Phenotypical difference of amyloid precursor protein (APP) V717L mutation in Japanese family

Clinical characteristics and genotype-phenotype correlation analysis of familial Alzheimer’s disease patients with pathogenic/likely pathogenic amyloid protein precursor mutations

Alzheimer’s disease (AD) is a progressive neurodegenerative disease associated with aging, environmental, and genetic factors. Amyloid protein precursor (APP) is a known pathogenic gene for familial Alzheimer’s disease (FAD), and now more than 70 APP mutations have been reported, but the genotype-phenotype correlation remains unclear. In this study, we collected clinical data from patients carrying APP mutations defined as pathogenic/likely pathogenic according to the American college of medical genetics and genomics (ACMG) guidelines. Then, we reanalyzed the clinical characteristics and identified genotype-phenotype correlations in APP mutations. Our results indicated that the clinical phenotypes of APP mutations are generally consistent with typical AD despite the fact that they show more non-demented symptoms and neurological symptoms. We also performed genotype-phenotype analysis according to the difference in APP processing caused by the mutations, and we found that there were indeed differences in onset age, behavioral and psychological disorders of dementia (BPSD) and myoclonus.

Processing of Mutant β-Amyloid Precursor Protein and the Clinicopathological Features of Familial Alzheimer's Disease

Alzheimer's disease (AD) is a complex, multifactorial disease involving many pathological mechanisms. Nonetheless, single pathogenic mutations in amyloid precursor protein (APP) or presenilin 1 or 2 can cause AD with almost all of the clinical and neuropathological features, and therefore, we believe an important mechanism of pathogenesis in AD could be revealed from examining pathogenic APP missense mutations. A comprehensive review of the literature, including clinical, neuropathological, cellular and animal model data, was conducted through PubMed and the databases of Alzforum mutations, HGMD, UniProt, and AD&FTDMDB. Pearson correlation analysis combining the clinical and neuropathological data and aspects of mutant APP processing in cellular models was performed. We find that an increase in Aβ42 has a significant positive correlation with the appearance of neurofibrillary tangles (NFTs) and tends to cause an earlier age of AD onset, while an increase in Aβ40 significantly increases the age at death. The increase in the α-carboxyl terminal fragment (CTF) has a significantly negative correlation with the age of AD onset, and β-CTF has a similar effect without statistical significance. Animal models show that intracellular Aβ is critical for memory defects. Based on these results and the fact that amyloid plaque burden correlates much less well with cognitive impairment than do NFT counts, we propose a "snowball hypothesis": the accumulation of intraneuronal NFTs caused by extracellular Aβ42 and the increase in intraneuronal APP proteolytic products (CTFs and Aβs) could cause cellular organelle stress that leads to neurodegeneration in AD, which then resembles the formation of abnormal protein "snowballs" both inside and outside of neurons.

Network abnormalities and interneuron dysfunction in Alzheimer disease

The function of neural circuits and networks can be controlled, in part, by modulating the synchrony of their components' activities. Network hypersynchrony and altered oscillatory rhythmic activity may contribute to cognitive abnormalities in Alzheimer disease (AD). In this condition, network activities that support cognition are altered decades before clinical disease onset, and these alterations predict future pathology and brain atrophy. Although the precise causes and pathophysiological consequences of these network alterations remain to be defined, interneuron dysfunction and network abnormalities have emerged as potential mechanisms of cognitive dysfunction in AD and related disorders. Here, we explore the concept that modulating these mechanisms may help to improve brain function in these conditions.