Genetic Association of CHAT rs3810950 and rs2177369 Polymorphisms with the Risk of Alzheimer's Disease: A Meta-Analysis

The Changes of Blood and CSF Ion Levels in Depressed Patients: a Systematic Review and Meta-analysis

Micronutrient deficiencies and excesses are closely related to developing and treating depression. Traditional and effective antidepressants include tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), and lithium. There is no consensus on the fluctuation of zinc (Zn2+), magnesium (Mg2+), calcium (Ca2+), copper (Cu2+), iron (Fe2+), and manganese (Mn2+) ion levels in depressed individuals before and after therapy. In order to determine whether there were changes in blood and cerebrospinal fluid (CSF) levels of these ions in depressed patients compared with healthy controls and depressed patients treated with TCAs, SSRIs, or lithium, we applied a systematic review and meta-analysis. Using the Stata 17.0 software, we performed a systematic review and meta-analysis of the changes in ion levels in human samples from healthy controls, depressive patients, and patients treated with TCAs, SSRIs, and lithium, respectively. By searching the PubMed, EMBASE, Google Scholar, Web of Science, China National Knowledge Infrastructure (CNKI), and WAN FANG databases, 75 published analyzable papers were chosen. In the blood, the levels of Zn2+ and Mg2+ in depressed patients had decreased while the Ca2+ and Cu2+ levels had increased compared to healthy controls, Fe2+ and Mn2+ levels have not significantly changed. After treatment with SSRIs, the levels of Zn2+ and Ca2+ in depressed patients increased while Cu2+ levels decreased. Mg2+ and Ca2+ levels were increased in depressed patients after Lithium treatment. The findings of the meta-analysis revealed that micronutrient levels were closely associated with the onset of depression and prompted more research into the underlying mechanisms as well as the pathophysiological and therapeutic implications.

Advanced stratification analyses in molecular association meta-analysis: methodology and application

Background

Stratification analyses have been widely utilized in molecular association meta-analyses to estimate the interaction between genetic and environmental factors or to control for the confounding variables linked to a disease. Two calculation methods utilized in practical research, which are known as the variants of factorial stratification analysis and confounder-controlling stratification analysis in our nomenclature, have been applied in previous studies, but none of which have presented a methodology and application for these analyses.

Methods

In this paper, these two approaches are integrated and further developed into a standard procedure for stratification analysis. We first propose the advanced statistical methodology and theoretical algorithm of these three types of stratification analysis and then provide two example applications in meta-analyses of molecular association to illustrate the computing processes and interpretation of the results.

Results

The standard stratification analysis synthesizes the advantages of the first two practical methods, including identifying and controlling confounding moderators or revealing and calculating gene-environment interactions, to efficiently classify the real influence of various investigated factors on a disease in the general population. Additional challenges concerning this method and their potential solutions are also discussed, such as the approach to utilizing only the partially stratified data available in meta-research practice.

Conclusions

The standard stratification method will be extensively applicable to rapidly expanding future research on the complex relationships among genetics, environment, disease, and other variables.

Meta-Analysis of Gene Expression and Identification of Biological Regulatory Mechanisms in Alzheimer's Disease

Alzheimer's disease (AD), also known as senile dementia, is a progressive neurodegenerative disease. The etiology and pathogenesis of AD have not yet been elucidated. We examined common differentially expressed genes (DEGs) from different AD tissue microarray datasets by meta-analysis and screened the AD-associated genes from the common DEGs using GCBI. Then we studied the gene expression network using the STRING database and identified the hub genes using Cytoscape. Furthermore, we analyzed the microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and single nucleotide polymorphisms (SNPs) associated with the AD-associated genes, and then identified feed-forward loops. Finally, we performed SNP analysis of the AD-associated genes. Our results identified 207 common DEGs, of which 57 have previously been reported to be associated with AD. The common DEG expression network identified eight hub genes, all of which were previously known to be associated with AD. Further study of the regulatory miRNAs associated with the AD-associated genes and other genes specific to neurodegenerative diseases revealed 65 AD-associated miRNAs. Analysis of the miRNA associated transcription factor-miRNA-gene-gene associated TF (mTF-miRNA-gene-gTF) network around the AD-associated genes revealed 131 feed-forward loops (FFLs). Among them, one important FFL was found between the gene SERPINA3, hsa-miR-27a, and the transcription factor MYC. Furthermore, SNP analysis of the AD-associated genes identified 173 SNPs, and also found a role in AD for miRNAs specific to other neurodegenerative diseases, including hsa-miR-34c, hsa-miR-212, hsa-miR-34a, and hsa-miR-7. The regulatory network constructed in this study describes the mechanism of cell regulation in AD, in which miRNAs and lncRNAs can be considered AD regulatory factors.

CHAT gene polymorphism rs3810950 is associated with the risk of Alzheimer's disease in the Czech population

Background

Cholinergic hypothesis of Alzheimer’s disease (AD) is based on the findings that a reduced and/or perturbed cholinergic activity in the central nervous system correlates with cognitive decline in patients with Alzheimer’s disease. The hypothesis resulted in the development of centrally-acting agents potentiating cholinergic neurotransmission; these drugs, however, only slowed down the cognitive decline and could not prevent it. Consequently, the perturbation of the central cholinergic signalling has been accepted as a part of the Alzheimer’s aetiology but not necessarily the primary cause of the disease. In the present study we have focused on the rs3810950 polymorphism of ChAT (choline acetyltransferase) gene that has not been studied in Czech population before.

Methods

We carried out an association study to test for a relationship between the rs3810950 polymorphism and Alzheimer’s disease in a group of 1186 persons; 759 patients with Alzheimer’s disease and 427 control subjects. Furthermore, we performed molecular modelling of the terminal domain (1st-126th amino acid residue) of one of the ChAT isoforms (M) to visualise in silico whether the rs3810950 polymorphism (A120T) can change any features of the tertiary structure of the protein which would have a potential to alter its function.

Results

The AA genotype of CHAT was associated with a 1.25 times higher risk of AD (p <  0.002) thus demonstrating that the rs3810950 polymorphism can have a modest but statistically significant effect on the risk of AD in the Czech population. Furthermore, the molecular modelling indicated that the polymorphism is likely to be associated with significant variations in the tertiary structure of the protein molecule which may impact its enzyme activity.

Conclusions

Our findings are consistent with the results of the meta-analytical studies of the relationship between rs3810950 polymorphism and AD and provide further material evidence for a direct (primary) involvement of cholinergic mechanisms in the etiopathogenesis of AD, particularly as a factor in cognitive decline and perturbed conscious awareness commonly observed in patients with AD.

Controlling for confounding factors and revealing their interactions in genetic association meta-analyses: a computing method and application for stratification analyses

Subgroup and stratification analyses have been widely applied in genetic association studies to compare the effects of different factors or control for the effects of the confounding variables associated with a disease. However, studies have not systematically provided application standards and computing methods for stratification analyses. Based on the Mantel-Haenszel and Inverse-Variant approaches and two practical computing methods described in previous studies, we propose a standard stratification method for meta-analyses that contains two sequential steps: factorial stratification analysis and confounder-controlling stratification analysis. Examples of genetic association meta-analyses are used to illustrate these points. The standard stratification analysis method identifies interacting effects on investigated factors and controls for confounding variables, and this method effectively reveals the real effects of these factors and confounding variables on a disease in an overall study population. We also discuss important issues concerning stratification for meta-analyses, such as conceptual confusion between subgroup and stratification analyses, and incorrect calculations previously used for factorial stratification analyses. This standard stratification method will have extensive applications in future research for increasing studies on the complicated relationships between genetics and disease.

EXOC3L2 rs597668 variant contributes to Alzheimer's disease susceptibility in Asian population

Recent genome-wide association studies have established the association between EXOC3L2 rs597668 variant and Alzheimer's disease (AD) in European population. However, recent studies reported inconsistent results in Asian population. Here, we performed a systematic review and meta-analysis to evaluate the impact of rs597668 on AD risk in Asian population using a total of 8686 samples including 2855 cases and 5831 controls. Meanwhile, we selected 17,008 AD cases and 37,154 controls in European population to evaluate the potential heterogeneity between East Asian and European populations. In East Asian population, we identified no potential heterogeneity with P=0.31 and I² = 15.8%. By meta-analysis, we identified positive association between rs597668 and AD risk with P=0.023, OR=0.93, 95% CI 0.87-0.99. We further found significant heterogeneity in pooled Asian and European populations with P<0.0001 and I² = 87.7%. The meta-analysis indicated negative association with P=0.66, OR=0.97, 95% CI 0.85-1.11. In summary, all these findings indicate that rs597668 C allele is a risk factor for AD in European population with OR=1.18 and P=2.49E-13. However the rs597668 C allele played a protective role in AD with OR=0.93 and P=0.023 in East Asian population.