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Frontiers in Molecular Biosciences 2024Alzheimer's disease (AD) is a progressive debilitating neurological disorder representing the most common neurodegenerative disease worldwide. Although the exact...
Alzheimer's disease (AD) is a progressive debilitating neurological disorder representing the most common neurodegenerative disease worldwide. Although the exact pathogenic mechanisms of AD remain unresolved, the presence of extracellular amyloid-β peptide 1-42 (Aβ) plaques in the parenchymal and cortical brain is considered one of the hallmarks of the disease. In this work, we investigated the Aβ fibrillogenesis timeline up to 48 h of incubation, providing morphological and chemo-structural characterization of the main assemblies formed during the aggregation process of Aβ, by atomic force microscopy (AFM) and surface enhanced Raman spectroscopy (SERS), respectively. AFM topography evidenced the presence of characteristic protofibrils at early-stages of aggregation, which form peculiar macromolecular networks over time. SERS allowed to track the progressive variation in the secondary structure of the aggregation species involved in the fibrillogenesis and to determine when the β-sheet starts to prevail over the random coil conformation in the aggregation process. Our research highlights the significance of investigating the early phases of fibrillogenesis to better understand the molecular pathophysiology of AD and identify potential therapeutic targets that may prevent or slow down the aggregation process.
PubMed: 38948077
DOI: 10.3389/fmolb.2024.1376411 -
Frontiers in Cellular Neuroscience 2024Mild traumatic brain injury (mTBI) resulting from low-intensity blast (LIB) exposure in military and civilian individuals is linked to enduring behavioral and cognitive...
Mild traumatic brain injury (mTBI) resulting from low-intensity blast (LIB) exposure in military and civilian individuals is linked to enduring behavioral and cognitive abnormalities. These injuries can serve as confounding risk factors for the development of neurodegenerative disorders, including Alzheimer's disease-related dementias (ADRD). Recent animal studies have demonstrated LIB-induced brain damage at the molecular and nanoscale levels. Nevertheless, the mechanisms linking these damages to cognitive abnormalities are unresolved. Challenges preventing the translation of preclinical studies into meaningful findings in "real-world clinics" encompass the heterogeneity observed between different species and strains, variable time durations of the tests, quantification of dosing effects and differing approaches to data analysis. Moreover, while behavioral tests in most pre-clinical studies are conducted at the group level, clinical tests are predominantly assessed on an individual basis. In this investigation, we advanced a high-resolution and sensitive method utilizing the CognitionWall test system and applying reversal learning data to the Boltzmann fitting curves. A flow chart was developed that enable categorizing individual mouse to different levels of learning deficits and patterns. In this study, rTg4510 mice, which represent a neuropathology model due to elevated levels of tau P301L, together with the non-carrier genotype were exposed to LIB. Results revealed distinct and intricate patterns of learning deficits and patterns within each group and in relation to blast exposure. With the current findings, it is possible to establish connections between mice with specific cognitive deficits to molecular changes. This approach can enhance the translational value of preclinical findings and also allow for future development of a precision clinical treatment plan for ameliorating neurologic damage of individuals with mTBI.
PubMed: 38948027
DOI: 10.3389/fncel.2024.1397046 -
Imaging Neuroscience (Cambridge, Mass.) Feb 2024Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens...
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
PubMed: 38947941
DOI: 10.1162/imag_a_00089 -
ArXiv Jun 2024Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical...
Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.
PubMed: 38947926
DOI: No ID Found -
ArXiv Jun 2024Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known...
Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. In this study, we leveraged structural and functional MRI to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce SNPs as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning-based classification framework where generative module employing Cycle GANs was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable AI method, Integrated Gradients, to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our model was able to reach the SOA in the classification of CN/AD reaching an average test accuracy of $0.926\pm0.02$. For the MCI task, we achieved an average prediction accuracy of $0.711\pm0.01$ using the pre-trained model for CN/AD. The interpretability analysis revealed significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights.
PubMed: 38947922
DOI: No ID Found -
ACS Omega Jun 2024Increased deposition of amyloid-β (Aβ) plaques in the brain is a frequent pathological feature observed in human immunodeficiency virus (HIV)-positive patients....
Increased deposition of amyloid-β (Aβ) plaques in the brain is a frequent pathological feature observed in human immunodeficiency virus (HIV)-positive patients. Emerging evidence indicates that HIV regulatory proteins, particularly the transactivator of transcription (TAT) protein, could interact with Aβ peptide, accelerating the formation of Aβ plaques in the brain and potentially contributing to the onset of Alzheimer's disease in individuals with HIV infection. Nevertheless, the molecular mechanisms underlying these processes remain unclear. In the present study, we have used long all-atom molecular dynamics simulations to probe the direct interactions between the TAT protein and Aβ peptide at the molecular level. Sampling over 28.0 μs, our simulations show that TAT protein induces a shift in the Aβ monomer ensemble toward elongated conformations, exposing aggregation-prone regions on the surface and thereby inducing subsequent aggregation. TAT protein also appears to enhance the stability of preformed Aβ fibrils, while increasing the β-sheet content within these fibrils. Our atomistically detailed simulations qualitatively agree with previous in vitro and in vivo studies. Importantly, our simulations identify key interactions between Aβ and the TAT protein that drive the Aβ aggregation process and stabilize the preformed Aβ aggregates, which are particularly challenging to obtain through current experimental techniques.
PubMed: 38947850
DOI: 10.1021/acsomega.4c02643 -
Cureus May 2024Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in... (Review)
Review
Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in early diagnosis and management. This narrative review explores the pivotal role of advanced neuroimaging techniques in detecting and managing these diseases at early stages, potentially slowing their progression through timely interventions. Recent advancements in MRI, such as ultra-high-field systems and functional MRI, have enhanced the sensitivity for detecting subtle structural and functional changes. Additionally, the development of novel amyloid-beta tracers and other emerging modalities like optical imaging and transcranial ultrasonography have improved the diagnostic accuracy and capability of existing methods. This review highlights the clinical applications of these technologies in Alzheimer's and Parkinson's diseases, where they have shown improved diagnostic performance, enabling earlier intervention and better prognostic outcomes. Moreover, the integration of artificial intelligence (AI) and longitudinal research is emerging as a promising enhancement to refine early detection strategies further. However, this review also addresses the technical, ethical, and accessibility challenges in the field, advocating for the more extensive use of advanced imaging technologies to overcome these barriers. Finally, we emphasize the need for a holistic approach that incorporates both neurological and psychiatric perspectives, which is crucial for optimizing patient care and outcomes in the management of neurodegenerative diseases.
PubMed: 38947709
DOI: 10.7759/cureus.61335 -
Cureus May 2024Advanced glycation end products (AGEs) accumulate in the brain, leading to neurodegenerative conditions such as Alzheimer's disease (AD). The pathophysiology of AD is... (Review)
Review
Advanced glycation end products (AGEs) accumulate in the brain, leading to neurodegenerative conditions such as Alzheimer's disease (AD). The pathophysiology of AD is influenced by receptors for AGEs and toll-like receptor 4 (TLR4). Protein glycation results in irreversible AGEs through a complicated series of reactions involving the formation of Schiff's base, the Amadori reaction, followed by the Maillard reaction, which causes abnormal brain glucose metabolism, oxidative stress, malfunctioning mitochondria, plaque deposition, and neuronal death. Amyloid plaque and other stimuli activate macrophages, which are crucial immune cells in AD development, triggering the production of inflammatory molecules and contributing to the disease's pathogenesis. The risk of AD is doubled by risk factors for atherosclerosis, dementia, advanced age, and type 2 diabetic mellitus (DM). As individuals age, the prevalence of neurological illnesses such as AD increases due to a decrease in glyoxalase levels and an increase in AGE accumulation. Insulin's role in proteostasis influences hallmarks of AD-like tau phosphorylation and amyloid β peptide clearance, affecting lipid metabolism, inflammation, vasoreactivity, and vascular function. The high-mobility group box 1 (HMGB1) protein, a key initiator and activator of a neuroinflammatory response, has been linked to the development of neurodegenerative diseases such as AD. The TLR4 inhibitor was found to improve memory and learning impairment and decrease Aβ build-up. Therapeutic research into anti-glycation agents, receptor for advanced glycation end products (RAGE) inhibitors, and AGE breakers offers hope for intervention strategies. Dietary and lifestyle modifications can also slow AD progression. Newer therapeutic approaches targeting AGE-related pathways are needed.
PubMed: 38947632
DOI: 10.7759/cureus.61373 -
Global Health & Medicine Jun 2024Alzheimer's disease (AD), first diagnosed over a century ago, remains one of the major healthcare crises around the globe. Currently, there is no cure or effective...
Alzheimer's disease (AD), first diagnosed over a century ago, remains one of the major healthcare crises around the globe. Currently, there is no cure or effective treatment. The majority of drug development efforts to date have targeted reduction of amyloid-β peptide (Aβ). Drug development through inhibition of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), resulted in promising early clinical studies. However, nearly all small molecule BACE1 inhibitor drugs failed to live up to expectations in later phase clinical trials, due to toxicity and efficacy issues. This commentary aims to provide a brief review of over two decades of BACE1 inhibitor drug development challenges and efforts for treatment of AD and prospects of future BACE1-based drugs.
PubMed: 38947412
DOI: 10.35772/ghm.2024.01033 -
Data Science in Science 2024In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When...
In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators and exposure-by-mediator ( -by- ) interactions. Although several high-dimensional mediation methods can naturally handle -by- interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select and -by- interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.
PubMed: 38947225
DOI: 10.1080/26941899.2024.2360892