Impact of Common Variations in PLD3 on Neuroimaging Phenotypes in Non-demented Elders

The Radiogenomics of Late-onset Alzheimer Disease

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

Rare Variants in PLD3 Increase Risk for Alzheimer's Disease in Han Chinese

Next-generation sequencing studies had reported that a rare coding variant p.V232M in PLD3 was associated with Alzheimer's disease (AD) and a two-fold increased AD risk in European cohorts. To test whether coding region variants of PLD3 were associated with AD in a large Han Chinese cohort, we performed sequencing to analyze all exons of PLD3, and demonstrated that rare variants p.I163M and c.1020-8G>A conferred considerable risk of late-onset AD (LOAD) in our cohort. Meanwhile, the previously reported p.V232M variant was identified in our AD group. These findings indicate that rare variants of PLD3 may play an important role in LOAD in northern Han Chinese.

PLD3 Rare Variants Identified in Late-Onset Alzheimer's Disease Affect Amyloid-β Levels in Cellular Model

Next-generation sequencing studies have reported that rare variants in PLD3 were associated with increased risk of late-onset Alzheimer’s disease (LOAD) in European cohorts. The association has been replicated in a Han Chinese cohort, two rare variants p.I163M in exon7 and p.R356H in exon11 of PLD3 were found to be associated with LOAD risk. Whether these variants have deleterious effects on protein function, and the underlying mechanisms by which they influence LOAD pathogenesis are unknown. Our results are the first to validate the hypothesis that these variants could lead to reduced PLD3 activity and affect amyloid-β levels in cellular model of AD, possibly via autophagy-dependent mTOR signaling pathway, indicating that PLD3 may represent a new therapeutic target for AD.

Impact of late-onset Alzheimer's genetic risk factors on beta-amyloid endocytic production

The increased production of the 42 aminoacids long beta-amyloid (Aβ42) peptide has been established as a causal mechanism of the familial early onset Alzheimer's disease (AD). In contrast, the causal mechanisms of the late-onset AD (LOAD), that affects most AD patients, remain to be established. Indeed, Aβ42 accumulation has been detected more than 30 years before diagnosis. Thus, the mechanisms that control Aβ accumulation in LOAD likely go awry long before pathogenesis becomes detectable. Early on, APOE4 was identified as the biggest genetic risk factor for LOAD. However, since APOE4 is not present in all LOAD patients, genome-wide association studies of thousands of LOAD patients were undertaken to identify other genetic variants that could explain the development of LOAD. PICALM, BIN1, CD2AP, SORL1, and PLD3 are now with APOE4 among the identified genes at highest risk in LOAD that have been implicated in Aβ42 production. Recent evidence indicates that the regulation of the endocytic trafficking of the amyloid precursor protein (APP) and/or its secretases to and from sorting endosomes is determinant for Aβ42 production. Thus, here, we will review the described mechanisms, whereby these genetic risk factors can contribute to the enhanced endocytic production of Aβ42. Dissecting causal LOAD mechanisms of Aβ42 accumulation, underlying the contribution of each genetic risk factor, will be required to identify therapeutic targets for novel personalized preventive strategies.

State of Play in Alzheimer's Disease Genetics

Alzheimer's disease (AD), the main form of dementia in the elderly, is the most common progressive neurodegenerative disease characterized by rapidly progressive cognitive dysfunction and behavior impairment. AD exhibits a considerable heritability and great advances have been made in approaches to searching the genetic etiology of AD. In AD genetic studies, methods have developed from classic linkage-based and candidate-gene-based association studies to genome-wide association studies (GWAS) and next generation sequencing (NGS). The identification of new susceptibility genes has provided deeper insights to understand the mechanisms underlying AD. In addition to searching novel genes associated with AD in large samples, the NGS technologies can also be used to shed light on the 'black matter' discovery even in smaller samples. The shift in AD genetics between traditional studies and individual sequencing will allow biomaterials of each patient as the central unit of genetic studies. This review will cover genetic findings in AD and consequences of AD genetic findings. Firstly, we will discuss the discovery of mutations in APP, PSEN1, PSEN2, APOE, and ADAM10. Then we will summarize and evaluate the information obtained from GWAS of AD. Finally, we will outline the efforts to identify rare variants associated with AD using NGS.

MS4A6A genotypes are associated with the atrophy rates of Alzheimer's disease related brain structures

Membrane-spanning 4-domains, subfamily A, member 6A (MS4A6A) has been identified as susceptibility loci of Alzheimer's disease (AD) by several recent genome-wide association studies (GWAS), whereas little is known about the potential roles of these variants in the brain structure and function of AD. In this study, we included a total of 812 individuals from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Using multiple linear regression models, we found MS4A6A genotypes were strongly related to atrophy rate of left middle temporal (rs610932: Pc = 0.017, rs7232: Pc = 0.022), precuneus (rs610932: Pc = 0.015) and entorhinal (rs610932, Pc = 0.022) on MRI in the entire group. In the subgroup analysis, MS4A6A SNPs were significantly accelerated the percentage of volume loss of middle temporal, precuneus and entorhinal, especially in the MCI subgroup. These findings reveal that MS4A6A genotypes affect AD specific brain structures which supported the possible role of MS4A6A polymorphisms in influencing AD-related neuroimaging phenotypes.

Unconventional Trafficking of Mammalian Phospholipase D3 to Lysosomes

Variants in the phospholipase D3 (PLD3) gene have genetically been linked to late-onset Alzheimer's disease. We present a detailed biochemical analysis of PLD3 and reveal its endogenous localization in endosomes and lysosomes. PLD3 reaches lysosomes as a type II transmembrane protein via a (for mammalian cells) uncommon intracellular biosynthetic route that depends on the ESCRT (endosomal sorting complex required for transport) machinery. PLD3 is sorted into intraluminal vesicles of multivesicular endosomes, and ESCRT-dependent sorting correlates with ubiquitination. In multivesicular endosomes, PLD3 is subjected to proteolytic cleavage, yielding a stable glycosylated luminal polypeptide and a rapidly degraded N-terminal membrane-bound fragment. This pathway closely resembles the delivery route of carboxypeptidase S to the yeast vacuole. Our experiments reveal a biosynthetic route of PLD3 involving proteolytic processing and ESCRT-dependent sorting for its delivery to lysosomes in mammalian cells.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.