Inference of Causative Genes for Alzheimer's Disease Due to Dosage Imbalance

Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer's disease

The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

The contribution of CNVs to the most common aging-related neurodegenerative diseases

Alzheimer and Parkinson's diseases are neurodegenerative aging-related pathological conditions, mainly caused by the interplay of genetic and non-genetic factors and whose incidence rate is going to drastically increase given the growing life expectancy. To address these complex multifactorial traits, a systems biology strategy is needed to highlight genotype-phenotype correlations as well as overlapping gene signatures. Copy number variants (CNVs) are structural chromosomal imbalances that can have pathogenic nature causing or contributing to the disease onset or progression. Moreover, neurons affected by CNVs have been found to decline in number depending on age in healthy controls and may be selectively vulnerable to aging-related cell-death. In this review, we aim to update the reader on the role of these variations in the pathogenesis of Alzheimer and Parkinson diseases. To widen the comprehension of pathogenic mechanisms underlying them, we discuss variations detected from blood or brain specimens, as well as overlapped signatures between the two pathologies.

Identifying potential biomarkers of nonalcoholic fatty liver disease via genome-wide analysis of copy number variation

BACKGROUND

The prevalence of Non-alcoholic fatty liver disease (NAFLD) is increasing and emerging as a global health burden. In addition to environmental factors, numerous studies have shown that genetic factors play an important role in the development of NAFLD. Copy number variation (CNV) as a genetic variation plays an important role in the evaluation of disease susceptibility and genetic differences. The aim of the present study was to assess the contribution of CNV to the evaluation of NAFLD in a Chinese population.

METHODS

Genome-wide analysis of CNV was performed using high-density comparative genomic hybridisation microarrays (ACGH). To validate the CNV regions, TaqMan real-time quantitative PCR (qPCR) was utilized.

RESULTS

A total of 441 CNVs were identified, including 381 autosomal CNVs and 60 sex chromosome CNVs. By merging overlapping CNVs, a genomic CNV map of NAFLD patients was constructed. A total of 338 autosomal CNVRs were identified, including 275 CNVRs with consistent trends (197 losses and 78 gains) and 63 CNVRs with inconsistent trends. The length of the 338 CNVRs ranged from 5.7 kb to 2.23 Mb, with an average size of 117.44 kb. These CNVRs spanned 39.70 Mb of the genome and accounted for ~ 1.32% of the genome sequence. Through Gene Ontology and genetic pathway analysis, we found evidence that CNVs involving nine genes may be associated with the pathogenesis of NAFLD progression. One of the genes (NLRP4 gene) was selected and verified by quantitative PCR (qPCR) method with large sample size. We found the copy number deletion of NLRP4 was related to the risk of NAFLD.

CONCLUSIONS

This study indicate the copy number variation is associated with NAFLD. The copy number deletion of NLRP4 was related to the risk of NAFLD. These results could prove valuable for predicting patients at risk of developing NAFLD.

Polymorphism in the MAGI2 Gene Modifies the Effect of Amyloid β on Neurodegeneration

INTRODUCTION

A weak association between amyloid β (Aβ) deposition and neurodegeneration biomarkers, such as brain atrophy, has been repeatedly reported in a subset of patients with Alzheimer disease, suggesting individual differences in response to Aβ deposition.

METHODS

Here, we performed a genome-wide interaction study to identify single-nucleotide polymorphism (SNP) that modify the effect of Aβ (measured by 18F-florbetapir positron emission tomography) on brain atrophy (measured by cortical thickness using magnetic resonance imaging). We used magnetic resonance imaging, positron emission tomography, cerebrospinal fluid, and genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database [discovery cohort, ADNI-GO/2 (n=723) and replication cohort, ADNI-1 (n=129)].

RESULTS

We identified a genome-wide suggestive interaction of rs3807779 SNP (β=-0.14, SE=0.029, P=9.08×10-7) in the discovery cohort. The greater dosage of rs3807779 SNP increased the detrimental effect of Aβ deposition on cortical thickness. In replication analyses, the congruent results were replicated to confirm our findings. Furthermore, rs3807779 SNP augmented the detrimental effect of Aβ deposition on cognitive function. Genetic profiling showed that rs3807779 has chromatin interactions with the promoter region of MAGI2 gene, suggesting its association with MAGI2 expression.

CONCLUSIONS

These findings demonstrate that subjects carrying the rs3807779 SNP are more susceptible to Aβ-related neurodegeneration.

Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation

Background

Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis.

Methods

In this study, we used HotNet2, a heat diffusion‐based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level.

Results

Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS‐derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS‐derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation‐caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation‐caused varied protein‐protein interactions. In addition, 3,802 genes (including 46 function‐unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments.

Conclusions

Systems genetics algorithm HotNet2 can efficiently establish genotype‐phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2‐calculated disease‐gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome‐wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.

Copy Number Variants in miR-138 as a Potential Risk Factor for Early-Onset Alzheimer's Disease

Early-onset Alzheimer's disease (EOAD) accounts for 5-10% of all AD cases, with a heritability ranging between 92% to 100%. With the exception of rare mutations in APP, PSEN1, and PSEN2 genes causing autosomal dominant EOAD, little is known about the genetic factors underlying most of the EOAD cases. In this study, we hypothesized that copy number variations (CNVs) in microRNA (miR) genes could contribute to risk for EOAD. miRs are short non-coding RNAs previously implicated in the regulation of AD-related genes and phenotypes. Using whole exome sequencing, we screened a series of 546 EOAD patients negative for autosomal dominant EOAD mutations and 597 controls. We identified 86 CNVs in miR genes of which 31 were exclusive to EOAD cases, including a duplication of the MIR138-2 locus. In functional studies in human cultured cells, we could demonstrate that miR-138 overexpression leads to higher Aβ production as well as tau phosphorylation, both implicated in AD pathophysiology. These changes were mediated in part by GSK-3β and FERMT2, a potential risk factor for AD. Additional disease-related genes were also prone to miR-138 regulation including APP and BACE1. This study suggests that increased gene dosage of MIR138-2 could contribute to risk for EOAD by regulating different biological pathways implicated in amyloid and tau metabolism. Additional studies are now required to better understand the role of miR-CNVs in EOAD.

Sensitivity to gene dosage and gene expression affects genes with copy number variants observed among neuropsychiatric diseases

BACKGROUND

Copy number variants (CNVs) have been reported to be associated with diseases, traits, and evolution. However, it is hard to determine which gene should have priority as a target for further functional experiments if a CNV is rare or a singleton. In this study, we attempted to overcome this issue by using two approaches: by assessing the influences of gene dosage sensitivity and gene expression sensitivity. Dosage sensitive genes derived from two-round whole-genome duplication in previous studies. In addition, we proposed a cross-sectional omics approach that utilizes open data from GTEx to assess the effect of whole-genome CNVs on gene expression.

METHODS

Affymetrix Genome-Wide SNP Array 6.0 was used to detect CNVs by PennCNV and CNV Workshop. After quality controls for population stratification, family relationship and CNV detection, 287 patients with narcolepsy, 133 patients with essential hypersomnia, 380 patients with panic disorders, 164 patients with autism, 784 patients with Alzheimer disease and 1280 healthy individuals remained for the enrichment analysis.

RESULTS

Overall, significant enrichment of dosage sensitive genes was found across patients with narcolepsy, panic disorders and autism. Particularly, significant enrichment of dosage-sensitive genes in duplications was observed across all diseases except for Alzheimer disease. For deletions, less or no enrichment of dosage-sensitive genes with deletions was seen in the patients when compared to the healthy individuals. Interestingly, significant enrichments of genes with expression sensitivity in brain were observed in patients with panic disorder and autism. While duplications presented a higher burden, deletions did not cause significant differences when compared to the healthy individuals. When we assess the effect of sensitivity to genome dosage and gene expression at the same time, the highest ratio of enrichment was observed in the group including dosage-sensitive genes and genes with expression sensitivity only in brain. In addition, shared CNV regions among the five neuropsychiatric diseases were also investigated.

CONCLUSIONS

This study contributed the evidence that dosage-sensitive genes are associated with CNVs among neuropsychiatric diseases. In addition, we utilized open data from GTEx to assess the effect of whole-genome CNVs on gene expression. We also investigated shared CNV region among neuropsychiatric diseases.

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

A Comprehensive Atlas of E3 Ubiquitin Ligase Mutations in Neurological Disorders

Protein ubiquitination is a posttranslational modification that plays an integral part in mediating diverse cellular functions. The process of protein ubiquitination requires an enzymatic cascade that consists of a ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2) and an E3 ubiquitin ligase (E3). There are an estimated 600-700 E3 ligase genes representing ~5% of the human genome. Not surprisingly, mutations in E3 ligase genes have been observed in multiple neurological conditions. We constructed a comprehensive atlas of disrupted E3 ligase genes in common (CND) and rare neurological diseases (RND). Of the predicted and known human E3 ligase genes, we found ~13% were mutated in a neurological disorder with 83 total genes representing 70 different types of neurological diseases. Of the E3 ligase genes identified, 51 were associated with an RND. Here, we provide an updated list of neurological disorders associated with E3 ligase gene disruption. We further highlight research in these neurological disorders and discuss the advanced technologies used to support these findings.

Evolutionary and genetic features of drug targets

In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of "more investments, fewer drugs" in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community.

Copy Number Variation Disorders

PURPOSE OF REVIEW

Copy number variation (CNV) disorders arise from the dosage imbalance of one or more gene(s), resulting from deletions, duplications or other genomic rearrangements that lead to the loss or gain of genetic material. Several disorders, characterized by multiple birth defects and neurodevelopmental abnormalities, have been associated with relatively large (>1 Mb) and often recurrent CNVs. CNVs have also been implicated in the etiology of neuropsychiatric disorders including autism and schizophrenia as well as other common complex diseases. Thus, CNVs have a significant impact on human health and disease.

RECENT FINDINGS

The use of increasingly higher resolution, genomewide analysis has greatly enhanced the detection of genetic variation, including CNVs. Furthermore, the availability of comprehensive genetic variation data from large cohorts of healthy controls has the potential to greatly improve the identification of disease associated genetic variants in patient samples.

SUMMARY

This review discusses the current knowledge about CNV disorders, including the mechanisms underlying their formation and phenotypic outcomes, and the advantages and limitations of current methods of detection and disease association.