Current Alzheimer Research (v.13, #7)

Meet Our Editorial Board Member by Rosanna Squitti (727-727).

Traumatic Brain Injury as a Risk Factor for Alzheimer's Disease: Is Inflammatory Signaling a Key Player? by Jelena Djordjevic, Mohammad Golam Sabbir, Benedict C. Albensi (730-738).
Traumatic brain injury (TBI) has become a significant medical and social concern within the last 30 years. TBI has acute devastating effects, and in many cases, seems to initiate long-term neurodegeneration. With advances in medical technology, many people are now surviving severe brain injuries and their long term consequences. Post trauma effects include communication problems, sensory deficits, emotional and behavioral problems, physical complications and pain, increased suicide risk, dementia, and an increased risk for chronic CNS diseases, such as Alzheimer's disease (AD).
In this review, we provide an introduction to TBI and hypothesize how it may lead to neurodegenerative disease in general and AD in particular. In addition, we discuss the evidence that supports the hypothesis that TBI may lead to AD. In particular, we focus on inflammatory responses as key processes in TBI-induced secondary injury, with emphasis on nuclear factor kappa B (NF-?B) signaling.

Peripheral Immune Signatures in Alzheimer Disease by David Goldeck, Jacek M. Witkowski, Tamas Fülop, Graham Pawelec (739-749).
According to the current paradigm, the main cause of AD is the accumulation of neurotoxic amyloid beta (A?) peptide aggregates resulting from the cleavage of the amyloid precursor protein into peptides of different length, with the 42 amino acid long A?42 being the most toxic form. A? can aggregate and form plaques in the brain. It further promotes the hyperphosphorylation of the tau protein which forms characteristic neurofibrillary tangles and thereby loses its important role in axonal transport and contributes to neurodegeneration. Therefore, treatments have targeted A?, but clinical trials of immunotherapies caused severe side effects and showed that A? clearance alone did not result in any cognitive improvement. This leads to the question: what else promotes AD pathology? Here, we review data on systemic inflammation and the possible roles that the immune system might play in AD. Microglia and astrocytes are activated and secrete inflammatory cytokines and chemokines. Via a disturbed blood-brain barrier, peripheral immune cells are activated and recruited towards inflamed brain lesions and amyloid plaques, but due to the chronic nature of the amyloid burden and their reduced function, these cells are not able to control inflammation and the associated detrimental immune responses. In addition, age-related inflammation and chronic infection with herpes viruses might contribute to the systemic inflammation and exacerbate attempts to restore the balance of inflammation.

Alzheimer Disease (AD) is the most common primary cause of dementia with a burgeoning epidemic as life expectancy and general medical care improve worldwide. Recent data from pathologic studies has shown that the cooccurrence of other neurodegenerative and vascular pathologies is in fact the rule rather than the exception. In late onset AD, cerebral small vessel disease (SVD) is almost invariably co-existent to a greater or lesser extent and is known to promote cognitive deterioration. Previous observational studies and clinical trials have largely sought to divide dementia based on predominant neurodegenerative or vascular mechanisms. Given the high degree of overlap, findings from such studies may be difficult to interpret and apply to population cohorts. Additionally opportunities may be lost for uncovering novel interventions that target interactions between co-existent vascular and neurodegenerative pathologies. In the current review, we consider potential pathophysiologic mechanisms through which SVD may be associated with and promote AD pathology. In particular we explore shared environmental and genetic associations and how these may converge via neuroinflammatory pathways potentially providing novel therapeutic targets. SVD has heterogenous manifestations on cerebral imaging and at pathology. We discuss how studying SVD topography may enable us to better identify those at risk for more rapid cognitive decline and improve future clinical trial design.

Urinary Metabolomics Reveals Alterations of Aromatic Amino Acid Metabolism of Alzheimer's Disease in the Transgenic CRND8 Mice by Zhi Tang, Liangfeng Liu, Yongle Li, Jiyang Dong, Min Li, Jiandong Huang, Shuhai Lin, Zongwei Cai (764-776).
Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with amyloid plaques accumulation as the key feature involved in its pathology. To date, however, the biochemical changes in AD have not been clearly characterized. Here, we present that urinary metabolomics based on high resolution mass spectrometry was employed for delineation of metabolic alterations in transgenic CRND8 mice. In this noninvasive approach, urinary metabolome reveals the biochemical changes in early onset of this AD mouse model. In virtue of comprehensive metabolite profiling and multivariate statistical analysis, a total of 73 differential metabolites of urine sample sets was identified in 12-week and 18-week transgenic mice compared to wild-type littermates, covering perturbations of aromatic amino acid metabolism, the Krebs cycle and one-carbon metabolism. Of particular interest is that divergent tryptophan metabolism, such as upregulation of serotonin pathway while downregulation of kynurenine pathway, was observed. Meanwhile, the accumulation of both N-acetylvanilalanine and 3-methoxytyrosine indicated aromatic L-amino acid decarboxylase deficiency. And the microbial metabolites derived from aromatic amino acid metabolism and drug-like phase II metabolic response via the glycine conjugation reactions were also highlighted, indicating that genetic modification in mouse brain not only alters genotype but also perturbs the gut microbiome. Together, our study demonstrated that the integrative approach employing mass spectrometry-based metabolomics and a transgenic mouse model for AD may provide new evidence for distinct metabolic signatures

Calcium homeostasis is an essential physiological process requiring tight control in the normal cell. The dysregulation of calcium homeostasis may play a key role in the onset of Alzheimer's disease (AD) and other disorders, whether through the loss of calcium binding or calcium sensing capacity. Calbindin D28k (CB-D28k), a calcium binding protein composed of six EF-hands, four of which can bind Ca2+, has been implicated in AD-related calcium dysregulation. In this study, docking and molecular dynamics calculations were employed to refine the protein data base model in order to understand the underlying structural variations between functional and non-functional EF-hands. Molecular modeling calculations improved the modelled protein structure: helix-loop-helix sequences were formed in all hands and most canonical interactions were formed in the four functional hands. The protein can also bind Zn2+, potentially altering the Ca2+ binding capability. Analysis of calculated structures of Zn2+ bound protein showed that only half of the correct EF-hand canonical interactions of Ca2+ were formed with loop residues. These results have important implications for the understanding of calcium dysregulation as well as for the development of novel therapeutic strategies in AD and other central nervous system disease processes, or in conditions of brain injury where calcium homeostasis is compromised.

High Content, Multi-Parameter Analyses in Buccal Cells to Identify Alzheimer's Disease by Maxime François, Michael F. Fenech, Philip Thomas, Maryam Hor, Alan Rembach, Ralph N. Martins, Stephanie R. Rainey-Smith, Colin L. Masters, David Ames, Christopher C. Rowe, S. Lance Macaulay, Andrew F. Hill, Wayne R. Leifert, The Australian Imaging, Biomarkers and Lifestyle Study Research Group (787-799).
Alzheimer's disease (AD) is a degenerative brain disorder and is the most common form of dementia. Minimally invasive approaches are required that combine biomarkers to identify individuals who are at risk of developing mild cognitive impairment (MCI) and AD, to appropriately target clinical trials for therapeutic discovery as well as lifestyle strategies aimed at prevention. Buccal mucosa cells from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing cohort (n=60) were investigated for cytological markers that could be used to identify both MCI and AD individuals. Visual scoring of the buccal cytome demonstrated a significantly lower frequency of basal and karyorrhectic cells in the MCI group compared with controls. A high content, automated assay was developed using laser scanning cytometry to simultaneously measure cell types, nuclear DNA content and aneuploidy, neutral lipid content, putative Tau and amyloid-? (A?) in buccal cells. DNA content, aneuploidy, neutral lipids and Tau were similar in all groups. However, there was significantly lower Tau protein in both basal and karyolytic buccal cell types compared with differentiated buccal cells. A?, as measured by frequency of cells containing A? signal, as well as area and integral of A? signal, was significantly higher in the AD group compared with the control group. Buccal cell A? was correlated with mini-mental state examination (MMSE) scores (r = -0.436, P=0.001) and several blood-based biomarkers. Combining newly identified biomarkers from buccal cells with those already established may offer a potential route for more specific biomarker panels which may substantially increase the likelihood of better predictive markers for earlier diagnosis of AD.

Cerebrospinal Fluid proNGF: A Putative Biomarker for Early Alzheimer's Disease by Scott E. Counts, Bin He, John G. Prout, Bernadeta Michalski, Lucia Farotti, Margaret Fahnestock, Elliott J. Mufson (800-808).
The discovery of biomarkers for the onset of Alzheimer's disease (AD) is essential for disease modification strategies. To date, AD biomarker studies have focused on brain imaging and cerebrospinal fluid (CSF) changes in amyloid- ? (A?) peptide and tau proteins. While reliable to an extent, this panel could be improved by the inclusion of novel biomarkers that optimize sensitivity and specificity. In this study, we determined whether CSF levels of the nerve growth factor (NGF) precursor protein, proNGF, increased during the progression of AD, mirroring its up regulation in postmortem brain samples of people who died with a clinical diagnosis of mild cognitive impairment (MCI) or AD. Immunoblot analysis was performed on ventricular CSF harvested from participants in the Rush Religious Orders Study with an antemortem clinical diagnosis of no cognitive impairment (NCI), amnestic MCI (aMCI, a putative prodromal AD stage), or mild/moderate AD. ProNGF levels were increased 55% in aMCI and 70% in AD compared to NCI. Increasing CSF proNGF levels correlated with impairment on cognitive test scores. In a complementary study, we found that proNGF was significantly increased by 30% in lumbar CSF samples derived from patients with a clinical dementia rating (CDR) of 0.5 or 1 compared to those with a CDR = 0. Notably, proNGF/A?1-42 levels were 50% higher in CDR 0.5 and CDR 1 compared to CDR 0 controls. By contrast, ELISA measurements of CSF brain-derived neurotrophic factor (BDNF) did not distinguish aMCI from NCI. Taken together, these results suggest that proNGF protein levels may augment the diagnostic accuracy of currently used CSF biomarker panels.

Background: Studies on the immunotherapy for Alzheimer's disease (AD) have increasingly gained attention since 1990s. However, there are pros (preventing of AD) and cons (incurred cost and side effects) regarding the administration of immunotherapy. Up to date, there has been lacking of economic evaluation for immunotherapy of AD. We aimed to assess the cost-effectiveness analysis of the vaccination for AD. Methods: A meta-analysis of randomized control trials after systemic review was conducted to evaluate the efficacy of the vaccine. A Markov decision model was constructed and applied to a 120,000-Taiwanese cohort aged ?65 years. Person years and quality-adjusted life years (QALY) were computed between the vaccinated group and the the unvaccinated group. Economic evaluation was performed to calculate the incremental cost-effectiveness ratio (ICER) and cost-effectiveness acceptability curve (CEAC). Results: Vaccinated group gained an additional 0.84 life years and 0.56 QALYs over 10-years and an additional 0.35 life years and 0.282 QALYs over 5-years of follow-up. The vaccinated group dominated the unvaccinated group by ICER over 5-years of follow-up. The ICERs of 10-year follow-up for the vaccinated group against the unvaccinated group were $13,850 per QALY and $9,038 per life year gained. Given the threshold of $20,000 of willingness to pay (WTP), the CEAC showed the probability of being cost-effective for vaccination with QALY was 70.7% and 92% for life years gained after 10-years of follow-up. The corresponding figures were 87.3% for QALY and 93.5% for life years gained over 5-years follow-up. Conclusion: The vaccination for AD was cost-effective in gaining QALY and life years compared with no vaccination, under the condition of a reasonable threshold of WTP.

Increased Epileptiform EEG Activity and Decreased Seizure Threshold in Arctic APP Transgenic Mouse Model of Alzheimer's Disease by Sofya Ziyatdinova, Annica Rönnbäck, Kestutis Gurevicius, Diana Miszczuk, Caroline Graff, Bengt Winblad, Asla Pitkänen, Heikki Tanila (817-830).
Several Alzheimer model mice carrying transgenic amyloid precursor protein (APP) with the Swedish mutation have been reported to exhibit spontaneous seizures and/or increased epileptiform EEG activity. The primary cause for the epilepsy phenotype is still under debate. In contrast to mice with APPswe mutation that develop extracellular amyloid plaques, mice with APP Arctic mutation (E693G) have no bias toward ?-secretase cleavage and display intracellular amyloid deposits but not plaques. We conducted a systematic long-term video-EEG recording in three two-week sessions on 21 APParc and 11 wild-type control mice between 3.5 and 8 months of age. Spontaneous seizures were not detected more often in APParc mice than in their wild-type control mice. Long (1 - 5 s) epileptiform discharges were occasionally detected in both APParc and wild-type mice, but short (0.5 - <1 s) epileptiform discharges were more common in APParc mice than in wild-types. However, they were far less frequent than in 6 APPswe/PS1dE9 mice recorded in parallel. In pentylenetetrazole test for seizure susceptibility, APParc mice displayed a shorter latency to sharp-wave discharges than wildtype mice but no increase in seizure duration. These data speak for a relatively mild epilepsy phenotype in APParc mice compared to APPswe mice despite even higher extent of APP overexpression. Thus extracellular amyloid plaques or increased ?-secretase cleavage products appear important for the epilepsy phenotype in APPswe mice.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease by F. Segovia, J. M. G&#243;rriz, J. Ram&#237;rez, C. Phillips, for the Alzheimer's Disease Neuroimaging Initiative (831-837).
Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease by D. Salas-Gonzalez, F. Segovia, F. J. Mart&#237;nez-Murcia, E. W. Lang, J. M. Gorriz, J. Ram|rez (838-844).
In this work, we present a fully automatic computer-aided diagnosis method for the early diagnosis of the Alzheimer's disease. We study the distance between classes (labelled as normal controls and possible Alzheimer's disease) calculated in 116 regions of the brain using the Welchs's t-test. We select the regions with highest Welchs's t-test value as features to perform classification. Furthermore, we also study the less discriminative region according to the t-test (regions with lowest t-test absolute values) in order to use them as reference. We show that the mean and standard deviation of the intensity values in these two regions, the less and most discriminative according to the Welch's ttest, can be combined as a vector. The modulus and phase of this vector reveal statistical differences between groups which can be used to improve the classification task. We show how they can be used as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70 SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.