Comparison of environmental and genetic factors for Parkinson's disease between Chinese and Caucasians

This review paper compares the differences in prevalence, and environmental and genetic risk factors for Parkinson's disease between Chinese and Caucasian subjects. Comparison of age-specific prevalence between Chinese people and Caucasians suggests that the prevalence is lower in the Chinese ( at least in the past), although the prevalence rate in China appears to be rising. Distinctions in environmental risk factors and genetic factors are discussed. The difference in prevalence may be due to distinctions in environmental and genetic risk factors as well as the complex interaction between these environmental and genetic factors, although discrepancies in methodology for prevalence surveys can also be an explanation. Copyright (C) 2004 S. Karger AG, Basel.

Genomic and proteomic analysis of synaptic protein expression in the brains of human alcoholics

Alcoholism results in changes in the human brain which reinforce the cycle of craving and dependency, and these changes are manifest in the pattern of expression of mRNA and proteins in key cells and brain areas. Long-term alcohol abuse also results in damage to selected regions of the cortex. We have used cDNA microarrays to show that less than 1% of mRNA transcripts differ signifi cantly between cases and controls in the susceptible area and that the expression profi le of a subset of these transcripts is suffi cient to distinguish alcohol abusers from controls. In addition, we have utilized a 2D gel proteomics based approach to determine the identity of proteins in the superior frontal cortex (SFC) of the human brain that show differential expression in controls and long term alcohol abusers. Overall, 182 proteins differed by the criterion of > 2-fold between case and control samples. Of these, 139 showed signifi cantly lower expression in alcoholics, 35 showed signifi cantly higher expression, and 8 were new or had disappeared. To date 63 proteins have been identifi ed. The expression of one family of proteins, the synucleins, has been further characterized using Real Time PCR and Western Blotting. The expression of alpha-synuclein mRNA was signifi cantly lower in the SFC of alcoholics compared with the same area in controls (P = 0.01) whereas no such difference in expression was found in the motor cortex. The expression of beta- and gamma- synuclein were not signifi cantly different between alcoholics and controls. In contrast, the pattern of alphasynuclein protein expression differs from that of the corresponding RNA transcript. Because of the key role of synaptic proteins in the pathogenesis of alcoholism, we are developing 2-D DIGE based techniques to quantify expression changes in synaptosomes prepared from the SFC of controls and alcoholics.

What determines the molecular composition of abnormal protein aggregates in neurodegenerative disease?

Abnormal protein aggregates, in the form of either extracellular plaques or intracellular inclusions, are an important pathological feature of the majority of neurodegenerative disorders. The major molecular constituents of these lesions, viz., beta-amyloid (Abeta), tau, and alpha-synuclein, have played a defining role in the diagnosis and classification of disease and in studies of pathogenesis. The molecular composition of a protein aggregate, however, is often complex and could be the direct or indirect consequence of a pathogenic gene mutation, be the result of cell degeneration, or reflect the acquisition of new substances by diffusion and molecular binding to existing proteins. This review examines the molecular composition of the major protein aggregates found in the neurodegenerative diseases including the Abeta and prion protein (PrP) plaques found in Alzheimer's disease (AD) and prion disease, respectively, and the cellular inclusions found in the tauopathies and synucleinopathies. The data suggest that the molecular constituents of a protein aggregate do not directly cause cell death but are largely the consequence of cell degeneration or are acquired during the disease process. These findings are discussed in relation to diagnosis and to studies of to disease pathogenesis.

Multiple system atrophy: laminar distribution of the pathological changes in frontal and temporal neocortex--a study in ten patients

OBJECTIVE: To determine the distribution of the pathological changes in the neocortex in multiple-system atrophy (MSA). METHOD: The vertical distribution of the abnormal neurons (neurons with enlarged or atrophic perikarya), surviving neurons, glial cytoplasmic inclusions (GCI) and neuronal cytoplasmic inclusions (NI) were studied in alpha-synuclein-stained material of frontal and temporal cortex in ten cases of MSA. RESULTS: Abnormal neurons exhibited two common patterns of distribution, viz., density was either maximal in the upper cortex or a bimodal distribution was present with a density peak in the upper and lower cortex. The NI were either located in the lower cortex or were more uniformly distributed down the cortical profile. The distribution of the GCI varied considerably between gyri and cases. The density of the glial cell nuclei was maximal in the lower cortex in the majority of gyri. In a number of gyri, there was a positive correlation between the vertical densities of the abnormal neurons, the total number of surviving neurons, and the glial cell nuclei. The vertical densities of the GCI were not correlated with those of the surviving neurons or glial cells but the GCI and NI were positively correlated in a small number of gyri. CONCLUSION: The data suggest that there is significant degeneration of the frontal and temporal lobes in MSA, the lower laminae being affected more significantly than the upper laminae. Cortical degeneration in MSA is likely to be secondary to pathological changes occurring within subcortical areas.

Dementia with Lewy bodies

Dementia with Lewy bodies (‘Lewy body dementia' or ‘diffuse Lewy body disease') (DLB) is the second commonest form of dementia after Alzheimer’s disease (AD). Characteristic of DLB are: (1) fluctuating cognitive ability with variations in attention and alertness, (2) recurrent visual hallucinations, and (3) motor features including akinesia, rigidity, and tremor. Various brain regions are affected in DLD including cortical and limbic regions. Histopathologically, alpha-synuclein-immunoreactive Lewy bodies (LB) are observed in the substantia nigra and in the cerebral cortex. DLB has affinities both with the parkinsonian syndromes including Parkinson’s disease (PD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA), and with AD, which can make differential diagnosis difficult. The presence of visual hallucinations may aid differential diagnosis of the parkinsononian syndromes and occipital hypometabolism may be a useful potential method of distinguishing DLB from AD. Treatment of CBD involves managing and reducing the effect of symptoms.

Modelo animal da Doença de Parkinson baseado na expressão de alfa - sinucleína: caracterização comportamental, eletrofisiológica e avaliação dos efei tos da estimulação da medula espinhal

Parkinson disease (PD) is associated with motor symptoms and dopaminergic cell loss in the nigrostriatal pathway. Alpha-synuclein is the major component of the Lewy bodies, the biological hallmarks of disease, and has been associated with familial cases of PD. Recently, the spinal cord stimulation (SCS) showed to be effective to alleviate the Parkinson symptoms in animal models and human patients. In this project, we characterized the motor and electrophysiological effects of alpha-synuclein overexpression in the substantia nigra of rats. We further investigated the effects of spinal electrical stimulation, AMPT and L-dopa administration in this model. Method: Sprague-Dawley rats were injected with empty viral vector or the vector carrying the gene for alpha-synuclein in the substantia nigra, and were tested weekly for 10 weeks in the open field and cylinder tests. A separated group of animals implanted with bilateral electrode arrays in the motor cortex and the striatum were recorded in the open field, during the SCS sessions and the pharmacological experiments. Results: Alpha-synuclein expression resulted in motor asymmetry, observed as the reduction in use of contralateral forepaw in the cylinder test. Animals showed an increase of local field potential activity in beta band three and four weeks after the virus injection, that was not evident after the 5th week. AMPT resulted in a sever parkinsonian state, with reduction in the locomotor activity and significant peak of oscillatory activity in cortex and striatum. SCS was effective to alleviate the motor asymmetry at long term, but did not reduce the corticostriatal low frequency oscillations observed 24 hs after the AMPT administration. These oscillations were attenuated by L-dopa that, even as SCS, was not effective to restore the locomotor activity during the severe dopaminergic depletion period. Discussion: The alpha-synuclein model reproduces the motor impairment and the progressive neurodegenerative process of PD. We demonstrated, by the first time, that this model also presents the increase in low frequency oscillatory activity in the corticostriatal circuit, compatible with parkinsonian condition; and that SCS has a therapeutic effect on motor symptom of this model.

The impact of Rab5 on the formation of glial cytoplasmic inclusion: implications for multiple system atrophy

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Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.