Huntington's disease cerebrospinal fluid seeds aggregation of mutant huntingtin.

Huntington's disease (HD), a progressive neurodegenerative disease, is caused by an expanded CAG triplet repeat producing a mutant huntingtin protein (mHTT) with a polyglutamine-repeat expansion. Onset of symptoms in mutant huntingtin gene-carrying individuals remains unpredictable. We report that synthetic polyglutamine oligomers and cerebrospinal fluid (CSF) from BACHD transgenic rats and from human HD subjects can seed mutant huntingtin aggregation in a cell model and its cell lysate. Our studies demonstrate that seeding requires the mutant huntingtin template and may reflect an underlying prion-like protein propagation mechanism. Light and cryo-electron microscopy show that synthetic seeds nucleate and enhance mutant huntingtin aggregation. This seeding assay distinguishes HD subjects from healthy and non-HD dementia controls without overlap (blinded samples). Ultimately, this seeding property in HD patient CSF may form the basis of a molecular biomarker assay to monitor HD and evaluate therapies that target mHTT.

A novel pool of protein phosphatase 2A is associated with microtubules and is regulated during the cell cycle.

Immunofluorescence microscopy revealed the presence of protein phosphatase 2A (PP2A) on microtubules in neuronal and nonneuronal cells. Interphase and mitotic spindle microtubules, as well as centrosomes, were all labeled with antibodies against individual PP2A subunits, showing that the AB alpha C holoenzyme is associated with microtubules. Biochemical analysis showed that PP2A could be reversibly bound to microtubules in vitro and that approximately 75% of the PP2A in cytosolic extracts could interact with microtubules. The activity of microtubule-associated PP2A was differentially regulated during the cell cycle. Enzymatic activity was high during S phase and intermediate during G1, while the activity in G2 and M was 20-fold lower than during S phase. The amount of microtubule-bound PP2A remained constant throughout the cell cycle, implying that cell cycle regulation of its enzymatic activity involves factors other than microtubules. These results raise the possibility that PP2A regulates cell cycle-dependent microtubule functions, such as karyokinesis and membrane transport.

Regulation of the phosphorylation state and microtubule-binding activity of Tau by protein phosphatase 2A.

Recently, we reported that a pool of protein phosphatase 2A (PP2A) is associated with microtubules. Here, we demonstrate that specific isoforms of PP2A bind and dephosphorylate the neuronal microtubule-associated protein tau. Coexpression of tau and SV40 small t, a specific inhibitor of PP2A, in CV-1, NIH 3T3, or NT2 cells induced the phosphorylation of tau at multiple sites, including Ser-199, Ser-202, Thr-205, Ser-396, and Ser-404. Immunofluorescent and biochemical analyses revealed that hyperphosphorylation correlated with dissociation of tau from microtubules and a loss of tau-induced microtubule stabilization. Taken together, these results support the hypothesis that PP2A controls the phosphorylation state of tau in vivo.

Simian virus 40 small t antigen cooperates with mitogen-activated kinases to stimulate AP-1 activity.

The simian virus 40 small tumor antigen (small t) specifically interacts with protein phosphatase type 2A (PP2A) in vivo and alters its catalytic activity in vitro. Among the substrates for PP2A in vitro are the activated forms of MEK and ERK kinases. Dephosphorylation of the activating phosphorylation sites on MEK and ERKs by PP2A in vitro results in a decrease in their respective kinase activities. Recently, it has been shown that overexpression of small t in CV-1 cells results in an inhibition of PP2A activity toward MEK and ERK2 and a constitutive upregulation of MEK and ERK2 activity. Previously, we have observed that overexpression of either ERK1, MEK1, or a constitutively active truncated form of c-Raf-1 (BXB) is insufficient to activate AP-1 in REF52 fibroblasts. We therefore examined whether overexpression of small t either alone or in conjunction with ERK1, MEK1, or BXB could activate AP-1. We found that coexpression of small t and either ERK1, MEK1, or BXB resulted in an increase in AP-1 activity, whereas expression of either small t or any of the kinases alone did not have any effect. Similarly, coexpression of small t and ERK1 activated serum response element-regulated promoters. Coexpression of kinase-deficient mutants of ERK1 and ERK2 inhibited the activation of AP-1 caused by expression of small t and either MEK1 or BXB. Coexpression of an interfering MEK, which inhibited AP-1 activation by small t and BXB, did not inhibit the activation of AP-1 caused by small t and ERK1. In contrast to REF52 cells, we observed that overexpression of either small or ERK1 alone in CV-1 cells was sufficient to stimulate AP-1 activity and that this stimulation was not enhanced by expression of small t and ERK1 together. These results show that the effects of small t on immediate-early gene expression depend on the cell type examined and suggest that the mitogen-activated protein kinase activation pathway is distinctly regulated in different cell types.

Protein phosphatase 2A: the Trojan Horse of cellular signaling.

Dynamic phosphorylation and dephosphorylation of proteins are fundamental mechanisms utilized by cells to transduce signals. Whereas transduction by protein kinases has been a major focus of studies in the last decade, protein phosphatase 2A (PP2A) enzymes emerge in this millenium as the most fashionable players in cellular signaling. Viral proteins target specific PP2A enzymes in order to deregulate chosen cellular pathways in the host and promote viral progeny. The observation that a variety of viruses utilize PP2A to alienate cellular behavior emphasizes the fundamental importance of PP2A in signal transduction. This review will primarily focus on discussing the uniqueness of PP2A regulation and uncovering the critical role played by protein-protein interactions in the modulation of PP2A signaling. Moreover, the place of PP2A in signaling pathways and its functional significance for human diseases will be discussed.

Cortical synapse loss in progressive supranuclear palsy.

Cortical synapse loss, the probable substrate of cognitive impairment in Alzheimer disease (AD), has not previously been evaluated in progressive supranuclear palsy (PSP). Hypothesizing that synapse loss would be greater in demented than non-demented PSP patients, we examined synaptophysin concentrations in 8 cases of PSP (5 demented and 3 nondemented cases). We found a decrease in mean synaptophysin concentration in these 8 cases in frontal, temporal, and parietal lobes, and in cerebellum, compared to the means in corresponding lobes of 16 controls. The decreases were similar to those in 28 cases of AD, but not as great. We determined synaptophysin concentration from motor cortex in only 4 of our PSP cases, 2 demented and 2 non-demented. The average concentrations in these 4 cases were lower than in AD motor cortex; both were lower than controls. When demented and non-demented PSP cases were compared, neocortical synaptophysin concentrations in non-demented PSP cases were lower than in demented cases. There appears to be a link between AD and PSP, in that synapse loss is found in both. However, the basis and significance of the prominent neocortical synapse loss in PSP, especially in non-demented subjects, remain to be explored.

Protein phosphatase 2A: a definite player in viral and parasitic regulation.

Cells use phosphorylation/dephosphorylation mechanisms to regulate the activity of several proteins required to transmit information from the cell surface to the nucleus. Recent studies have significantly increased our knowledge regarding the structure/function of one major regulator of cell phosphorylation: protein phosphatase 2A (PP2A). This review will discuss the role of PP2A in virology and parasitology.

Contribution of asymmetric synapse loss to lateralizing clinical deficits in frontotemporal dementias.

BACKGROUND: Synapse loss has been found to be the major correlate of cognitive decline in Alzheimer disease (AD), and prefrontal synapse loss has been found in patients with frontotemporal dementia (FTD). OBJECTIVE: To see if our hypothesis that within each FTD case, regional synapse loss would correlate with lateralizing neuropsychologic and neurobehavioral deficits would be correct. DESIGN: We analyzed synaptophysin as a marker for synapse loss in snap-frozen brain samples, using an enzyme-linked immunosorbent assay technique. Quantitative results were obtained by comparing patient data with a standard curve made by analyzing serial dilutions of a recombinant synaptophysin protein fragment. A board-certified neuropsychologist and a board-certified neurologist, both unaware of the synaptophysin results, determined areas of primary neuropsychologic and neurobehavioral dysfunction. Relationships between areas of primary dysfunction and synapse loss were analyzed using the binomial test. PATIENTS: Six cases of FTD, 28 cases of AD, and 16 nondemented control subjects. RESULTS: Five of 6 FTD cases had regional synaptophysins correlating with lateralizing frontal or temporal deficits. Three of 6 correlated in 2 or more regions. Although our results were higher than that expected based on a pure-chance mechanism (50% vs 34%), statistical significance was not attained. CONCLUSIONS: We found a trend for an association between synapse loss and lateralizing neuropsychologic and neurobehavioral deficits in FTD. Studies in larger numbers of FTD cases are planned with the goal of attaining statistically significant conclusions.

Feedback stabilization using two-hidden-layer nets.

The representational capabilities of one-hidden-layer and two-hidden-layer nets consisting of feedforward interconnections of linear threshold units are compared. It is remarked that for certain problems two hidden layers are required, contrary to what might be in principle expected from the known approximation theorems. The differences are not based on numerical accuracy or number of units needed, nor on capabilities for feature extraction, but rather on a much more basic classification into direct and inverse problems. The former correspond to the approximation of continuous functions, while the latter are concerned with approximating one-sided inverses of continuous functions, and are often encountered in the context of inverse kinematics determination or in control questions. A general result is given showing that nonlinear control systems can be stabilized using two hidden layers, but not, in general, using just one.

On the complexity of training neural networks with continuous activation functions.

Deals with computational issues of loading a fixed-architecture neural network with a set of positive and negative examples. This is the first result on the hardness of loading a simple three-node architecture which does not consist of the binary-threshold neurons, but rather utilizes a particular continuous activation function, commonly used in the neural-network literature. The authors observe that the loading problem is polynomial-time if the input dimension is constant. Otherwise, however, any possible learning algorithm based on particular fixed architectures faces severe computational barriers. Similar theorems have already been proved by Megiddo and by Blum and Rivest, to the case of binary-threshold networks only. The authors' theoretical results lend further suggestion to the use of incremental (architecture-changing) techniques for training networks rather than fixed architectures. Furthermore, they imply hardness of learnability in the probably approximately correct sense as well.

Remarks on feedforward circuits, adaptation, and pulse memory.

This note studies feedforward circuits as models for perfect adaptation to step signals in biological systems. A global convergence theorem is proved in a general framework, which includes examples from the literature as particular cases. A notable aspect of these circuits is that they do not adapt to pulse signals, because they display a memory phenomenon. Estimates are given of the magnitude of this effect.

Monotone bifurcation graphs.

In recent work by Angeli and the authors, it was shown that the stability and global behaviour of strongly monotone dynamical systems may be profitably studied using a technique that involves feedback decompositions into 'well-behaved' subsystems. The present paper generalizes the approach, so that it applies to a far larger class of systems. As an illustration, the techniques are used in the analysis of a nine-variable autoregulatory transcription network. Also, extensions to delay and reaction diffusion systems are considered.

Proximity of intracellular regulatory networks to monotone systems.

Networks that contain only sign-consistent loops, such as positive feedforward and feedback loops, function as monotone systems. Simulated using differential equations, monotone systems display well-ordered behaviour that excludes the possibility for chaotic dynamics. Perturbations of such systems have unambiguous global effects and a predictability characteristic that confers robustness and adaptability. The authors assess whether the topology of biological regulatory networks is similar to the topology of monotone systems. For this, three intracellular regulatory networks are analysed where links are specified for the directionality and the effects of interactions. These networks were assembled from functional studies in the experimental literature. It is found that the three biological networks contain far more positive 'sign-consistent' feedback and feedforward loops than negative loops. Negative loops can be 'eliminated' from the real networks by the removal of fewer links as compared with the corresponding shuffled networks. The abundance of positive feedforward and feedback loops in real networks emerges from the presence of hubs that are enriched with either negative or positive links. These observations suggest that intracellular regulatory networks are 'close-to-monotone', a characteristic that could contribute to the dynamical stability observed in cellular behaviour.

Regulation of cell adhesion by PP2A and SV40 small tumor antigen: an important link to cell transformation.

The serine/threonine protein phosphatase 2A (PP2A) represents a large family of highly conserved heterotrimeric enzymes. Their critical importance in cell homeostasis is underlined by the fact that they are targets of natural toxins like the tumor promoter okadaic acid, and of simian virus 40 small tumor antigen (SV40 small t), a viral protein known to promote cell transformation. Furthermore, mutated or lower expression levels of PP2A subunits have been found in certain cancers. One major known event in PP2A-dependent cell transformation is the alteration of key signaling pathways that control cell growth and survival. In this review, we focus on how PP2A enzymes also affect cell adhesion and cytoskeletal dynamics, the disruption of which is linked to loss of cell polarity, increased cell motility and invasiveness. We also examine how those various pathways participate in the transforming activity of SV40 small t.

Methods of robustness analysis for Boolean models of gene control networks.

As a discrete approach to genetic regulatory networks, Boolean models provide an essential qualitative description of the structure of interactions among genes and proteins. Boolean models generally assume only two possible states (expressed or not expressed) for each gene or protein in the network, as well as a high level of synchronisation among the various regulatory processes. Two possible methods of adapting qualitative models to incorporate the continuous-time character of regulatory networks, are discussed and compared. The first method consists of introducing asynchronous updates in the Boolean model. In the second method, the approach introduced by Glass is adopted to obtain a set of piecewise linear differential equations that continuously describe the states of each gene or protein in the network. Both methods are applied to a Boolean model of the segment polarity gene network of Drosophila melanogaster. The dynamics of the model is analysed, and a theoretical characterisation of the model's gene pattern prediction is provided as a function of the timescales of the various processes.

Parameter estimation in models combining signal transduction and metabolic pathways: the dependent input approach.

Biological complexity and limited quantitative measurements pose severe challenges to standard engineering methodologies for modelling and simulation of genes and gene products integrated in a functional network. In particular, parameter quantification is a bottleneck, and therefore parameter estimation, identifiability, and optimal experiment design are important research topics in systems biology. An approach is presented in which unmodelled dynamics are replaced by fictitious 'dependent inputs'. The dependent input approach is particularly useful in validation experiments, because it allows one to fit model parameters to experimental data generated by a reference cell type ('wild-type') and then test this model on data generated by a variation ('mutant'), so long as the mutations only affect the unmodelled dynamics that produce the dependent inputs. Another novel feature of the approach is in the inclusion of a priori information in a multi-objective identification criterion, making it possible to obtain estimates of parameter values and their variances from a relatively limited experimental data set. The pathways that control the nitrogen uptake fluxes in baker's yeast (Saccharomyces cerevisiae) have been studied. Well-defined perturbation experiments were performed on cells growing in steady-state. Time-series data of extracellular and intracellular metabolites were obtained, as well as mRNA levels. A nonlinear model was proposed and was shown to be structurally identifiable given data of its inputs and outputs. The identified model is a reliable representation of the metabolic system, as it could correctly describe the responses of mutant cells and different perturbations.

Some new directions in control theory inspired by systems biology.

This paper, addressed primarily to engineers and mathematicians with an interest in control theory, argues that entirely new theoretical problems arise naturally when addressing questions in the field of systems biology. Examples from the author's recent work are used to illustrate this point.

Neural systems as nonlinear filters.

Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are dynamic; their "weight" changes on a short timescale by several hundred percent in dependence of the past input to the synapse. In this article we address the question how this inherent synaptic dynamics (which should not be confused with long term learning) affects the computational power of a neural network. In particular, we analyze computations on temporal and spatiotemporal patterns, and we give a complete mathematical characterization of all filters that can be approximated by feedforward neural networks with dynamic synapses. It turns out that even with just a single hidden layer, such networks can approximate a very rich class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics. Our characterization result provides for all nonlinear filters that are approximable by Volterra series a new complexity hierarchy related to the cost of implementing such filters in neural systems.