Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments

Biomarkers of cognitive and memory decline in psychotropic drug users

Psychotropic drugs are vital in psychiatry, aiding in the management of mental health disorders. Their use requires an understanding of their pharmacological properties, therapeutic applications, and potential side effects. Ongoing research aims to improve their efficacy and safety. Biomarkers play a crucial role in understanding and predicting memory decline in psychotropic drug users. A comprehensive understanding of biomarkers, including neuroimaging, biochemical, genetic, and cognitive assessments, is essential for developing targeted interventions and preventive strategies. In this narrative review, we performed a comprehensive search on PubMed and Google using review-specific terms. Clinicians should use a multifaceted approach, including neurotransmitter analysis, neurotrophic factors, miRNA profiling, and cognitive tasks for early intervention and personalized treatment. Anxiolytics' mechanisms involve various neurotransmitter systems and emerging targets. Research on biomarkers for memory decline in anxiolytic users can lead to early detection and intervention, enhancing clinical practices and aligning with precision medicine. Mood stabilizer users can benefit from early detection of memory decline through RNA, neurophysiological, and inflammatory biomarkers, promoting timely interventions. Performance-enhancing drugs may boost athletic performance in the short term, but their long-term health risks and ethical issues make their use problematic. Long-term use of psychotropic performance enhancers in athletes shows changes in biomarkers of cognitive decline, necessitating ongoing monitoring and intervention strategies. Understanding these genetic influences on memory decline helps pave the way for personalized approaches to prevent or mitigate cognitive deterioration, emphasizing the importance of genetic screening and early interventions based on an individual's genetic profile. Future research should focus on refining these biomarkers and protective measures against cognitive deterioration. Overall, a comprehensive understanding of biomarkers in psychotropic drug users is essential for developing targeted interventions and preventive strategies.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects

BACKGROUND

Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression.

METHODS

This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.

RESULTS

On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers.

CONCLUSIONS

The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Simple model systems reveal conserved mechanisms of Alzheimer's disease and related tauopathies

The lack of effective therapies that slow the progression of Alzheimer's disease (AD) and related tauopathies highlights the need for a more comprehensive understanding of the fundamental cellular mechanisms underlying these diseases. Model organisms, including yeast, worms, and flies, provide simple systems with which to investigate the mechanisms of disease. The evolutionary conservation of cellular pathways regulating proteostasis and stress response in these organisms facilitates the study of genetic factors that contribute to, or protect against, neurodegeneration. Here, we review genetic modifiers of neurodegeneration and related cellular pathways identified in the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster, focusing on models of AD and related tauopathies. We further address the potential of simple model systems to better understand the fundamental mechanisms that lead to AD and other neurodegenerative disorders.

Amyloid-β Pathology-Specific Cytokine Secretion Suppresses Neuronal Mitochondrial Metabolism

Introduction

Neuroinflammation and metabolic dysfunction are early alterations in Alzheimer's disease (AD) brain that are thought to contribute to disease onset and progression. Glial activation due to protein deposition results in cytokine secretion and shifts in brain metabolism, which have been observed in AD patients. However, the mechanism by which this immunometabolic feedback loop can injure neurons and cause neurodegeneration remains unclear.

Methods

We used Luminex XMAP technology to quantify hippocampal cytokine concentrations in the 5xFAD mouse model of AD at milestone timepoints in disease development. We used partial least squares regression to build cytokine signatures predictive of disease progression, as compared to healthy aging in wild-type littermates. We applied the disease-defining cytokine signature to wild-type primary neuron cultures and measured downstream changes in gene expression using the NanoString nCounter system and mitochondrial function using the Seahorse Extracellular Flux live-cell analyzer.

Results

We identified a pattern of up-regulated IFNγ, IP-10/CXCL10, and IL-9 as predictive of advanced disease. When healthy neurons were exposed to these cytokines in proportions found in diseased brain, gene expression of mitochondrial electron transport chain complexes, including ATP synthase, was suppressed. In live cells, basal and maximal mitochondrial respiration were impaired following cytokine stimulation.

Conclusions

We identify a pattern of cytokine secretion predictive of progressing amyloid-β pathology in the 5xFAD mouse model of AD that reduces expression of mitochondrial electron transport complexes and impairs mitochondrial respiration in healthy neurons. We establish a mechanistic link between disease-specific immune cues and impaired neuronal metabolism, potentially causing neuronal vulnerability and susceptibility to degeneration in AD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00782-y.

Toxicity of Per- and Polyfluoroalkyl Substances to Nematodes

Per- and polyfluoroalkyl substances (PFASs) are a class of compounds that persist in the environment globally. Besides being transported to the soil and sediments, which act as their sinks, PFASs can be transferred to several species of higher organisms directly or via bacteria, eliciting a wide range of adverse effects. Caenorhabditis elegans has been widely used in toxicological studies and life science research owing to its numerous advantages over traditional vertebrate models; notably, C. elegans has 65% conserved human-disease-associated genes and does not require ethical approvals for experimental use. This review covers a range of topics, from reported accumulation characteristics and lethal concentrations of PFAS in C. elegans to the mechanisms underlying the toxicity of PFAS at different levels, including reproductive, developmental, cellular, neurologic, oxidative, metabolic, immune, and endocrine toxicities. Additionally, the toxicity levels of some PFAS substitutes are summarized. Lastly, we discuss the toxicological mechanisms of these PFAS substitutes and the importance and promising potential of nematodes as in vivo models for life science research, epidemiological studies (obesity, aging, and Alzheimer's disease research), and toxicological investigations of PFASs and other emerging pollutants compared with other soil animals or model organisms.

Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease

Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies

Background

Alzheimer's disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.

Methods

The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.

Results

The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets.

Conclusion

The pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.

Alzheimer's disease-specific cytokine secretion suppresses neuronal mitochondrial metabolism

Introduction

Neuroinflammation and metabolic dysfunction are early alterations in Alzheimer's disease brain that are thought to contribute to disease onset and progression. Glial activation due to protein deposition results in cytokine secretion and shifts in brain metabolism, which have been observed in Alzheimer's disease patients. However, the mechanism by which this immunometabolic feedback loop can injure neurons and cause neurodegeneration remains unclear.

Methods

We used Luminex XMAP technology to quantify hippocampal cytokine concentrations in the 5xFAD mouse model of Alzheimer's disease at milestone timepoints in disease development. We used partial least squares regression to build cytokine signatures predictive of disease progression, as compared to healthy aging in wild-type littermates. We applied the disease-defining cytokine signature to wild-type primary neuron cultures and measured downstream changes in gene expression using the NanoString nCounter system and mitochondrial function using the Seahorse Extracellular Flux live-cell analyzer.

Results

We identified a pattern of up-regulated IFNγ, IP-10, and IL-9 as predictive of advanced disease. When healthy neurons were exposed to these cytokines in proportions found in diseased brain, gene expression of mitochondrial electron transport chain complexes, including ATP synthase, was suppressed. In live cells, basal and maximal mitochondrial respiration were impaired following cytokine stimulation.

Conclusions

An Alzheimer's disease-specific pattern of cytokine secretion reduces expression of mitochondrial electron transport complexes and impairs mitochondrial respiration in healthy neurons. We establish a mechanistic link between disease-specific immune cues and impaired neuronal metabolism, potentially causing neuronal vulnerability and susceptibility to degeneration in Alzheimer's disease.

Modeling Alzheimer's Disease in Caenorhabditis elegans

Alzheimer’s disease (AD) is the most frequent cause of dementia. After decades of research, we know the importance of the accumulation of protein aggregates such as β-amyloid peptide and phosphorylated tau. We also know that mutations in certain proteins generate early-onset Alzheimer’s disease (EOAD), and many other genes modulate the disease in its sporadic form. However, the precise molecular mechanisms underlying AD pathology are still unclear. Because of ethical limitations, we need to use animal models to investigate these processes. The nematode Caenorhabditis elegans has received considerable attention in the last 25 years, since the first AD models overexpressing Aβ peptide were described. We review here the main results obtained using this model to study AD. We include works studying the basic molecular mechanisms of the disease, as well as those searching for new therapeutic targets. Although this model also has important limitations, the ability of this nematode to generate knock-out or overexpression models of any gene, single or combined, and to carry out toxicity, recovery or survival studies in short timeframes with many individuals and at low cost is difficult to overcome. We can predict that its use as a model for various diseases will certainly continue to increase.

Shared genetic architecture between the two neurodegenerative diseases: Alzheimer's disease and glaucoma

BACKGROUND

Considered as the representatives of neurodegenerative diseases, Alzheimer's disease (AD) and glaucoma are complex progressive neuropathies affected by both genetic and environmental risk factors and cause irreversible damages. Current research indicates that there are common features between AD and glaucoma in terms of epidemiology and pathophysiology. However, the understandings and explanations of their comorbidity and potential genetic overlaps are still limited and insufficient.

METHOD

Genetic pleiotropy analysis was performed using large genome-wide association studies summary statistics of AD and glaucoma, with an independent cohort of glaucoma for replication. Conditional and conjunctional false discovery rate methods were applied to identify the shared loci. Biological function and network analysis, as well as the expression level analysis were performed to investigate the significance of the shared genes.

RESULTS

A significant positive genetic correlation between AD and glaucoma was identified, indicating that there were significant polygenetic overlaps. Forty-nine shared loci were identified and mapped to 11 shared protein-coding genes. Functional genomic analyses of the shared genes indicate their modulation of critical physiological processes in human cells, including those occurring in the mitochondria, nucleus, and cellular membranes. Most of the shared genes indicated a potential modulation of metabolic processes in human cells and tissues. Furthermore, human protein-protein interaction network analyses revealed that some of the shared genes, especially MTCH2, NDUFS3, and PTPMT1, as well as SPI1 and MYBPC3, may function concordantly. The modulation of their expressions may be related to metabolic dysfunction and pathogenic processes.

CONCLUSION

Our study identified a shared genetic architecture between AD and glaucoma, which may explain their shared features in epidemiology and pathophysiology. The potential involvement of these shared genes in molecular and cellular processes reflects the "inter-organ crosstalk" between AD and glaucoma. These results may serve as a genetic basis for the development of innovative and effective therapeutics for AD, glaucoma, and other neurodegenerative diseases.

A network-based method for brain disease gene prediction by integrating brain connectome and molecular network

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein-protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer's disease, Parkinson's disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.

Generation and characterization of a tractable C. elegans model of tauopathy

Alzheimer's disease(AD) is an age-associated neurodegenerative disease that results in deterioration of memory and cognitive function. As a currently untreatable disorder, AD has emerged as one of the defining biomedical challenges of our time. Thus, new approaches that can examine the cellular and molecular mechanisms underlying age-related AD pathology are sorely needed. One of the hallmarks of Alzheimer's disease is the hyperphosphorylation of the tau protein. Caenorhabditis elegans have been previously used to study the genetic pathways impacted by tau proteotoxic stress; however, currently, available C. elegans tau models express the human protein solely in neurons, which are unresponsive to global RNA interference (RNAi). This limits powerful RNAi screening methods from being utilized effectively in these disease models. Our goal was to develop a C. elegans tau model that has pronounced tau-induced disease phenotypes in cells that can be modified by feeding RNAi methods. Towards this end, we generated a novel C. elegans transgenic line with codon-optimized human 0N4R V337M tau expressed in the body wall muscle under the myo-3 promoter. Immunoblotting experiments revealed that the expressed tau is phosphorylated on epitopes canonically associated with human AD pathology. The tau line has significantly reduced health metrics, including egg laying, growth rate, paralysis, thrashing frequency, crawling speed, and lifespan. These defects are suppressed by RNAi directed against the tau mRNA. Taken together, our results suggest that this alternative tau genetic model could be a useful tool for uncovering the mechanisms that influence the hyperphosphorylation and toxicity of human tau via RNAi screening and other approaches.

Dynamic Interplay between Copper Toxicity and Mitochondrial Dysfunction in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder, affecting millions of people worldwide, a number expected to exponentially increase in the future since no effective treatments are available so far. AD is characterized by severe cognitive dysfunctions associated with neuronal loss and connection disruption, mainly occurring in specific brain areas such as the hippocampus, cerebral cortex, and amygdala, compromising memory, language, reasoning, and social behavior. Proteomics and redox proteomics are powerful techniques used to identify altered proteins and pathways in AD, providing relevant insights on cellular pathways altered in the disease and defining novel targets exploitable for drug development. Here, we review the main results achieved by both -omics techniques, focusing on the changes occurring in AD mitochondria under oxidative stress and upon copper exposure. Relevant information arises by the comparative analysis of these results, evidencing alterations of common mitochondrial proteins, metabolic cycles, and cascades. Our analysis leads to three shared mitochondrial proteins, playing key roles in metabolism, ATP generation, oxidative stress, and apoptosis. Their potential as targets for development of innovative AD treatments is thus suggested. Despite the relevant efforts, no effective drugs against AD have been reported so far; nonetheless, various compounds targeting mitochondria have been proposed and investigated, reporting promising results.

Bridging heterogeneous mutation data to enhance disease gene discovery

Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a 'Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)' pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass significant test but represent supportive evidences of functional biological process in literature. Eventually, we applied GDAMDB in Alzheimer's disease (AD) and predicted 79 AD-associated genes. Besides, 12 of them from the original GWAS, 60 of them are supported to be AD-related by other GWAS or literature report, and rest of them are newly predicted genes. Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

Exploring the conservation of Alzheimer-related pathways between H. sapiens and C. elegans: a network alignment approach

Alzheimer disease (AD) is a neurodegenerative disorder with an –as of yet– unclear etiology and pathogenesis. Research to unveil disease processes underlying AD often relies on the use of neurodegenerative disease model organisms, such as Caenorhabditis elegans. This study sought to identify biological pathways implicated in AD that are conserved in Homo sapiens and C. elegans. Protein–protein interaction networks were assembled for amyloid precursor protein (APP) and Tau in H. sapiens—two proteins whose aggregation is a hallmark in AD—and their orthologs APL-1 and PTL-1 for C. elegans. Global network alignment was used to compare these networks and determine similar, likely conserved, network regions. This comparison revealed that two prominent pathways, the APP-processing and the Tau-phosphorylation pathways, are highly conserved in both organisms. While the majority of interactions between proteins in those pathways are known to be associated with AD in human, they remain unexamined in C. elegans, signifying the need for their further investigation. In this work, we have highlighted conserved interactions related to AD in humans and have identified specific proteins that can act as targets for experimental studies in C. elegans, aiming to uncover the underlying mechanisms of AD.

ATP synthase and Alzheimer's disease: putting a spin on the mitochondrial hypothesis

It is estimated that over 44 million people across the globe have dementia, and half of these cases are believed to be Alzheimer’s disease (AD). As the proportion of the global population which is over the age 60 increases so will the number of individuals living with AD. This will result in ever-increasing demands on healthcare systems and the economy. AD can be either sporadic or familial, but both present with similar pathobiology and symptoms. Three prominent theories about the cause of AD are the amyloid, tau and mitochondrial hypotheses. The mitochondrial hypothesis focuses on mitochondrial dysfunction in AD, however little attention has been given to the potential dysfunction of the mitochondrial ATP synthase in AD. ATP synthase is a proton pump which harnesses the chemical potential energy of the proton gradient across the inner mitochondrial membrane (IMM), generated by the electron transport chain (ETC), in order to produce the cellular energy currency ATP. This review presents the evidence accumulated so far that demonstrates dysfunction of ATP synthase in AD, before highlighting two potential pharmacological interventions which may modulate ATP synthase.

The new genetic landscape of Alzheimer's disease: from amyloid cascade to genetically driven synaptic failure hypothesis?

A strong genetic predisposition (60–80% of attributable risk) is present in Alzheimer’s disease (AD). In view of this major genetic component, identification of the genetic risk factors has been a major objective in the AD field with the ultimate aim to better understand the pathological processes. In this review, we present how the genetic risk factors are involved in APP metabolism, β-amyloid peptide production, degradation, aggregation and toxicity, innate immunity, and Tau toxicity. In addition, on the basis of the new genetic landscape, resulting from the recent high-throughput genomic approaches and emerging neurobiological information, we propose an over-arching model in which the focal adhesion pathway and the related cell signalling are key elements in AD pathogenesis. The core of the focal adhesion pathway links the physiological functions of amyloid precursor protein and Tau with the pathophysiological processes they are involved in. This model includes several entry points, fitting with the different origins for the disease, and supports the notion that dysregulation of synaptic plasticity is a central node in AD. Notably, our interpretation of the latest data from genome wide association studies complements other hypotheses already developed in the AD field, i.e., amyloid cascade, cellular phase or propagation hypotheses. Genetically driven synaptic failure hypothesis will need to be further tested experimentally within the general AD framework.

Identifying and ranking potential driver genes of Alzheimer's disease using multiview evidence aggregation

MOTIVATION

Late onset Alzheimer's disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.

RESULTS

We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer's. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer's and are enriched in pathways that have been previously associated with the disease.

AVAILABILITY AND IMPLEMENTATION

Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

Novel methods for integration and visualization of genomics and genetics data in Alzheimer's disease

INTRODUCTION

Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required.

METHODS

We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways.

RESULTS

We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways.

DISCUSSION

Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.

Distinct functional roles of Vps41-mediated neuroprotection in Alzheimer's and Parkinson's disease models of neurodegeneration

Commonalities and, in some cases, pathological overlap between neurodegenerative diseases have led to speculation that targeting of underlying mechanisms might be of potentially shared therapeutic benefit. Alzheimer's disease is characterized by the formation of plaques, composed primarily of the amyloid-β 1-42 (Aβ) peptide in the brain, resulting in neurodegeneration. Previously, we have shown that overexpression of the lysosomal-trafficking protein, human Vps41 (hVps41), is neuroprotective in a transgenic worm model of Parkinson's disease, wherein progressive dopaminergic neurodegeneration is induced by α-synuclein overexpression. Here, we report the results of a systematic comparison of hVps41-mediated neuroprotection between α-synuclein and Aβ in transgenic nematode models of Caenorhabditis elegans. Our results indicate that an ARF-like GTPase gene product, ARL-8, mitigates endocytic Aβ neurodegeneration in a VPS-41-dependent manner, rather than through RAB-7 and AP3 as with α-synuclein. Furthermore, the neuroprotective effect of ARL-8 or hVps41 appears to be dependent on their colocalization and the activity of ARL-8. Additionally, we demonstrate that the LC3 orthologue, LGG-2, plays a critical role in Aβ toxicity with ARL-8. Further analysis of functional effectors of Aβ protein processing via the lysosomal pathway will assist in the elucidation of the underlying mechanism involving VPS-41-mediated neuroprotection. These results reveal functional distinctions in the intracellular management of neurotoxic proteins that serve to better inform the path for development of therapeutic interventions to halt neurodegeneration.

Environmental and Dietary Exposure to Copper and Its Cellular Mechanisms Linking to Alzheimer's Disease

Metals are commonly found in the environment, household, and workplaces in various forms, and a significant segment of the population is routinely exposed to the trace amount of metals from variety of sources. Exposure to metals, such as aluminum, lead, iron, and copper, from environment has long been debated as a potential environmental risk factor for Alzheimer's disease (AD) for decades, yet results from in vitro, in vivo, and human population remain controversial. In the case of copper, the neurotoxic mechanism of action was classically viewed as its strong affinity to amyloid-beta (Aβ) to help its aggregation and increase oxidative stress via Fenton reaction. Thus, it has been thought that accumulation of copper mediates neurotoxicity, and removing it from the brain prevents or reverse Aβ plaque burden. Recent evidence, however, suggests dyshomeostasis of copper and its valency in the body, instead of the accumulation and interaction with Aβ, are major determinants of its beneficial effects as an essential metal or its neurotoxic counterpart. This notion is also supported by the fact that genetic loss-of-function mutations on copper transporters lead to severe neurological symptoms. Along with its altered distribution, recent studies have also proposed novel mechanisms of copper neurotoxicity mediated by nonneuronal cell lineages in the brain, such as capillary endothelial cells, leading to development of AD neuropathology. This review covers recent findings of multifactorial toxic mechanisms of copper and discusses the risk of environmental exposure as a potential factor in accounting for the variability of AD incidence.

ApoE-associated modulation of neuroprotection from Aβ-mediated neurodegeneration in transgenic Caenorhabditis elegans

Allele-specific distinctions in the human apolipoprotein E (APOE) locus represent the best-characterized genetic predictor of Alzheimer's disease (AD) risk. Expression of isoform APOEε2 is associated with reduced risk, while APOEε3 is neutral and APOEε4 carriers exhibit increased susceptibility. Using Caenorhabditis elegans, we generated a novel suite of humanized transgenic nematodes to facilitate neuronal modeling of amyloid-beta peptide (Aβ) co-expression in the context of distinct human APOE alleles. We found that co-expression of human APOEε2 with Aβ attenuated Aβ-induced neurodegeneration, whereas expression of the APOEε4 allele had no effect on neurodegeneration, indicating a loss of neuroprotective capacity. Notably, the APOEε3 allele displayed an intermediate phenotype; it was not neuroprotective in young adults but attenuated neurodegeneration in older animals. There was no functional impact from the three APOE isoforms in the absence of Aβ co-expression. Pharmacological treatment that examined neuroprotective effects of APOE alleles on calcium homeostasis showed allele-specific responses to changes in ER-associated calcium dynamics in the Aβ background. Additionally, Aβ suppressed survival, an effect that was rescued by APOEε2 and APOEε3, but not APOEε4. Expression of the APOE alleles in neurons, independent of Aβ, exerted no impact on survival. Taken together, these results illustrate that C. elegans provides a powerful in vivo platform with which to explore how AD-associated neuronal pathways are modulated by distinct APOE gene products in the context of Aβ-associated neurotoxicity. The significance of both ApoE and Aβ to AD highlights the utility of this new pre-clinical model as a means to dissect their functional inter-relationship.This article has an associated First Person interview with the first author of the paper.

A Meta-Analysis of Alzheimer's Disease Brain Transcriptomic Data

BACKGROUND

Microarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer's disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations.

OBJECTIVE

Meta-analyze publicly available transcriptomic data from multiple brain-related disorders to identify robust transcriptomic changes specific to AD brains.

METHODS

Twenty-two AD, eight schizophrenia, five bipolar disorder, four Huntington's disease, two major depressive disorder, and one Parkinson's disease dataset totaling 2,667 samples and mapping to four different brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum) were analyzed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction.

RESULTS

Meta-analysis identified 323, 435, 1,023, and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe, and cerebellum brain regions, respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-Seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. In addition, biological pathways involved in the "metabolism of proteins" and viral components were significantly enriched across AD brains.

CONCLUSION

This study identified transcriptomic changes specific to AD brains, which could make a significant contribution toward the understanding of AD disease mechanisms and may also provide new therapeutic targets.

Systematic Analysis and Biomarker Study for Alzheimer's Disease

Revealing the relationship between dysfunctional genes in blood and brain tissues from patients with Alzheimer’s Disease (AD) will help us to understand the pathology of this disease. In this study, we conducted the first such large systematic analysis to identify differentially expressed genes (DEGs) in blood samples from 245 AD cases, 143 mild cognitive impairment (MCI) cases, and 182 healthy control subjects, and then compare these with DEGs in brain samples. We evaluated our findings using two independent AD blood datasets and performed a gene-based genome-wide association study to identify potential novel risk genes. We identified 789 and 998 DEGs common to both blood and brain of AD and MCI subjects respectively, over 77% of which had the same regulation directions across tissues and disease status, including the known ABCA7, and the novel TYK2 and TCIRG1. A machine learning classification model containing NDUFA1, MRPL51, and RPL36AL, implicating mitochondrial and ribosomal function, was discovered which discriminated between AD patients and controls with 85.9% of area under the curve and 78.1% accuracy (sensitivity = 77.6%, specificity = 78.9%). Moreover, our findings strongly suggest that mitochondrial dysfunction, NF-κB signalling and iNOS signalling are important dysregulated pathways in AD pathogenesis.

Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

The Systems Biology of Single-Cell Aging

Aging is a leading cause of human morbidity and mortality, but efforts to slow or reverse its effects are hampered by an incomplete understanding of its multi-faceted origins. Systems biology, the use of quantitative and computational methods to understand complex biological systems, offers a toolkit well suited to elucidating the root cause of aging. We describe the known components of the aging network and outline innovative techniques that open new avenues of investigation to the aging research community. We propose integration of the systems biology and aging fields, identifying areas of complementarity based on existing and impending technological capabilities.

Using High-Throughput Animal or Cell-Based Models to Functionally Characterize GWAS Signals

PURPOSE OF REVIEW

The advent of genome-wide association studies (GWASs) constituted a breakthrough in our understanding of the genetic architecture of multifactorial diseases. For Alzheimer's disease (AD), more than 20 risk loci have been identified. However, we are now facing three new challenges: (i) identifying the functional SNP or SNPs in each locus, (ii) identifying the causal gene(s) in each locus, and (iii) understanding these genes' contribution to pathogenesis.

RECENT FINDINGS

To address these issues and thus functionally characterize GWAS signals, a number of high-throughput strategies have been implemented in cell-based and whole-animal models. Here, we review high-throughput screening, high-content screening, and the use of the Drosophila model (primarily with reference to AD).

SUMMARY

We describe how these strategies have been successfully used to functionally characterize the genes in GWAS-defined risk loci. In the future, these strategies should help to translate GWAS data into knowledge and treatments.

Mitochondrial Molecular Abnormalities Revealed by Proteomic Analysis of Hippocampal Organelles of Mice Triple Transgenic for Alzheimer Disease

Mitochondrial dysfunction is implicated in the pathogenesis of Alzheimer’s disease (AD). However, the precise mitochondrial molecular deficits in AD remain poorly understood. Mitochondrial and nuclear proteomic analysis in mature male triple transgenic AD mice (PS1M146V/APPSwe/TauP301L) by two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MALDI-TOF-MS/MS, bio-informatics analysis and immunofluorescent staining were performed in this study. In addition to impaired spatial memory impairment and intracellular accumulation of amyloid 1–42 (Aβ_1–42) in the 3xTg-AD mice, a well-accepted mouse model of the human disease, we also found significantly increased DNA oxidative damage in entorhinal cortex, hippocampal CA1, CA3 and dental gyrus (DG), as evidenced by the positive staining of 8-hydroxyguanosine, a biomarker of mild cognitive impairment early in AD. We identified significant differences in 27 hippocampal mitochondrial proteins (11 increased and 16 decreased), and 37 hippocampal nuclear proteins (12 increased and 25 decreased) in 3xTg-AD mice compared with the wild-type (WT) mice. Differentially expressed mitochondrial and nuclear proteins were mainly involved in energy metabolism (>55%), synapses, DNA damage, apoptosis and oxidative stress. Two proteins were differentially expressed in both hippocampal mitochondria and nuclei, namely electron transport chain (ETC)-related protein ATP synthase subunit d (ATP5H) was significantly decreased, and apoptosis-related dynamin-1 (DYN1), a pre-synaptic and mitochondrial division-regulated protein that was significantly increased. In sum, perturbations of hippocampus mitochondrial energy metabolism-related proteins responsible for ATP generation via oxidation phosphorylation (OXPHOS), especially nuclear-encoded OXPHOS proteins, correlated with the amyloid-associated cognitive deficits of this murine AD model. The molecular changes in respiratory chain-related proteins and DYN1 may represent novel biomarkers of AD.

Chronic exposure to a glyphosate-containing pesticide leads to mitochondrial dysfunction and increased reactive oxygen species production in Caenorhabditis elegans

Glyphosate-containing herbicides are among the most widely-used in the world. Although glyphosate itself is relatively non-toxic, growing evidence suggests that commercial herbicide formulations may lead to increased oxidative stress and mitochondrial inhibition. In order to assess these mechanisms in vivo, we chronically (24h) exposed Caenorhabditis elegans to various concentrations of the glyphosate-containing herbicide TouchDown (TD). Following TD exposure, we evaluated the function of specific mitochondrial electron transport chain complexes. Initial oxygen consumption studies demonstrated inhibition in mid- and high-TD concentration treatment groups compared to controls. Results from tetramethylrhodamine ethyl ester and ATP assays indicated reductions in the proton gradient and ATP levels, respectively. Additional studies were designed to determine whether TD exposure resulted in increased reactive oxygen species (ROS) production. Data from hydrogen peroxide, but not superoxide or hydroxyl radical, assays showed statistically significant increases in this specific ROS. Taken together, these data indicate that exposure of Caenorhabditis elegans to TD leads to mitochondrial inhibition and hydrogen peroxide production.