Amphiphysin-1 protein level changes associated with tau-mediated neurodegeneration
ABSTRACT Tauopathies are a family of neurodegenerative diseases that have the pathological hallmark of intraneuronal accumulation of filaments composed of hyperphosphorylated tau proteins that tend to aggregate in an ultrastructure known as neurofibrillary tangles. The identification of mutations on the tau gene in familial cases of tauopathies underscores the pathological role of the tau protein. However, the molecular process that underlines tau-mediated neurodegeneration is not understood. Here, a proteomics approach was used to identify proteins that may be affected during the course of tau-mediated neurodegeneration in the tauopathy mouse model JNPL3. The JNPL3 mice express human tau proteins bearing a P301L mutation, which mimics the neurodegenerative process observed in humans with tauopathy. The results showed that the protein amphiphysin-1 (AMPH1) is significantly reduced in terminally ill JNPL3 mice. Specifically, the AMPH1 protein level is reduced in brain regions known to accumulate aggregates of hyperphosphorylated tau proteins. The AMPH1 protein reduction was validated in Alzheimer's disease cases. Taken together, the results suggest that the reduction of the AMPH1 protein level is a molecular event associated with the progression of tau-mediated neurodegeneration.
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ABSTRACT: Molecular diagnostic tools with non-invasive properties that allow detection of pathological events in Alzheimer's disease (AD) and other neurodegenerative tauopathies are essential for the development of therapeutics. Several diagnostic strategies based on the identification of biomarkers have been proposed. However, its specificity among neurodegenerative disorders is disputable as the association with pathological events remains elusive. Recently, we showed that Amphiphysin-1 (AMPH1) protein's abundance is reduced in the central nervous system (CNS) of the tauopathy mouse model JNPL3 and AD brains. AMPH1 is a synaptic protein that plays an important role in clathrin-mediated endocytosis and associates with BIN1, one of the most important risk loci for AD. Also, it has been associated with a rare neurological disease known as Stiff-Person Syndrome (SPS). Auto-antibodies against AMPH1 are used as diagnostic biomarkers for a paraneoplastic variant of SPS. Therefore, we set up to evaluate the presence and abundance of auto-AMPH1 antibodies in tau-mediated neurodegeneration. Immunoblots and enzyme-linked immunosorbent assays (ELISA) were conducted to detect the presence of auto-AMPH1 antibodies in sera from euthanized mice that developed neurodegeneration (JNPL3) and healthy control mice (NTg). Results showed increased levels of auto-AMPH1 antibodies in JNPL3 sera compared to NTg controls. The abundance of auto-AMPH1 antibodies correlated with motor impairment and AMPH1 protein level decrease in the CNS. The results suggest that auto-AMPH1 antibodies could serve as a biomarker for the progression of tau-mediated neurodegeneration in JNPL3 mice.Frontiers in Neuroscience 01/2014; 7:277. DOI:10.3389/fnins.2013.00277 · 3.70 Impact Factor
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ABSTRACT: Alzheimer's disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers then candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.BMC Genomics 03/2014; 15(1):199. DOI:10.1186/1471-2164-15-199 · 4.04 Impact Factor