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Shuangshuang Ma
Author with expertise in Molecular Mechanisms of Synaptic Plasticity and Neurological Disorders
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Changes of Protein Phosphorylation Are Associated with Synaptic Functions during the Early Stage of Alzheimer’s Disease

Yuewen Chen et al.Aug 19, 2019
Alzheimer's disease is an irreversible neurodegenerative disorder for which we have limited knowledge of the mechanisms underlying its pathogenesis, especially the molecular events that trigger the deterioration of neuronal functions in the early stage. Protein phosphorylation and dephosphorylation are highly dynamic and reversible post-translational modifications that control protein signaling and hence neuronal functions, aberrations of which are implicated in various neurodegenerative diseases including Alzheimer's disease. We conducted a quantitative phosphoproteomic analysis in the brains of APP/PS1 mice, an Aβ-deposition transgenic mouse model, at 3 months old, the stage at which amyloid pathology just initiates. Compared to the wild-type mouse brains, we found that changes in serine phosphorylation were predominant in the APP/PS1 mouse brains, and that the occurrence of proline-directed phosphorylation was most common among the overrepresented phosphopeptides. Further analysis of the 167 phosphoproteins that were significantly up- or downregulated in APP/PS1 mouse brains revealed the enrichment of these proteins in synapse-related pathways. In particular, Western blot analysis validated the increased phosphorylation of chromogranin B, a protein enriched in large dense-core vesicles, in APP/PS1 mouse brains. These findings collectively suggest that changes in the phosphoprotein network may be associated with the deregulation of synaptic functions during the pathogenesis of Alzheimer's disease.
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Deep learning methods improve polygenic risk analysis and prediction for Alzheimer’s disease

Xiaopu Zhou et al.Sep 7, 2021
Abstract Recent advances in genetic sequencing have enabled comprehensive genetic analyses of human diseases, resulting in the identification of numerous genetic risk factors for heritable disorders including Alzheimer’s disease (AD). Such analyses enable AD risk prediction well before disease onset, which is critical for early interventions. However, current analytical approaches have limited ability to accurately estimate the risk effects of genetic variants owing to epistatic effects, which have been overlooked in most previous studies, resulting in unsatisfactory disease risk prediction. Herein, we modeled AD polygenic risk using deep learning methods, which outperformed existing models (i.e., weighted polygenic risk score and lasso models) for classifying disease risk. Moreover, by examining the associations between the outcomes from deep learning methods and multi-omics data obtained from our in-house Chinese AD cohorts, we identified the pathways that are potentially regulated by AD polygenic risk, including immune-associated signaling pathways. Thus, our results demonstrate the utility of deep learning methods for modeling the genetic risks of human diseases, which can facilitate both disease risk classification and the study of disease mechanisms.
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