Identifying genetic interactions associated with late-onset Alzheimer's disease

Alzheimer's Disease: An Updated Overview of Its Genetics

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

Alzheimer's disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment

Recent proteome and transcriptome profiling of Alzheimer's disease (AD) brains reveals RNA splicing dysfunction and U1 small nuclear ribonucleoprotein (snRNP) pathology containing U1-70K and its N-terminal 40-KDa fragment (N40K). Here we present a causative role of U1 snRNP dysfunction to neurodegeneration in primary neurons and transgenic mice (N40K-Tg), in which N40K expression exerts a dominant-negative effect to downregulate full-length U1-70K. N40K-Tg recapitulates N40K insolubility, erroneous splicing events, neuronal degeneration and cognitive impairment. Specifically, N40K-Tg shows the reduction of GABAergic synapse components (e.g., the GABA receptor subunit of GABRA2), and concomitant postsynaptic hyperexcitability that is rescued by a GABA receptor agonist. Crossing of N40K-Tg and the 5xFAD amyloidosis model indicates that the RNA splicing defect synergizes with the amyloid cascade to remodel the brain transcriptome and proteome, deregulate synaptic proteins, and accelerate cognitive decline. Thus, our results support the contribution of U1 snRNP-mediated splicing dysfunction to AD pathogenesis.

The interaction of early life factors and depression-associated loci affecting the age at onset of the depression

Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10^-3) and males (β = 1.421, P = 7.64 × 10^-4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10^-8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10^-6) and KIRREL3 (rs535603, P = 4.84 × 10^-6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.

Effects of Specific Inhibitors for CaMK1D on a Primary Neuron Model for Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia worldwide. Despite extensive research and targeting of the main molecular components of the disease, beta-amyloid (Aβ) and tau, there are currently no treatments that alter the progression of the disease. Here, we examine the effects of two specific kinase inhibitors for calcium/calmodulin-dependent protein kinase type 1D (CaMK1D) on Aβ-mediated toxicity, using mouse primary cortical neurons. Tau hyperphosphorylation and cell death were used as AD indicators. These specific inhibitors were found to prevent Aβ induced tau hyperphosphorylation in culture, but were not able to protect cells from Aβ induced toxicity. While inhibitors were able to alter AD pathology in cell culture, they were insufficient to prevent cell death. With further research and development, these inhibitors could contribute to a multi-drug strategy to combat AD.

5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Diagnostic Biomarkers for Late-Onset Alzheimer's Disease

BACKGROUND

5-Hydroxymethylcytosine (5hmC) is an epigenetic DNA modification that is highly abundant in central nervous system. It has been reported that DNA 5hmC dysregulation play a critical role in Alzheimer's disease (AD) pathology. Changes in 5hmC signatures can be detected in circulating cell-free DNA (cfDNA), which has shown potential as a non-invasive liquid biopsy material.

OBJECTIVE

However, the genome-wide profiling of 5hmC in cfDNA and its potential for the diagnosis of AD has not been reported to date.

METHODS

We carried out a case-control study and used a genome-wide chemical capture followed by high-throughput sequencing to detect the genome-wide profiles of 5hmC in human cfDNA and identified differentially hydroxymethylated regions (DhMRs) in late-onset AD patients and the control.

RESULTS

We discovered significant differences of 5hmC enrichment in gene bodies which were linked to multiple AD pathogenesis-associated signaling pathways in AD patients compared with cognitively normal controls, indicating they can be well distinguished from normal controls by DhMRs in cfDNA. Specially, we identified 7 distinct genes (RABEP1, CPNE4, DNAJC15, REEP3, ROR1, CAMK1D, and RBFOX1) with predicting diagnostic potential based on their significant correlations with MMSE and MoCA scores of subjects.

CONCLUSION

The present results suggest that 5hmC markers derived from plasma cfDNA can served as an effective, minimally invasive biomarkers for clinical auxiliary diagnosis of late-onset AD.

GSAP regulates lipid homeostasis and mitochondrial function associated with Alzheimer's disease

Biochemical, pathogenic, and human genetic data confirm that GSAP (γ-secretase activating protein), a selective γ-secretase modulatory protein, plays important roles in Alzheimer's disease (AD) and Down's syndrome. However, the molecular mechanism(s) underlying GSAP-dependent pathogenesis remains largely elusive. Here, through unbiased proteomics and single-nuclei RNAseq, we identified that GSAP regulates multiple biological pathways, including protein phosphorylation, trafficking, lipid metabolism, and mitochondrial function. We demonstrated that GSAP physically interacts with the Fe65-APP complex to regulate APP trafficking/partitioning. GSAP is enriched in the mitochondria-associated membrane (MAM) and regulates lipid homeostasis through the amyloidogenic processing of APP. GSAP deletion generates a lipid environment unfavorable for AD pathogenesis, leading to improved mitochondrial function and the rescue of cognitive deficits in an AD mouse model. Finally, we identified a novel GSAP single-nucleotide polymorphism that regulates its brain transcript level and is associated with an increased AD risk. Together, our findings indicate that GSAP impairs mitochondrial function through its MAM localization and that lowering GSAP expression reduces pathological effects associated with AD.

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.

Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data

Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.

Novel Alzheimer's disease risk variants identified based on whole-genome sequencing of APOE ε4 carriers

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E-07) and rs12594991 (P = 2.03E-07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

Molecular Genetics of Early- and Late-Onset Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia in the elderly and this complex disorder is associated with environmental as well as genetic factors. Early-onset AD (EOAD) and late-onset AD (LOAD, more common) are major identified types of AD. The genetics of EOAD is extensively understood, with three gene variants such as APP, PSEN1, and PSEN2 leading to the disease. Some common alleles, including APOE, are effectively associated with LOAD identified, but the genetics of LOAD is not clear to date. It has been accounted that about 5-10% of EOAD patients can be explained through mutations in the three familiar genes of EOAD. The APOE ε4 allele augmented the severity of EOAD risk in carriers, and the APOE ε4 allele was considered as a hallmark of EOAD. A great number of EOAD patients, who are not genetically explained, indicate that it is not possible to identify disease-triggering genes yet. Although several genes have been identified by using the technology of next-generation sequencing in EOAD families, including SORL1, TYROBP, and NOTCH3. A number of TYROBP variants are identified through exome sequencing in EOAD patients and these TYROBP variants may increase the pathogenesis of EOAD. The existence of the ε4 allele is responsible for increasing the severity of EOAD. However, several ε4 allele carriers propose the presence of other LOAD genetic as well as environmental risk factors that are not identified yet. It is urgent to find out missing genetics of EOAD and LOAD etiology to discover new potential genetic facets which will assist in understanding the pathological mechanism of AD. These investigations should contribute to developing a new therapeutic candidate for alleviating, reversing and preventing AD. This article, based on current knowledge, represents the overview of the susceptible genes of EOAD, and LOAD. Next, we represent the probable molecular mechanism that might elucidate the genetic etiology of AD and highlight the role of massively parallel sequencing technologies for novel gene discoveries.

The exploration of novel Alzheimer's therapeutic agents from the pool of FDA approved medicines using drug repositioning, enzyme inhibition and kinetic mechanism approaches

Novel drug development is onerous, time consuming and overpriced process with particularly low success and relatively high enfeebling rates. To overcome this burden, drug repositioning approach is being used to predict the possible therapeutic effects of FDA approved drugs in different diseases. Herein, we designed a computational and enzyme inhibitory mechanistic approach to fetch the promising drugs from the pool of FDA approved drugs against AD. The binding interaction patterns and conformations of screened drugs within active region of AChE were confirmed through molecular docking profiles. The possible associations of selected drugs with AD genes were predicted by pharmacogenomics analysis and confirmed through data mining. The stability behaviour of docked complexes (Drugs-AChE) were checked by MD simulations. The possible therapeutic potential of repositioned drugs against AChE were checked by in vitro analysis. Taken together, Cinitapride displayed a comparable results with standard and can be used as possible therapeutic agent in the treatment of AD.

A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images

Background

It is becoming increasingly clear that the quantification of mitochondria and synapses is of great significance to understand the function of biological nervous systems. Electron microscopy (EM), with the necessary resolution in three directions, is the only available imaging method to look closely into these issues. Therefore, estimating the number of mitochondria and synapses from the serial EM images is coming into prominence. Since previous studies have achieved preferable 2D segmentation performance, it holds great promise to obtain the 3D connection relationship from the 2D segmentation results.

Results

In this paper, we improve upon Matlab’s function bwconncomp and propose a fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images. To benchmark the performance of the proposed method, two EM datasets with the annotated ground truth are produced for mitochondria and synapses, respectively. Experimental results show that the proposed method can achieve the preferable connection performance that closely matches the ground truth. Moreover, it greatly reduces the computational burden and alleviates the memory requirements compared with the function bwconncomp.

Conclusions

The proposed method can be deemed as an effective strategy to obtain the 3D connection relationship from serial mitochondria and synapse segmentations. It is helpful to accurately and quickly quantify the statistics of the numbers, volumes, surface areas, and lengths, which will greatly facilitate the data analysis of neurobiology research.

LEAP: biomarker inference through learning and evaluating association patterns

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability.