The Synaptic Vesicle Protein 2a Interacts With Key Pathogenic Factors in Alzheimer’s Disease: Implications for Treatment

Abstract Background:Alzheimer’s Disease(AD), a serious neurodegenerative disease,is pathologicallycharacterized by synaptic loss and dysfunction.Synaptic vesicle protein 2A (SV2A) is an indispensable vesicular protein specifically expressed in synapses and can be used as a biomarker for synaptic density. Nevertheless, the involvement of SV2A in the pathogenesis and development of ADand its relation to other hallmarks of AD pathology, such as amyloid precursor protein (APP), β-amyloid (Aβ), and tau protein are not fully understood.Methods:We first examined and compared the mRNA levels of SV2A in the hippocampus of AD patients and non-AD subject in the Allen Brain database,thenwe constructed SV2A knockout mouse model.Using PET imagingwe compared the expression of Aβ in SV2A knockout mice and WT mice, analyze the relationship between SV2A and AD related proteins by quantitative real-time polymerase chain reaction (PCR), western blotting and ELISA.Results:Our results showed thatthe expression of SV2A was downregulated in the hippocampus of AD patients.In addition,SV2A colocalized with APPandwas downregulated at Aβ deposition. Moreover, we used APPswe293T cells lines to either silence or overexpress SV2A and found that SV2A deficiency leads to a simultaneous increase in Aβand Tau hyperphosphorylation,whileSV2A overexpression was associated with down-regulation of BACE1 and APOE. In addition, evidence gained in the study points PI3K signaling pathway as a possible mediator in SV2A regulation influencing the incidence and development of AD. Conclusions:Our research demonstrated that SV2A is an important regulator of AD, close interplay between SV2A and AD related proteins demonstrated in our studyprovide novel diagnostic and therapeutic opportunities of AD. This study provides guidelines and information regarding the mechanism of SV2A influence in the regulation of AD and possible future research of neurological diseases.

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The Synaptic Vesicle Protein 2A Interacts With Key Pathogenic Factors in Alzheimer’s Disease: Implications for Treatment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.609908 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yanyan Kong ◽

Lin Huang ◽

Weihao Li ◽

Xuanting Liu ◽

Yinping Zhou ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Synaptic Vesicle ◽

Diagnostic Methods ◽

Simultaneous Increase ◽

Synaptic Vesicle Protein ◽

Pathogenic Factors ◽

Vesicle Protein ◽

Related Proteins ◽

Synaptic Vesicle Protein 2A

Alzheimer’s disease (AD), a serious neurodegenerative disease, is pathologically characterized by synaptic loss and dysfunction. Synaptic vesicle protein 2A (SV2A) is an indispensable vesicular protein specifically expressed in synapses and can be used as a biomarker for synaptic density. We found that the expression of SV2A was down-regulated in the hippocampus of AD patients, yet the relation of SV2A to other hallmarks of AD pathology such as amyloid precursor protein (APP), β-amyloid (Aβ), and Tau protein is not thoroughly clear. In addition, SV2A colocalized with APP and was down-regulated at Aβ deposition. Moreover, we found that SV2A deficiency leads to a simultaneous increase in Aβ and Tau hyperphosphorylation, while SV2A overexpression was associated with downregulation of β-site APP cleaving enzyme 1 and apolipoprotein E genes. In addition, evidence gained in the study points to the phosphatidylinositol 3-kinase signaling pathway as a possible mediator in SV2A regulation influencing the incidence and development of AD. With limited effective diagnostic methods for AD, a close interplay between SV2A and AD-related proteins demonstrated in our study may provide novel and innovative diagnostic and therapeutic opportunities.

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The Implication of STEP in Synaptic Plasticity and Cognitive Impairments in Alzheimer’s Disease and Other Neurological Disorders

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.680118 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yacoubou Abdoul Razak Mahaman ◽

Fang Huang ◽

Kidane Siele Embaye ◽

Xiaochuan Wang ◽

Feiqi Zhu

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Synaptic Plasticity ◽

Ampa Receptor ◽

Neurological Disorders ◽

Neurological Diseases ◽

Cognitive Impairments ◽

Tyrosine Phosphatase ◽

Receptor Complexes ◽

Related Proteins

STriatal-Enriched protein tyrosine Phosphatase (STEP) is a tyrosine phosphatase that has been implicated in Alzheimer’s disease (AD), the most common form of dementia, and many other neurological diseases. The protein level and activity of STEP have been found to be elevated in most of these disorders, and specifically in AD as a result of dysregulation of different pathways including PP2B/DARPP32/PP1, PKA as well as impairments of both proteasomal and lysosomal systems. The upregulation in STEP leads to increased binding to, and dephosphorylation of, its substrates which are mainly found to be synaptic plasticity and thus learning and memory related proteins. These proteins include kinases like Fyn, Pyk2, ERK1/2 and both NMDA and AMPA receptor subunits GluN2B and GluA2. The dephosphorylation of these molecules results in inactivation of these kinases and internalization of NMDA and AMPA receptor complexes leading to synapse loss and cognitive impairments. In this study, we aim to review STEP regulation and its implications in AD as well as other neurological disorders and then summarize data on targeting STEP as therapeutic strategy in these diseases.

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Alzheimer’s Disease Targeted Nano-Based Drug Delivery Systems

Current Drug Targets ◽

10.2174/1389450120666191118123151 ◽

2020 ◽

Vol 21 (7) ◽

pp. 628-646

Author(s):

Gülcem Altinoglu ◽

Terin Adali

Keyword(s):

Alzheimer’S Disease ◽

Drug Delivery ◽

Alzheimer's Disease ◽

Neurological Diseases ◽

Sustained Drug Release ◽

Physiochemical Properties ◽

Conventional Drug ◽

Health Care Burden ◽

The Central Nervous System ◽

Effective Drugs

Alzheimer’s disease (AD) is the most common neurodegenerative disease, and is part of a massive and growing health care burden that is destroying the cognitive function of more than 50 million individuals worldwide. Today, therapeutic options are limited to approaches with mild symptomatic benefits. The failure in developing effective drugs is attributed to, but not limited to the highly heterogeneous nature of AD with multiple underlying hypotheses and multifactorial pathology. In addition, targeted drug delivery to the central nervous system (CNS), for the diagnosis and therapy of neurological diseases like AD, is restricted by the challenges posed by blood-brain interfaces surrounding the CNS, limiting the bioavailability of therapeutics. Research done over the last decade has focused on developing new strategies to overcome these limitations and successfully deliver drugs to the CNS. Nanoparticles, that are capable of encapsulating drugs with sustained drug release profiles and adjustable physiochemical properties, can cross the protective barriers surrounding the CNS. Thus, nanotechnology offers new hope for AD treatment as a strong alternative to conventional drug delivery mechanisms. In this review, the potential application of nanoparticle based approaches in Alzheimer’s disease and their implications in therapy is discussed.

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The Involvement of Post-Translational Modifications in Alzheimer's Disease

Current Alzheimer Research ◽

10.2174/1567205014666170505095109 ◽

2018 ◽

Vol 15 (4) ◽

pp. 313-335 ◽

Cited By ~ 19

Author(s):

Serena Marcelli ◽

Massimo Corbo ◽

Filomena Iannuzzi ◽

Lucia Negri ◽

Fabio Blandini ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Protein Modification ◽

Neurodegenerative Disorder ◽

Amyloid Β ◽

Normal Function ◽

Ageing Population ◽

Covalent Bonds ◽

Post Translational Modifications ◽

Related Proteins

Background: Alzheimer's disease (AD) is a neurodegenerative disorder recognized as the most common cause of chronic dementia among the ageing population. AD is histopathologically characterized by progressive loss of neurons and deposits of insoluble proteins, primarily composed of amyloid-β pelaques and neurofibrillary tangles (NFTs). Methods: Several molecular processes contribute to the formation of AD cellular hallmarks. Among them, post-translational modifications (PTMs) represent an attractive mechanism underlying the formation of covalent bonds between chemical groups/peptides to target proteins, which ultimately result modified in their function. Most of the proteins related to AD undergo PTMs. Several recent studies show that AD-related proteins like APP, Aβ, tau, BACE1 undergo post-translational modifications. The effect of PTMs contributes to the normal function of cells, although aberrant protein modification, which may depend on many factors, can drive the onset or support the development of AD. Results: Here we will discuss the effect of several PTMs on the functionality of AD-related proteins potentially contributing to the development of AD pathology. Conclusion: We will consider the role of Ubiquitination, Phosphorylation, SUMOylation, Acetylation and Nitrosylation on specific AD-related proteins and, more interestingly, the possible interactions that may occur between such different PTMs.

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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Brain Informatics ◽

10.1186/s40708-020-00112-2 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Manan Binth Taj Noor ◽

Nusrat Zerin Zenia ◽

M Shamim Kaiser ◽

Shamim Al Mamun ◽

Mufti Mahmud

Keyword(s):

Alzheimer’S Disease ◽

Parkinson’S Disease ◽

Alzheimer's Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Magnetic Resonance ◽

Neurological Disorders ◽

Magnetic Resonance Images ◽

Future Research ◽

Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms22157911 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7911

Author(s):

Eugene Lin ◽

Chieh-Hsin Lin ◽

Hsien-Yuan Lane

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Future Research ◽

Learning Approaches ◽

Learning Models ◽

Learning Techniques ◽

Neuroimaging Data ◽

Similarities And Differences ◽

Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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Role of the lncRNA BACE1-AS in shared disease mechanisms between heart failure and Alzheimer's disease

European Heart Journal ◽

10.1093/ehjci/ehaa946.0840 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

S Greco ◽

A Made' ◽

A.S Tascini ◽

J Garcia Manteiga ◽

S Castelvecchio ◽

...

Keyword(s):

Heart Failure ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

In Situ Hybridization ◽

Cell Apoptosis ◽

Common Disease ◽

Funding Source ◽

Mrna Levels ◽

Rna Seq

Abstract Background BACE1 encodes for β-secretase, the key enzyme involved in β-amyloid (βA) generation, a peptide well known for its involvement in Alzheimer's disease (AD). Of note, heart failure (HF) and AD share several risk factors and effectors. We recently showed that, in the heart of ischemic HF patients, the levels of both BACE1, its antisense RNA BACE1-AS and βA are all increased. BACE1-AS positively regulates the expression of BACE1, triggering βA intracellular accumulation, and its overexpression or βA administration induce cardiovascular-cell apoptosis. Aim To characterize the transcripts of the BACE1 locus and to investigate the molecular mechanisms underpinning BACE1-AS regulation of cell vitality. Methods By PCR and sequencing, we studied in the heart the expression of a variety of antisense BACE1 transcripts predicted by FANTOM CAT Epigenome. We studied BACE1 RNA stability by BrdU pulse chase experiments (BRIC assay). The cellular localization of BACE1-AS RNA was investigated by in situ hybridization assay. BACE1-AS binding RNAs were evaluated by BACE1-AS-MS2-Tag pull-down in AC16 cardiomyocytes followed by RNA-seq. Enriched RNAs were validated by qPCR and analysed by bioinformatics comparison with publicly available gene expression datasets of AD brains. Results We readily detected several antisense BACE1 transcripts expressed in AC16 cardiomyocytes; however, only BACE1-AS RNAs overlapping exon 6 of BACE1 positively regulated BACE1 mRNA levels, acting by increasing its stability. BACE1 silencing reverted cell apoptosis induced by BACE1-AS expression, indicating that BACE1 is a functional target of BACE1-AS. However, in situ hybridization experiments indicated a mainly nuclear localization for BACE1-AS, which displayed a punctuated distribution, compatible with chromatin association and indicative of potential additional targets. To identify other BACE1-AS binding RNAs, a BACE1-AS-MS2-tag pull-down was performed and RNA-seq of the enriched RNAs identified 698 BACE1-AS interacting RNAs in cardiomyocytes. Gene ontology of the BACE1-AS binding RNAs identified categories of relevance for cardiovascular or neurological diseases, such as dopaminergic synapse, glutamatergic synapse, calcium signalling pathway and voltage-gated channel activity. In spite of the differences between brain and heart transcriptomes, BACE1-AS-interacting RNAs identified in cardiomyocytes were significantly enriched in transcripts differentially expressed in AD brains as well as in RNAs expressed by enhancer genomic regions that are significantly hypomethylated in AD brains. Conclusions These data shed a new light on the complexity of BACE1-AS locus and on the existence of RNAs interacting with BACE1-AS with a potential as enhancer-RNAs. Moreover, the dysregulation of the BACE1-AS/BACE1/βA pathway may be a common disease mechanism shared by cardiovascular and neurological degenerative diseases. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Italian Health Ministery_Ricerca Corrente 2020

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Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1611073113 ◽

2016 ◽

Vol 113 (42) ◽

pp. E6535-E6544 ◽

Cited By ~ 68

Author(s):

Xiuming Zhang ◽

Elizabeth C. Mormino ◽

Nanbo Sun ◽

Reisa A. Sperling ◽

Mert R. Sabuncu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Executive Function ◽

Bayesian Model ◽

Late Onset ◽

Temporal Cortex ◽

Cortical Atrophy ◽

Future Research ◽

Dementia Patients ◽

With Memory

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

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