The glial-specific hypermethylated 3' untranslated region of histone deacetylase 1 may modulates several signal pathways in Alzheimer's disease

DNA Methylation in Alzheimer's Disease

To date, DNA methylation is the best characterized epigenetic modification in Alzheimer's disease. Involving the addition of a methyl group to the fifth carbon of the cytosine pyrimidine base, DNA methylation is generally thought to be associated with the silencing of gene expression. It has been hypothesized that epigenetics may mediate the interaction between genes and the environment in the manifestation of Alzheimer's disease, and therefore studies investigating DNA methylation could elucidate novel disease mechanisms. This chapter comprehensively reviews epigenomic studies, undertaken in human brain tissue and purified brain cell types, focusing on global methylation levels, candidate genes, epigenome wide approaches, and recent meta-analyses. We discuss key differentially methylated genes and pathways that have been highlighted to date, with a discussion on how new technologies and the integration of multiomic data may further advance the field.

Histone deacetylase inhibitors VPA and WT161 ameliorate the pathological features and cognitive impairments of the APP/PS1 Alzheimer's disease mouse model by regulating the expression of APP secretases

BACKGROUND

Alzheimer's disease (AD) is a degenerative neurological disorder. Recent studies have indicated that histone deacetylases (HDACs) are among the most prominent epigenetic therapy targets and that HDAC inhibitors have therapeutic effects on AD. Here, we identified sodium valproate (VPA), a pan-HDAC inhibitor, and WT161, a novel HDAC6 selective inhibitor, as potential therapeutic agents for AD. Underlying molecular mechanisms were investigated.

METHODS

A cellular model, N2a-APPswe, was established via lentiviral infection, and the APPswe/PSEN1dE9 transgenic mouse model was employed in the study. LC-MS/MS was applied to quantify the concentration of WT161 in the mouse brain. Western blotting, immunohistochemical staining, thioflavin-S staining and ELISA were applied to detect protein expression in cells, tissues, or serum. RNA interference was utilized to knockdown the expression of specific genes in cells. The cognitive function of mice was assessed via the nest-building test, novel object recognition test and Morris water maze test.

RESULTS

Previous studies have focused mainly on the impact of HDAC inhibitors on histone deacetylase activity. Our study discovered that VPA and WT161 can downregulate the expression of multiple HDACs, such as HDAC1 and HDAC6, in both AD cell and mouse models. Moreover, they also affect the expression of APP and APP secretases (BACE1, PSEN1, ADAM10). RNA interference and subsequent vitamin C induction further confirmed that the expression of APP and APP secretases is indeed regulated by HDAC1 and HDAC6, with the JNK pathway being the intermediate link in this regulatory process. Through the above pathways, VPA and WT161 effectively reduced Aβ deposition in both AD cell and mouse models and significantly improved cognitive function in AD mice.

CONCLUSIONS

In general, we have discovered that the HDAC6-JNK-APP secretases cascade is an important pathway for VPA and WT161 to exert their therapeutic effects on AD. Investigations into the safety and efficacy of VPA and WT161 were also conducted, providing essential preclinical evidence for assessing these two epigenetic drugs for the treatment of AD.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

ADmeth: A Manually Curated Database for the Differential Methylation in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease. More and more evidence show that DNA methylation is closely related to the pathological mechanism of AD. Many AD-associated differentially methylated genes, regions and CpG sites have been identified in recent researches, which may have great potential in clinical research. However, there is no dedicated database to collect AD-related differential methylation up to now. To provide a reference to researchers, we design a database named ADmeth by manually curating relevant articles, which contains a total of 16,709 AD-related differentially methylated items identified from different brain regions and different cell types in the blood, involving 209 genes, 2,229 regions and 14,271 CpG sites. The ADmeth database provides user-friendly pages to search, submit and download data. We hope that the ADmeth database can facilitate researchers to select candidate AD-associated methylation markers in revealing the pathological mechanism of AD and promote the cell-free DNA based non-invasive diagnosis of AD. The ADmeth database is available at http://www.biobdlab.cn/ADmeth.

Cancer and Alzheimer's Inverse Correlation: an Immunogenetic Analysis

Numerous studies have demonstrated an inverse link between cancer and Alzheimer's disease (AD), with data suggesting that people with Alzheimer's have a decreased risk of cancer and vice versa. Although other studies have investigated mechanisms to explain this relationship, the connection between these two diseases remains largely unexplained. Processes seen in cancer, such as decreased apoptosis and increased cell proliferation, seem to be reversed in AD. Given the need for effective therapeutic strategies for AD, comparisons with cancer could yield valuable insights into the disease process and perhaps result in new treatments. Here, through a review of existing literature, we compared the expressions of genes involved in cell proliferation and apoptosis to establish a genetic basis for the reciprocal association between AD and cancer. We discuss an array of genes involved in the aforementioned processes, their relevance to both diseases, and how changes in those genes produce varying effects in either disease.

DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: Constructing a methylation risk score using a machine learning method

Treatment-resistant schizophrenia (TRS) is defined as a non-response to at least two trials of antipsychotic medication with an adequate dose and duration. We aimed to evaluate the discriminant abilities of DNA methylation probes and methylation risk score between treatment-resistant schizophrenia and non-treatment-resistant schizophrenia. This study recruited 96 schizophrenia patients (TRS and non-TRS) and 56 healthy controls (HC). Participants were divided into a discovery set and a validation set. In the discovery set, we conducted genome-wide methylation analysis (human MethylationEPIC 850K BeadChip) on the subject's blood DNA and discriminated significant methylation signatures, then verified these methylation signatures in the validation set. Based on genome-wide scans of TRS versus non-TRS, thirteen differentially methylated probes were identified at FDR <0.05 and >20% differences in DNA methylation β-values. Next, we selected six probes within gene coding regions (LOC404266, LOXL2, CERK, CHMP7, and SLC17A9) to conduct verification in the validation set using quantitative methylation-specific PCR (qMSP). These six methylation probes showed satisfactory discrimination between TRS patients and non-TRS patients, with an AUC ranging from 0.83 to 0.92, accuracy ranging from 77.8% to 87.3%, sensitivity ranging from 80% to 90%, and specificity ranging from 65.6% to 85%. This methylation risk score model showed satisfactory discrimination between TRS patients and non-TRS patients, with an accuracy of 88.3%. These findings support that methylation signatures may be used as an indicator of TRS vulnerability and provide a model for the clinical use of methylation to identify TRS.

Morphological and biomolecular targets in retina and vitreous from Reelin-deficient mice (Reeler): Potential implications for age-related macular degeneration in Alzheimer’s dementia

The neurosensory retina is an outgrowth of the Central Nervous System (CNS), and the eye is considered “a window to the brain.” Reelin glycoprotein is directly involved in neurodevelopment, in synaptic plasticity, learning and memory. Consequently, abnormal Reelin signaling has been associated with brain neurodegeneration but its contributing role in ocular degeneration is still poorly explored. To this aim, experimental procedures were assayed on vitreous or retinas obtained from Reeler mice (knockout for Reelin protein) at different postnatal days (p) p14, p21 and p28. At p28, a significant increase in the expression of Amyloid Precursor Protein (APP) and its amyloidogenic peptide (Aβ1-42 along with truncated tau fragment (i.e., NH2htau)- three pathological hallmarks of Alzheimer’s disease (AD)-were found in Reeler mice when compared to their age-matched wild-type controls. Likewise, several inflammatory mediators, such as Interleukins, or crucial biomarkers of oxidative stress were also found to be upregulated in Reeler mice by using different techniques such as ELLA assay, microchip array or real-time PCR. Taken together, these findings suggest that a dysfunctional Reelin signaling enables the expression of key pathological features which are classically associated with AD neurodegenerative processes. Thus, this work suggests that Reeler mouse might be a suitable animal model to study not only the pathophysiology of developmental processes but also several neurodegenerative diseases, such as AD and Age-related Macular Degeneration (AMD), characterized by accumulation of APP and/or Aβ1-42, NH2htau and inflammatory markers.

Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as "GTP cyclohydrolase I type 2" through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.