The Alzheimer's associated 5' region of the SORL1 gene cis regulates SORL1 transcripts expression

In Alzheimer-prone brain regions, metabolism and risk-gene expression are strongly correlated

Neuroimaging in the preclinical phase of Alzheimer’s disease provides information crucial to early intervention, particularly in people with a high genetic risk. Metabolic network modularity, recently applied to the study of dementia, is increased in Alzheimer’s disease patients compared with controls, but network modularity in cognitively unimpaired elderly with various risks of developing Alzheimer’s disease needs to be determined. Based on their 5-year cognitive progression, we stratified 117 cognitively normal participants (78.3 ± 4.0 years of age, 52 women) into three age-matched groups, each with a different level of risk for Alzheimer’s disease. From their fluorodeoxyglucose PET we constructed metabolic networks, evaluated their modular structures using the Louvain algorithm, and compared them between risk groups. As the risk for Alzheimer’s disease increased, the metabolic connections among brain regions weakened and became more modular, indicating network fragmentation and functional impairment of the brain. We then set out to determine the correlation between regional brain metabolism, particularly in the modules derived from the previous analysis, and the regional expression of Alzheimer-risk genes in the brain, obtained from the Allen Human Brain Atlas. In all risk groups of this elderly population, the regional brain expression of most Alzheimer-risk genes showed a strong correlation with brain metabolism, particularly in the module that corresponded to regions of the brain that are affected earliest and most severely in Alzheimer’s disease. Among the genes, APOE and CD33 showed the strongest negative correlation and SORL1 showed the strongest positive correlation with brain metabolism. The Pearson correlation coefficients remained significant when contrasted against a null-hypothesis distribution of correlation coefficients across the whole transcriptome of 20 736 genes (SORL1: P = 0.0130; CD33, P = 0.0136; APOE: P = 0.0093). The strong regional correlation between Alzheimer-related gene expression in the brain and brain metabolism in older adults highlights the role of brain metabolism in the genesis of dementia.

Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model

Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.

Group sparse reduced rank regression for neuroimaging genetic study

The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression model to take the relations of both the phenotypes and the genotypes for the neuroimaging genetic study. Specifically, we propose designing a graph sparsity constraint as well as a reduced rank constraint to simultaneously conduct subspace learning and feature selection. The group sparsity constraint conducts feature selection to identify genotypes highly related to neuroimaging data, while the reduced rank constraint considers the relations among neuroimaging data to conduct subspace learning in the feature selection model. Furthermore, an alternative optimization algorithm is proposed to solve the resulting objective function and is proved to achieve fast convergence. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method has superiority on predicting the phenotype data by the genotype data, than the alternative methods under comparison.

An alternative transcript of the Alzheimer's disease risk gene SORL1 encodes a truncated receptor

SORL1 encodes a 250-kDa protein named sorLA, a functional sorting receptor for the amyloid precursor protein (APP). Several single nucleotide polymorphisms of the gene SORL1, encoding sorLA, are genetically associated with Alzheimer's disease (AD). In the existing literature, SORL1 is insufficiently described at the transcriptional level, and there is very limited amount of functional data defining different transcripts. We have characterized a SORL1 transcript containing a novel exon 30B. The transcript is expressed in most brain regions with highest expression in the temporal lobe and hippocampus. Exon 30B is spliced to exon 31, leading to a mature transcript that encodes an 829 amino acid sorLA receptor. This receptor variant lacks the binding site for APP and is unlikely to function in APP sorting. This transcript is expressed in equal amounts in the cerebellum from AD and non-AD individuals. Our data describe a transcript that encodes a truncated sorLA receptor, suggesting novel neuronal functions for sorLA and that alternative transcription provides a mechanism for SORL1 activity regulation.

Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers

In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-Wide association study. Specifically, we impose a low-rank constraint on the weight coefficient matrix and then decompose it into two low-rank matrices, which find relationships in genetic features and in brain imaging features, respectively. We also introduce a sparse acyclic digraph with sparsity-inducing penalty to take further into account the correlations among the genetic variables, by which it can be possible to identify the representative SNPs that are highly associated with the brain imaging features. We optimize our objective function by jointly tackling low-rank regression and variable selection in a framework. In our method, the low-rank constraint allows us to conduct variable selection with the low-rank representations of the data; the learned low-sparsity weight coefficients allow discarding unimportant variables at the end. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method could select the important SNPs to more accurately estimate the brain imaging features than the state-of-the-art methods.

Alzheimer's Disease: Insights from Genetic Mouse Models and Current Advances in Human IPSC-Derived Neurons

Alzheimer's disease was first described in 1906 and since then tremendous efforts have been made to fully understand the disease pathology and to find a cure for this neurodegenerative disease. The diagnosis of Alzheimer's is still difficult, especially in early stages of the disease. Current treatment of Alzheimer's only ameliorates the symptoms but fails to provide a therapy. Over the last decades, animal models have been proven valuable in elucidating insights of the pathology. In vitro models using patient-derived cells are currently emerging and hold great promise in understanding the disease pathophysiology. Here, we introduce the neurobiology and genetic features of Alzheimer's and describe what we have learned from studies employing mouse models and patient-derived induced pluripotent stem cells.

Risk factor SORL1: from genetic association to functional validation in Alzheimer's disease

Alzheimer's disease (AD) represents one of the most dramatic threats to healthy aging and devising effective treatments for this devastating condition remains a major challenge in biomedical research. Much has been learned about the molecular concepts that govern proteolytic processing of the amyloid precursor protein to amyloid-β peptides (Aβ), and how accelerated accumulation of neurotoxic Aβ peptides underlies neuronal cell death in rare familial but also common sporadic forms of this disease. Out of a plethora of proposed modulators of amyloidogenic processing, one protein emerged as a key factor in AD pathology, a neuronal sorting receptor termed SORLA. Independent approaches using human genetics, clinical pathology, or exploratory studies in animal models all converge on this receptor that is now considered a central player in AD-related processes by many. This review will provide a comprehensive overview of the evidence implicating SORLA-mediated protein sorting in neurodegenerative processes, and how receptor gene variants in the human population impair functional receptor expression in sporadic but possibly also in autosomal-dominant forms of AD.

Novel susceptibility loci for Alzheimer's disease

Late-onset Alzheimer's disease (AD), a highly prevalent neurodegenerative disorder characterized by progressive deterioration in cognition, function and behavior terminating in incapacity and death, is a clinically and pathologically heterogeneous disease with a substantial heritable component. During the past 5 years, the technological developments in next-generation high-throughput genome technologies have led to the identification of more than 20 novel susceptibility loci for AD, and have implicated specific pathways in the disease, in particular intracellular trafficking/endocytosis, inflammation and immune response and lipid metabolism. These observations have significantly advanced our understanding of underlying pathogenic mechanisms and potential therapeutic targets. This review article summarizes these recent advances in AD genomics and discusses the value of identified susceptibility loci for diagnosis and prognosis of AD.

Alzheimer disease (AD) specific transcription, DNA methylation and splicing in twenty AD associated loci

Genome-wide association studies have identified twenty loci associated with late-onset Alzheimer disease (LOAD). We examined each of the twenty loci, specifically the ±50kb region surrounding the most strongly associated variant, for changes in gene(s) transcription specific to LOAD. Post-mortem human brain samples were examined for expression, methylation, and splicing differences. LOAD specific differences were detected by comparing LOAD to normal and "disease" controls. Eight loci, prominently ABCA7, contain LOAD specific differences. Significant changes in the CELF1 and ZCWPW1 loci occurred in genes not located nearest the associated variant, suggesting that these genes should be investigated further as LOAD candidates.

Elucidating molecular phenotypes caused by the SORL1 Alzheimer's disease genetic risk factor using human induced pluripotent stem cells

Predisposition to sporadic Alzheimer's disease (SAD) involves interactions between a person's unique combination of genetic variants and the environment. The molecular effect of these variants may be subtle and difficult to analyze with standard in vitro or in vivo models. Here we used hIPSCs to examine genetic variation in the SORL1 gene and possible contributions to SAD-related phenotypes in human neurons. We found that human neurons carrying SORL1 variants associated with an increased SAD risk show a reduced response to treatment with BDNF, at the level of both SORL1 expression and APP processing. shRNA knockdown of SORL1 demonstrates that the differences in BDNF-induced APP processing between genotypes are dependent on SORL1 expression. We propose that the variation in SORL1 expression induction by BDNF is modulated by common genetic variants and can explain how genetic variation in this one locus can contribute to an individual's risk of developing SAD.

The SORL1 gene and convergent neural risk for Alzheimer's disease across the human lifespan

Prior to intervention trials in individuals genetically at-risk for late-onset Alzheimer's disease, critical first steps are identifying where (neuroanatomic effects), when (timepoint in the lifespan) and how (gene expression and neuropathology) Alzheimer's risk genes impact the brain. We hypothesized that variants in the sortilin-like receptor (SORL1) gene would affect multiple Alzheimer's phenotypes before the clinical onset of symptoms. Four independent samples were analyzed to determine effects of SORL1 genetic risk variants across the lifespan at multiple phenotypic levels: (1) microstructural integrity of white matter using diffusion tensor imaging in two healthy control samples (n=118, age 18-86; n=68, age 8-40); (2) gene expression using the Braincloud postmortem healthy control sample (n=269, age 0-92) and (3) Alzheimer's neuropathology (amyloid plaques and tau tangles) using a postmortem sample of healthy, mild cognitive impairment (MCI) and Alzheimer's individuals (n=710, age 66-108). SORL1 risk variants predicted lower white matter fractional anisotropy in an age-independent manner in fronto-temporal white matter tracts in both samples at 5% family-wise error-corrected thresholds. SORL1 risk variants also predicted decreased SORL1 mRNA expression, most prominently during childhood and adolescence, and significantly predicted increases in amyloid pathology in postmortem brain. Importantly, the effects of SORL1 variation on both white matter microstructure and gene expression were observed during neurodevelopmental phases of the human lifespan. Further, the neuropathological mechanism of risk appears to primarily involve amyloidogenic pathways. Interventions targeted toward the SORL1 amyloid risk pathway may be of greatest value during early phases of the lifespan.

Zingiberene attenuates hydrogen peroxide-induced toxicity in neuronal cells

In this experimental design, we explored the neuroprotective potential of zingiberene (ZGB), a monocyclic sesquiterpene, in hydrogen peroxide (H2O2)-induced toxicity in newborn rat cerebral cortex cell cultures for the first time. The rats were exposed to H2O2 for 6 h to determine the oxidative stress levels. To evaluate cell viability, both 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolium bromide and lactate dehydrogenase assays were carried out. Total antioxidant capacity (TAC) and total oxidative stress (TOS) parameters were used to evaluate oxidative changes. Besides determining 8-hydroxy-2-deoxyguanosine (8-OH-dG) levels in vitro, single-cell gel electrophoresis was also performed to measure the resistance of neuronal DNA to H2O2- exposed rats. Our results showed that survival and TAC levels of the cells decreased, while TOS, 8-OH-dG levels and the mean values of the total scores of cells showing DNA damage increased in the H2O2 alone-treated cultures. But pretreatment of ZGB suppressed the cytotoxicity, genotoxicity and oxidative stress that were increased by H2O2. Based on these observations, it is suggested that the sesquiterpene ZGB can be used as a novel and natural potential therapeutic in counteracting oxidative damages in the field of neurodegenerative disorders.

Protective effects of cyclosativene on H2O 2-induced injury in cultured rat primary cerebral cortex cells

Sesquiterpenes have attracted much interest with respect to their protective effect against oxidative damage that may be the cause of many diseases including several neurodegenerative disorders and cancer. Our previous unpublished work suggested that cyclosativene (CSV), a tetracyclic sesquiterpene, has antioxidant and anticarcinogenic features. However, little is known about the effects of CSV on oxidative stress induced neurotoxicity. We used hydrogen peroxide (H2O2) exposure for 6 h to model oxidative stress. Therefore, this experimental design allowed us to explore the neuroprotective potential of CSV in H2O2-induced toxicity in new-born rat cerebral cortex cell cultures for the first time. For this aim, MTT and lactate dehydrogenase release assays were carried out to evaluate cytotoxicity. Total antioxidant capacity (TAC) and total oxidative stress (TOS) parameters were used to evaluate oxidative changes. In addition to determining of 8-hydroxy-2-deoxyguanosine (8-OH-dG) levels, the single cell gel electrophoresis (or Comet assay) was also performed for measuring the resistance of neuronal DNA to H2O2-induced challenge. Our results showed that survival and TAC levels of the cells decreased, while TOS, 8-OH-dG levels and the mean values of the total scores of cells showing DNA damage (Comet assay) increased in the H2O2 alone treated cultures. But pre-treatment of CSV suppressed the cytotoxicity, genotoxicity and oxidative stress which were increased by H2O2. On the basis of these observations, it is suggested that CSV as a natural product with an antioxidant capacity in mitigating oxidative injuries in the field of neurodegenerative disorders.

Sortilin receptor 1 predicts longitudinal cognitive change

The gene encoding sortilin receptor 1 (SORL1) has been associated with Alzheimer's disease risk. We examined 15 SORL1 variants and single nucleotide polymorphism (SNP) set risk scores in relation to longitudinal verbal, spatial, memory, and perceptual speed performance, testing for age trends and sex-specific effects. Altogether, 1609 individuals from 3 population-based Swedish twin studies were assessed up to 5 times across 16 years. Controlling for apolipoprotein E genotype (APOE), multiple simple and sex-moderated associations were observed for spatial, episodic memory, and verbal trajectories (p = 1.25E-03 to p = 4.83E-02). Five variants (rs11600875, rs753780, rs7105365, rs11820794, rs2070045) were associated across domains. Notably, in those homozygous for the rs2070045 risk allele, men demonstrated initially favorable performance but accelerating declines, and women showed overall lower performance. SNP set risk scores predicted spatial (Card Rotations, p = 5.92E-03) and episodic memory trajectories (Thurstone Picture Memory, p = 3.34E-02), where higher risk scores benefited men's versus women's performance up to age 75 but with accelerating declines. SORL1 is associated with cognitive aging, and might contribute differentially to change in men and women.