scholarly journals Using Context-Sensitive Text Mining to Identify miRNAs in Different Stages of Atherosclerosis

2019 ◽  
Vol 119 (08) ◽  
pp. 1247-1264 ◽  
Author(s):  
Markus Joppich ◽  
Christian Weber ◽  
Ralf Zimmer
Keyword(s):  
Data Analysis ◽  
Text Mining ◽  
High Throughput ◽  
Target Genes ◽  
Mirna Gene ◽  
Cell Types ◽  
Gene Interactions ◽  
Messenger Rnas ◽  
New Findings ◽  
Micro Rnas

790 human and mouse micro-RNAs (miRNAs) are involved in diseases. More than 26,428 miRNA–gene interactions are annotated in humans and mice. Most of these interactions are posttranscriptional regulations: miRNAs bind to the messenger RNAs (mRNAs) of genes and induce their degradation, thereby reducing the gene expression of target genes. For atherosclerosis, 667 miRNA–gene interactions for 124 miRNAs and 343 genes have been identified and described in numerous publications. Some interactions were observed through high-throughput experiments, others were predicted using bioinformatic methods, and some were determined by targeted experiments. Several reviews collect knowledge on miRNA–gene interactions in (specific aspects of) atherosclerosis.Here, we use our bioinformatics resource (atheMir) to give an overview of miRNA–gene interactions in the context of atherosclerosis. The interactions are based on public databases and context-based text mining of 28 million PubMed abstracts. The miRNA–gene interactions are obtained from more than 10,000 publications, of which more than 1,000 are in a cardiovascular disease context (266 in atherosclerosis). We discuss interesting miRNA–gene interactions in atherosclerosis, grouped by specific processes in different cell types and six phases of atherosclerotic progression. All evidence is referenced and easily accessible: Relevant interactions are provided by atheMir as supplementary tables for further evaluation and, for example, for the subsequent data analysis of high-throughput measurements as well as for the generation and validation of hypotheses. The atheMir approach has several advantages: (1) the evidence is easily accessible, (2) regulatory interactions are uniformly available for subsequent high-throughput data analysis, and (3) the resource can incrementally be updated with new findings.

scholarly journals Micro RNAs in Regulation of Cellular Redox Homeostasis

2021 ◽  
Vol 22 (11) ◽  
pp. 6022
Author(s):  
Sylwia Ciesielska ◽  
Izabella Slezak-Prochazka ◽  
Patryk Bil ◽  
Joanna Rzeszowska-Wolny
Keyword(s):  
Target Genes ◽  
Redox Homeostasis ◽  
Messenger Rnas ◽  
Cellular Redox ◽  
Cell Responses ◽  
Regulatory Circuits ◽  
Micro Rnas ◽  
Cellular Behaviors ◽  
Homeostasis Regulation ◽  
Thioredoxin Reductases

In living cells Reactive Oxygen Species (ROS) participate in intra- and inter-cellular signaling and all cells contain specific systems that guard redox homeostasis. These systems contain both enzymes which may produce ROS such as NADPH-dependent and other oxidases or nitric oxide synthases, and ROS-neutralizing enzymes such as catalase, peroxiredoxins, thioredoxins, thioredoxin reductases, glutathione reductases, and many others. Most of the genes coding for these enzymes contain sequences targeted by micro RNAs (miRNAs), which are components of RNA-induced silencing complexes and play important roles in inhibiting translation of their targeted messenger RNAs (mRNAs). In this review we describe miRNAs that directly target and can influence enzymes responsible for scavenging of ROS and their possible role in cellular redox homeostasis. Regulation of antioxidant enzymes aims to adjust cells to survive in unstable oxidative environments; however, sometimes seemingly paradoxical phenomena appear where oxidative stress induces an increase in the levels of miRNAs which target genes which are supposed to neutralize ROS and therefore would be expected to decrease antioxidant levels. Here we show examples of such cellular behaviors and discuss the possible roles of miRNAs in redox regulatory circuits and further cell responses to stress.

scholarly journals Pulmonary microRNA profiling in a mouse model of ventilator-induced lung injury

2012 ◽  
Vol 303 (3) ◽  
pp. L199-L207 ◽  
Author(s):  
Katerina Vaporidi ◽  
Eleni Vergadi ◽  
Evangelos Kaniaris ◽  
Maria Hatziapostolou ◽  
Eleni Lagoudaki ◽  
...  
Keyword(s):  
Lung Injury ◽  
Mirna Expression ◽  
Gene Networks ◽  
Target Genes ◽  
Statistical Significance ◽  
Mirna Gene ◽  
Lung Compliance ◽  
Negative Control ◽  
Arterial Oxygen ◽  
Micro Rnas

The aim of this study was to investigate the changes induced by high tidal volume ventilation (HVTV) in pulmonary expression of micro-RNAs (miRNAs) and identify potential target genes and corresponding miRNA-gene networks. Using a real-time RT-PCR-based array in RNA samples from lungs of mice subjected to HVTV for 1 or 4 h and control mice, we identified 65 miRNAs whose expression changed more than twofold upon HVTV. An inflammatory and a TGF-β-signaling miRNA-gene network were identified by in silico pathway analysis being at highest statistical significance ( P = 10−43 and P = 10−28, respectively). In the inflammatory network, IL-6 and SOCS-1, regulated by miRNAs let-7 and miR-155, respectively, appeared as central nodes. In TGF-β-signaling network, SMAD-4, regulated by miR-146, appeared as a central node. The contribution of miRNAs to the development of lung injury was evaluated in mice subjected to HVTV treated with a precursor or antagonist of miR-21, a miRNA highly upregulated by HVTV. Lung compliance was preserved only in mice treated with anti-miR-21 but not in mice treated with pre-miR-21 or negative-control miRNA. Both alveolar-arterial oxygen difference and protein levels in bronchoalveolar lavage were lower in mice treated with anti-miR-21 than in mice treated with pre-miR-21 or negative-control miRNA (DA-a: 66 ± 27 vs. 131 ± 22, 144 ± 10 mmHg, respectively, P < 0.001; protein concentration: 1.1 ± 0.2 vs. 2.3 ± 1, 2.1 ± 0.4 mg/ml, respectively, P < 0.01). Our results show that HVTV induces changes in miRNA expression in mouse lungs. Modulation of miRNA expression can affect the development of HVTV-induced lung injury.

scholarly journals HSF1Base: A Comprehensive Database of HSF1 (Heat Shock Factor 1) Target Genes

2019 ◽  
Vol 20 (22) ◽  
pp. 5815 ◽  
Author(s):  
Kovács ◽  
Sigmond ◽  
Hotzi ◽  
Bohár ◽  
Fazekas ◽  
...  
Keyword(s):  
Heat Shock ◽  
High Throughput ◽  
Ribosome Biogenesis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Heat Shock Factor ◽  
Heat Shock Factor 1 ◽  
Genes Encoding ◽  
Shock Proteins

: HSF1 (heat shock factor 1) is an evolutionarily conserved master transcriptional regulator of the heat shock response (HSR) in eukaryotic cells. In response to high temperatures, HSF1 upregulates genes encoding molecular chaperones, also called heat shock proteins, which assist the refolding or degradation of damaged intracellular proteins. Accumulating evidence reveals however that HSF1 participates in several other physiological and pathological processes such as differentiation, immune response, and multidrug resistance, as well as in ageing, neurodegenerative demise, and cancer. To address how HSF1 controls these processes one should systematically analyze its target genes. Here we present a novel database called HSF1Base (hsf1base.org) that contains a nearly comprehensive list of HSF1 target genes identified so far. The list was obtained by manually curating publications on individual HSF1 targets and analyzing relevant high throughput transcriptomic and chromatin immunoprecipitation data derived from the literature and the Yeastract database. To support the biological relevance of HSF1 targets identified by high throughput methods, we performed an enrichment analysis of (potential) HSF1 targets across different tissues/cell types and organisms. We found that general HSF1 functions (targets are expressed in all tissues/cell types) are mostly related to cellular proteostasis. Furthermore, HSF1 targets that are conserved across various animal taxa operate mostly in cellular stress pathways (e.g., autophagy), chromatin remodeling, ribosome biogenesis, and ageing. Together, these data highlight diverse roles for HSF1, expanding far beyond the HSR.

scholarly journals EAGLE: an algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions

2019 ◽  
Author(s):  
Tianshun Gao ◽  
Jiang Qian
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Cross Validation ◽  
Cell Types ◽  
Gene Interactions ◽  
Tissue Cell ◽  
Cell Type ◽  
Genomic Features ◽  
Link Type ◽  
Cell Type Specific

AbstractLong-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.Author summaryEnhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, it’s difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs.Finally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

scholarly journals ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Target Genes ◽  
Chromatin State ◽  
Response Analysis ◽  
Rna Seq ◽  
High Throughput Data ◽  
Micro Rnas ◽  
Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

scholarly journals Computational Model for Predicting the Relationship Between Micro-RNAs and Their Target Messenger RNAs in Breast and Colon Cancers

2018 ◽  
Vol 17 ◽  
pp. 117693511878514
Author(s):  
Shinuk Kim
Keyword(s):  
Breast Cancer ◽  
Colon Cancer ◽  
Target Gene ◽  
Target Genes ◽  
Computational Method ◽  
Numerical Code ◽  
Messenger Rnas ◽  
Unknown Parameters ◽  
Micro Rnas ◽  
The Relationship

Motivation: Uncovering the relationship between micro-RNAs (miRNAs) and their target messenger RNAs (mRNAs) can provide critical information regarding the mechanisms underlying certain types of cancers. In this context, we have proposed a computational method, referred to as prediction analysis by optimization method (PAOM), to predict miRNA-mRNA relations using data from normal and cancer tissues, and then applying the relevant algorithms to colon and breast cancers. Specifically, we used 26 miRNAs and 26 mRNAs with 676 (= 26 × 26) relationships to be recovered as unknown parameters. Results: Optimization methods were used to detect 61 relationships in breast cancer and 32 relationships in colon cancer. Using sequence filtering, we detected 18 relationships in breast cancer and 15 relationships in colon cancer. Among the 18 relationships, CD24 is the target gene of let-7a and miR-98, and E2F1 is the target gene of miR-20. In addition, the frequencies of the target genes of miR-223, miR-23a, and miR-20 were significant in breast cancer, and the frequencies of the target genes of miR-17, miR-124, and miR-30a were found to be significant in colon cancer. Availability: The numerical code is available from the authors on request.

scholarly journals Simultaneous learning of individual microRNA-gene interactions and regulatory comodules

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michael Roth ◽  
Pranjal Jain ◽  
Jinkyu Koo ◽  
Somali Chaterji
Keyword(s):  
Mirna Gene ◽  
Regulation Of Gene Expression ◽  
Biological Significance ◽  
Gene Interactions ◽  
Messenger Rnas ◽  
Sequencing Data ◽  
Functional Roles ◽  
Microrna Gene ◽  
Mirna Clusters ◽  
Post Transcriptional Regulation

Abstract Background MicroRNAs (miRNAs) function in post-transcriptional regulation of gene expression by binding to target messenger RNAs (mRNAs). Because of the key part that miRNAs play, understanding the correct regulatory role of miRNAs in diverse patho-physiological conditions is of great interest. Although it is known that miRNAs act combinatorially to regulate genes, precise identification of miRNA-gene interactions and their specific functional roles in regulatory comodules remains a challenge. We developed Theia, an effective method for simultaneously predicting miRNA-gene interactions and regulatory comodules, which group functionally related miRNAs and genes via non-negative matrix factorization (NMF). Results We apply Theia to RNA sequencing data from breast invasive carcinoma samples and demonstrate its effectiveness in discovering biologically significant regulatory comodules that are significantly enriched in spatial miRNA clusters, biological pathways, and various cancers. Conclusions Theia is a theoretically rigorous optimization algorithm that simultaneously predicts the strength and direction (i.e., up-regulation or down-regulation) of the effect of modules of miRNAs on a gene. We posit that if Theia is capable of recovering known clusters of genes and miRNA, then the clusters found by our method not previously identified by literature are also likely to have biological significance. We believe that these novel regulatory comodules found by our method will be a springboard for further research into the specific functional roles of these new functional ensembles of miRNAs and genes,especially those related to diseases like breast cancer.

scholarly journals High-throughput small RNA and transcriptome sequencing reveal reproduction-related microRNAs and mRNA in ovary of Geese in Counter-Season Production

2019 ◽  
Author(s):  
Jin-Shan Ran ◽  
Ling-Qian Yin ◽  
Jing-Jing Li ◽  
Yan-Qiang Tang ◽  
Jian Huang ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Target Genes ◽  
Mirna Gene ◽  
Egg Laying ◽  
Differentially Expressed ◽  
Ovary Development ◽  
Regulation Mechanism ◽  
Pathway Network ◽  
Apoptosis Pathways

Abstract Background Broodiness is a phenomenon that occurs in most avian species and significantly reduces productivity. Several genes are known to play an important role in regulating the progress of reproduction, but the molecular regulation mechanism of broodiness remains unclear. In the current study, via high throughput sequencing, we identified and explored the differentially expressed miRNAs and mRNAs involved in ovarian atrophy. Results We identified a total of 901 mRNAs and 50 miRNAs that were differentially expressed in egg-laying and atrophic ovaries. Among them, numerous DEGs transcripts and target genes for miRNAs were significantly enriched in reproductive processes, cell proliferation, and apoptosis pathways. In addition, a miRNA- gene-pathway network was constructed by considering target relationships and correlation of the expression levels between ovary development-related genes and miRNAs. Conclusions We discovered mRNA and miRNAs transcripts that are candidate regulators of ovary development in broody geese. Our findings expanded our understanding of the functional of miRNAs in ovarian atrophy and demonstrated that RNA-Seq is a powerful tool for examining the molecular mechanism in regulating broodiness.

scholarly journals ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Target Genes ◽  
Chromatin State ◽  
Response Analysis ◽  
Rna Seq ◽  
High Throughput Data ◽  
Micro Rnas ◽  
Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

scholarly journals miR-301a-5p Regulates TGFB2 during Chicken Spermatogenesis

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1695
Author(s):  
Qixin Guo ◽  
Yong Jiang ◽  
Hao Bai ◽  
Guohong Chen ◽  
Guobin Chang
Keyword(s):  
Growth Factor ◽  
Signaling Pathway ◽  
Transforming Growth Factor Beta ◽  
Target Genes ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Cell Types ◽  
Germline Cell ◽  
Micro Rnas ◽  
Growth Factor Beta

The process of spermatogenesis is complex and systemic, requiring the cooperation of many regulators. However, little is known about how micro RNAs (miRNAs) regulate spermatogenesis in poultry. In this study, we investigated key miRNAs and their target genes that are involved in spermatogenesis in chickens. Next-generation sequencing was conducted to determine miRNA expression profiles in five cell types: primordial germ cells (PGCs), spermatogonial stem cells (SSCs), spermatogonia (Spa), and chicken sperm. Next, we analyzed and identified several key miRNAs that regulate spermatogenesis in the four germline cell miRNA profiles. Among the enriched miRNAs, miRNA-301a-5p was the key miRNA in PGCs, SSCs, and Spa. Through reverse transcription quantitative PCR (RT-qPCR), dual-luciferase, and miRNA salience, we confirmed that miR-301a-5p binds to transforming growth factor-beta 2 (TGFβ2) and is involved in the transforming growth factor-beta (TGF-β) signaling pathway and germ cell development. To the best of our knowledge, this is the first demonstration of miR-301a-5p involvement in spermatogenesis by direct binding to TGFβ2, a key gene in the TGF-β signaling pathway. This finding contributes to the insights into the molecular mechanism through which miRNAs regulate germline cell differentiation and spermatogenesis in chickens.

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