Functional enrichment analysis of three Alzheimer's disease genome-wide association studies identities DAB1 as a novel candidate liability/protective gene

Whole genome analysis in APOE4 homozygotes identifies the DAB1-RELN pathway in Alzheimer's disease pathogenesis

The APOE-ε4 allele is known to predispose to amyloid deposition and consequently is strongly associated with Alzheimer's disease (AD) risk. There is debate as to whether the APOE gene accounts for all genetic variation of the APOE locus. Another question which remains is whether APOE-ε4 carriers have other genetic factors influencing the progression of amyloid positive individuals to AD. We conducted a genome-wide association study in a sample of 5,390 APOE-ε4 homozygous (ε4ε4) individuals (288 cases and 5102 controls) aged 65 or over in the UK Biobank. We found no significant associations of SNPs in the APOE locus with AD in the sample of ε4ε4 individuals. However, we identified a novel genome-wide significant locus associated to AD, mapping to DAB1 (rs112437613, OR = 2.28, CI = 1.73-3.01, p = 5.4 × 10^-9). This identification of DAB1 led us to investigate other components of the DAB1-RELN pathway for association. Analysis of the DAB1-RELN pathway indicated that the pathway itself was associated with AD, therefore suggesting an epistatic interaction between the APOE locus and the DAB1-RELN pathway.

Relevance of a Novel Circuit-Level Model of Episodic Memories to Alzheimer's Disease

The medial temporal lobe memory system has long been identified as the brain region showing the first histopathological changes in early Alzheimer's disease (AD), and the functional decline observed in patients also points to a loss of function in this brain area. Nonetheless, the exact identity of the neurons and networks that undergo deterioration has not been determined so far. A recent study has identified the entorhinal and hippocampal neural circuits responsible for encoding new episodic memories. Using this novel model we describe the elements of the episodic memory network that are especially vulnerable in early AD. We provide a hypothesis of how reduced reelin signaling within such a network can promote AD-related changes. Establishing novel associations and creating a temporal structure for new episodic memories are both affected in AD. Here, we furnish a reasonable explanation for both of these previous observations.

Mutant Presenilin 1 Dysregulates Exosomal Proteome Cargo Produced by Human-Induced Pluripotent Stem Cell Neurons

The accumulation and propagation of hyperphosphorylated tau (p-Tau) is a neuropathological hallmark occurring with neurodegeneration of Alzheimer's disease (AD). Extracellular vesicles, exosomes, have been shown to initiate tau propagation in the brain. Notably, exosomes from human-induced pluripotent stem cell (iPSC) neurons expressing the AD familial A246E mutant form of presenilin 1 (mPS1) are capable of inducing tau deposits in the mouse brain after in vivo injection. To gain insights into the exosome proteome cargo that participates in propagating tau pathology, this study conducted proteomic analysis of exosomes produced by human iPSC neurons expressing A246E mPS1. Significantly, mPS1 altered the profile of exosome cargo proteins to result in (1) proteins present only in mPS1 exosomes and not in controls, (2) the absence of proteins in the mPS1 exosomes which were present only in controls, and (3) shared proteins which were upregulated or downregulated in the mPS1 exosomes compared to controls. These results show that mPS1 dysregulates the proteome cargo of exosomes to result in the acquisition of proteins involved in the extracellular matrix and protease functions, deletion of proteins involved in RNA and protein translation systems along with proteasome and related functions, combined with the upregulation and downregulation of shared proteins, including the upregulation of amyloid precursor protein. Notably, mPS1 neuron-derived exosomes displayed altered profiles of protein phosphatases and kinases involved in regulating the status of p-tau. The dysregulation of exosome cargo proteins by mPS1 may be associated with the ability of mPS1 neuron-derived exosomes to propagate tau pathology.

Hypoxia-inducible factor-2α is crucial for proper brain development

Sufficient tissue oxygenation is required for regular brain function; thus oxygen supply must be tightly regulated to avoid hypoxia and irreversible cell damage. If hypoxia occurs the transcription factor complex hypoxia-inducible factor (HIF) will accumulate and coordinate adaptation of cells to hypoxia. However, even under atmospheric O₂ conditions stabilized HIF-2α protein was found in brains of adult mice. Mice with a neuro-specific knockout of Hif-2α showed a reduction of pyramidal neurons in the retrosplenial cortex (RSC), a brain region responsible for a range of cognitive functions, including memory and navigation. Accordingly, behavioral studies showed disturbed cognitive abilities in these mice. In search of the underlying mechanisms for the specific loss of pyramidal cells in the RSC, we found deficits in migration in neural stem cells from Hif-2α knockout mice due to altered expression patterns of genes highly associated with neuronal migration and positioning.

Genetic variants beyond amyloid and tau associated with cognitive decline: A cohort study

OBJECTIVE

To identify single nucleotide polymorphisms (SNPs) associated with cognitive decline independent of β-amyloid (Aβ) and tau pathology in Alzheimer disease (AD).

METHODS

Discovery and replication datasets consisting of 414 individuals (94 cognitively normal control [CN], 185 with mild cognitive impairment [MCI], and 135 with AD) and 72 individuals (22 CN, 39 with MCI, and 11 with AD), respectively, were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Genome-wide association analysis was conducted to identify SNPs associated with individual cognitive function (measured with the Mini-Mental State Examination and Alzheimer's Disease Assessment Scale-Cognitive Subscale ) while controlling for the level of Aβ and tau (measured as CSF phosphorylated-tau/Aβ_1-42). Gene ontology analysis was performed on SNP-associated genes.

RESULTS

We identified 1 significant (rs55906536, β = -1.91, standard error 0.34, p = 4.07 × 10^-8) and 4 suggestive variants on chromosome 6 that were associated with poorer cognitive function. Congruent results were found in the replication data. A structural equation model showed that the identified SNP deteriorated cognitive function partially through cortical thinning of the brain in a region-specific manner. Furthermore, a bioinformatics analysis showed that the identified SNPs were associated with genes related to glutathione metabolism.

CONCLUSIONS

In this study, we identified SNPs related to cognitive decline in a manner that could not be explained by Aβ and tau levels. Our findings provide insight into the complexity of AD pathogenesis and support the growing literature on the role of glutathione in AD.

Environment and Gene Association With Obesity and Their Impact on Neurodegenerative and Neurodevelopmental Diseases

Obesity is a multifactorial disease in which environmental conditions and several genes play an important role in the development of this disease. Obesity is associated with neurodegenerative diseases (Alzheimer, Parkinson, and Huntington diseases) and with neurodevelopmental diseases (autism disorder, schizophrenia, and fragile X syndrome). Some of the environmental conditions that lead to obesity are physical activity, alcohol consumption, socioeconomic status, parent feeding behavior, and diet. Interestingly, some of these environmental conditions are shared with neurodegenerative and neurodevelopmental diseases. Obesity impairs neurodevelopment abilities as memory and fine-motor skills. Moreover, maternal obesity affects the cognitive function and mental health of the offspring. The common biological mechanisms involved in obesity and neurodegenerative/neurodevelopmental diseases are insulin resistance, pro-inflammatory cytokines, and oxidative damage, among others, leading to impaired brain development or cell death. Obesogenic environmental conditions are not the only factors that influence neurodegenerative and neurodevelopmental diseases. In fact, several genes implicated in the leptin-melanocortin pathway (LEP, LEPR, POMC, BDNF, MC4R, PCSK1, SIM1, BDNF, TrkB, etc.) are associated with obesity and neurodegenerative and neurodevelopmental diseases. Moreover, in the last decades, the discovery of new genes associated with obesity (FTO, NRXN3, NPC1, NEGR1, MTCH2, GNPDA2, among others) and with neurodegenerative or neurodevelopmental diseases (APOE, CD38, SIRT1, TNFα, PAI-1, TREM2, SYT4, FMR1, TET3, among others) had opened new pathways to comprehend the common mechanisms involved in these diseases. In conclusion, the obesogenic environmental conditions, the genes, and the interaction gene-environment would lead to a better understanding of the etiology of these diseases.

Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder partly induced by dysregulation of different brain regions. Prefrontal cortex (PFC) dysregulation has been reported to associate with mental symptoms such as delusion, apathy, and depression in AD patients. However, the internal mechanisms have not yet been well-understood. This study aims to identify the potential therapeutic target genes and related pathways in PFC of AD. First, differential expression analyses were performed on transcriptome microarray of PFC between AD specimens and non-AD controls. Second, protein-protein interaction networks were constructed based on the identified differentially expressed genes to explore candidate therapeutic target genes. Finally, these candidate genes were validated through biological experiments. The enrichment analyses showed that the differentially expressed genes were significantly enriched in protein functions and pathways related to AD. Furthermore, the top ten hub genes in the protein-protein interaction network (ELAVL1, CUL3, MAPK6, FBXW11, YWHAE, YWHAZ, GRB2, CLTC, YWHAQ, and PDHA1) were proved to be directly or indirectly related to AD. Besides, six genes (PDHA1, CLTC, YWHAE, MAPK6, YWHAZ, and GRB2) of which were validated to significantly altered in AD mice by biological experiments. Importantly, the most significantly changed gene, PDHA1, was proposed for the first time that may be serve as a target gene in AD treatment. In summary, several genes and pathways that play critical roles in PFC of AD patients have been uncovered, which will provide novel insights on molecular targets for treatment and diagnostic biomarkers of AD.

A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression

In this study, we present a novel, multiple coefficient of determination (R²_M)-based method for parsing SNPs located within the chromosomal neighborhood of a gene into semi-independent families, each of which corresponds to one or more functional variants that regulate transcription of the gene. Specifically, our method utilizes a matrix equation framework to calculate R²_M values for SNPs within a chromosome region of interest (ROI) based upon the choices of 1-4 "index" SNPs (iSNPs) that serve as proxies for underlying regulatory variants. Exhaustive testing of sets of 1-4 candidate iSNPs identifies iSNP models that best account for estimated R² values derived from single-variable linear regression analysis of correlations between mRNA expression and genotypes of individual SNPs. Subsequent genotype-based estimation of pairwise r² linkage disequilibrium (LD) coefficients between each iSNP and the other ROI SNPs allows the SNPs to be parsed into semi-independent families. Analysis of mRNA expression and genotypes data downloaded from Gene Expression Omnibus (GEO) and database for Genotypes and Phenotypes (dbGAP) demonstrates the usefulness of this method for parsing SNPs based on experimental data. We believe that this method will be widely applicable for the analysis of the genetic basis of mRNA expression and visualizing the contributions of multiple genetic variants to the regulation of individual genes.

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework

Alzheimer’s disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer’s disease.

Identification of co-evolving temporal networks

BACKGROUND

Biological networks describes the mechanisms which govern cellular functions. Temporal networks show how these networks evolve over time. Studying the temporal progression of network topologies is of utmost importance since it uncovers how a network evolves and how it resists to external stimuli and internal variations. Two temporal networks have co-evolving subnetworks if the evolving topologies of these subnetworks remain similar to each other as the network topology evolves over a period of time. In this paper, we consider the problem of identifying co-evolving subnetworks given a pair of temporal networks, which aim to capture the evolution of molecules and their interactions over time. Although this problem shares some characteristics of the well-known network alignment problems, it differs from existing network alignment formulations as it seeks a mapping of the two network topologies that is invariant to temporal evolution of the given networks. This is a computationally challenging problem as it requires capturing not only similar topologies between two networks but also their similar evolution patterns.

RESULTS

We present an efficient algorithm, Tempo, for solving identifying co-evolving subnetworks with two given temporal networks. We formally prove the correctness of our method. We experimentally demonstrate that Tempo scales efficiently with the size of network as well as the number of time points, and generates statistically significant alignments-even when evolution rates of given networks are high. Our results on a human aging dataset demonstrate that Tempo identifies novel genes contributing to the progression of Alzheimer's, Huntington's and Type II diabetes, while existing methods fail to do so.

CONCLUSIONS

Studying temporal networks in general and human aging specifically using Tempo enables us to identify age related genes from non age related genes successfully. More importantly, Tempo takes the network alignment problem one huge step forward by moving beyond the classical static network models.

Gene expression dynamics following mithramycin treatment: A possible model for post-chemotherapy cognitive impairment

Chemotherapy-induced cognitive changes is a major burden on a substantial number of cancer survivors. The mechanism of this sequel is unknown. In this study, we followed long-term effects of early in life mithramycin (MTR) treatment on behaviour and on the normal course of alterations of gene expression in brain. Between post-natal days (PND) 7 and 10, male rats were divided into 2 groups, 1 receiving MTR (0.1 mg/kg s.c. per day) and the other receiving saline. At PND11, frontal cortex tissue samples were dissected from 4 rats from each group. At PND 65 the remaining rats underwent behavioural tests after which all the rats were decapitated and their prefrontal cortex incised. Rats treated transiently with MTR early in life, showed impairments in spatial working memory and anxious-like behaviour in adulthood. The immediate molecular effect of MTR was expressed in a limited number of altered genes of different unconnected trajectories, which were simultaneously distorted by the drug. In contrast, 3 months later we observed a change in the expression of more than 1000 genes that converged into specific cellular processes. Time-dependent gene expression dynamics of several genes was significantly different between treated and untreated rats. The differences in the total number of altered genes and in gene expression trends, immediately and long after MTR treatment cessation, suggest the evolution of a new cellular homeostatic set point, which can lead to behavioural abnormalities following chemotherapy treatment.

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning

Background

Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.

Methods

We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome.

Results

We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale.

Conclusions

In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Electronic supplementary material

The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.

Neuroprotective effect of melatonin on soluble Aβ1-42-induced cortical neurodegeneration via Reelin-Dab1 signaling pathway

OBJECTIVE

Soluble Aβ_1-42 oligomers play a vital role in the development and pathogenesis of Alzheimer's disease (AD). Melatonin could delay the progress of AD through multiple mechanisms. Reelin-Dab1 signaling plays an important role in AD, including neuronal function and synaptic plasticity. However, whether melatonin could exert its neuroprotective function against soluble Aβ_1-42-induced neurotoxicity during AD development through regulating Reelin-Dab1 signaling remains poorly understood.

METHODS

AD rat model was established by soluble Aβ_1-42 repeated intracerebroventricular injection. Using immunohistochemistry and Western blot analyses, the effect of melatonin on synaptic plasticity, neuritic degeneration, and astrocyte activation was investigated in cerebral cortex. Meanwhile, the expression of Reelin and Dab1 was also examined in cerebral cortex. In our in vitro study, Reelin-Dab1 signaling was inhibited by Reelin antibody, and neuroprotective effect of melatonin against Aβ_1-42 was further determined.

RESULTS

Melatonin ameliorated the neurotoxiciy and astrocyte activation induced by Aβ_1-42 in the cerebral cortex. Melatonin also blocked the reduction in Reelin and Dab1 expression induced by Aβ_1-42. Using in vitro study, Reelin inactivation completely abolished the protective effect of melatonin against Aβ_1-42-induced neurotoxicity.

DISCUSSION

Melatonin might play its neuroprotective role against Aβ_1-42 through mediating Reelin-Dab1 signaling pathway. Melatonin could be a safe and remarkable therapeutic candidate for AD and other aged-associated neurodegenerative diseases.