Proteomics of Alzheimer's disease: Unveiling protein dysregulation in complex neuronal systems

Alzheimer's disease (AD) and its progressive dementia is multifactorial, develops relatively sporadically, and involves multiple pathologies in the complex biological system of the brain. For these reasons, genetics alone is not likely to explain the molecular basis of this disease. Proteomics is contributing valuable information about the underlying molecular defects involved in AD development and progression, which includes oxidative damage to specific proteins, altered levels of synaptic components, and protein compositions of Aβ plaques and neurofibrillary tangles (pathological hallmarks of AD). However, emerging strategies in the field of proteomics which include more specific targeting of disease-related anatomical regions, targeting of specific subcellular compartments of functional relevance, novel approaches for large scale identification of regulatory post-translational modifications such as phosphorylation and O-GlcNAc, improved chromatographic separations of peptides for more comprehensive analysis of samples, and high-throughput quantitative strategies directly coupled with MS should greatly enhance the future of AD proteomics. The characterization of AD-specific alterations in proteomes should provide further insight into mechanisms of disease development and progression, provide biomarkers predicting disease development, and provide novel targets for therapeutic intervention.     

Redox proteomics studies of in vivo amyloid beta-peptide animal models of Alzheimer's disease: Insight into the role of oxidative stress

Alzheimer's disease (AD) is an age-related neurodegenerative disease. AD is characterized by the presence of senile plaques, neurofibrillary tangles, and synaptic loss. Amyloid β-peptide (Aβ), a component of senile plaques, has been proposed to play an important role in oxidative stress in AD brain and could be one of the key factors in the pathogenesis of AD. In the present review, we discuss some of the AD animal models that express Aβ, and compare the proteomics-identified oxidatively modified proteins between AD brain and those of Aβ models. Such a comparison would allow better understanding of the role of Aβ in AD pathogenesis thereby helping in developing potential therapeutics to treat or delay AD.     

卷积神经网络诊断阿尔兹海默症的方法

针对阿尔兹海默症（AD）通常会导致海马体区域萎缩的现象,提出一种使用卷积神经网络（CNN）对脑部磁共振成像（MRI）的海马体区域进行AD识别的方法。测试数据来自ADNI数据库提供的188位患者和229位正常人的脑部MRI图像。首先,将所有脑图像进行颅骨剥离,并配准到标准模板;其次,使用线性回归进行脑部萎缩的年龄矫正;然后,经过预处理后,从每个对象的3D脑图像的海马体区域提取出多幅2.5D的图像;最后,使用CNN对这些图像进行训练和识别,将同一个对象的图像识别结果用于对该对象的联合诊断。通过多次十折交叉验证方式进行实验,实验结果表明所提方法的平均识别准确率达到88.02%。与堆叠自动编码器（SAE）方法进行比较,比较结果表明,所提方法在仅使用MRI进行诊断的情况下效果比SAE方法有较大提高。     

Pilot proteomic analysis of cerebrospinal fluid in Alzheimer's disease

Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study.     

Lectin-affinity chromatography brain glycoproteomics and Alzheimer disease: insights into protein alterations consistent with the pathology and progression of this dementing disorder

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the accumulation of senile plaques and neurofibrillary tangles, and both these pathological hallmarks of AD are extensively modified by glycosylation. Mounting evidence shows that alterations in glycosylation patterns influence the pathogenesis and progression of AD, but the vast number of glycan motifs and potential glycosylation sites of glycoproteins has made the field of glycobiology difficult. However, the advent of glycoproteomics has produced major strides in glycoprotein identification and glycosylation site mapping. The use of lectins, proteins that have strong affinity for specific carbohydrate epitopes, to enrich glycoprotein fractions coupled with modern MS, have yielded techniques to elucidate the glycoproteome in AD. Proteomic studies have identified brain proteins in AD and arguably the earliest form of AD, mild cognitive impairment, with altered affinity for Concanavalin-A and wheat germ agglutinin lectins that are consistent with the pathology and progression of this disorder. This is a relatively nascent field of proteomics research in brain, so future studies of lectin-based brain protein separations may lead to additional insights into AD pathogenesis and progression.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Apolipoprotein A-I: Insights from redox proteomics for its role in neurodegeneration

Proteomics has a wide range of applications, including determination of differences in the proteome in terms of expression and post-translational protein modifications. Redox proteomics allows the identification of specific targets of protein oxidation in a biological sample. Using proteomic techniques, apolipoprotein A-I (ApoA-I) has been found at decreased levels in subjects with a variety of neurodegenerative disorders including in the serum and cerebrospinal fluid (CSF) of Alzheimer disease (AD), Parkinson disease (PD), and Down syndrome (DS) with gout subjects. ApoA-I plays roles in cholesterol transport and regulation of inflammation. Redox proteomics further showed ApoA-I to be highly oxidatively modified and particularly susceptible to modification by 4-hydroxy-2-trans-nonenal (HNE), a lipid peroxidation product. In the current review, we discuss the consequences of oxidation of ApoA-I in terms of neurodegeneration. ROS-associated chemotherapy related ApoA-I oxidation leads to elevation of peripheral levels of tumor necrosis factor-α (TNF-α) that can cross the blood-brain barrier (BBB) causing a signaling cascade that can contribute to neuronal death, likely a contributor to what patients refer to as “chemobrain.” Current evidence suggests ApoA-I to be a promising diagnostic marker as well as a potential target for therapeutic strategies in these neurodegenerative disorders.     

Identification and validation of novel CSF biomarkers for early stages of Alzheimer's disease

The pathology of Alzheimer's disease (AD) begins years prior to clinical diagnosis. The development of antecedent biomarkers that indicate the presence of AD pathology and predict risk for decline in both cognitively normal and mildly impaired individuals will be useful as effective therapies are developed. While cerebrospinal fluid (CSF) markers such as amyloid-β (Aβ) 42 and tau are useful, additional biomarkers are needed. To identify new markers, we utilized 2-D difference gel electrophoresis (2-D DIGE) of individual CSF samples from subjects with very mild AD versus controls after depletion of high-abundant proteins. Protein spots displaying differential abundance between the two groups were identified with MS. A number of candidate biomarkers were identified in 18 gel features. Selected candidates were quantified in a larger clinical set using ELISA. The mean levels of α1-antichymotrypsin (ACT), antithrombin III (ATIII), and zinc-α2-glycoprotein (ZAG) were significantly higher in the mild AD group, and the mean level of carnosinase 1 (CNDP1) was decreased. When these biomarkers are optimally combined, there is a strong trend toward greater specificity and sensitivity based on clinical diagnosis than when used individually. Our findings provide novel biomarker candidates for very mild and mild AD that can be further assessed as antecedent markers and predictors of clinical progression.     

Analysis of protein profiling studies of β‐thalassemia/Hb E disease

A number of studies have used global protein profiling technologies on a range of patient samples to detect proteins that are differentially expressed in β‐thalassemia/Hb E as an aid for understanding the physiopathology of this disease. Seven studies have identified a total of 111 unique, differentially expressed proteins. Seven proteins (prothrombin, alpha‐1‐antichymotrypsin, fibrinogen beta chain, hemoglobin beta, selenium‐binding protein, microtubule‐actin cross‐linking factor and adenomatous polyposis coli protein 2) have been identified in two independent studies, whereas two proteins (carbonic anhydrase 1 and peroxiredoxin‐2) have been identified in three independent studies. Both of these latter two proteins were consistently upregulated in the studies that identified them. Ontological analysis of all differentially regulated proteins identified “response to inorganic substances” as the most significant functional annotation cluster, which is consistent with iron overload being a major pathological consequence of this disease. Despite the range of samples investigated and the relatively small number of studies undertaken, a coherent picture of the mediators of the pathological consequences of β‐thalassemia/Hb E disease is starting to emerge.     

3DPCANet在阿尔茨海默症功能磁共振成像图像分类中的应用

阿尔茨海默症(AD)是一种起病隐匿的进行性神经退行性疾病,会使患者的大脑脑区结构发生改变.为辅助医生对AD患者的病情做出正确判断,提出了一种改进的三维主成分分析网络(3DPCANet)模型,并结合被试者全脑均值低频波动振幅(mALFF)图像来对AD进行分类.首先,对功能磁共振成像(fMRI)数据进行预处理,计算出全脑m...     

Investigation of an albumin‐enriched fraction of human serum and its albuminome

The removal of albumin and other high abundance proteins is a routine first step in the analysis of serum and plasma proteomes. However, as albumin can bind proteins and peptides, there is a universal concern as to how the serum proteome is changed by the removal of albumin. To address this concern, the current study was designed to identify proteins and peptides removed from the serum during albumin depletion; to determine which of these are bound to albumin (rather than copurified) and whether the bound proteins are intact proteins or peptide fragments. Sequential, independent analyses including both anti‐albumin antibody (anti‐HSA) affinity chromatography and SEC were used to isolate albumin‐bound proteins. RP‐HPLC and 1‐D SDS‐PAGE were then used to further separate the proteins prior to identification by MS/MS. Finally, whole protein molecular weight (MW) MS measurements coupled with protein coverage obtained by MS were combined to assess whether the bound proteins were intact or peptide fragments. Combining the results from multiple approaches, 35 proteins, of which 24 are intact, were found to be associated with albumin, and they include both known high and low abundance proteins.     

Characterization of proteomic changes in cardiac mitochondria in streptozotocin-diabetic rats using iTRAQ™ isobaric tags

Diabetes now affects more than 5% of the world's population and heart failure is the most common cause of death amongst diabetic patients. Accumulating evidence supports a view that myocardial mitochondrial structural and functional changes are central to the onset of diabetic heart failure, but the exact nature of these changes at the proteomic level remains unclear.Here we report on proteomic changes in diabetic rat heart mitochondria following 120 days of streptozotocin‐diabetes using the recently developed iTRAQ? labeling method, which permits quantification of proteins directly from complex mixtures, bypassing the limitations associated with gel‐based methods such as 2‐DE. Of 252 unique proteins identified, 144 were represented in at least three of six individual paired experiments. Relative amounts of 65 proteins differed significantly between the groups, confirming that the cardiac mitochondrial proteome is indeed impacted by diabetes. The most significant changes were increased protein levels of enzymes involved in mitochondrial oxidation of long‐chain fatty acids, which was also confirmed by enzyme assays, and decreased levels of multiple enzymes involved in oxidative phosphorylation and catabolism of short‐chain fatty acids and branched‐chain amino acids. We also found significant changes in levels of several enzymes linked to oxidative stress.     

A multi‐GPU algorithm for large‐scale neuronal networks

Large‐scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low‐cost solution for many high‐performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large‐scale networks composed of biologically realistic Hodgkin–Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad‐core CPU. Copyright © 2010 John Wiley & Sons, Ltd.     

Synaptic proteome changes in the superior frontal gyrus and occipital cortex of the alcoholic brain

Cognitive deficits and behavioral changes that result from chronic alcohol abuse are a consequence of neuropathological changes that alter signal transmission through the neural network. To focus on the changes that occur at the point of connection between the neural network cells, synaptosomal preparations from post‐mortem human brain of six chronic alcoholics and six non‐alcoholic controls were compared using 2‐D differential in‐gel electrophoresis (DIGE). Functionally affected and spared regions (superior frontal gyrus, SFG, and occipital cortex, OC, respectively) were analyzed from both groups to further investigate the specific pathological response that alcoholism has on the brain. Forty‐nine proteins were differentially regulated between the SFG of alcoholics and the SFG of controls and 94 proteins were regulated in the OC with an overlap of 23 proteins. Additionally, the SFG was compared to the OC within each group (alcoholics or controls) to identify region‐specific differences. A selection was identified by MALDI‐TOF mass spectrometry revealing proteins involved in vesicle transport, metabolism, folding and trafficking, and signal transduction, all of which have the potential to influence synaptic activity. A number of proteins identified in this study have been previously related to alcoholism; however, the focus on synaptic proteins has also uncovered novel alcoholism‐affected proteins. Further exploration of these proteins will illuminate the mechanisms altering synaptic plasticity, and thus neuronal signaling and response, in the alcoholic brain.     

Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information

The aim of the present study was to analyse the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD), one of the most frequent disorders among elderly population. For this pilot study, we recorded the MEGs with a 148-channel whole-head magnetometer in 20 patients with probable AD and 21 age-matched control subjects. Artefact-free epochs of 3392 samples were analysed with auto-mutual information (AMI). Average AMI decline rates were lower for the AD patients' recordings than for control subjects' ones. Statistically significant differences were found using a Student's t-test (p<0.01) in 144 channels. Mean AMI values were analysed with a receiver operating characteristic curve. Sensitivity, specificity and accuracy values of 75%, 90.5% and 82.9% were obtained. Our results show that AMI estimations of the magnetic brain activity are different in both groups, hence indicating an abnormal type of dynamics associated with AD. This study suggests that AMI might help medical doctors in the diagnosis of the disease.     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.     

Proteomics analysis of plasma for potential biomarkers in the diagnosis of Alzheimer's disease

The objective of this study was to search for biological markers associated with Alzheimer's disease (AD). Plasma specimens obtained from ten pathologically diagnosed AD patients and ten non-demented (ND) control subjects were analyzed by a combination of 2-DE and MS. This strategy allowed us to identify six plasma proteins (alpha-1-antitrypsin, vitamin D-binding protein, inter-alpha-trypsin inhibitor family heavy chain-related protein, apolipoprotein J precursor, cAMP-dependent protein kinase catalytic subunit alpha 1, and an orf) whose 2-DE spot densities were different between the AD and ND groups. Due to their involvements in AD amyloid plaque formation, the plasma concentrations of alpha-1-antitrypsin and apolipoprotein J were further validated using either ELISA or Western blot. The results revealed that the plasma levels of alpha-1-antitrypsin in AD were higher than those of controls, confirming the 2-DE findings. However, no difference in total apolipoprotein J concentration was observed between the AD and ND groups. Considering the difference in resolving power to differentially quantitate protein isoforms provided by 2-DE and Western blot, 2-DE analysis combined with MS protein identification offers distinctive advantages when a disease-related protein isoform-specific variance is investigated.     

Proteomic identification and early validation of complement 1 inhibitor and pigment epithelium-derived factor: Two novel biomarkers of Alzheimer's disease in human plasma

Emerging disease modifying therapeutic strategies for Alzheimer's disease (AD) have generated a critical need for biomarkers of early stage disease. Here, we describe the identification and assessment of a number of candidate biomarkers in patients with mild to moderate probable AD. Plasma from 47 probable Alzheimer's patients and 47 matched controls were analysed by proteomics to define a significant number of proteins whose expression appeared to be associated with AD. These were compared to a similar proteomic comparison of a mouse transgenic model of amyloidosis, which showed encouraging overlap with the human data. From these studies a prioritised list of 31 proteins were then analysed by immunoassay and/or functional assay in the same human cohort to verify the changes observed. Eight proteins continued to show significance by either immunoassay or functional assay in the human plasma and these were tested in a further set of 100 probable AD patients and 100 controls from the original cohort. From our data it appeared that two proteins, serpin F1 (pigment epithelium-derived factor) and complement C1 inhibitor are down-regulated in plasma from AD patients.     

A non‐smooth lower bound on ν

A non‐smooth optimization technique to directly compute a lower bound on the skew structured singular value ν is developed. As corroborated by several real‐world challenging applications, the proposed technique can provide tighter lower bounds when compared with currently available techniques. Moreover, in many cases, the determined lower bound equals the true value of ν. Thanks to the efficiency of the non‐smooth technique, the algorithm can be applied to problems involving even a significant number of uncertain parameters. Another appealing feature of the proposed non‐smooth approach is that the dimension of repeated scalar uncertainties in the overall structured uncertainty matrix has little impact on the computational time. The technique can be used to compute a lower bound on the structured singular value μ as well. Copyright © 2012 John Wiley & Sons, Ltd.