Genetic and epigenetic MTHFR gene variants in the mothers of attention-deficit/hyperactivity disorder affected children as possible risk factors for neurodevelopmental disorders

Epigenomics ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 813-823
Author(s):  
Ignazio Stefano Piras ◽  
Anna Costa ◽  
Maria Cristina Tirindelli ◽  
Andrea Stoccoro ◽  
Matthew J Huentelman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Promoter Methylation ◽  
Mthfr Gene ◽  
Expression Levels ◽  
Hyperactivity Disorder ◽  
Genotype Frequencies ◽  
Healthy Control ◽  
Gene Expression Levels

Aim: To assess promoter methylation levels, gene expression levels and 677C>T/1298A>C genotype and allele frequencies of the MTHFR gene in 45 mothers of attention-deficit/hyperactivity disorder affected child/children (ADHDM) and compare it with age matched healthy control mothers (HCM). Materials & methods: High resolution melting analysis, quantitative real time PCR and PCR-RFLP were performed to assess methylation, gene expression and genotyping, respectively. Significance between ADHDM and HCM was assessed by linear (methylation and gene expression) and logistic regression (genotypes). Results: MTHFR gene expression levels were significantly higher in the ADHDM compared with the HCM group (adj-p < 7.7E-04). No differences in MTHFR promoter methylation level and 677C>T/1298A>C genotype frequencies were detected between ADHDM and HCM. Conclusion: We observed increased MTHFR expression levels not resulting from promoter methylation changes in ADHDM respect to HMC, potentially contributing to the ADHD condition in their children and deserving further investigation.

scholarly journals D-GPM: A Deep Learning Method for Gene Promoter Methylation Inference

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 807 ◽  
Author(s):  
Pan ◽  
Liu ◽  
Wen ◽  
Liu ◽  
Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Target Genes ◽  
Gene Promoter ◽  
Support Vector ◽  
Whole Genome ◽  
Expression Levels ◽  
Gene Expression Levels

Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. This inspired us to propose that the methylation level of the promoters in landmark genes might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling. Here, we propose a deep learning model called Deep-Gene Promoter Methylation (D-GPM) to predict the whole-genome promoter methylation level based on the promoter methylation profile of the landmark genes from The Cancer Genome Atlas (TCGA). D-GPM-15%-7000 × 5, the optimal architecture of D-GPM, acquires the least overall mean absolute error (MAE) and the highest overall Pearson correlation coefficient (PCC), with values of 0.0329 and 0.8186, respectively, when testing data. Additionally, the D-GPM outperforms the regression tree (RT), linear regression (LR), and the support vector machine (SVM) in 95.66%, 92.65%, and 85.49% of the target genes by virtue of its relatively lower MAE and in 98.25%, 91.00%, and 81.56% of the target genes based on its relatively higher PCC, respectively. More importantly, the D-GPM predominates in predicting 79.86% and 78.34% of the target genes according to the model distribution of the least MAE and the highest PCC, respectively.

scholarly journals Spatial organization of cells and variable expression of autophagy, apoptosis, and neurodevelopmental genes might underlie selective brain region vulnerability in Attention-Deficit/Hyperactivity Disorder

2019 ◽  
Author(s):  
Jonathan L. Hess ◽  
Nevena V. Radonjić ◽  
Jameson Patak ◽  
Stephen J. Glatt ◽  
Stephen V. Faraone
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Brain Region ◽  
Spatial Organization ◽  
Brain Regions ◽  
Brain Atlas ◽  
Expression Levels ◽  
Variable Expression ◽  
Hyperactivity Disorder ◽  
Imaging Results

AbstractGenetically influenced changes in brain organization occur over the course of development. Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of imaging results from the ADHD ENIGMA Consortium along with high-resolution postmortem brain microarray data from Allen Brain Atlas from 22 sub-cortical and cortical brain regions to investigate our selective brain region vulnerability (SBRV) hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and non-neuronal cells in the brain. We then assessed the relationships between gene set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with reductions in regional brain sizes in ADHD, along with associations to regional abundances of dopaminergic neurons, astrocytes, oligodendrocytes, and neural progenitor cells. This works provides novel mechanistic insights into SBRV in ADHD.

scholarly journals The Effect of 8-Weeks of Low-Intensity Swimming Training on Promyelocytic Leukemia Zinc Finger Protein and Spermatid Transition Nuclear Protein Gene Expression in Azoospermic Rats Model

2020 ◽  
Vol 26 (4) ◽  
pp. 332-347
Author(s):  
Leila Zohrabi Karani ◽  
◽  
Parvin Farzanegi ◽  
Mohammad Ali Azarbayjani ◽  
◽  
...  
Keyword(s):  
Gene Expression ◽  
Zinc Finger ◽  
Nuclear Protein ◽  
Promyelocytic Leukemia ◽  
Swimming Exercise ◽  
Promyelocytic Leukemia Zinc Finger ◽  
Expression Levels ◽  
Low Intensity ◽  
Healthy Control ◽  
Gene Expression Levels

Aims: One of the causes of infertility in men is the azoospermia disease, which is attributed to the lack of sperm in each sperm. The primary function of spermatogenesis is the maintenance, proliferation, and differentiation of spermatogonial cells. Thus, the present study aimed to investigate the changes in Promyelocytic Leukemia Zinc Finger (PLZF) and spermatid Transition Nuclear Protein (TNP) gene expression levels in an azoospermic rat model after 8 weeks of low-intensity aerobic training. Methods & Materials: In this experimental study, 15 adult male Wistar rats were randomly divided into three groups of healthy control, with azoospermia, and exercise plus azoospermia after creating an azoospermia model. The patient plus exercise group performed a low-intensity swimming exercise 30 minutes a day, five days a week for 8 weeks, after the creation of the azoospermic rats. A One-way ANOVA test was used for data analysis. Findings: The results showed that a period of swimming exercise program in the exercise plus azoospermia group significantly reduced PLZF gene expression compared to the healthy control groups (P=0.001) and no significant increase to the azoospermia group (P=0.06). There was also a significant decrease in TNP gene expression levels in the exercise plus azoospermia group compared to the healthy control group (P=0.001) and a significant increase in the azoospermia group (P=0.057). Conclusion: Based on these Findings, it can be stated that the alteration of key molecules or signaling pathways and expression of the PLZF and TNP genes in the spermatogenesis process may increase infertility, but regular aerobic exercise, such as low-intensity swimming, helps to control the effects of infertility by increasing the maintenance and development of spermatogonial stem cells.

Serum Interleukin-6 Level in Children With Attention-Deficit Hyperactivity Disorder (ADHD)

2018 ◽  
Vol 34 (2) ◽  
pp. 61-67 ◽  
Author(s):  
Amira Hamed Darwish ◽  
Tarek Mohamed Elgohary ◽  
Nahla A. Nosair
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Interleukin 6 ◽  
Rating Scale ◽  
Enzyme Linked Immunosorbent Assay ◽  
Parent Rating ◽  
Hyperactivity Disorder ◽  
Diagnostic And Statistical Manual ◽  
Healthy Control ◽  
And Control

Introduction: Attention-deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder in children, but its specific etiology and pathophysiology are still incompletely understood. Objectives: This case-control study aimed to measure the level of serum interleukin-6 (IL-6) as a predictor of the immunologic status in children with ADHD, and to study its correlation with severity of symptoms. Subjects and Methods: 60 ADHD children who met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, criteria for ADHD and 60 control children were subjected to complete history taking, clinical examination, and psychometric tests. Serum interleukin-6 of ADHD patients and control children was measured by enzyme-linked immunosorbent assay. Results: The mean serum level of IL-6 was 22.35 (95% confidence interval [CI], 17.68-26.99) in ADHD patients, and it was 5.44 (95% CI, 4.81-6.06) in controls. A significantly higher level of IL-6 was reported in ADHD patients compared with controls ( P = .001). No significant correlation was found between serum IL-6 level and either the Intelligence Quotient (IQ) or the Conners’ Parent Rating Scale score. Conclusion: Serum IL-6 values were significantly higher in ADHD patients compared to healthy control children. Increased production of IL-6 may play a role in the pathogenesis of ADHD.

scholarly journals Exploring Genetic Variation that Influences Brain Methylation in Attention-Deficit/Hyperactivity Disorder

2018 ◽  
Author(s):  
Laura Pineda-Cirera ◽  
Anu Shivalikanjli ◽  
Judit Cabana-Domínguez ◽  
Ditte Demontis ◽  
Veera M. Rajagopal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Neurodevelopmental Disorder ◽  
Epigenetic Mechanism ◽  
Brain Volumes ◽  
Altered Expression ◽  
Hyperactivity Disorder ◽  
Cpg Sites

ABSTRACTAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder caused by an interplay of genetic and environmental factors. Epigenetics is crucial to lasting changes in gene expression in the brain. Recent studies suggest a role for DNA methylation in ADHD. We explored the contribution to ADHD of allele-specific methylation (ASM), an epigenetic mechanism that involves SNPs correlating with differential levels of DNA methylation at CpG sites. We selected 3,896 tagSNPs reported to influence methylation in human brain regions and performed a case-control association study using the summary statistics from the largest GWAS meta-analysis of ADHD, comprising 20,183 cases and 35,191 controls. We identified associations with eight tagSNPs that were significant at a 5% False Discovery Rate (FDR). These SNPs correlated with methylation of CpG sites lying in the promoter regions of six genes. Since methylation may affect gene expression, we inspected these ASM SNPs together with 52 ASM SNPs in high LD with them for eQTLs in brain tissues and observed that the expression of three of those genes was affected by them. ADHD risk alleles correlated with increased expression (and decreased methylation) of ARTN and PIDD1 and with a decreased expression (and increased methylation) of C2orf82. Furthermore, these three genes were predicted to have altered expression in ADHD, and genetic variants in C2orf82 correlated with brain volumes. In summary, we followed a systematic approach to identify risk variants for ADHD that correlated with differential cis-methylation, identifying three novel genes contributing to the disorder.

scholarly journals D-GPM: a deep learning method for gene promoter methylation inference

2018 ◽  
Author(s):  
Xingxin Pan ◽  
Biao Liu ◽  
Xingzhao Wen ◽  
Yulu Liu ◽  
Xiuqing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Target Genes ◽  
Gene Promoter ◽  
Support Vector ◽  
Whole Genome ◽  
Expression Levels ◽  
Gene Expression Levels

AbstractBackgroundGene promoter methylation plays a critical role in a wide range of biological processes, such as transcriptional expression, gene imprinting, X chromosome inactivation,etc. Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. Moreover, the methylation level of the promoter is usually negatively correlated with its corresponding gene expression. This result inspired us to propose that the methylation level of the promoters might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling.ResultsHere, we developed a deep learning model (D-GPM) to predict the whole-genome promoter methylation level based on the methylation profile of the landmark genes. We benchmarked D-GPM against three machine learning methods, namely, linear regression (LR), regression tree (RT) and support vector machine (SVM), based on two criteria: the mean absolute deviation (MAE) and the Pearson correlation coefficient (PCC). After profiling the methylation beta value (MBV) dataset from the TCGA, with respect to MAE and PCC, we found that D-GPM outperforms LR by 9.59% and 4.34%, RT by 27.58% and 22.96% and SVM by 6.14% and 3.07% on average, respectively. For the number of better-predicted genes, D-GPM outperforms LR in 92.65% and 91.00%, RT in 95.66% and 98.25% and SVM in 85.49% and 81.56% of the target genes.ConclusionsD-GPM acquires the least overall MAE and the highest overall PCC on MBV-te compared to LR, RT, and SVM. For a genewise comparative analysis, D-GPM outperforms LR, RT, and SVM in an overwhelming majority of the target genes, with respect to the MAE and PCC. Most importantly, D-GPM predominates among the other models in predicting a majority of the target genes according to the model distribution of the least MAE and the highest PCC for the target genes.

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