The molecular biology of senile plaques and neurofibrillary tangles in Alzheimer’s disease

Since the earliest descriptions of the disease, senile plaques (SP) and neurofibrillary tangles (NFT) have been regarded as the pathological 'hallmarks' of Alzheimer's disease (AD). Whether or not SP and NFT are sufficient cause to explain the neurodegeneration of AD is controversial. The major molecular constituents of these lesions, viz., beta-amyloid (Ass) and tau, have played a defining role both in the diagnosis of the disease and in studies of pathogenesis. The molecular biology of SP and NFT, however, is complex with many chemical constituents. An individual constituent could be the residue of a pathogenic gene mutation, result from cellular degeneration, or reflect the acquisition of new proteins by diffusion and molecular binding. This review proposes that the molecular composition of SP and NFT is largely a consequence of cell degeneration and the later acquisition of proteins. Such a conclusion has implications both for the diagnosis of AD and in studies of disease pathogenesis.

Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Molecular biology and biochemistry of brain tumours

ßElucidating some molecular mechanisms and biochemistry of brain tumours is an important step towards the development of adjuvant medical therapies. The present study concentrates on cholecystokinin (CCK), a gut-brain peptide that has been described to be able to induce mitosis of rat gliomas as well as hormone secretion by the anterior pituitary, via the CCK-B receptor. The significance of a polymorphism in the growth hormone releasing hormone (GHRH) receptor (GHRH-R) gene was also determined. Finally, defects in the ß-catenin gene, an important component of the developmental pathway, in a sub-set of craniopharyngiomas were investigated. Reverse transcription-polymerase chain reaction (RT-PCR), restriction digestion analysis and direct sequencing demonstrated expression of CCK peptide itself and its A and B receptors by human gliomas, meningiomas and pituitary tumours. CCK peptides stimulated growth of cultured gliomas and meningiomas as well as in vitro hormone secretion [growth hormone (GH), luteinizing hormone (LH) and follicle stimulating hormone (FSH)] by human pituitary tumours. These biological effects were reduced or abolished by CCK antagonists. In addition, an antibody to CCK reduced mitosis by gliomas and meningiomas, and the same antibody inhibited hormone secretion by cultured human pituitary tumours. CCK peptides stimulated phosphatidylinositol (PI) hydrolysis, indicating coupling of the CCK receptors to phopsholipase C. Cyclic AMP was unaffected. In addition, caspase-3 activity was significantly and markedly increased, whilst proteasome activity was decreased. Taken together, these results may indicate an autocrine/paracrine role of CCK in the control of growth and/or functioning of gliomas, meningiomas and pituitary tumours. Primer induced restriction analysis (PIRA) of a rarer and alternative polymorphism in the GHRH-R receptor, in which Thr replaces Ala at codon 57, in human GH-secreting pituitary tumours was investigated. Whilst the rarer form correlated with an increased response of the pituitary cells to GHRH in vitro, allele distribution studies revealed that it is unlikely that the polymorphism contributes to increased risk of developing GH-secreting tumours and therefore acromegaly. Further findings of this study, using PCR and direct sequencing, were the demonstration of an association between b-catenin gene alterations and craniopharyngiomas of the adamantinomatous type. Since this gene product is involved with development, these results suggest that p-catenin mutations may contribute to the initiation and subsequent growth of congenital adamantinomatous craniopharyngiomas.

In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Large-scale production of secreted proteins in Pichia pastoris

The production of recombinant therapeutic proteins is an active area of research in drug development. These bio-therapeutic drugs target nearly 150 disease states and promise to bring better treatments to patients. However, if new bio-therapeutics are to be made more accessible and affordable, improvements in production performance and optimization of processes are necessary. A major challenge lies in controlling the effect of process conditions on production of intact functional proteins. To achieve this, improved tools are needed for bio-processing. For example, implementation of process modeling and high-throughput technologies can be used to achieve quality by design, leading to improvements in productivity. Commercially, the most sought after targets are secreted proteins due to the ease of handling in downstream procedures. This chapter outlines different approaches for production and optimization of secreted proteins in the host Pichia pastoris. © 2012 Springer Science+business Media, LLC.

Membrane protein production in the Yeast, S. cerevisiae

The first crystal structures of recombinant mammalian membrane proteins were solved in 2005 using protein that had been produced in yeast cells. One of these, the rabbit Ca2+-ATPase SERCA1a, was synthesized in Saccharomyces cerevisiae. All host systems have their specific advantages and disadvantages, but yeast has remained a consistently popular choice in the eukaryotic membrane protein field because it is quick, easy and cheap to culture, whilst being able to post-translationally process eukaryotic membrane proteins. Very recent structures of recombinant membrane proteins produced in S. cerevisiae include those of the Arabidopsis thaliana NRT1.1 nitrate transporter and the fungal plant pathogen lipid scramblase, TMEM16. This chapter provides an overview of the methodological approaches underpinning these successes.

In silico adjuvant design and validation

Adjuvants are substances that boost the protective immune response to vaccine antigens. The majority of known adjuvants have been identified through the use of empirical approaches. Our aim was to identify novel adjuvants with well-defined cellular and molecular mechanisms by combining a knowledge of immunoregulatory mechanisms with an in silico approach. CD4 + CD25 + FoxP3 + regulatory T cells (Tregs) inhibit the protective immune responses to vaccines by suppressing the activation of antigen presenting cells such as dendritic cells (DCs). In this chapter, we describe the identification and functional validation of small molecule antagonists to CCR4, a chemokine receptor expressed on Tregs. The CCR4 binds the chemokines CCL22 and CCL17 that are produced in large amounts by activated innate cells including DCs. In silico identified small molecule CCR4 antagonists inhibited the migration of Tregs both in vitro and in vivo and when combined with vaccine antigens, significantly enhanced protective immune responses in experimental models.

Complex temporal patterns in molecular dynamics:a direct measure of the phase-space exploration by the trajectory at macroscopic time scales

Computer simulated trajectories of bulk water molecules form complex spatiotemporal structures at the picosecond time scale. This intrinsic complexity, which underlies the formation of molecular structures at longer time scales, has been quantified using a measure of statistical complexity. The method estimates the information contained in the molecular trajectory by detecting and quantifying temporal patterns present in the simulated data (velocity time series). Two types of temporal patterns are found. The first, defined by the short-time correlations corresponding to the velocity autocorrelation decay times (â‰0.1â€ps), remains asymptotically stable for time intervals longer than several tens of nanoseconds. The second is caused by previously unknown longer-time correlations (found at longer than the nanoseconds time scales) leading to a value of statistical complexity that slowly increases with time. A direct measure based on the notion of statistical complexity that describes how the trajectory explores the phase space and independent from the particular molecular signal used as the observed time series is introduced. © 2008 The American Physical Society.

Origins of molecular neurobiology:The role of the physicists

The revolution in the foundations of physics at the beginning of the twentieth century suggested to several of its most prominent workers that biology was ripe for something similar. In consequence, a number of physicists moved into biology. They were highly influential in initiating a molecular biology in the 1950s. Two decades later it seemed to several of these migrants, and those they had influenced, that the major problems in molecular biology had been solved, and that it was time to move on to what seemed to them the final problem: the nervous system, consciousness, and the age-old mind-body problem. This paper reviews this "double migration" and shows how the hopes of the first generation of physicist-biologists were both realized and dashed. No new physical principles were discovered at work in the foundations of biology or neuroscience. On the other hand, the mind-set of those trained in physics proved immensely valuable in analyzing fundamental issues in both biology and neuroscience. It has been argued that the outcome of the molecular biology of the 1950s was a change in the concept of the gene from that of "a mysterious entity into that of a real molecular object" (Watson, 1965, p.6); the gates and channels which play such crucial roles in the functioning of nervous systems have been transformed in a similar way. Studies on highly simplified systems have also opened the prospect of finding the neural correlatives of numerous behaviors and neuropathologies. This increasing understanding at the molecular level is invaluable not only in devising rational therapies but also, by defining the material substrate of consciousness, in bringing the mind-body problem into sharper focus. Copyright © Taylor & Francis Inc.

Computational mechanics reveals nanosecond time correlations in molecular dynamics of liquid systems

Statistical complexity, a measure introduced in computational mechanics has been applied to MD simulated liquid water and other molecular systems. It has been found that statistical complexity does not converge in these systems but grows logarithmically without a limit. The coefficient of the growth has been introduced as a new molecular parameter which is invariant for a given liquid system. Using this new parameter extremely long time correlations in the system undetectable by traditional methods are elucidated. The existence of hundreds of picosecond and even nanosecond long correlations in bulk water has been demonstrated. © 2008 Elsevier B.V. All rights reserved.

The implementation of a design of experiments strategy to increase recombinant protein yields in yeast (review)

Biological processes are subject to the influence of numerous factors and their interactions, which may be non-linear in nature. In a recombinant protein production experiment, understanding the relative importance of these factors, and their influence on the yield and quality of the recombinant protein being produced, is an essential part of its optimisation. In many cases, implementing a design of experiments (DoE) approach has delivered this understanding. This chapter aims to provide the reader with useful pointers in applying a DoE strategy to improve the yields of recombinant yeast cultures.