Altered proteins of the anterior cingulate cortex white matter proteome in schizophrenia

Alterations in the circuitry between and within different brain regions including the anterior cingulate cortex (ACC) is implicated in the neuropathology of schizophrenia. The involvement of white matter in schizophrenia is becoming increasingly apparent with reports of structural, morphological and genetic alterations occurring in the disease. The 2‐DE was employed to reveal significantly altered proteins within the ACC white matter proteome in a schizophrenia cohort (n = 10) relative to controls (n = 10). From the 423 matched spots between the two groups, the levels of 32 protein spots were altered in the schizophrenia ACC white matter. Of these, 30 spots were identified using MS. The majority of the altered proteins in schizophrenia function in metabolism, the cytoskeleton, and the synapse. These proteomic data suggest that the brain circuitry involving the ACC white matter tracts is altered in schizophrenia, possibly caused by depleted glucose metabolism and altered structural components of this region.     

Identifying stage of Alzheimer disease using multiclass particle swarm optimisation technique

ABSTRACT

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Alzheimer Disease (AD) is a progressive neurodegenerative disorder that causes structural changes in patient’s brain. As such, it is essential to develop an algorithm for identifying the biomarkers of this disease stage. We developed a novel volumetric analysis of anatomical components of brain with multiclass particle swam optimisation technique (MPSO) approach to detect the stages of AD as potential biomarkers. To avoid image distortion bias correction is applied. We have used anatomical structures i.e. tissue and ventricle volume are used as criteria to categorise image features into four classes such as Alzheimer Mild cognitive decline, Alzheimer Moderate Cognitive decline and Alzheimer Severe Cognitive decline and healthy subject. This work was experimented with 30 AD and 10 normal cases. We observed that grey matter content was reduced from 4 to 20% of normal brain and volume of ventricle is increasing gradually from mild to severe cognitive decline. The statistical performance measures are calculated for proposed and existing work. The value shows that our empirical evaluation has superior diagnosis performance. We found that AD patient’s brain has reduced volume in grey matter and subsequently shrunk the volume of brain. The size of ventricle is also the major concern to predict the severity of AD disease. Therefore, the volumes of grey matter and ventricle size more discriminately classify the AD patient with severity from normal subject.     

Analysis of membrane microdomain-associated proteins in the insular cortex of post-mortem human brain

Membrane microdomains (MM) are membrane rafts within the cell membrane enriched in cholesterol and glycosphingolipids that have been implicated in the trafficking and sorting of membrane proteins, secretory and endocytotic pathways, and signal transduction. To date, MM have not been characterised in the human brain. We reason that by identifying MM in the normal human cortex, we may better understand the molecular mechanisms of human brain dysfunction. To characterize the protein composition of MM in the human brain, we have carried out a comprehensive proteomic analysis of detergent resistant membranes (DRMs) associated proteins derived from human postmortem insular cortex using 1-DE separation prior to LC coupled to MS/MS or GeLC-MS/MS. Eighty five proteins were identified including 57 unique to human brain cortex DRMs (by comparison with DRM proteins reported in other cell types). High levels of signal transduction, cell adhesion, cell transport and cell trafficking proteins were identified including synaptic proteins such as synapsin II and synaptic vesicle membrane protein, mitochondrial proteins such as ATPase subunits and metabolic enzymes such as malate dehydrogenase. This data will facilitate our understanding of protein expression changes within membranes in candidate brain regions in human brain diseases such as schizophrenia, bipolar disorder and other psychiatric and neurodegenerative disorders.     

White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions.     

Abnormal pathways in the genu of the corpus callosum in schizophrenia pathogenesis: a proteome study

Abnormalities within the corpus callosum (CC) have been identified in schizophrenia brains and are thought to affect inter-hemispheric communication, which in-turn is postulated to underlie some schizophrenia symptoms. Furthermore, hemisphere asymmetry of fractional anisotropy, detected by diffusion tensor imaging, left-higher-than-right- has been observed in normal individuals in the CC genu. This asymmetry is significantly reduced in the left CC genu of first-episode and chronic schizophrenia subjects. We examined the protein expression profile of the CC genu, including the profiles from the left and right hemisphere, in schizophrenia brains compared to controls using two-dimensional gel electrophoresis and mass spectrometry techniques. Proteins involved in cytoskeletal structure and function, neuroprotective function and energy metabolism were identified as differentially expressed, suggesting these proteins may underlie abnormal CC genu structure and function. Proteins in these functional categories also displayed different expression levels in the left CC genu compared to the right in both control and schizophrenia brains and therefore may be involved in normal CC asymmetry and reduced asymmetry in schizophrenia individuals. This initial pool of protein candidates and abnormal functional pathways opens up avenues for further investigation of molecular mechanisms involving the CC in schizophrenia pathogenesis and symptoms.     

Multiscale Segmentation of Three-Dimensional MR Brain Images

Segmentation of MR brain images using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A multiscale method to MRI brain segmentation is presented which uses both edge and intensity information. First a multiscale representation of an image is created, which can be made edge dependent to favor intra-tissue diffusion over inter-tissue diffusion. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of objects based on intensity. It is shown that both an improvement in accuracy and a reduction in image post-processing can be achieved if edge dependent diffusion is used instead of linear diffusion. The combination of edge dependent diffusion and intensity based linking facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. To segment the total brain (white matter plus grey matter) morphological operations are applied to remove small bridges between the brain and cranium. If the total brain is segmented, grey matter, white matter and cerebrospinal fluid can be segmented by joining a small number of segments. Using a supervised segmentation technique and MRI simulations of a brain phantom for validation it is shown that the errors are in the order of or smaller than reported in literature.     

Computer-assisted categorization of brain computerized tomography pixels into cerebrospinal fluid, white matter, and gray matter

A computer-assisted method was employed to estimate the amounts of cerebrospinal fluid (CSF), white matter, and gray matter in individual computerized tomography (CT) scans of brains. By means of an image processing procedure (DMORPH), the means +/- SD CT numbers of "pure" CSF, white matter, and gray matter were determined in each scan and stored. A CATSEG program used these means to define ranges for CT numbers for each of the three tissues on each scan, and to assign each pixel in a scan to one of the three categories. Summing over seven serial scans provided volumetric estimates of CSF, white matter, and gray matter in a brain segment. For 10 subjects aged 21 to 43 years, CSF volume equaled 1.4 to 4.7% of the total segment volume, white matter equaled 37.5 to 48.2%, and gray matter equaled 50.2 to 58.9%. Image processing hardware and software which allow standardized sampling from CT images for the evaluation of surface areas and CT numbers are described. These procedures, as applied to CT scans of the human brain, can be used to estimate the volumes of CSF, white matter, and gray matter in a selected intracranial segment.     

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.     

三维脑白质纤维束成像方法?

从Riemannian流形的角度分析扩散张量成像,将脑白质中任意两点间的纤维束生成问题转化为计算Riemannian流形中两点间测地线的问题,通过Level-Set方法计算测地线,并将其作为脑白质中两点间的纤维束。利用模拟脑白质纤维束对该算法和传统算法进行比较,实验结果表明,该算法在准确性、鲁棒性等方面有较大改进。     

Relationship between tau,neuroinflammation and atrophy in Alzheimer's disease: The NIMROD study

In addition to beta-amyloid accumulation, misfolded tau and activated microglia are also present in Alzheimer's disease (AD). It is important to study the relationship amongst these pathologies in vivo and their effects on the cognitive deficits for developing effective trails and future therapeutic or preventive strategies for AD. To investigate the relationships amongst different pathologies in AD, in particular how they interact resulting in cognitive impairments, we conducted a study of sixty-six subjects (15 AD, 24 Mild Cognitive Impairment (MCI) and 27 similarly aged healthy controls), who underwent standardised clinical and neuropsychological assessments followed by dynamic PET using [¹⁸F]AV1451 (tau) and [¹¹C]PK11195 (activated microglia) and multimodal 3T MRI. MCI patients also underwent [¹¹C]PIB (beta-amyloid) PET. We compared regional PET binding and grey matter atrophy amongst AD, amyloid positive MCI and controls, as well as their spatial distribution across different brain areas. We also applied a mediation analysis to infer the direct and indirect effects of tau, neuroinflammation and grey matter atrophy on cognitive functioning. We found increased [¹⁸F]AV1451 and [¹¹C]PK11195 binding as well as grey matter atrophy in AD, with a strong spatial overlap amongst these AD related biomarkers suggesting them interacting with each other. We demonstrated that both tau ([¹⁸F]AV1451) and neuroinflammation ([¹¹C]PK11195) have significant effects on cognition however their effects were fully mediated by grey matter atrophy. No mediation effect between tau and neuroinflammation were found with respect to cognition. In conclusion, grey matter atrophy not only spatially overlapped with tau and microglia activity in AD, but also mediate them in affecting cognitive impairments. The mediation analysis enabled data fusion across multiple imaging modalities (PET and MRI) and multiple PET tracers. Our results have significant implications for trials targeting tau and inflammation, and future therapeutic or preventive strategies for AD.     

Subacute proteome changes following traumatic injury of the developing brain: Implications for a dysregulation of neuronal migration and neurite arborization

Traumatic brain injury (TBI) is a major cause of morbidity and mortality among children and adolescents. To gain insight into developmental events influenced by TBI, we analyzed subacute mouse brain proteome changes in a percussion head trauma model at P7 ipsi- and contralateral to the site of injury. The comparison of brain proteomes of trauma mice and controls revealed reproducible changes in the intensity of 28 proteins (30 protein spots) in response to trauma. The changes detected suggest that TBI leads to apoptosis, inflammation, and oxidative stress. These changes were consistent with our results of histological and biochemical evaluation of the brains which revealed widespread apoptotic neurodegeneration, microglia activation, and increased levels of protein carbonyls. Furthermore, we detected changes in proteins involved in neuronal migration as well as axonal and dendritic growth and guidance, suggesting interference of trauma with these developmental events.     

Systematic investigation of lycopene effects in LNCaP cells by use of novel large‐scale proteomic analysis software

Lycopene, the red pigment of tomatoes, is a carotenoid with potent antioxidant properties. Although lycopene may function as a prostate cancer chemoprevention agent, little is known about its effects at the cellular level. To define general changes induced by treatment of cells with lycopene, and to gain insights into the possible chemoprevention properties of lycopene, we investigated changes in protein expression after lycopene treatment in human LNCaP cells. The high throughput proteomics data were then visualized and analyzed by novel biological protein pathway modeling software. Differentially expressed proteins were identified, and the data were analyzed by protein pathway simulation software, without the need for specialized programming, by importing pathway models from a number of sources or by creating our own. One notable outcome was the identification of a group of upregulated proteins involved in detoxification of reactive oxygen species. This finding suggests that a possible mechanism of lycopene chemoprevention is the stimulation of detoxification enzymes associated with the antioxidant response element. Novel biological pathway modeling software enhances analysis of large proteomics data. When applied to the analysis of proteins differentially expressed in prostate cancer cells upon treatment with lycopene, the up‐regulation of detoxification enzymes was identified.     

An efficient multi class Alzheimer detection using hybrid equilibrium optimizer with capsule auto encoder

Alzheimer is an advanced nervous brain disease. In old aged people, Alzheimer is also causing the death. The earlier prediction of Alzheimer’s disease (AD) helps to proper treatment and protects from brain tissue damages. In earlier works, different machine learning techniques are presented and the techniques are lacks in the detection performance. This work presented an innovative methodology for the Alzheimer detection in brain image. Initially, an input image is pre-processed by the skull stripping, and normalized linear smoothing and median joint (NLSMJ) filtering. In the next stage, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) brain regions are segmented from the filtered images using adaptive fuzzy based atom search optimizer which is the high convergence rate optimizer for enhancing the segmentation performance. After the image segmentation, GM is registered with the filtered images using the improved affine transformation. Subsequently, features are extracted utilizing improved Zernike features and hybrid wavelet walsh features. Afterwards, features are selected utilizing adaptive rain optimization. Finally, hybrid equilibrium optimizer with capsule auto encoder (HEOCAE) framework is utilized for the detection of Alzheimer, normal and mild cognitive impairment images. The implementation platform used in this work is MATLAB. The presented technique is tested with the ADNI dataset images. The experimental results of the presented technique provide improved performance than the existing techniques in regards of accuracy (98.21%), sensitivity (97.31%), specificity (98.64%), precision (97.45%), NPV (0.098), F1 measure (97.37%) and AUC score (98.29%).

     

Proteomic expression analysis and comparison of protein and mRNA expression profiles in human malignant gliomas

Gliomas are highly heterogeneous and therapy resistant tumors with a poor prognosis. Novel experimental therapeutic approaches have shown some promising results, but often target specific molecular mechanisms or antigens, and careful characterization of the molecular subgroup of the tumors will therefore likely be important. Thorough investigations of gene and protein alterations are also important to better understand the tumorigenic mechanisms. We have undertaken a proteomic approach, using 2-D DIGE and LC-MS/MS protein identification, to investigate 38 human gliomas and normal brains. We show that the proteome profile can discriminate between normal brain and tumors, and between tumors of varying grade by a supervised classifier. Furthermore, an analysis of the identified proteins shows an enrichment of proteins associated to pathways known to be central in gliomas, such as MEK/Erk signaling and actin cytoskeleton. It also shows a shift between different glial fibrillary acidic protein (GFAP) representatives in different grades. In a previous study the gene expression profile was characterized in an almost identical set of tumors, which enabled a paired analysis of the gene and protein expression profiles. We show that there is often a weak correlation between the mRNA and protein level. This, together with the ability of proteomics to identify PTMs, emphasizes the benefit of characterization on a protein level.     

Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker

The aim of this study was to characterize the proteome of normal and malignant colonic tissue. We previously studied the colon proteome using 2‐DE and MALDI‐MS and identified 734 proteins (Roeßler, M., Rollinger, W., Palme S., Hagmann, M.‐L., et al.., Clin. Cancer Res. 2005, 11, 6550–6557). Here we report the identification of additional colon proteins from the same set of tissue samples using a complementary nano‐flow 2‐D‐LC‐ESI‐MS. In total, 484 proteins were identified in colon. Of these, 252 had also been identified by the 2‐DE/MALDI‐MS approach, whereas 232 proteins were unique to the 2‐D‐LC‐ESI‐MS analysis. Comparing protein expression in neoplastic and normal colon tissue indicated elevated expression of several proteins in colorectal cancer, among them the well established tumor marker carcinoembryonic antigen, as well as calnexin, 40S ribosomal protein S15a, serpin H1, and S100A12. Overexpression of these proteins was confirmed by immunoblotting. Serum levels of S100A12 were determined by ELISA and were found to be strongly elevated in colorectal cancer patients compared to healthy individuals. We conclude, that 2‐D‐LC‐ESI‐MS is a powerful approach to identify and compare protein profiles of tissue samples, that it is complementary to 2‐DE/MALDI‐MS approaches and has the potential to identify novel biomarkers.     

A non-local fuzzy segmentation method: Application to brain MRI

The Fuzzy C-Means (FCM) algorithm is a widely used and flexible approach to automated image segmentation, especially in the field of brain tissue segmentation from 3D MRI, where it addresses the problem of partial volume effects. In order to improve its robustness to classical image deterioration, namely noise and bias field artifacts, which arise in the MRI acquisition process, we propose to integrate into the FCM segmentation methodology concepts inspired by the non-local (NL) framework, initially defined and considered in the context of image restoration. The key algorithmic contributions of this article are the definition of an NL data term and an NL regularisation term to efficiently handle intensity inhomogeneities and noise in the data. The resulting new energy formulation is then built into an NL-FCM brain tissue segmentation algorithm. Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms.     

Proteomic analysis in cerebrospinal fluid of patients with atypical nonketotic hyperglycinemia and pulmonary hypertension - A pilot study

A variant phenotype of nonketotic hyperglycinemia has been described by our group associated with pulmonary hypertension. The aim of this study is to investigate the cerebrospinal fluid proteomes to get an insight into this neurodegenerative process producing leukoencephalopathy with white matter spongiform degeneration. DIGE and MALDI-TOF-TOF analyses were performed to carry out the proteomic study of four patients against three normal controls and one additional control of a classical nonketotic hyperglycinemia. The differential proteomic analysis showed a displacement of some series of spots toward the acidic side. The shifted proteins showed a high degree of carbonylation and increased methionine sulfoxidation was found in cystatin C and in vitamin-D-binding protein. These findings in addition to the increase of serum malondialdehyde concentration provide evidence of an oxidative stress in the patients under study, which is probably systemic rather than mainly confined to the CNS. The similarities of our findings with those found in other neurodegenerative diseases suggest that oxidative damage is commonly involved in these pathologies. DIGE technology improves the 2-D PAGE differential analysis and it is suitable in proteomic studies with a small number of cases.     

3D Graph cut with new edge weights for cerebral white matter segmentation

Accurate and efficient automatic or semi-automatic brain image segmentation methods are of great interest to both scientific and clinical researchers of the human central neural system. Cerebral white matter segmentation in brain Magnetic Resonance Imaging (MRI) data becomes a challenging problem due to a combination of several factors like low contrast, presence of noise and imaging artifacts, partial volume effects, intrinsic tissue variation due to neurodevelopment and neuropathologies, and the highly convoluted geometry of the cortex. In this paper, we propose a new set of edge weights for the traditional graph cut algorithm (Boykov and Jolly, 2001) to correctly segment the cerebral white matter from T1-weighted MRI sequence. In this algorithm, the edge weights of Boykov and Jolly (2001) are modified by comparing the probabilities of an individual voxel and its neighboring voxels to belong to different segmentation classes. A shape prior in form of a series of ellipses is used next to model the contours of the human skull in various 2D slices in the sequence. This shape constraint is imposed to prune the original graph constructed from the input to form a subgraph consisting of voxels within the skull contours. Our graph cut algorithm with new set of edge weights is applied to the above subgraph, thereby increasing the segmentation accuracy as well as decreasing the computation time. Average segmentation errors for the proposed algorithm, the graph cut algorithm (Boykov and Jolly, 2001), and the Expectation Maximization Segmentation (EMS) algorithm Van Leemput et al., 2001 in terms of Dice coefficients are found to be (3.72 ± 1.12)%, (14.88 ± 1.69)%, and (11.95 ± 5.2)%, respectively.     

Candidate antigens specifically detected by cerebrospinal fluid-IgG in oligoclonal IgG bands-positive multiple sclerosis patients

The aim of the present study was to detect antigenic proteins that react specifically with cerebrospinal fluid (CSF)-IgG from oligoclonal IgG bands (OB)-positive multiple sclerosis (MS) patients. To identify such antigenic proteins, we developed a rat brain proteome map using 2-DE and applied it to the immunoscreening of brain proteins that react with CSF-IgG but not with serum-IgG in OB-positive MS patients. After sequential MALDI-TOF mass spectrometry, eight proteins [two neuronal proteins (tubulin β-2 and γ enolase-2), HSP-1, Tpi-1 protein and cellular enzymes (creatine kinase, phosphopyruvate hydratase, triosephosphate isomerase and phosphoglycerate kinase-1)] were identified as candidate antigens in seven MS patients. Reactivity to tubulin was seen in Western blotting in four patients, and CSF-specific anti-tubulin IgG was detected in one patient. In addition, CSF-specific anti-gamma enolase IgG was found in another patient. These findings suggest that intrathecal immune responses may occur against a broad range of proteins in MS.     

Redox proteomics identification of 4‐hydroxynonenal‐modified brain proteins in Alzheimer's disease: Role of lipid peroxidation in Alzheimer's disease pathogenesis

Numerous studies have shown that neuronal lipids are highly susceptible to oxidative stress including in those brain areas directly involved in the neurodegenerative process of Alzheimer's disease (AD). Lipid peroxidation directly damages membranes and also generates a number of secondary biologically active products (toxic aldehydes)that are capable of easily attacking lipids, proteins, and DNA. Accumulating evidence has demonstrated regionally increased brain lipid peroxidation in patients with AD; however, extensive studies on specific targets of lipid peroxidation‐induced damage are still missing. The present study represents a further step in understanding the relationship between oxidative modification of protein and neuronal death associated with AD. We used a proteomics approach to determine specific targets of lipid peroxidation in AD brain, both in hippocampus and inferior parietal lobule, by coupling immunochemical detection of 4‐hydroxynonenal‐bound proteins with 2‐D polyacrylamide gel electrophoresis and MS analysis. We identified 4‐hydroxynonenal‐bound proteins in the hippocampus and inferior parietal lobule brain regions of subjects with AD. The identified proteins play different biological functions including energy metabolism, antioxidant system, and structural proteins, thus impairing multiple molecular pathways. Our results provide further evidence for the role of lipid peroxidation in the pathogenesis of AD.