Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts

Amyloid-induced neurodegeneration: A comprehensive review through aggregomics perception of proteins in health and pathology

Amyloidosis of protein caused by fibrillation and aggregation are some of the most exciting new edges not only in protein sciences but also in molecular medicines. The present review discusses recent advancements in the field of neurodegenerative diseases and therapeutic applications with ongoing clinical trials, featuring new areas of protein misfolding resulting in aggregation. The endogenous accretion of protein fibrils having fibrillar morphology symbolizes the beginning of neuro-disorders. Prognostic amyloidosis is prominent in numerous degenerative infections such as Alzheimer's and Parkinson's disease, Amyotrophic lateral sclerosis (ALS), etc. However, the molecular basis determining the intracellular or extracellular evidence of aggregates, playing a significant role as a causative factor in neurodegeneration is still unclear. Structural conversions and protein self-assembly resulting in the formation of amyloid oligomers and fibrils are important events in the pathophysiology of the disease. This comprehensive review sheds light on the evolving landscape of potential treatment modalities, highlighting the ongoing clinical trials and the potential socio-economic impact of novel therapeutic interventions in the realm of neurodegenerative diseases. Furthermore, many drugs are undergoing different levels of clinical trials that would certainly help in treating these disorders and will surely improve the socio-impact of human life.

Development of Computational Correlations among Known Drug Scaffolds and their Target-Specific Non-Coding RNA Scaffolds of Alzheimer's Disease

BACKGROUND

Alzheimer's disease is the most common neurodegenerative disorder. Recent development in sciences has also identified the pivotal role of microRNAs (miRNAs) in AD pathogenesis.

OBJECTIVES

We proposed a novel method to identify AD pathway-specific statistically significant miRNAs from the targets of known AD drugs. Moreover, microRNA scaffolds and corresponding drug scaffolds of different pathways were also discovered.

MATERIAL AND METHODS

A Wilcoxon signed-rank test was performed to identify pathway-specific significant miRNAs. We generated feed-forward loop regulations of microRNA-TF-gene-based networks, studied the minimum free energy structures of pre-microRNA sequences, and clustered those microRNAs with their corresponding structural motifs of robust transcription factors. Conservation analyses of significant microRNAs were done, and the phylogenetic trees were constructed. We identified 3'UTR binding sites and chromosome locations of these significant microRNAs.

RESULTS

In this study, hsa-miR-4261, hsa-miR-153-5p, hsa-miR-6766, and hsa-miR-4319 were identified as key miRNAs for the ACHE pathway and hsa-miR-326, hsa-miR-6133, hsa-miR-4251, hsa-miR-3148, hsa-miR-10527-5p, hsa-miR-527, and hsa-miR-518a were identified as regulatory miRNAs for the NMDA pathway. These miRNAs were regulated by several AD-specific TFs, namely RAD21, FOXA1, and ESR1. It has been observed that anisole and adamantane are important chemical scaffolds to regulate these significant miRNAs.

CONCLUSION

This is the first study that developed a detailed correlation between known AD drug scaffolds and their AD target-specific miRNA scaffolds. This study identified chromosomal locations of microRNAs and corresponding structural scaffolds of transcription factors that may be responsible for miRNA co-regulation for Alzheimer's disease. Our study provides hope for therapeutic improvements in the existing microRNAs by regulating pathways and targets.

Identification of significant genes and therapeutic agents for breast cancer by integrated genomics

Breast cancer is the most commonly diagnosed malignancy in women; thus, more cancer prevention research is urgently needed. The aim of this study was to predict potential therapeutic agents for breast cancer and determine their molecular mechanisms using integrated bioinformatics. Summary data from a large genome-wide association study of breast cancer was derived from the UK Biobank. The gene expression profile of breast cancer was from the Oncomine database. We performed a network-wide association study and gene set enrichment analysis to identify the significant genes in breast cancer. Then, we performed Gene Ontology analysis using the STRING database and conducted Kyoto Encyclopedia of Genes and Genomes pathway analysis using Cytoscape software. We verified our results using the Gene Expression Profile Interactive Analysis, PROgeneV2, and Human Protein Atlas databases. Connectivity map analysis was used to identify small-molecule compounds that are potential therapeutic agents for breast cancer. We identified 10 significant genes in breast cancer based on the gene expression profile and genome-wide association study. A total of 65 small-molecule compounds were found to be potential therapeutic agents for breast cancer.

System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis

A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between Aβ, aging, and neurodegeneration within the most vulnerable neurons in AD.

Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease

Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson's disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer's patients or healthy subjects to determine the generalizability of our algorithm.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

The Alexipharmic Mechanisms of Five Licorice Ingredients Involved in CYP450 and Nrf2 Pathways in Paraquat-Induced Mice Acute Lung Injury

Oxidative stress is an important mechanism in acute lung injury (ALI) induced by paraquat (PQ), one of the most widely used herbicides in developing countries. In clinical prophylaxis and treatment, licorice is a widely used herbal medicine in China due to its strong alexipharmic characteristics. However, the corresponding biochemical mechanism of antioxidation and detoxification enzymes induced by licorice's ingredients is still not fully demonstrated. In this study, the detoxification effect of licorice was evaluated in vivo and in vitro. The detoxification and antioxidation effect of its active ingredients involved in the treatment was screened systematically according to Absorption, Distribution, Metabolism, and Excretion (ADME): predictions and evidence-based literature mining methods in silico approach. Data shows that licorice alleviate pulmonary edema and fibrosis, decrease Malondialdehyde (MDA) contents and increase Superoxide Dismutase (SOD) activity in PQ-induced ALI mice, protect the morphologic appearance of lung tissues, induce cytochrome 3A4 (CYA3A4) and Nuclear factor erythroid 2-related factor 2 (Nrf2) expression to active detoxification pathways, reduce the accumulation of PQ in vivo, protect or improve the liver and renal function of mice, and increase the survival rate. The 104 genes of PPI network contained all targets of licorice ingredients and PQ, which displayed the two redox regulatory enzymatic group modules cytochrome P450 (CYP450) and Nrf2 via a score-related graphic theoretic clustering algorithm in silico. According to ADME properties, glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A were employed due to their oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.1, and being highly associated with CYP450 and Nrf2 pathways, as potential activators to halt PQ-induced cells death in vitro. Both 3A4 inhibitor and silenced Nrf2 gene decreased the alexipharmic effects of those ingredients significantly. All these disclosed the detoxification and antioxidation effects of licorice on acute lung injury induced by PQ, and glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A upregulated CYP450 and Nrf2 pathways underlying the alexipharmic mechanisms of licorice.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.

A Network of Genetic Effects on Non-Demented Cognitive Aging: Alzheimer's Genetic Risk (CLU + CR1 + PICALM) Intensifies Cognitive Aging Genetic Risk (COMT + BDNF) Selectively for APOEɛ4 Carriers

BACKGROUND

Trajectories of complex neurocognitive phenotypes in preclinical aging may be produced differentially through selective and interactive combinations of genetic risk.

OBJECTIVE

We organize three possible combinations into a "network" of genetic risk indices derived from polymorphisms associated with normal and impaired cognitive aging, as well as Alzheimer's disease (AD). Specifically, we assemble and examine three genetic clusters relevant to non-demented cognitive trajectories: 1) Apolipoprotein E (APOE), 2) a Cognitive Aging Genetic Risk Score (CA-GRS; Catechol-O-methyltransferase + Brain-derived neurotrophic factor), and 3) an AD-Genetic Risk Score (AD-GRS; Clusterin + Complement receptor 1 + Phosphatidylinositol-binding clathrin assembly protein).

METHOD

We use an accelerated longitudinal design (n = 634; age range = 55-95 years) to test whether AD-GRS (low versus high) moderates the effect of increasing CA-GRS risk on executive function (EF) performance and change as stratified by APOE status (ɛ4+ versus ɛ4-).

RESULTS

APOEɛ4 carriers with high AD-GRS had poorer EF performance at the centering age (75 years) and steeper 9-year decline with increasing CA-GRS but this association was not present in APOEɛ4 carriers with low AD-GRS.

CONCLUSIONS

APOEɛ4 carriers with high AD-GRS are at elevated risk of cognitive decline when they also possess higher CA-GRS risk. Genetic risk from both common cognitive aging and AD-related indices may interact in intensification networks to differentially predict (1) level and trajectories of EF decline and (2) potential selective vulnerability for transitions into impairment and dementia.

Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's disease

MOTIVATION

Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)].

RESULTS

A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases.

AVAILABILITY AND IMPLEMENTATION

The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.

Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Recent genome-wide association studies have identified more than one hundred susceptibility loci associated with CAD. However, the underlying mechanism of these genetic loci to CAD susceptibility is still largely unknown. We performed a tissue-specific network analysis of CAD using the summary statistics from one of the largest genome-wide association studies. Variant-level associations were summarized into gene-level associations, and a CAD-related interaction network was built using experimentally validated gene interactions and gene coexpression in coronary artery. The network contained 102 genes, of which 53 were significantly associated with CAD. Pathway enrichment analysis revealed that many genes in the network were involved in the regulation of peripheral arteries. In summary, we performed a tissue-specific network analysis and found abnormalities in the peripheral arteries might be an important pathway underlying the pathogenesis of CAD. Future functional characterization might further validate our findings and identify potential therapeutic targets for CAD.

Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

MOTIVATION

Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity.

RESULTS

We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype.

AVAILABILITY AND IMPLEMENTATION

The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/.

CONTACT

shenli@iu.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study

OBJECTIVE

To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study.

METHODS

MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.

CONCLUSIONS

The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES

Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.

Pathway and network-based strategies to translate genetic discoveries into effective therapies

One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.