scholarly journals Correlation between miRNA-targeted-gene promoter methylation and miRNA regulation of target genes

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 21 ◽  
Author(s):  
Y-h Taguchi
Keyword(s):  
Gene Expression ◽  
Promoter Methylation ◽  
Target Gene ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Gene Promoter ◽  
Gene Expression Omnibus ◽  
Mirna Regulation ◽  
Regulate Gene Expression ◽  
Mirna Targeting

Background miRNA regulation of target genes and promoter methylation are known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied. Methods Gene expression and promoter methylation profiles of 271 distinct human cell lines were obtained from gene expression omnibus. P-values that describe both miRNA-targeted-gene promoter methylaion and miRNA regulation of target genes were computed using the MiRaGE method proposed recently by the author.Results Significant changes in promoter methylation were associated with miRNA targeting. It was also found that miRNA-targeted-gene promoter hypomethylation was related to differential target gene expression; the genes with miRNA-targeted-gene promoter hypomethylation were downregulated during cell senescence and upregulated during cellular differentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs.Conclusions It was found that promoter methylation was affected by miRNA targeting. Furthermore, miRNA-targeted-gene promoter hypomethylation is suggested to facilitate gene regulation by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs).

scholarly journals Correlation between miRNA-targeted-gene promoter methylation and miRNA regulation of target genes

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 21 ◽  
Author(s):  
Y-h Taguchi
Keyword(s):  
Gene Expression ◽  
Promoter Methylation ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Gene Promoter ◽  
Gene Expression Omnibus ◽  
Seed Region ◽  
Mirna Regulation ◽  
Regulate Gene Expression ◽  
Mirna Targeting

Background miRNA regulation of target genes and promoter methylation are known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied.Methods Gene expression and promoter methylation profiles of 270 distinct human cell lines were obtained from Gene Expression Omnibus. P-values that describe both miRNA-targeted-gene promoter methylation and miRNA regulation of target genes were computed using the MiRaGE method proposed recently by the author.Results Significant changes in promoter methylation were associated with miRNA targeting. It was also found that miRNA-targeted-gene promoter hypomethylation was related to differential target gene expression; the genes with miRNA-targeted-gene promoter hypomethylation were downregulated during cell senescence and upregulated during cellular differentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs. miRNA-targeted-gene promoter methylation may also be related to the seed region features of miRNA.Conclusions It was found that promoter methylation was correlated to miRNA targeting. Furthermore, miRNA-targeted-gene promoter hypomethylation was especially enhanced in promoters of genes targeted by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs) and was suggested to be highly related to some seed region features of miRNAs.

scholarly journals Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 21
Author(s):  
Y-h Taguchi
Keyword(s):  
Gene Expression ◽  
Promoter Methylation ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Gene Expression Omnibus ◽  
Mirna Regulation ◽  
P Values ◽  
Regulate Gene Expression ◽  
Mirna Targeting ◽  
Regulate Gene

Background miRNA regulation of target genes and promoter methylation were known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied. Methods Gene expression and promoter methylation profiles of 271 distinct human cell lines were obtained from gene expression omnibus. P-values that describe both miRNA-targeting-specific promoter methylation and miRNA regulation of target genes were computed with the MiRaGE method proposed recently by the author. Results We found that promoter methylation was miRNA-targeting-specific. In other words, changes in promoter methylation were associated with miRNA binding at target genes. It was also found that miRNA-targeting-specific promoter hypomethylation was related to miRNA regulation; the genes with miRNA-targeting-specific promoter hypomethylation were downregulated during cell senescence and upregulated during cellulardierentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, and primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs. Conclusions It was found that promoter methylation was affected by miRNA targeting. Furthermore, miRNA-targeting-specific promoter hypomethylation was suggested to facilitate gene regulation by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs).

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 Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

2018 ◽  
Author(s):  
Heather E. Wheeler ◽  
Sally Ploch ◽  
Alvaro N. Barbeira ◽  
Rodrigo Bonazzola ◽  
Angela Andaleon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Complex Traits ◽  
Target Gene ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nucleic Acid Binding ◽  
Expression Of Genes ◽  
Gene Associations ◽  
Made In

AbstractRegulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available GTEx tissues and find 2356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

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.

scholarly journals The Impact of lncRNAs in Diabetes Mellitus: A Systematic Review and In Silico Analyses

2021 ◽  
Vol 12 ◽  
Author(s):  
Cristine Dieter ◽  
Natália Emerim Lemos ◽  
Nathalia Rodrigues de Faria Corrêa ◽  
Taís Silveira Assmann ◽  
Daisy Crispim
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Systematic Review ◽  
Target Genes ◽  
Gene Expression Omnibus ◽  
Bioinformatics Analyses ◽  
Regulate Gene Expression ◽  
The Subject ◽  
The Impact ◽  
In Silico Analyses

Long non-coding RNAs (lncRNAs) are non-coding transcripts that have emerged as one of the largest and diverse RNA families that regulate gene expression. Accumulating evidence has suggested a number of lncRNAs are involved in diabetes mellitus (DM) pathogenesis. However, results about lncRNA expressions in DM patients are still inconclusive. Thus, we performed a systematic review of the literature on the subject followed by bioinformatics analyses to better understand which lncRNAs are dysregulated in DM and in which pathways they act. Pubmed, Embase, and Gene Expression Omnibus (GEO) repositories were searched to identify studies that investigated lncRNA expression in cases with DM and non-diabetic controls. LncRNAs consistently dysregulated in DM patients were submitted to bioinformatics analysis to retrieve their target genes and identify potentially affected signaling pathways under their regulation. Fifty-three eligible articles were included in this review after the application of the inclusion and exclusion criteria. Six hundred and thirty-eight lncRNAs were differentially expressed between cases and controls in at least one study. Among them, six lncRNAs were consistently dysregulated in patients with DM (Anril, Hotair, Malat1, Miat, Kcnq1ot1, and Meg3) compared to controls. Moreover, these six lncRNAs participate in several metabolism-related pathways, evidencing their importance in DM. This systematic review suggests six lncRNAs are dysregulated in DM, constituting potential biomarkers of this disease.

ATF4, DLX3, FRA1, MSX2, C/EBP-ζ, and C/EBP-α Shape the Molecular Basis of Therapeutic Effects of Zoledronic Acid in Bone Disorders

2020 ◽  
Vol 20 (18) ◽  
pp. 2274-2284
Author(s):  
Faroogh Marofi ◽  
Jalal Choupani ◽  
Saeed Solali ◽  
Ghasem Vahedi ◽  
Ali Hassanzadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Zoledronic Acid ◽  
Mrna Expression ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Target Genes ◽  
Osteoblastic Differentiation ◽  
Therapeutic Effects ◽  
Significant Difference ◽  
Scheduled Time

Objective: Zoledronic Acid (ZA) is one of the common treatment choices used in various boneassociated conditions. Also, many studies have investigated the effect of ZA on Osteoblastic-Differentiation (OSD) of Mesenchymal Stem Cells (MSCs), but its clear molecular mechanism(s) has remained to be understood. It seems that the methylation of the promoter region of key genes might be an important factor involved in the regulation of genes responsible for OSD. The present study aimed to evaluate the changes in the mRNA expression and promoter methylation of central Transcription Factors (TFs) during OSD of MSCs under treatment with ZA. Materials and Methods: MSCs were induced to be differentiated into the osteoblastic cell lineage using routine protocols. MSCs received ZA during OSD and then the methylation and mRNA expression levels of target genes were measured by Methylation Specific-quantitative Polymerase Chain Reaction (MS-qPCR) and real.time PCR, respectively. The osteoblastic differentiation was confirmed by Alizarin Red Staining and the related markers to this stage. Results: Gene expression and promoter methylation level for DLX3, FRA1, ATF4, MSX2, C/EBPζ, and C/EBPa were up or down-regulated in both ZA-treated and untreated cells during the osteodifferentiation process on days 0 to 21. ATF4, DLX3, and FRA1 genes were significantly up-regulated during the OSD processes, while the result for MSX2, C/EBPζ, and C/EBPa was reverse. On the other hand, ATF4 and DLX3 methylation levels gradually reduced in both ZA-treated and untreated cells during the osteodifferentiation process on days 0 to 21, while the pattern was increasing for MSX2 and C/EBPa. The methylation pattern of C/EBPζ was upward in untreated groups while it had a downward pattern in ZA-treated groups at the same scheduled time. The result for FRA1 was not significant in both groups at the same scheduled time (days 0-21). Conclusion: The results indicated that promoter-hypomethylation of ATF4, DLX3, and FRA1 genes might be one of the mechanism(s) controlling their gene expression. Moreover, we found that promoter-hypermethylation led to the down-regulation of MSX2, C/EBP-ζ and C/EBP-α. The results implicate that ATF4, DLX3 and FRA1 may act as inducers of OSD while MSX2, C/EBP-ζ and C/EBP-α could act as the inhibitor ones. We also determined that promoter-methylation is an important process in the regulation of OSD. However, yet there was no significant difference in the promoter-methylation level of selected TFs in ZA-treated and control cells, a methylation- independent pathway might be involved in the regulation of target genes during OSD of MSCs.

scholarly journals The EAR Motif in the Arabidopsis MADS Transcription Factor AGAMOUS-Like 15 Is Not Necessary to Promote Somatic Embryogenesis

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 758
Author(s):  
Sanjay Joshi ◽  
Christian Keller ◽  
Sharyn E. Perry
Keyword(s):  
Gene Expression ◽  
Somatic Embryogenesis ◽  
Target Gene ◽  
Target Genes ◽  
Domain Family ◽  
Binding Factor ◽  
Ear Motif ◽  
Carboxyl Terminal ◽  
Responsive Element ◽  
Carboxy Terminal

AGAMOUS-like 15 (AGL15) is a member of the MADS domain family of transcription factors (TFs) that can directly induce and repress target gene expression, and for which promotion of somatic embryogenesis (SE) is positively correlated with accumulation. An ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif of form LxLxL within the carboxyl-terminal domain of AGL15 was shown to be involved in repression of gene expression. Here, we examine whether AGL15′s ability to repress gene expression is needed to promote SE. While a form of AGL15 where the LxLxL is changed to AxAxA can still promote SE, another form with a strong transcriptional activator at the carboxy-terminal end, does not promote SE and, in fact, is detrimental to SE development. Select target genes were examined for response to the different forms of AGL15.

scholarly journals On the way toward regulatable expression systems in acetic acid bacteria: target gene expression and use cases

2021 ◽  
Author(s):  
Philipp Moritz Fricke ◽  
Angelika Klemm ◽  
Michael Bott ◽  
Tino Polen
Keyword(s):  
Gene Expression ◽  
Acetic Acid ◽  
Literature Search ◽  
Target Gene ◽  
Target Genes ◽  
Recombinant Dna ◽  
Acetic Acid Bacteria ◽  
Homoserine Lactone ◽  
Expression Systems ◽  
Target Gene Expression

Abstract Acetic acid bacteria (AAB) are valuable biocatalysts for which there is growing interest in understanding their basics including physiology and biochemistry. This is accompanied by growing demands for metabolic engineering of AAB to take advantage of their properties and to improve their biomanufacturing efficiencies. Controlled expression of target genes is key to fundamental and applied microbiological research. In order to get an overview of expression systems and their applications in AAB, we carried out a comprehensive literature search using the Web of Science Core Collection database. The Acetobacteraceae family currently comprises 49 genera. We found overall 6097 publications related to one or more AAB genera since 1973, when the first successful recombinant DNA experiments in Escherichia coli have been published. The use of plasmids in AAB began in 1985 and till today was reported for only nine out of the 49 AAB genera currently described. We found at least five major expression plasmid lineages and a multitude of further expression plasmids, almost all enabling only constitutive target gene expression. Only recently, two regulatable expression systems became available for AAB, an N-acyl homoserine lactone (AHL)-inducible system for Komagataeibacter rhaeticus and an l-arabinose-inducible system for Gluconobacter oxydans. Thus, after 35 years of constitutive target gene expression in AAB, we now have the first regulatable expression systems for AAB in hand and further regulatable expression systems for AAB can be expected. Key points • Literature search revealed developments and usage of expression systems in AAB. • Only recently 2 regulatable plasmid systems became available for only 2 AAB genera. • Further regulatable expression systems for AAB are in sight.

A TRIPARTITE CLUSTERING ANALYSIS ON MICRORNA, GENE AND DISEASE MODEL

2012 ◽  
Vol 10 (01) ◽  
pp. 1240007 ◽  
Author(s):  
CHENGCHENG SHEN ◽  
YING LIU
Keyword(s):  
Gene Expression ◽  
Clustering Algorithm ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Disease Model ◽  
Relational Information ◽  
Microrna Gene ◽  
Research Findings ◽  
Family Based

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

Sign in / Sign up

Export Citation Format

Share Document