scholarly journals The Impact of MicroRNAs on Brain Aging and Neurodegeneration

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Stephan P. Persengiev ◽  
Ivanela I. Kondova ◽  
Ronald E. Bontrop
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Brain Aging ◽  
Functional Decline ◽  
System Level ◽  
Model Organisms ◽  
Small Noncoding Rnas ◽  
The Impact ◽  
Gene Expression Levels

The molecular instructions that govern gene expression regulation are encoded in the genome and ultimately determine the morphology and functional specifications of the human brain. As a consequence, changes in gene expression levels might be directly related to the functional decline associated with brain aging. Small noncoding RNAs, including miRNAs, comprise a group of regulatory molecules that modulate the expression of hundred of genes which play important roles in brain metabolism. Recent comparative studies in humans and nonhuman primates revealed that miRNAs regulate multiple pathways and interconnected signaling cascades that are the basis for the cognitive decline and neurodegenerative disorders during aging. Identifying the roles of miRNAs and their target genes in model organisms combined with system-level studies of the brain would provide more comprehensive understanding of the molecular basis of brain deterioration during the aging process.

scholarly journals Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

2019 ◽  
Author(s):  
Jan Zrimec ◽  
Filip Buric ◽  
Azam Sheikh Muhammad ◽  
Rhongzen Chen ◽  
Vilhelm Verendel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Model Organisms ◽  
Regulatory Structure ◽  
Expression Levels ◽  
Coding Regions ◽  
Dna Regulatory Motifs ◽  
Gene Regulatory ◽  
The Individual ◽  
Gene Expression Levels

AbstractUnderstanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets in 7 model organisms ranging from bacteria to Human. We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we show that the correct combination of all regulatory regions must be established in order to accurately control gene expression levels. Therefore, the holistic system that spans the entire gene regulatory structure is required to analyse, understand, and design any future gene expression systems.

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 RNAseq Studies Reveal Distinct Transcriptional Response to Vitamin a (VA) Deficiency in Small Intestine (SI) versus Colon, Discovering Novel VA-regulated Genes (P15-002-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Zhi Chai ◽  
Yafei Lyu ◽  
Qiuyan Chen ◽  
Cheng-Hsin Wei ◽  
Lindsay Snyder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Small Intestine ◽  
Vitamin A ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Gene Expression Profiles ◽  
Illumina Hiseq ◽  
The Impact

Abstract Objectives To characterize and compare the impact of vitamin A (VA) deficiency on gene expression patterns in the small intestine (SI) and the colon, and to discover novel target genes in VA-related biological pathways. Methods vitamin A deficient (VAD) mice were generated by feeding VAD diet to pregnant C57/BL6 dams and their post-weaning offspring. Total mRNA extracted from SI and colon were sequenced using Illumina HiSeq 2500 platform. Differentially Expressed Gene (DEG), Gene Ontology (GO) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to characterize expression patterns and co-expression patterns. Results The comparison between vitamin A sufficient (VAS) and VAD groups detected 49 and 94 DEGs in SI and colon, respectively. According to GO information, DEGs in the SI demonstrated significant enrichment in categories relevant to retinoid metabolic process, molecule binding, and immune function. Immunity related pathways, such as “humoral immune response” and “complement activation,” were positively associated with VA in SI. On the contrary, in colon, “cell division” was the only enriched category and was negatively associated with VA. WGCNA identified modules significantly correlated with VA status in SI and in colon. One of those modules contained five known retinoic acid targets. Therefore we have prioritized the other module members (e.g., Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) to be investigated as candidate genes regulated by VA. Comparison of co-expression modules between SI and colon indicated distinct VA effects on these two organs. Conclusions The results show that VA deficiency alters the gene expression profiles in SI and colon quite differently. Some immune-related genes (Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) may be novel targets under the control of VA in SI. Funding Sources NIH training grant and NIH research grant. Supporting Tables, Images and/or Graphs

scholarly journals Codon usage is an important determinant of gene expression levels largely through its effects on transcription

2016 ◽  
Vol 113 (41) ◽  
pp. E6117-E6125 ◽  
Author(s):  
Zhipeng Zhou ◽  
Yunkun Dang ◽  
Mian Zhou ◽  
Lin Li ◽  
Chien-hung Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Important Determinant ◽  
Mrna Translation ◽  
Mrna Levels ◽  
Protein Coding ◽  
Expression Levels ◽  
Highly Expressed Genes ◽  
The Impact ◽  
Gene Expression Levels

Codon usage biases are found in all eukaryotic and prokaryotic genomes, and preferred codons are more frequently used in highly expressed genes. The effects of codon usage on gene expression were previously thought to be mainly mediated by its impacts on translation. Here, we show that codon usage strongly correlates with both protein and mRNA levels genome-wide in the filamentous fungus Neurospora. Gene codon optimization also results in strong up-regulation of protein and RNA levels, suggesting that codon usage is an important determinant of gene expression. Surprisingly, we found that the impact of codon usage on gene expression results mainly from effects on transcription and is largely independent of mRNA translation and mRNA stability. Furthermore, we show that histone H3 lysine 9 trimethylation is one of the mechanisms responsible for the codon usage-mediated transcriptional silencing of some genes with nonoptimal codons. Together, these results uncovered an unexpected important role of codon usage in ORF sequences in determining transcription levels and suggest that codon biases are an adaptation of protein coding sequences to both transcription and translation machineries. Therefore, synonymous codons not only specify protein sequences and translation dynamics, but also help determine gene expression levels.

Identification of reference genes for gene expression studies during seed germination and seedling establishment in Ricinus communis L.

2014 ◽  
Vol 24 (4) ◽  
pp. 341-352 ◽  
Author(s):  
Paulo R. Ribeiro ◽  
Bas J. W. Dekkers ◽  
Luzimar G. Fernandez ◽  
Renato D. de Castro ◽  
Wilco Ligterink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Seed Germination ◽  
Reference Genes ◽  
Target Genes ◽  
Ricinus Communis ◽  
Quantitative Polymerase Chain Reaction ◽  
Optimal Combination ◽  
Expression Levels ◽  
Gene Expression Studies ◽  
Gene Expression Levels

AbstractReverse transcription-quantitative polymerase chain reaction (RT-qPCR) is an important technology to analyse gene expression levels during plant development or in response to different treatments. An important requirement to measure gene expression levels accurately is a properly validated set of reference genes. In this context, we analysed the potential use of 17 candidate reference genes across a diverse set of samples, including several tissues, different stages and environmental conditions, encompassing seed germination and seedling growth in Ricinus communis L. These genes were tested by RT-qPCR and ranked according to the stability of their expression using two different approaches: GeNorm and NormFinder. GeNorm and Normfinder indicated that ACT, POB and PP2AA1 comprise the optimal combination for normalization of gene expression data in inter-tissue (heterogeneous sample panel) studies. We also describe the optimal combination of reference genes for a subset of root, endosperm and cotyledon samples. In general, the most stable genes suggested by GeNorm are very consistent with those indicated by NormFinder, which highlights the strength of the selection of reference genes in our study. We also validated the selected reference genes by normalizing the expression levels of three target genes involved in energy metabolism with the reference genes suggested by GeNorm and NormFinder. The approach used in this study to identify stably expressed genes, and thus potential reference genes, was applied successfully for R. communis and it provides important guidelines for RT-qPCR studies in seeds and seedlings for other species (especially in those cases where extensive microarray data are not available).

scholarly journals SAT-726 Estrogen Receptor Alpha as a Potential Target for Bisphenol A-Mediated Epigenetic Reprogramming: An in Vitro Analysis

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Morgan Gallo ◽  
Lindsey S Treviño ◽  
Tiffany A Katz
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Target Genes ◽  
Hepatic Cell ◽  
Time Points ◽  
Target Gene Expression ◽  
Er Alpha ◽  
Hepatic Cell Lines ◽  
Gene Expression Levels

Abstract Perinatal exposure to bisphenol A (BPA) has been shown to reprogram the hepatic epigenome of rodents and may promote the development of various metabolic diseases later in life, such as nonalcoholic fatty liver disease (NAFLD). This developmental reprogramming is characterized by the creation of “super promoters” at target genes implicated in metabolic pathways. While it is unclear how these “super promoters” are created, their creation is potentially mediated through BPA and estrogen receptor (ER) interaction. In order to test this potential mechanism, in vitro methods were used to examine ER target gene expression via RT-qPCR in 2 human hepatic cell lines transiently transfected with the ER isoform, ER alpha, prior to BPA exposure for various lengths of time. Within individual time points, there were no significant differences in target gene expression levels between cells that had been transfected with ER alpha and the vector control. Gene expression levels in the target genes were visibly increased at the 24-hour exposure mark in both transfection groups in comparison to the 0- and 6-hour time points, however only a fraction of these increases were found to be statistically significant. These gene expression patterns are not only consistent with previous studies examining target gene expression in BPA-treated hepatic cell lines, but more importantly, suggest BPA does not act via ER alpha to orchestrate the epigenetic changes seen in vitro. BPA may interact with a different ER isoform or an unknown target to create the observed “super promoters” at target genes, reinforcing the promiscuity of BPA and other xenoestrogens in facilitating epigenetic modifications, and ultimately, disease phenotypes.

scholarly journals Effectiveness of orally-delivered double-stranded RNA on gene silencing in the stinkbug Plautia stali

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245081
Author(s):  
Yudai Nishide ◽  
Daisuke Kageyama ◽  
Yoshiaki Tanaka ◽  
Kakeru Yokoi ◽  
Akiya Jouraku ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Insect Pest ◽  
Crop Protection ◽  
High Sensitivity ◽  
Double Stranded Rna ◽  
Expression Levels ◽  
Rnai Efficiency ◽  
Plautia Stali ◽  
Gene Expression Levels

Development of a reliable method for RNA interference (RNAi) by orally-delivered double-stranded RNA (dsRNA) is potentially promising for crop protection. Considering that RNAi efficiency considerably varies among different insect species, it is important to seek for the practical conditions under which dsRNA-mediated RNAi effectively works against each pest insect. Here we investigated RNAi efficiency in the brown-winged green stinkbug Plautia stali, which is notorious for infesting various fruits and crop plants. Microinjection of dsRNA into P. stali revealed high RNAi efficiency–injection of only 30 ng dsRNA into last-instar nymphs was sufficient to knockdown target genes as manifested by their phenotypes, and injection of 300 ng dsRNA suppressed the gene expression levels by 80% to 99.9%. Knockdown experiments by dsRNA injection showed that multicopper oxidase 2 (MCO2), vacuolar ATPase (vATPase), inhibitor of apoptosis (IAP), and vacuolar-sorting protein Snf7 are essential for survival of P. stali, as has been demonstrated in other insects. By contrast, P. stali exhibited very low RNAi efficiency when dsRNA was orally administered. When 1000 ng/μL of dsRNA solution was orally provided to first-instar nymphs, no obvious phenotypes were observed. Consistent with this, RT-qPCR showed that the gene expression levels were not affected. A higher concentration of dsRNA (5000 ng/μL) induced mortality in some cohorts, and the gene expression levels were reduced to nearly 50%. Simultaneous oral administration of dsRNA against potential RNAi blocker genes did not improve the RNAi efficiency of the target genes. In conclusion, P. stali shows high sensitivity to RNAi with injected dsRNA but, unlike the allied pest stinkbugs Halyomorpha halys and Nezara viridula, very low sensitivity to RNAi with orally-delivered dsRNA, which highlights the varied sensitivity to RNAi across different species and limits the applicability of the molecular tool for controlling this specific insect pest.

scholarly journals RegVar: Tissue-specific Prioritization of Noncoding Regulatory Variants

2021 ◽  
Author(s):  
Hao Lu ◽  
Luyu Ma ◽  
Lei Li ◽  
Cheng Quan ◽  
Yiming Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Human Genetics ◽  
Computational Framework ◽  
Tissue Specific ◽  
Regulatory Variants ◽  
Regulatory Impact ◽  
Genome Variations ◽  
The Impact ◽  
Specific Impact

Noncoding genomic variants constitute the majority of trait-associated genome variations; however, identification of functional noncoding variants is still a challenge in human genetics, and a method systematically assessing the impact of regulatory variants on gene expression and linking them to potential target genes is still lacking. Here we introduce a deep neural network (DNN)-based computational framework, RegVar, that can accurately predict the tissue-specific impact of noncoding regulatory variants on target genes. We show that, by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current noncoding variants prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a webserver at http://regvar.cbportal.org/.

scholarly journals hTFtarget: a comprehensive database for regulations of human transcription factors and their targets

2019 ◽  
Author(s):  
Qiong Zhang
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Chromatin Immunoprecipitation ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Epigenetic Modification ◽  
Wide Range ◽  
Chromatin Immunoprecipitation Sequencing ◽  
User Friendly

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF-target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of Chromatin immunoprecipitation sequencing (ChIP-Seq) provides great opportunities to discover the TF-target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7,190 ChIP-Seq samples of 659 TFs and high confident TF binding sites of 699 TFs) and epigenetic modification information to predict accurate TF-target regulations. hTFtarget offers the following functions for users to explore TF-target regulations: 1) Browse or search general targets of a query TF across datasets; 2) Browse TF-target regulations for a query TF in a specific dataset or tissue; 3) Search potential TFs for a given target gene or ncRNA; 4) Investigate co-association between TFs in cell lines; 5) Explore potential co-regulations for given target genes or TFs; 6) Predict candidate TFBSs on given DNA sequences; 7) View ChIP-Seq peaks for different TFs and conditions in genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF-target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.

scholarly journals An Arabidopsis gene expression predictor enables inference of transcriptional regulators

2020 ◽  
Author(s):  
Haiying Geng ◽  
Meng Wang ◽  
Jiazhen Gong ◽  
Yupu Xu ◽  
Shisong Ma
Keyword(s):  
Gene Expression ◽  
Lateral Root ◽  
Secondary Cell Wall ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Transcriptional Regulators ◽  
Endoplasmic Reticulum Stress Response ◽  
Rna Seq ◽  
Average Correlation ◽  
Cell Wall Development

ABSTRACTGene expression regulation by transcription factors (TF) has long been studied, but no model exists yet that can accurately predict transcriptome profiles based on TF activities. We have constructed a universal predictor for Arabidopsis to predict the expression of 28192 non-TF genes using 1678 TFs. Applied to bulk RNA-Seq samples from diverse tissues, the predictor produced accurate predicted transcriptomes correlating well with actual expression, with average correlation coefficient of 0.986. Having recapitulated the quantitative relationships between TFs and target genes, the predictor further enabled downstream inference of TF regulators for genes and pathways, i.e. those involved in suberin, flavonoid, glucosinolate metabolism, lateral root, xylem, secondary cell wall development, and endoplasmic reticulum stress response. Our predictor provides an innovative approach to study transcriptional regulation.

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