scholarly journals Regional protein expression in human Alzheimer’s brain correlates with disease severity

2018 ◽  
Author(s):  
Jingshu Xu ◽  
Stefano Patassini ◽  
Nitin Rustogi ◽  
Isabel Riba-Garcia ◽  
Benjamin D. Hale ◽  
...  
Keyword(s):  
Protein Expression ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Expression Change ◽  
Protein Expression Change ◽  
Progression Of Disease ◽  
Brain Correlates ◽  
Molecular Pathophysiology ◽  
Human Brains ◽  
The Brain

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen both the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions show varying and region-specific changes in protein expression. These changes provide novel insights into the progression of disease, novel AD-related pathways, the presence of a ‘gradient’ of protein expression change from less to more affected regions, and the presence of a ‘protective’ protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and opens new avenues of interest to enhance our understanding of molecular pathophysiology of AD, provides new targets for intervention and broadens the conceptual frameworks for future AD research.

scholarly journals Efficacy and immunogenicity of MultiTEP-based DNA vaccines targeting human α-synuclein: prelude for IND enabling studies

npj Vaccines ◽  
2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Changyoun Kim ◽  
Armine Hovakimyan ◽  
Karen Zagorski ◽  
Tatevik Antonyan ◽  
Irina Petrushina ◽  
...  
Keyword(s):  
Dna Vaccines ◽  
Neurodegenerative Disorder ◽  
Neuronal Damage ◽  
Lewy Bodies ◽  
Amyloid Β ◽  
The Elderly ◽  
Brain Regions ◽  
Dependent Manner ◽  
Lewy Neurites ◽  
The Brain

AbstractAccumulation of misfolded proteins such as amyloid-β (Aβ), tau, and α-synuclein (α-Syn) in the brain leads to synaptic dysfunction, neuronal damage, and the onset of relevant neurodegenerative disorder/s. Dementia with Lewy bodies (DLB) and Parkinson’s disease (PD) are characterized by the aberrant accumulation of α-Syn intracytoplasmic Lewy body inclusions and dystrophic Lewy neurites resulting in neurodegeneration associated with inflammation. Cell to cell propagation of α-Syn aggregates is implicated in the progression of PD/DLB, and high concentrations of anti-α-Syn antibodies could inhibit/reduce the spreading of this pathological molecule in the brain. To ensure sufficient therapeutic concentrations of anti-α-Syn antibodies in the periphery and CNS, we developed four α-Syn DNA vaccines based on the universal MultiTEP platform technology designed especially for the elderly with immunosenescence. Here, we are reporting on the efficacy and immunogenicity of these vaccines targeting three B-cell epitopes of hα-Syn aa85–99 (PV-1947D), aa109–126 (PV-1948D), aa126–140 (PV-1949D) separately or simultaneously (PV-1950D) in a mouse model of synucleinopathies mimicking PD/DLB. All vaccines induced high titers of antibodies specific to hα-Syn that significantly reduced PD/DLB-like pathology in hα-Syn D line mice. The most significant reduction of the total and protein kinase resistant hα-Syn, as well as neurodegeneration, were observed in various brain regions of mice vaccinated with PV-1949D and PV-1950D in a sex-dependent manner. Based on these preclinical data, we selected the PV-1950D vaccine for future IND enabling preclinical studies and clinical development.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

scholarly journals Extensive Expression Analysis of Htt Transcripts in Brain Regions from the zQ175 HD Mouse Model Using a QuantiGene Multiplex Assay

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Aikaterini S. Papadopoulou ◽  
Casandra Gomez-Paredes ◽  
Michael A. Mason ◽  
Bridget A. Taxy ◽  
David Howland ◽  
...  
Keyword(s):  
Mouse Model ◽  
Neurodegenerative Disorder ◽  
Simultaneous Detection ◽  
Brain Regions ◽  
Cag Repeat ◽  
Highly Pathogenic ◽  
Exon 2 ◽  
Mouse Tissues ◽  
Exon 1 ◽  
The Brain

Abstract Huntington’s disease (HD) is an inherited neurodegenerative disorder caused by a CAG repeat expansion within exon 1 of the huntingtin (HTT) gene. HTT mRNA contains 67 exons and does not always splice between exon 1 and exon 2 leading to the production of a small polyadenylated HTTexon1 transcript, and the full-length HTT mRNA has three 3′UTR isoforms. We have developed a QuantiGene multiplex panel for the simultaneous detection of all of these mouse Htt transcripts directly from tissue lysates and demonstrate that this can replace the more work-intensive Taqman qPCR assays. We have applied this to the analysis of brain regions from the zQ175 HD mouse model and wild type littermates at two months of age. We show that the incomplete splicing of Htt occurs throughout the brain and confirm that this originates from the mutant and not endogenous Htt allele. Given that HTTexon1 encodes the highly pathogenic exon 1 HTT protein, it is essential that the levels of all Htt transcripts can be monitored when evaluating HTT lowering approaches. Our QuantiGene panel will allow the rapid comparative assessment of all Htt transcripts in cell lysates and mouse tissues without the need to first extract RNA.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Angiotensin-converting enzyme 2 (ACE2) is upregulated in Alzheimer’s disease brain

2020 ◽  
Author(s):  
Qiyue Ding ◽  
Nataliia V. Shults ◽  
Brent T. Harris ◽  
Yuichiro J. Suzuki
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Angiotensin Converting Enzyme ◽  
Protein Expression ◽  
Neurodegenerative Disorder ◽  
Host Cells ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
The Brain

AbstractAlzheimer’s disease is a chronic neurodegenerative disorder and represents the main cause of dementia. Currently, the world is suffering from the pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that uses angiotensin-converting enzyme 2 (ACE2) as a receptor to enter the host cells. In COVID-19, neurological manifestations have been reported to occur. The present study demonstrates that the protein expression level of ACE2 is upregulated in the brain of Alzheimer’s disease patients. The increased ACE2 expression is not age-dependent, suggesting the direct relationship between Alzheimer’s disease and the ACE2 expression. Oxidative stress has been implicated in the pathogenesis of Alzheimer’s disease, and Alzheimer’s disease brains examined in this study also exhibited higher carbonylated proteins as well as increased thiol oxidation state of peroxiredoxin 6 (Prx6). The positive correlation was found between the increased ACE2 protein expression and oxidative stress in Alzheimer’s disease brain. Thus, the present study reveals the relationships between Alzheimer’s disease and ACE2, the receptor for SARS-CoV-2. These results warrant monitoring Alzheimer’s disease patients with COVID-19 carefully for the possible higher viral load in the brain and long-term adverse neurological consequences.

scholarly journals Using Diffusion Tensor Imaging to examine brain structural plasticity and language experience

2019 ◽  
Author(s):  
Gigi Luk ◽  
Christos Pliatsikas
Keyword(s):  
Language Processing ◽  
Brain Structure ◽  
Language Use ◽  
Diffusion Tensor ◽  
Brain Plasticity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Language Experience ◽  
Brain Correlates ◽  
The Brain

Recent advances in neuroimaging methods have led to a renewed interest in the brain correlates of language processing. Most intriguing is how experiences of language use relates to variation in brain structure and how brain structure predicts language acquisition. These two lines of inquiry have important implications on considering language use as an experience-dependent mechanism that induces brain plasticity. This paper focuses on the structural connectivity of the brain, as delivered by white matter, i.e. the collections of the axons of the brain neurons that provide connectivity between brain regions. Tract-Based Spatial Statistics (TBSS), a method commonly used in the field, will be presented in detail. Readers will be introduced to procedures for the extraction of indices of variation in WM structure such as fractional anisotropy. Furthermore, the role of individual differences in WM and changes in WM pertaining to bilingual experience and language processing will be used as examples to illustrate the applicability of this method.

scholarly journals Amplification, not spreading limits rate of tau aggregate accumulation in Alzheimer’s disease

2020 ◽  
Author(s):  
Georg Meisl ◽  
Yukun Zuo ◽  
Kieren Allinson ◽  
Timothy Rittman ◽  
Sarah DeVos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Replication Rate ◽  
Promising Strategy ◽  
Human Brains ◽  
The Brain ◽  
Tau Aggregate ◽  
Rate Of Accumulation

AbstractBoth the replication of protein aggregates and their spreading throughout the brain are implicated in the progression of Alzheimer’s disease (AD). However, the rates of these processes are unknown and the identity of the rate-determining process in humans has therefore remained elusive. By bringing together chemical kinetics with measurements of tau seeds and aggregates across brain regions, we are able to quantify their replication rate in human brains. Remarkably, we obtain comparable rates in several different datasets, with 5 different methods of tau quantification, from seed amplification assays in vitro to tau PET studies in living patients. Our results suggest that the overall rate of accumulation of tau in neocortical regions is limited not by spreading between brain regions but by local replication, which doubles the number of seeds every ~5 years. Thus, we propose that limiting local replication constitutes the most promising strategy to control tau accumulation during AD.

scholarly journals Sexual Dimorphism in the Brain Correlates of Adult-Onset Depression: A Pilot Structural and Functional 3T MRI Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Maria Chiara Piani ◽  
Eleonora Maggioni ◽  
Giuseppe Delvecchio ◽  
Adele Ferro ◽  
Davide Gritti ◽  
...  
Keyword(s):  
Disease Onset ◽  
Executive Dysfunction ◽  
The Elderly ◽  
Brain Regions ◽  
Brain Morphology ◽  
Parahippocampal Gyrus ◽  
Adult Onset ◽  
Sex Effects ◽  
Brain Correlates ◽  
The Brain

Major Depressive Disorder (MDD) is a disabling illness affecting more than 5% of the elderly population. Higher female prevalence and sex-specific symptomatology have been observed, suggesting that biologically-determined dimensions might affect the disease onset and outcome. Rumination and executive dysfunction characterize adult-onset MDD, but sex differences in these domains and in the related brain mechanisms are still largely unexplored. The present pilot study aimed to explore any interactions between adult-onset MDD and sex on brain morphology and brain function during a Go/No-Go paradigm. We hypothesized to detect diagnosis by sex effects on brain regions involved in self-referential processes and cognitive control. Twenty-four subjects, 12 healthy (HC) (mean age 68.7 y, 7 females and 5 males) and 12 affected by adult-onset MDD (mean age 66.5 y, 5 females and 7 males), underwent clinical evaluations and a 3T magnetic resonance imaging (MRI) session. Diagnosis and diagnosis by sex effects were assessed on regional gray matter (GM) volumes and task-related functional MRI (fMRI) activations. The GM volume analyses showed diagnosis effects in left mid frontal cortex (p < 0.01), and diagnosis by sex effects in orbitofrontal, olfactory, and calcarine regions (p < 0.05). The Go/No-Go fMRI analyses showed MDD effects on fMRI activations in left precuneus and right lingual gyrus, and diagnosis by sex effects on fMRI activations in right parahippocampal gyrus and right calcarine cortex (p < 0.001, ≥ 40 voxels). Our exploratory results suggest the presence of sex-specific brain correlates of adult-onset MDD–especially in regions involved in attention processing and in the brain default mode–potentially supporting cognitive and symptom differences between sexes.

scholarly journals Protein Expression of Angiotensin-Converting Enzyme 2 (ACE2) is Upregulated in Brains with Alzheimer’s Disease

2021 ◽  
Vol 22 (4) ◽  
pp. 1687
Author(s):  
Qiyue Ding ◽  
Nataliia V. Shults ◽  
Sergiy G. Gychka ◽  
Brent T. Harris ◽  
Yuichiro J. Suzuki
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Angiotensin Converting Enzyme ◽  
Protein Expression ◽  
Neurodegenerative Disorder ◽  
Host Cells ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
The Brain

Alzheimer’s disease is a chronic neurodegenerative disorder and represents the main cause of dementia globally. Currently, the world is suffering from the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus that uses angiotensin-converting enzyme 2 (ACE2) as a receptor to enter the host cells. In COVID-19, neurological manifestations have been reported to occur. The present study demonstrates that the protein expression level of ACE2 is upregulated in the brain of patients with Alzheimer’s disease. The increased ACE2 expression is not age-dependent, suggesting the direct relationship between Alzheimer’s disease and ACE2 expression. Oxidative stress has been implicated in the pathogenesis of Alzheimer’s disease, and brains with the disease examined in this study also exhibited higher carbonylated proteins, as well as an increased thiol oxidation state of peroxiredoxin 6 (Prx6). A moderate positive correlation was found between the increased ACE2 protein expression and oxidative stress in brains with Alzheimer’s disease. In summary, the present study reveals the relationships between Alzheimer’s disease and ACE2, the receptor for SARS-CoV-2. These results suggest the importance of carefully monitoring patients with both Alzheimer’s disease and COVID-19 in order to identify higher viral loads in the brain and long-term adverse neurological consequences.

scholarly journals Delineating visual, auditory and motor regions in the human brain with functional neuroimaging: a BrainMap-based meta-analytic synthesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marisa K. Heckner ◽  
Edna C. Cieslik ◽  
Vincent Küppers ◽  
Peter T. Fox ◽  
Simon B. Eickhoff ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Motor Task ◽  
Brain Regions ◽  
Affective Processing ◽  
Resting State Functional Connectivity ◽  
Motor Processes ◽  
Brain Correlates ◽  
Meta Analyses ◽  
The Brain

AbstractMost everyday behaviors and laboratory tasks rely on visual, auditory and/or motor-related processes. Yet, to date, there has been no large-scale quantitative synthesis of functional neuroimaging studies mapping the brain regions consistently recruited during such perceptuo-motor processing. We therefore performed three coordinate-based meta-analyses, sampling the results of neuroimaging experiments on visual (n = 114), auditory (n = 122), or motor-related (n = 251) processing, respectively, from the BrainMap database. Our analyses yielded both regions known to be recruited for basic perceptual or motor processes and additional regions in posterior frontal cortex. Comparing our results with data-driven network definitions based on resting-state functional connectivity revealed good overlap in expected regions but also showed that perceptual and motor task-related activations consistently involve additional frontal, cerebellar, and subcortical areas associated with “higher-order” cognitive functions, extending beyond what is captured when the brain is at “rest.” Our resulting sets of domain-typical brain regions can be used by the neuroimaging community as robust functional definitions or masks of regions of interest when investigating brain correlates of perceptual or motor processes and their interplay with other mental functions such as cognitive control or affective processing. The maps are made publicly available via the ANIMA database.

Sign in / Sign up

Export Citation Format

Share Document