Impacts of AD-Related ABCA7 and CLU Variants on Default Mode Network Connectivity in Healthy Middle-Age Adults

ABCA7-dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling

Genetic variants in ABCA7, an Alzheimer's disease (AD)-associated gene, elevate AD risk, yet its functional relevance to the etiology is unclear. We generated a CRISPR-Cas9-mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single-cell transcriptomics in heterozygous abca7+/- knockout combined with Aβ42 toxicity revealed that ABCA7 is crucial for neuropeptide Y (NPY), brain-derived neurotrophic factor (BDNF), and nerve growth factor receptor (NGFR) expressions, which are crucial for synaptic integrity, astroglial proliferation, and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density, which are rescuable with ectopic NPY. In induced pluripotent stem cell-derived human neurons exposed to Aβ42, ABCA7-/- suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages, genetic variants in NPY associated with AD, and epigenetic changes in NPY, NGFR, and BDNF promoters linked to ABCA7 variants. Therefore, ABCA7-dependent NPY signaling via BDNF-NGFR maintains synaptic integrity, implicating its impairment in increased AD risk through reduced brain resilience.

The ABC's of Alzheimer risk gene ABCA7

Alzheimer's disease (AD) is a growing problem worldwide. Since ABCA7's identification as a risk gene, it has been extensively researched for its role in the disease. We review its recently characterized structure and what the mechanistic insights teach us about its function. We furthermore provide an overview of identified ABCA7 mutations, their presence in different ancestries and protein domains and how they might cause AD. For ABCA7 PTC variants and a VNTR expansion, haploinsufficiency is proposed as the most likely mode-of-action, although splice events could further influence disease risk. Overall, the need to better understand expression of canonical ABCA7 and its isoforms in disease is indicated. Finally, ABCA7's potential functions in lipid metabolism, phagocytosis, amyloid deposition, and the interplay between these three, is described. To conclude, in this review, we provide a comprehensive overview and discussion about the current knowledge on ABCA7 in AD, and what research questions remain. HIGHLIGHTS: Alzheimer's risk-increasing variants in ABCA7 can be found in up to 7% of AD patients. We review the recently characterized protein structure of ABCA7. We present latest insights in genetics, expression patterns, and functions of ABCA7.

ABCA7-dependent Neuropeptide-Y signalling is a resilience mechanism required for synaptic integrity in Alzheimer's disease

Alzheimer's disease (AD) remains a complex challenge characterized by cognitive decline and memory loss. Genetic variations have emerged as crucial players in the etiology of AD, enabling hope for a better understanding of the disease mechanisms; yet the specific mechanism of action for those genetic variants remain uncertain. Animal models with reminiscent disease pathology could uncover previously uncharacterized roles of these genes. Using CRISPR/Cas9 gene editing, we generated a knockout model for abca7, orthologous to human ABCA7 - an established AD-risk gene. The abca7 +/- zebrafish showed reduced astroglial proliferation, synaptic density, and microglial abundance in response to amyloid beta 42 (Aβ42). Single-cell transcriptomics revealed abca7 -dependent neuronal and glial cellular crosstalk through neuropeptide Y (NPY) signaling. The abca7 knockout reduced the expression of npy, bdnf and ngfra , which are required for synaptic integrity and astroglial proliferation. With clinical data in humans, we showed reduced NPY in AD correlates with elevated Braak stage, predicted regulatory interaction between NPY and BDNF , identified genetic variants in NPY associated with AD, found segregation of variants in ABCA7, BDNF and NGFR in AD families, and discovered epigenetic changes in the promoter regions of NPY, NGFR and BDNF in humans with specific single nucleotide polymorphisms in ABCA7 . These results suggest that ABCA7-dependent NPY signaling is required for synaptic integrity, the impairment of which generates a risk factor for AD through compromised brain resilience.

Abstract Figure

ABCA7-Associated Clinical Features and Molecular Mechanisms in Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of neurodegenerative disease and its pathogenesis is still unclear. Genetic factors are thought to account for a large proportion of the overall AD phenotypes. ATP-binding cassette transporter A7 (ABCA7) is one of the most important risk gene for AD. Multiple forms of ABCA7 variants significantly increase the risk of AD, such as single-nucleotide polymorphisms, premature termination codon variants, missense variants, variable number tandem repeat, mutations, and alternative splicing. AD patients with ABCA7 variants usually exhibit typical clinical and pathological features of traditional AD with a wide age of onset range. ABCA7 variants can alter ABCA7 protein expression levels and protein structure to affect protein functions such as abnormal lipid metabolism, amyloid precursor protein (APP) processing, and immune cell function. Specifically, ABCA7 deficiency can cause neuronal apoptosis by inducing endoplasmic reticulum stress through the PERK/eIF2α pathway. Second, ABCA7 deficiency can increase Aβ production by upregulating the SREBP2/BACE1 pathway and promoting APP endocytosis. In addition, the ability of microglia to phagocytose and degrade Aβ is destroyed by ABCA7 deficiency, leading to reduced clearance of Aβ. Finally, disturbance of lipid metabolism may also be an important method by which ABCA7 variants influence the incidence rate of AD. In the future, more attention should be given to different ABCA7 variants and ABCA7 targeted therapies for AD.

A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer's disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions. In this method, the deep subspace reconstruction technique was applied to retrieve the nonlinear properties of the original data, and hypergraphs were used to mine the high-order correlation between two types of rebuilt data. The molecular biological analysis of the experimental findings demonstrated that our algorithm was capable of extracting more valuable time series correlation from the real data obtained by the AD neuroimaging program and finding AD biomarkers across multiple time points. Additionally, we used regression analysis to verify the close relationship between the extracted top brain areas and top genes and found the deep subspace reconstruction approach with a multi-layer neural network was helpful in enhancing clustering performance.

Association of adenosine triphosphate-related genes to major depression and suicidal behavior: Cognition as a potential mediator

BACKGROUND

Soluble epoxide hydrolase (sEH, encoded by EPHX2) and P2X2 (a subtype of ATP receptors) may mediate the antidepressant-like effects of ATP. We sought to determine whether polymorphisms and mRNA expression of EPHX2 and P2X2 are associated with depression and suicidal behavior and how cognition may mediate such associations.

METHOD

We examined 83 single nucleotide polymorphisms (SNPs) of EPHX2 and P2X2. Subjects were MDD suicide attempters (N = 143), MDD non-suicide attempters (N = 248), and healthy volunteers (HV, N = 110). Data on demographics, depression severity, and suicide attempts were collected. Participants completed a set of cognitive tasks. Polymorphisms were genotyped using MALDI-TOF MS within the MassARRAY system. The expression of mRNA was measured using real-time polymerase chain reaction (RT-PCR).

RESULTS

Cognitive function was a significant mediator (p = 0.006) of the genetic effect on depression. Allele C of rs202059124 was associated with depression risk (OR = 11.57, 95%CI: 2.33-209.87, p = 0.0181). A significant relationship was found between P2X2 mRNA expression and depression (OR = 0.68, 95%CI: 0.49-0.94, p = 0.0199). One haploblock (rs9331942 and rs2279590) was associated with suicide attempts: subjects with haplotype GC (frequency = 19.8 %, p = 0.017) and AT (frequency = 35.2 %, p < 0.001) had a lower rate of suicide attempts.

CONCLUSIONS

Our results confirmed that cognitive impairment plays a role in the effect of rs9331949 on depression. Moreover, we confirmed a relationship between P2X2, EPHX2, and MDD in humans and presented preliminary haplotype-based evidence that implicates EPHX2 in suicide.

LIMITATIONS

The main limitation of this study is the limited sample size. More comprehensive and multi-domain cognition tasks and different assessment measures are required in further study.

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Objectives

The abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.

Methods

Resting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.

Results

We found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.

Conclusion

The decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.

Clinical Trial Registration

: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

ABCA7, a Genetic Risk Factor Associated with Alzheimer's Disease Risk in African Americans

African American/Black adults are twice as likely to have Alzheimer's disease (AD) compared to non-Hispanic White adults. Genetics partially contributes to this disparity in AD risk, among other factors, as there are several genetic variants associated with AD that are more prevalent in individuals of African or European ancestry. The phospholipid-transporting ATPase ABCA7 (ABCA7) gene has stronger associations with AD risk in individuals with African ancestry than in individuals with European ancestry. In fact, ABCA7 has been shown to have a stronger effect size than the apolipoprotein E (APOE) ɛ4 allele in African American/Black adults. ABCA7 is a transmembrane protein involved in lipid homeostasis and phagocytosis. ABCA7 dysfunction is associated with increased amyloid-beta production, reduced amyloid-beta clearance, impaired microglial response to inflammation, and endoplasmic reticulum stress. This review explores the impact of ABCA7 mutations that increase AD risk in African American/Black adults on ABCA7 structure and function and their contributions to AD pathogenesis. The combination of biochemical/biophysical and 'omics-based studies of these variants needed to elucidate their downstream impact and molecular contributions to AD pathogenesis is highlighted.

Complement System in Alzheimer's Disease

Alzheimer’s disease is a type of dementia characterized by problems with short-term memory, cognition, and difficulties with activities of daily living. It is a progressive, neurodegenerative disorder. The complement system is an ancient part of the innate immune system and comprises of more than thirty serum and membrane-bound proteins. This system has three different activating pathways and culminates into the formation of a membrane attack complex that ultimately causes target cell lysis (usually pathogens) The complement system is involved in several important functions in the central nervous system (CNS) that include neurogenesis, synaptic pruning, apoptosis, and neuronal plasticity. Here, we discuss how the complement system is involved in the effective functioning of CNS, while also contributing to chronic neuroinflammation leading to neurodegenerative disorders such as Alzheimer’s disease. We also discuss potential targets in the complement system for stopping its harmful effects via neuroinflammation and provide perspective for the direction of future research in this field.

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).