involves a combination of predisposing variations in the genome and other factors such as environmental triggers. APECED (autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy) is a rare, recessively inherited, autoimmune disease caused by mutations in a single gene. Patients with APECED suffer from several organ-specific autoimmune disorders, often affecting the endocrine glands. The defective gene, AIRE, codes for a transcriptional regulator. The AIRE (autoimmune regulator) protein controls the expression of hundreds of genes, representing a substantial subset of tissue-specific antigens which are presented to developing T cells in the thymus and has proven to be a key molecule in the establishment of immunological tolerance. However, the molecular mechanisms by which AIRE mediates its functions are still largely obscure. The aim of this thesis has been to elucidate the functions of AIRE by studying the molecular interactions it is involved in by utilizing different cultured cell models. A potential molecular mechanism for exceptional, dominant, inheritance of APECED in one family, carrying a glycine 228 to tryptophan (G228W) mutation, was described in this thesis. It was shown that the AIRE polypeptide with G228W mutation has a dominant negative effect by binding the wild type AIRE and inhibiting its transactivation capacity in vitro. The data also emphasizes the importance of homomultimerization of AIRE in vivo. Furthermore, two novel protein families interacting with AIRE were identified. The importin alpha molecules regulate the nuclear import of AIRE by binding to the nuclear localization signal of AIRE, delineated as a classical monopartite signal sequence. The interaction of AIRE with PIAS E3 SUMO ligases, indicates a link to the sumoylation pathway, which plays an important role in the regulation of nuclear architecture. It was shown that AIRE is not a target for SUMO modification but enhances the localization of SUMO1 and PIAS1 proteins to nuclear bodies. Additional support for the suggestion that AIRE would preferably up-regulate genes with tissue-specific expression pattern and down-regulate housekeeping genes was obtained from transactivation studies performed with two models: human insulin and cystatin B promoters. Furthermore, AIRE and PIAS activate the insulin promoter concurrently in a transactivation assay, indicating that their interaction is biologically relevant. Identification of novel interaction partners for AIRE provides us information about the molecular pathways involved in the establishment of immunological tolerance and deepens our understanding of the role played by AIRE not only in APECED but possibly also in several other autoimmune diseases.

A Team of Continuous-Action Learning Automata for Noise-Tolerant Learning of Half-Spaces

Learning automata are adaptive decision making devices that are found useful in a variety of machine learning and pattern recognition applications. Although most learning automata methods deal with the case of finitely many actions for the automaton, there are also models of continuous-action-set learning automata (CALA). A team of such CALA can be useful in stochastic optimization problems where one has access only to noise-corrupted values of the objective function. In this paper, we present a novel formulation for noise-tolerant learning of linear classifiers using a CALA team. We consider the general case of nonuniform noise, where the probability that the class label of an example is wrong may be a function of the feature vector of the example. The objective is to learn the underlying separating hyperplane given only such noisy examples. We present an algorithm employing a team of CALA and prove, under some conditions on the class conditional densities, that the algorithm achieves noise-tolerant learning as long as the probability of wrong label for any example is less than 0.5. We also present some empirical results to illustrate the effectiveness of the algorithm.

Uplink Power Control and Base Station Association in Multichannel Cellular Networks

A combined base station association and power control problem is studied for the uplink of multichannel multicell cellular networks, in which each channel is used by exactly one cell (i.e., base station). A distributed association and power update algorithm is proposed and shown to converge to a Nash equilibrium of a noncooperative game. We consider network models with discrete mobiles (yielding an atomic congestion game), as well as a continuum of mobiles (yielding a population game). We find that the equilibria need not be Pareto efficient, nor need they be system optimal. To address the lack of system optimality, we propose pricing mechanisms. It is shown that these mechanisms can be implemented in a distributed fashion.