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1
Guo, Li, Yang Zhao, Sheng Yang, Hui Zhang, and Feng Chen. "Integrative Analysis of miRNA-mRNA and miRNA-miRNA Interactions." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/907420.
Повний текст джерелаАнотація:
MicroRNAs (miRNAs) are small, noncoding regulatory molecules. They are involved in many essential biological processes and act by suppressing gene expression. The present work reports an integrative analysis of miRNA-mRNA and miRNA-miRNA interactions and their regulatory patterns using high-throughput miRNA and mRNA datasets. Aberrantly expressed miRNA and mRNA profiles were obtained based on fold change analysis, and qRT-PCR was used for further validation of deregulated miRNAs. miRNAs and target mRNAs were found to show various expression patterns. miRNA-miRNA interactions and clustered/homologous miRNAs were also found to contribute to the flexible and selective regulatory network. Interacting miRNAs (e.g., miRNA-103a and miR-103b) showed more pronounced differences in expression, which suggests the potential “restricted interaction” in the miRNA world. miRNAs from the same gene clusters (e.g., miR-23b gene cluster) or gene families (e.g., miR-10 gene family) always showed the same types of deregulation patterns, although they sometimes differed in expression levels. These clustered and homologous miRNAs may have close functional relationships, which may indicate collaborative interactions between miRNAs. The integrative analysis of miRNA-mRNA based on biological characteristics of miRNA will further enrich miRNA study.
2
Muniategui, Ander, Rubén Nogales-Cadenas, Miguél Vázquez, et al. "Quantification of miRNA-mRNA Interactions." PLoS ONE 7, no. 2 (2012): e30766. http://dx.doi.org/10.1371/journal.pone.0030766.
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Naderi, Elnaz, Mehdi Mostafaei, Akram Pourshams, and Ashraf Mohamadkhani. "Network of microRNAs-mRNAs Interactions in Pancreatic Cancer." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/534821.
Повний текст джерелаАнотація:
Background.MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death.Methods.56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool.Results.This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated.Conclusion.Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.
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Subat, Sophia, Kentaro Inamura, Hironori Ninomiya, Hiroko Nagano, Sakae Okumura, and Yuichi Ishikawa. "Unique MicroRNA and mRNA Interactions in EGFR-Mutated Lung Adenocarcinoma." Journal of Clinical Medicine 7, no. 11 (2018): 419. http://dx.doi.org/10.3390/jcm7110419.
Повний текст джерелаАнотація:
The EGFR gene was one of the first molecules to be selected for targeted gene therapy. EGFR-mutated lung adenocarcinoma, which is responsive to EGFR inhibitors, is characterized by a distinct oncogenic pathway in which unique microRNA (miRNA)–mRNA interactions have been observed. However, little information is available about the miRNA–mRNA regulatory network involved. Both miRNA and mRNA expression profiles were investigated using microarrays in 155 surgically resected specimens of lung adenocarcinoma with a known EGFR mutation status (52 mutated and 103 wild-type cases). An integrative analysis of the data was performed to identify the unique miRNA–mRNA regulatory network in EGFR-mutated lung adenocarcinoma. Expression profiling of miRNAs and mRNAs yielded characteristic miRNA/mRNA signatures (19 miRNAs/431 mRNAs) in EGFR-mutated lung adenocarcinoma. Five of the 19 miRNAs were previously listed as EGFR-mutation-specific miRNAs (i.e., miR-532-3p, miR-500a-3p, miR-224-5p, miR-502-3p, and miR-532-5p). An integrative analysis of miRNA and mRNA expression revealed a refined list of putative miRNA–mRNA interactions, of which 63 were potentially involved in EGFR-mutated tumors. Network structural analysis provided a comprehensive view of the complex miRNA–mRNA interactions in EGFR-mutated lung adenocarcinoma, including DUSP4 and MUC4 axes. Overall, this observational study provides insight into the unique miRNA–mRNA regulatory network present in EGFR-mutated tumors. Our findings, if validated, would inform future research examining the interplay of miRNAs and mRNAs in EGFR-mutated lung adenocarcinoma.
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Mukushkina, D. D., S. Labeit, and A. T. Ivashchenko. "CHARACTERISTICS OF miRNA INTERACTION WITH mRNA OF ISCHEMIC HEART DISEASE CANDIDATE GENES." REPORTS 335, no. 1 (2021): 74–82. http://dx.doi.org/10.32014/2021.2518-1483.11.
Повний текст джерелаАнотація:
Ischemic heart disease (IHD) is the most serious cardiovascular disease and one of the leading causes of death worldwide. An important role in the pathophysiology of IHD play such processes as the processes of inflammation and immune response, metabolism of homocysteine and folate, development processes of endothelial dysfunction and oxidative stress and homeostasis system. Accordingly, the identified genes that are directly involved in these processes. In addition, miRNA (mRNA-inhibiting RNA) may affect the expression of these candidate genes. Using bioinformatics methods, the most efficient associations of miRNA and target genes were established. This research presents the characteristics of miRNA interactions with mRNA of candidate IHD genes. Candidate genes were identified that had a free energy of interaction with miRNA equal to -120 kJ / mole and higher in the following interactions: in 5’UTR - ALDH2 and ID02142.3p-miR; CELSR2 and ID00457.3p-miR; DDAH2 and ID01272.3p-miR; DNMT1 and ID02052.5p-miR; DOCK7 and ID00061.3p-miR; EGFR and ID02457.3p-miR; FOLH1 and ID01428.3p-miR; IL6R and miR-6089; NOS3 and ID02363.5p-miR; NPC1 and ID00551.3p-miR; PPP1R17 and ID01693.5p-miR; PRKCH and ID00520.5p-miR; SERPINE1 and ID01098.3p-miR; in CDS - ABCG8 and ID03064.3p-miR; ADORA2A and ID02697.3p-miR; APOA1 and ID00457.3p-miR; CDKN2B and ID02899.3p-miR; IL6R and ID01806.3p-miR; TIMP2 and ID00098.5p-miR; TNF and ID02050.3p-miR; TRIB1 and ID03208.5p-miR; VWF and ID01238.5p-miR. Associations were also revealed in the 3'UTR region with an interaction free energy of -115 kJ/mole and higher: AGTR2 and ID01213.5p-miR; APLNR and ID00616.5p-miR; CXCL12 and ID00483.3p-miR; FADS2 and miR-1224-3p; FCGR2A and miR-1273g-3p; GCKR and ID02928.3p-miR; IL6R and ID00913.5p-miR; KCNJ11 and ID03288.5p-miR; PPP1R3B and ID00913.5p-miR; TFPI and miR-1273g-3p; TIMP2 and ID01941.5p-miR. The results obtained could be used as molecular genetic markers of IHD for the diagnosis of this disease.
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Bencun, Maja, Thiago Britto-Borges, Jessica Eschenbach, and Christoph Dieterich. "New Tricks with Old Dogs: Computational Identification and Experimental Validation of New miRNA–mRNA Regulation in hiPSC-CMs." Biomedicines 10, no. 2 (2022): 391. http://dx.doi.org/10.3390/biomedicines10020391.
Повний текст джерелаАнотація:
Cardiovascular disease is still the leading cause of morbidity and mortality worldwide. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have become a valuable widespread in vitro model to study cardiac disease. Herein, we employ the hiPSC-CM model to identify novel miRNA–mRNA interaction partners during cardiac differentiation and β-adrenergic stress. Whole transcriptome and small RNA sequencing data were combined to identify novel miRNA–mRNA interactions. Briefly, mRNA and miRNA expression profiles were integrated with miRNA target predictions to identify significant statistical dependencies between a miRNA and its candidate target set. We show by experimental validation that our approach discriminates true from false miRNA target predictions. Thereby, we identified several differentially expressed miRNAs and focused on the two top candidates: miR-99a-5p in the context of cardiac differentiation and miR-212-3p in the context of β-adrenergic stress. We validated some target mRNA candidates by 3′UTR luciferase assays as well as in transfection experiments in the hiPSC-CM model system. Our data show that iPSC-derived cardiomyocytes and computational modeling can be used to uncover new valid miRNA–mRNA interactions beyond current knowledge.
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Alshalalfa, Mohammed. "MicroRNA Response Elements-Mediated miRNA-miRNA Interactions in Prostate Cancer." Advances in Bioinformatics 2012 (November 4, 2012): 1–10. http://dx.doi.org/10.1155/2012/839837.
Повний текст джерелаАнотація:
The cell is a highly organized system of interacting molecules including proteins, mRNAs, and miRNAs. Analyzing the cell from a systems perspective by integrating different types of data helps revealing the complexity of diseases. Although there is emerging evidence that microRNAs have a functional role in cancer, the role of microRNAs in mediating cancer progression and metastasis remains not fully explored. As the amount of available miRNA and mRNA gene expression data grows, more systematic methods combining gene expression and biological networks become necessary to explore miRNA function. In this work I integrated functional miRNA-target interactions with mRNA and miRNA expression to infer mRNA-mediated miRNA-miRNA interactions. The inferred network represents miRNA modulation through common targets. The network is used to characterize the functional role of microRNA response element (MRE) to mediate interactions between miRNAs targeting the MRE. Results revealed that miRNA-1 is a key player in regulating prostate cancer progression. 11 miRNAs were identified as diagnostic and prognostic biomarkers that act as tumor suppressor miRNAs. This work demonstrates the utility of a network analysis as opposed to differential expression to find important miRNAs that regulate prostate cancer.
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Stebel, Sophie, Janina Breuer, and Oliver Rossbach. "Studying miRNA–mRNA Interactions: An Optimized CLIP-Protocol for Endogenous Ago2-Protein." Methods and Protocols 5, no. 6 (2022): 96. http://dx.doi.org/10.3390/mps5060096.
Повний текст джерелаАнотація:
Transcriptome-wide analysis of RNA-binding partners is commonly achieved using UV crosslinking and immunoprecipitation (CLIP). Individual-nucleotide-resolution CLIP (iCLIP)enables identification of the specific position of the protein–RNA interaction. In addition to RNA-binding proteins (RBPs), microRNA (miRNA)–mRNA interactions also play a crucial role in the regulation of gene expression. Argonaute-2 (Ago2) mediates miRNA binding to a multitude of mRNA target sites, enabling the identification of miRNA–mRNA interactions by employing modified Ago2-CLIP protocols. Here, we describe an Ago2-specific CLIP protocol optimized for the use of small quantities of cell material, targeting endogenous Ago2 while avoiding possible methodological biases such as metabolic labeling or Ago2 overexpression and applying the latest advances in CLIP library preparation, the iCLIP2 protocol. In particular, we focus on the optimization of lysis conditions and improved radioactive labeling of the 5′ end of the miRNA.
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Afonso-Grunz, Fabian, and Sören Müller. "Principles of miRNA–mRNA interactions: beyond sequence complementarity." Cellular and Molecular Life Sciences 72, no. 16 (2015): 3127–41. http://dx.doi.org/10.1007/s00018-015-1922-2.
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Wang, Zixing, Wenlong Xu, Haifeng Zhu, and Yin Liu. "A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information." Cancer Informatics 13s7 (January 2014): CIN.S16348. http://dx.doi.org/10.4137/cin.s16348.
Повний текст джерелаАнотація:
MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.
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Tseng, Chia-Chun, Ling-Yu Wu, Wen-Chan Tsai, et al. "Differential Expression Profiles of the Transcriptome and miRNA Interactome in Synovial Fibroblasts of Rheumatoid Arthritis Revealed by Next Generation Sequencing." Diagnostics 9, no. 3 (2019): 98. http://dx.doi.org/10.3390/diagnostics9030098.
Повний текст джерелаАнотація:
Using next-generation sequencing to decipher the molecular mechanisms underlying aberrant rheumatoid arthritis synovial fibroblasts (RASF) activation, we performed transcriptome-wide RNA-seq and small RNA-seq on synovial fibroblasts from rheumatoid arthritis (RA) subject and normal donor. Differential expression of mRNA and miRNA was integrated with interaction analysis, functional annotation, regulatory network mapping and experimentally verified miRNA–target interaction data, further validated with microarray expression profiles. In this study, 3049 upregulated mRNA and 3552 downregulated mRNA, together with 50 upregulated miRNA and 35 downregulated miRNA in RASF were identified. Interaction analysis highlighted contribution of miRNA to altered transcriptome. Functional annotation revealed metabolic deregulation and oncogenic signatures of RASF. Regulatory network mapping identified downregulated FOXO1 as master transcription factor resulting in altered transcriptome of RASF. Differential expression in three miRNA and corresponding targets (hsa-miR-31-5p:WASF3, hsa-miR-132-3p:RB1, hsa-miR-29c-3p:COL1A1) were also validated. The interactions of these three miRNA–target genes were experimentally validated with past literature. Our transcriptomic and miRNA interactomic investigation identified gene signatures associated with RASF and revealed the involvement of transcription factors and miRNA in an altered transcriptome. These findings help facilitate our understanding of RA with the hope of serving as a springboard for further discoveries relating to the disease.
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Łuczkowska, Karolina, Dorota Rogińska, Zofia Ulańczyk, Edyta Paczkowska, Christian Andreas Schmidt, and Bogusław Machaliński. "Molecular Mechanisms of Bortezomib Action: Novel Evidence for the miRNA–mRNA Interaction Involvement." International Journal of Molecular Sciences 21, no. 1 (2020): 350. http://dx.doi.org/10.3390/ijms21010350.
Повний текст джерелаАнотація:
Bortezomib is an anti-tumor agent, which inhibits 26S proteasome degrading ubiquitinated proteins. While apoptotic transcription-associated activation in response to bortezomib has been suggested, mechanisms related to its influence on post-transcriptional gene silencing mediated regulation by non-coding RNAs remain not fully elucidated. In the present study, we examined changes in global gene and miRNA expression and analyzed the identified miRNA–mRNA interactions after bortezomib exposure in human neuroblastoma cells to define pathways affected by this agent in this type of cells. Cell viability assays were performed to assess cytotoxicity of bortezomib. Global gene and miRNA expression profiles of neuroblastoma cells after 24-h incubation with bortezomib were determined using genome-wide RNA and miRNA microarray technology. Obtained results were then confirmed by qRT-PCR and Western blot. Further bioinformatical analysis was performed to identify affected biological processes and pathways. In total, 719 genes and 28 miRNAs were downregulated, and 319 genes and 61 miRNAs were upregulated in neuroblastoma cells treated with bortezomib. Possible interactions between dysregulated miRNA/mRNA, which could be linked to bortezomib-induced neurotoxicity, affect neurogenesis, cellular calcium transport, and neuron death. Bortezomib might exert toxic effects on neuroblastoma cells and regulate miRNA–mRNA interactions influencing vital cellular functions. Further studies on the role of specific miRNA–mRNA interactions are needed to elucidate mechanisms of bortezomib action.
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Wei, Chao, Lei Wang, and Han Zhang. "An ensemble method to predict target genes and pathways in uveal melanoma." Open Life Sciences 13, no. 1 (2018): 90–96. http://dx.doi.org/10.1515/biol-2018-0013.
Повний текст джерелаАнотація:
AbstractObjectiveThis work proposes to predict target genes and pathways for uveal melanoma (UM) based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC), a causal inference method (IDA) and a regression method (Lasso) utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.
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Zhang, Yunpeng, Wei Liu, Yanjun Xu, et al. "Identification of Subtype Specific miRNA-mRNA Functional Regulatory Modules in Matched miRNA-mRNA Expression Data: Multiple Myeloma as a Case." BioMed Research International 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/501262.
Повний текст джерелаАнотація:
Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.
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Mukushkina, D. D., A. T. Ivashchenko, and S. Labeit. "FEATURES OF miRNA ASSOCIATIONS WITH mRNA OF MYOCARDIAL INFARCTION CANDIDATE GENES." REPORTS 2, no. 336 (2021): 46–53. http://dx.doi.org/10.32014/2021.2518-1483.29.
Повний текст джерелаАнотація:
Cardiovascular diseases, in particular myocardial infarction, are one of the most common causes of death in the world. To date, the risk assessment strategy infarction and post-infarction complications represent a significant problem sensitivity and predictive value of modern methods and markers, so the identification of new genetic markers is an actual problem. In this research, functionally significant candidate genes were studied, which are involved in the processes associated with the pathogenesis of myocardial infarction, in lipid metabolism, thrombus formation, endothelial dysfunction, and inflammatory reactions. However, in addition to genes, it has been determined that miRNA is also involved in the development of myocardial infarction by regulating the expression of target genes. This paper presents characteristics of miRNA interactions with mRNAs of candidate myocardial infarction genes. We have identified 34, 51 and 36 target genes that have miRNA binding sites in the 5'UTR, CDS, and 3'UTR regions, respectively. Based on the criteria chosen in our study, candidate genes were identified that have a free energy of interaction with miRNA equal to -120 kJ/mole and higher in the following associations: in 5’UTR - ID02142.3p-miR and ALDH2; ID00909.3p-miR and ALOX5; ID00216.3p-miR and CD40; ID01272.3p-miR and DDAH2; ID01774.5p-miR and IL6R; miR-6752-5p and KLF4; ID03332.3p-miR and LAMA3; ID02363.5p-miR and NOS3; ID02800.3p-miR and OPA1; ID01310.3p-miR and PDE4D; ID03397.3p-miR and PTGS2; ID01098.3p-miR and SERPINE1; ID01018.3p-miR and SGPP1; ID02430.3p-miR and SHH; ID01652.3p-miR and THBS1; ID01770.3p-miR and ZNF202; in CDS - ID00457.3p-miR and APOA1; ID00425.5p-miR and BTN2A1; ID01632.5p- miR and CCL5; ID02899.3p-miR and CDKN2B; miR-6894-5p and CYP1A2; ID01806.3p-miR and IL6R; ID01403.5p-miR and PLAUR; ID02950.3p-miR and SEMA3F; ID03332.3p-miR and SGPP1; ID02062.3p-miR and SIRT6; ID02050.3p-miR and TNF; ID01804.3p-miR and XBP1; ID00182.5p-miR and ZNF202; in 3’UTR - ID01293.5p-miR and SMTN; ID01882.5p-miR and TNNI3. The identified associations can be used as genetic markers in the diagnosis of myocardial infarction.
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Wu, Wei, Lingxiang Wu, Mengyan Zhu, et al. "miRNA Mediated Noise Making of 3′UTR Mutations in Cancer." Genes 9, no. 11 (2018): 545. http://dx.doi.org/10.3390/genes9110545.
Повний текст джерелаАнотація:
Somatic mutations in 3′-untranslated regions (3′UTR) do not alter amino acids and are considered to be silent in cancers. We found that such mutations can promote tumor progression by altering microRNA (miRNA) targeting efficiency and consequently affecting miRNA–mRNA interactions. We identified 67,159 somatic mutations located in the 3′UTRs of messenger RNAs (mRNAs) which can alter miRNA–mRNA interactions (functional somatic mutations, funcMutations), and 69.3% of these funcMutations (the degree of energy change > 12 kcal/mol) were identified to significantly promote loss of miRNA-mRNA binding. By integrating mRNA expression profiles of 21 cancer types, we found that the expression of target genes was positively correlated with the loss of absolute affinity level and negatively correlated with the gain of absolute affinity level. Functional enrichment analysis revealed that genes carrying funcMutations were significantly enriched in the MAPK and WNT signaling pathways, and analysis of regulatory modules identified eighteen miRNA modules involved with similar cellular functions. Our findings elucidate a complex relationship between miRNA, mRNA, and mutations, and suggest that 3′UTR mutations may play an important role in tumor development.
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Li, A., J. Zhang, Z. Zhou, L. Wang, X. Sun, and Y. Liu. "Genome-scale identification of miRNA-mRNA and miRNA-lncRNA interactions in domestic animals." Animal Genetics 46, no. 6 (2015): 716–19. http://dx.doi.org/10.1111/age.12329.
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Yan, Li, Demin Jiao, Huizhen Hu, et al. "Identification of lymph node metastasis-related microRNAs in lung adenocarcinoma and analysis of the underlying mechanisms using a bioinformatics approach." Experimental Biology and Medicine 242, no. 7 (2016): 709–17. http://dx.doi.org/10.1177/1535370216677353.
Повний текст джерелаАнотація:
This study aimed to screen lymphatic metastasis-related microRNAs (miRNAs) in lung adenocarcinoma and explore their underlying mechanisms using bioinformatics. The miRNA expression in primary lung adenocarcinoma, matched adjacent non-tumorigenic and lymph node metastasis tissues of patients were profiled via microarray. The screened metastasis-related miRNAs were then validated using quantitative real-time PCR in a second cohort of lung adenocarcinoma patients with lymphatic metastasis. Significance was determined using a paired t-test. Target genes of the metastasis-related miRNAs were predicted using TargetScan, and transcription factors (TFs) were predicted based on the TRANSFAC and ENCODE databases. Furthermore, the related long non-coding RNAs (lncRNAs) were screened with starBase v2.0. The miRNA-TF-mRNA and lncRNA-miRNA-mRNA networks were constructed to determine the key interactions associated with lung adenocarcinoma metastasis. According to the miRNA microarray results, there were 10 miRNAs that were differentially expressed in metastatic tissues compared with primary tumor and adjacent non-tumorigenic tissues. Among them were increased levels of miR-146a-5p, miR-342-3p, and miR-150-5p, which were validated in the second cohort. Based on the miRNA-TF-mRNA network, vascular endothelial growth factor A and transcription factors (TFs) including TP53, SMAD4, and EP300 were recognized as critical targets of the three miRNAs. Interactions involving SNHG16–miR-146a-5p–SMAD4 and RP6-24A23.7–miR-342-3p/miR-150-5p–EP300 were highlighted according to the lncRNA-miRNA-mRNA network. miR-146a-5p, miR-342-3p, and miR-150-5p are lymphatic metastasis-related miRNAs in lung adenocarcinoma. Bioinformatics analyses demonstrated that SNHG16 might inhibit the interaction between miR-146a-5p and SMAD4, while RP6-24A23.7 might weaken miR-342-3p–EP300 and miR-150-5p–EP300 interactions in metastasis.
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Fang, Yi, Xiaoyong Pan, and Hong-Bin Shen. "Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction." Symmetry 14, no. 7 (2022): 1302. http://dx.doi.org/10.3390/sym14071302.
Повний текст джерелаАнотація:
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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Frohn, Anne, H. Christian Eberl, Julia Stöhr, et al. "Dicer-dependent and -independent Argonaute2 Protein Interaction Networks in Mammalian Cells." Molecular & Cellular Proteomics 11, no. 11 (2012): 1442–56. http://dx.doi.org/10.1074/mcp.m112.017756.
Повний текст джерелаАнотація:
Argonaute (Ago) proteins interact with small regulatory RNAs such as microRNAs (miRNAs) and facilitate gene-silencing processes. miRNAs guide Ago proteins to specific mRNAs leading to translational silencing or mRNA decay. In order to understand the mechanistic details of miRNA function, it is important to characterize Ago protein interactors. Although several proteomic studies have been performed, it is not clear how the Ago interactome changes on miRNA or mRNA binding. Here, we report the analysis of Ago protein interactions in miRNA-containing and miRNA-depleted cells. Using stable isotope labeling in cell culture in conjunction with Dicer knock out mouse embryonic fibroblasts, we identify proteins that interact with Ago2 in the presence or the absence of Dicer. In contrast to our current view, we find that Ago-mRNA interactions can also take place in the absence of miRNAs. Our proteomics approach provides a rich resource for further functional studies on the cellular roles of Ago proteins.
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Abu-Halima, Masood, Viktoria Wagner, Lea Simone Becker, et al. "Integrated microRNA and mRNA Expression Profiling Identifies Novel Targets and Networks Associated with Ebstein’s Anomaly." Cells 10, no. 5 (2021): 1066. http://dx.doi.org/10.3390/cells10051066.
Повний текст джерелаАнотація:
Little is known about abundance level changes of circulating microRNAs (miRNAs) and messenger RNAs (mRNA) in patients with Ebstein’s anomaly (EA). Here, we performed an integrated analysis to identify the differentially abundant miRNAs and mRNA targets and to identify the potential therapeutic targets that might be involved in the mechanisms underlying EA. A large panel of human miRNA and mRNA microarrays were conducted to determine the genome-wide expression profiles in the blood of 16 EA patients and 16 age and gender-matched healthy control volunteers (HVs). Differential abundance level of single miRNA and mRNA was validated by Real-Time quantitative PCR (RT-qPCR). Enrichment analyses of altered miRNA and mRNA abundance levels were identified using bioinformatics tools. Altered miRNA and mRNA abundance levels were observed between EA patients and HVs. Among the deregulated miRNAs and mRNAs, 76 miRNAs (49 lower abundance and 27 higher abundance, fold-change of ≥2) and 29 mRNAs (25 higher abundance and 4 lower abundance, fold-change of ≥1.5) were identified in EA patients compared to HVs. Bioinformatics analysis identified 37 pairs of putative miRNA-mRNA interactions. The majority of the correlations were detected between the lower abundance level of miRNA and higher abundance level of mRNA, except for let-7b-5p, which showed a higher abundance level and their target gene, SCRN3, showed a lower abundance level. Pathway enrichment analysis of the deregulated mRNAs identified 35 significant pathways that are mostly involved in signal transduction and cellular interaction pathways. Our findings provide new insights into a potential molecular biomarker(s) for the EA that may guide the development of novel targeting therapies.
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Li, Jianqing, Xue Yin, Bingyu Zhang, Chen Li, and Peirong Lu. "Bioinformatical Analysis of miRNA-mRNA Interaction Network Underlying Macrophage Aging and Cholesterol-Responsive Difference between Young and Aged Macrophages." BioMed Research International 2020 (June 13, 2020): 1–11. http://dx.doi.org/10.1155/2020/9267475.
Повний текст джерелаАнотація:
Purpose. Macrophage aging is involved with the occurrence and progression of age-related macular degeneration (AMD). The purpose of this study was to identify the specific microRNAs (miRNA), mRNAs, and their interactions underlying macrophage aging and response to cholesterol through bioinformatical analysis in order to get a better understanding of the mechanism of AMD. Methods. The microarray data were obtained from Gene Expression Omnibus (accession GSE111304 and GSE111382). The age-related differentially expressed genes in macrophages were identified using R software. Further miRNA-mRNA interactions were analyzed through miRWalk, mirTarBase, starBase, and then produced by Cytoscape. The functional annotations including Gene Ontology and KEGG pathways of the miRNA target genes were performed by the DAVID and the STRING database. In addition, protein-protein interaction network was constructed to identify the key genes in response to exogenous cholesterol. Results. When comparing aged and young macrophages, a total of 14 miRNAs and 101 mRNAs were detected as differentially expressed. Besides, 19 validated and 544 predicted miRNA-mRNA interactions were detected. Lipid metabolic process was found to be associated with macrophage aging through functional annotations of the miRNA targets. After being treated with oxidized and acetylated low-density lipoprotein, miR-714 and 16 mRNAs differentially expressed in response to both kinds of cholesterol between aged and young macrophages. Among them, 6 miRNA-mRNA predicted pairs were detected. The functional annotations were mainly related to lipid metabolism process and farnesyl diphosphate farnesyl transferase 1 (FDFT1) was identified to be the key gene in the difference of response to cholesterol between aged and young macrophages. Conclusions. Lipid metabolic process was critical in both macrophage aging and response to cholesterol thus was regarded to be associated with the occurrence and progression of AMD. Moreover, miR-714-FDFT1 may modulate cholesterol homeostasis in aged macrophages and have the potential to be a novel therapeutic target for AMD.
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Riolo, Giulia, Silvia Cantara, Carlotta Marzocchi, and Claudia Ricci. "miRNA Targets: From Prediction Tools to Experimental Validation." Methods and Protocols 4, no. 1 (2020): 1. http://dx.doi.org/10.3390/mps4010001.
Повний текст джерелаАнотація:
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.
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Guo, Li, Yang Zhao, Sheng Yang, Hui Zhang, and Feng Chen. "An Integrated Analysis of miRNA, lncRNA, and mRNA Expression Profiles." BioMed Research International 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/345605.
Повний текст джерелаАнотація:
Increasing amounts of evidence indicate that noncoding RNAs (ncRNAs) have important roles in various biological processes. Here, miRNA, lncRNA, and mRNA expression profiles were analyzed in human HepG2 and L02 cells using high-throughput technologies. An integrative method was developed to identify possible functional relationships between different RNA molecules. The dominant deregulated miRNAs were prone to be downregulated in tumor cells, and the most abnormal mRNAs and lncRNAs were always upregulated. However, the genome-wide analysis of differentially expressed RNA species did not show significant bias between up- and downregulated populations. miRNA-mRNA interaction was performed based on their regulatory relationships, and miRNA-lncRNA and mRNA-lncRNA interactions were thoroughly surveyed and identified based on their locational distributions and sequence correlations. Aberrantly expressed miRNAs were further analyzed based on their multiple isomiRs. IsomiR repertoires and expression patterns were varied across miRNA loci. Several specific miRNA loci showed differences between tumor and normal cells, especially with respect to abnormally expressed miRNA species. These findings suggest that isomiR repertoires and expression patterns might contribute to tumorigenesis through different biological roles. Systematic and integrative analysis of different RNA molecules with potential cross-talk may make great contributions to the unveiling of the complex mechanisms underlying tumorigenesis.
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Balakrishnan, Ilango, Xiaodong Yang, Beverly Torok-Storb, Jay Hesselberth, and Manoj Pillai. "High Throughput Sequencing Following Cross-Linked Immune Precipitation (HITS-CLIP) of Argonaute (AGO) Identifies Mir-193a as a Regulator of Jagged1 In Marrow Stromal Cells." Blood 116, no. 21 (2010): 3847. http://dx.doi.org/10.1182/blood.v116.21.3847.3847.
Повний текст джерелаАнотація:
Abstract Abstract 3847 MicroRNAs (miRNAs) are small non-coding RNAs with important roles in the post-transcriptional regulation of up to 30% of all vertebrate genes. Traditional methods to determine miRNA-mRNA interactions have included transcriptional profiling of miRNAs, bio-informatic prediction of miRNA-mRNA binding, analysis of 3` untranslated region (3`UTR) binding of miRNAs and over-expression of miRNAs in relevant cell types. These studies however fall short of demonstrating direct interaction between a miRNA and its target mRNAs. We applied a recently described biochemical technique of high throughput sequencing following cross-linked immune precipitation (HITS-CLIP) to dissecting the miRNA-mRNA interactions in two functionally distinct human marrow stromal cell lines. HITS-CLIP relies on the ability of ultraviolet (UV) radiation to cross-link RNA to proteins they are bound to, followed by immune-precipitation of the RNA-protein complex to isolate the cross-linked RNA and sequencing by high throughput techniques. As miRNA-mRNA interactions occur in close proximity to the argonaute proteins (AGO), an anti-argonaute monoclonal antibody was used to isolate the Ago-miRNA-mRNA complexes. The two stromal cell lines analyzed by HITS-CLIP (designated HS5 and HS27a) were isolated from a normal marrow primary long term culture (LTC), immortalized and extensively characterized for both function and expression profiles (mRNA and miRNA). HS5 was found to secrete growth factors that stimulate proliferation and differentiation of hematopoietic progenitors (G-CSF, IL-6, IL-1α and IL1β), whereas HS27a expresses activities associated with the stem cell niche (CXCL12, Angiopoietin-1, Jag1 etc). In keeping with this, HS5 conditioned media stimulated proliferation and differentiation of isolated CD34+ cells whereas HS27a supported CD34+ cells in an undifferentiated state. Sequence reads from the HITS-CLIP analysis from each of the cell lines were aligned to the human genome using the UCSC genome browser to identify Ago-mRNA and Ago-miRNA binding sites in both the cell lines. Interestingly, corresponding datasets from HS5 and HS27a were similar for the majority of mRNAs and miRNAs, but distinct for those mRNAs (such as Jag1, CXCL12, IL6 and GCSF) and miRNAs (such as miR-886-3p, miR-221, miR-181a and miR-193a) known to be differentially expressed between the two cell lines. We then validated the use of the HITS-CLIP strategy in stromal cells by analyzing one such Ago-mRNA binding site for Jagged1 (Jag1). Jag1 is a ligand for Notch1 and is expressed in those cells that support the hematopoietic stem cell (HSC) niche. The Notch pathway is a highly conserved signaling system critical in regulating several tissue systems including hematopoietic cells. This binding site, 1749 bp downstream of the transcriptional start-site for Jag1 was significantly more enriched in HS5 compared to HS27a. The site was also a predicted binding site for miR-193a, a miRNA over-expressed in HS5 compared to HS27a cells. Over-expression of miR-193a in HS27a cells resulted in the down-regulation of Jag1 protein (as measured by Western blotting). To confirm the direct interaction between Jag1 and miR-193a, we cloned this purported binding site downstream of the luciferase gene and co-transfected the plasmid with miR-193a. Luciferase activity was down-regulated greater than 50% when compared to control transfections suggesting a direct effect of miR-193a on Jag1 transcript. In summary, our data suggest that HITS-CLIP methodology can be used to define in vivo spatial interactions between miRNA and mRNAs in the marrow microenvironment (ME). It can also be used to define miRNA-based regulation of specific genes such as Jag1, which are critical to defining functional niches in the ME. Disclosures: No relevant conflicts of interest to declare.
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Yu, Liwei, Tengfei Yao, Zhoulei Jiang, and Tong Xu. "Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head." Evidence-Based Complementary and Alternative Medicine 2021 (August 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/8076598.
Повний текст джерелаАнотація:
Osteonecrosis of the femoral head (ONFH) accounts for as many as 18% of total hip arthroplasties. Knowledge of genetic changes and molecular abnormalities could help identify individuals considered to be at a higher risk of developing ONFH. In this study, we sought to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs) associated with ONFH by integrated bioinformatics analyses as well as to construct the miRNA-mRNA regulatory network involving in the pathogenesis of ONFH. We performed differential expression analysis using a gene expression profile GSE123568 and a miRNA expression profile GSE89587 deposited in the Gene Expression Omnibus and identified 47 DEmiRs (24 upregulated miRNAs and 23 downregulated miRNAs) and 529 DEGs (218 upregulated genes and 311 downregulated genes). Gene Ontology enrichment analyses of DEGs suggested that DEGs were significantly enriched in neutrophil activation, cytosol, and ubiquitin-protein transferase activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs revealed that DEGs were significantly enriched in transcriptional misregulation in cancer. DEGs-based miRNA-mRNA regulatory networks were obtained by searching miRNA-mRNA prediction databases, TargetScan, miTarBase, miRMap, miRDB, and miRanda databases. Then, overlapped miRNAs were selected between these putative miRNAs and DEmiRs between ONFH and non-ONFH, and pairs of the DEmiR-DEG regulatory network were finally depicted. There were 12 nodes and 64 interactions for upDEmiR-downDEG regulatory networks and 6 nodes and 16 interactions for downDEmiR-upDEG regulatory networks. Using the STRING database, we established a protein-protein interaction network based on the overlapped DEGs between ONFH and non-ONFH. C5AR1, CDC27, CDC34, KAT2B, CPPED1, TFDP1, and MX2 were identified as the hub genes. The present study characterizes the miRNA profile, gene profile, and miRNA-mRNA regulatory network in ONFH, which may contribute to the interpretation of the pathogenesis of ONFH and the identification of novel biomarkers and therapeutic targets for ONFH.
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Yang, Zhifeng, Zili Liu, Lingqiu Meng, and Shuyan Ma. "Identification of key pathways regulated by a set of competitive long non-coding RNAs in oral squamous cell carcinoma." Journal of International Medical Research 47, no. 4 (2019): 1758–65. http://dx.doi.org/10.1177/0300060519827190.
Повний текст джерелаАнотація:
Objective The aim of this study was to identify important pathways regulated by a set of long non-coding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC). Methods A lncRNA-mediated competitive endogenous RNA network (LMCN) was constructed using information on microRNA (miRNA)–mRNA interactions and lncRNA–miRNA intersections from the E-GEOD-37991 transcription profiling data in the ArrayExpress database. A random walk with restart ranking algorithm was then applied to evaluate the influences of protein-coding genes regulated by competitive lncRNAs. Pathway enrichment scores were calculated based on the propagation scores of protein-coding genes. Finally, permutation tests were used to estimate the significance of the pathways. Results We obtained lncRNA–mRNA interactions based on miRNAs common to both miRNA–mRNA interactions and lncRNA–miRNA intersections, and used interactions with a z-score > 0.7 to construct a LMCN. Ten lncRNAs were identified as source nodes in the LMCN, and nine pathways with enrichment scores >0.8, including ‘Cell cycle’, ‘Endocytosis’, and ‘Pathways in cancer’, were significantly enriched by these source nodes. Conclusions Nine significant pathways regulated by a set of competitive lncRNAs were identified in OSCC, which may play important roles in the development of OSCC via the cell cycle and endocytosis.
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Tai, Yang, Chong Zhao, Jinhang Gao, Tian Lan, and Huan Tong. "Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis." PeerJ 9 (August 6, 2021): e11910. http://dx.doi.org/10.7717/peerj.11910.
Повний текст джерелаАнотація:
Background Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. Methods miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. Results By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. Conclusions We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.
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Solé, Anna, Núria Mencia, Xenia Villalobos, Véronique Noé, and Carlos J. Ciudad. "Validation of miRNA-mRNA interactions by electrophoretic mobility shift assays." BMC Research Notes 6, no. 1 (2013): 454. http://dx.doi.org/10.1186/1756-0500-6-454.
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Liu, Yanwei, Kai Tang, Wei Yan, et al. "Identifying Ki-67 specific miRNA–mRNA interactions in malignant astrocytomas." Neuroscience Letters 546 (June 2013): 36–41. http://dx.doi.org/10.1016/j.neulet.2013.04.030.
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Cloonan, Nicole. "Re‐thinking miRNA‐mRNA interactions: Intertwining issues confound target discovery." BioEssays 37, no. 4 (2015): 379–88. http://dx.doi.org/10.1002/bies.201400191.
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da Silveira, Willian, Ludivine Renaud, Jonathan Simpson, et al. "miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks." Genes 9, no. 9 (2018): 458. http://dx.doi.org/10.3390/genes9090458.
Повний текст джерелаАнотація:
It is estimated that 30% of all genes in the mammalian cells are regulated by microRNA (miRNAs). The most relevant miRNAs in a cellular context are not necessarily those with the greatest change in expression levels between healthy and diseased tissue. Differentially expressed (DE) miRNAs that modulate a large number of messenger RNA (mRNA) transcripts ultimately have a greater influence in determining phenotypic outcomes and are more important in a global biological context than miRNAs that modulate just a few mRNA transcripts. Here, we describe the development of a tool, “miRmapper”, which identifies the most dominant miRNAs in a miRNA–mRNA network and recognizes similarities between miRNAs based on commonly regulated mRNAs. Using a list of miRNA–target gene interactions and a list of DE transcripts, miRmapper provides several outputs: (1) an adjacency matrix that is used to calculate miRNA similarity utilizing the Jaccard distance; (2) a dendrogram and (3) an identity heatmap displaying miRNA clusters based on their effect on mRNA expression; (4) a miRNA impact table and (5) a barplot that provides a visual illustration of this impact. We tested this tool using nonmetastatic and metastatic bladder cancer cell lines and demonstrated that the most relevant miRNAs in a cellular context are not necessarily those with the greatest fold change. Additionally, by exploiting the Jaccard distance, we unraveled novel cooperative interactions between miRNAs from independent families in regulating common target mRNAs; i.e., five of the top 10 miRNAs act in synergy.
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Fan, Weiyang, Rui Shi, Minyi Guan, et al. "The Effects of Naringenin on miRNA-mRNA Profiles in HepaRG Cells." International Journal of Molecular Sciences 22, no. 5 (2021): 2292. http://dx.doi.org/10.3390/ijms22052292.
Повний текст джерелаАнотація:
Naringenin, a natural flavonoid widely found in citrus fruits, has been reported to possess anti-oxidant, anti-inflammatory, and hepatoprotective properties as a natural dietary supplement. However, the regulatory mechanism of naringenin in human liver remains unclear. In the present study, messenger RNA sequencing (mRNA-seq), microRNA sequencing (miRNA-seq), and real-time qPCR were used to distinguish the expression differences between control and naringenin-treated HepaRG cells. We obtained 1037 differentially expressed mRNAs and 234 miRNAs. According to the target prediction and integration analysis in silico, we found 20 potential miRNA-mRNA pairs involved in liver metabolism. This study is the first to provide a perspective of miRNA–mRNA interactions in the regulation of naringenin via an integrated analysis of mRNA-seq and miRNA-seq in HepaRG cells, which further characterizes the nutraceutical value of naringenin as a food additive.
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Nersisyan, Stepan, Alexei Galatenko, Vladimir Galatenko, Maxim Shkurnikov, and Alexander Tonevitsky. "miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence." PLOS ONE 16, no. 4 (2021): e0249424. http://dx.doi.org/10.1371/journal.pone.0249424.
Повний текст джерелаАнотація:
Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).
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Misiak, Danny, Marcus Bauer, Jana Lange, et al. "MiRNA Deregulation Distinguishes Anaplastic Thyroid Carcinoma (ATC) and Supports Upregulation of Oncogene Expression." Cancers 13, no. 23 (2021): 5913. http://dx.doi.org/10.3390/cancers13235913.
Повний текст джерелаАнотація:
Anaplastic thyroid carcinoma (ATC) is the most fatal and rapidly evolving endocrine malignancy invading the head and neck region and accounts for up to 50% of thyroid cancer-associated deaths. Deregulation of the microRNA (miRNA) expression promotes thyroid carcinoma progression by modulating the reorganization of the ATC transcriptome. Here, we applied comparative miRNA–mRNA sequencing on a cohort of 28 thyroid carcinomas to unravel the association of deregulated miRNA and mRNA expression. This identified 85 miRNAs significantly deregulated in ATC. By establishing a new analysis pipeline, we unraveled 85 prime miRNA–mRNA interactions supporting the downregulation of candidate tumor suppressors and the upregulation of bona fide oncogenes such as survivin (BIRC5) in ATC. This miRNA-dependent reprogramming of the ATC transcriptome provided an mRNA signature comprising 65 genes sharply distinguishing ATC from other thyroid carcinomas. The validation of the deregulated protein expression in an independent thyroid carcinoma cohort demonstrates that miRNA-dependent oncogenes comprised in this signature, the transferrin receptor TFRC (CD71) and the E3-ubiquitin ligase DTL, are sharply upregulated in ATC. This upregulation is sufficient to distinguish ATC even from poorly differentiated thyroid carcinomas (PDTC). In sum, these findings provide new diagnostic tools and a robust resource to explore the key miRNA–mRNA regulation underlying the progression of thyroid carcinoma.
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Williams, Allison Lesher, Vedbar S. Khadka, Ma C. T. Anagaran, et al. "miR-125 family regulates XIRP1 and FIH in response to myocardial infarction." Physiological Genomics 52, no. 8 (2020): 358–68. http://dx.doi.org/10.1152/physiolgenomics.00041.2020.
Повний текст джерелаАнотація:
MicroRNAs (miRNAs) are powerful regulators of protein expression. Many play important roles in cardiac development and disease. While several miRNAs and targets have been well characterized, the abundance of miRNAs and the numerous potential targets for each suggest that the vast majority of these interactions have yet to be described. The goal of this study was to characterize miRNA expression in the mouse heart after coronary artery ligation (LIG) and identify novel mRNA targets altered during the initial response to ischemic stress. We performed small RNA sequencing (RNA-Seq) of ischemic heart tissue 1 day and 3 days after ligation and identified 182 differentially expressed miRNAs. We then selected relevant mRNA targets from all potential targets by correlating miRNA and mRNA expression from a corresponding RNA-Seq data set. From this analysis we chose to focus, as proof of principle, on two miRNAs from the miR-125 family, miR-125a and miR-351, and two of their potential mRNA targets, Xin actin-binding repeat-containing protein 1 ( XIRP1) and factor inhibiting hypoxia-inducible factor ( FIH). We found miR-125a to be less abundant and XIRP1 more abundant after ligation. In contrast, the related murine miRNA miR-351 was substantially upregulated in response to ischemic injury, and FIH expression correspondingly decreased. Luciferase reporter assays confirmed direct interactions between these miRNAs and targets. In summary, we utilized a correlative analysis strategy combining miRNA and mRNA expression data to identify functional miRNA-mRNA relationships in the heart after ligation. These findings provide insight into the response to ischemic injury and suggest future therapeutic targets.
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Lin, Lihui, Yuting Liang, Tianyu Cao, et al. "Transcriptome profiling and ceRNA network of small extracellular vesicles from resting and degranulated mast cells." Epigenomics 15, no. 17 (2023): 845–62. http://dx.doi.org/10.2217/epi-2023-0175.
Повний текст джерелаАнотація:
Aim: This study aimed to investigate the transcriptomic characteristics and interactions between competitive endogenous RNAs (ceRNAs) within small extracellular vesicles (sEVs) derived from mast cells (MCs). Methods: Transcriptome sequencing analyzed lncRNA, circRNA and mRNA expression in resting and degranulated MC-derived sEVs. Constructed ceRNA regulatory network through correlation analysis and target gene prediction. Results: Differentially expressed 1673 mRNAs, 173 lncRNAs and 531 circRNAs were observed between resting and degranulated MCs-derived sEVs. Enrichment analysis revealed involvement of neurodegeneration, infection and tumor pathways. CeRNA networks included interactions between lncRNA–miRNA, circRNA–miRNA and miRNA–mRNA, targeting genes in the hippo and wnt signaling pathways linked to tumor immune regulation. Conclusion: This study provides valuable insights into MC-sEV molecular mechanisms, offering significant data resources for further investigations.
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Bhaumik, Panchalee, Chandrasekhar Gopalakrishnan, Balu Kamaraj, and Rituraj Purohit. "Single Nucleotide Polymorphisms in MicroRNA Binding Sites: Implications in Colorectal Cancer." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/547154.
Повний текст джерелаАнотація:
Cancer is a complex genetic disorder, characterised by uncontrolled cell proliferation and caused by altered expression of oncogenes and tumour suppressor genes. When cell proliferation pertains to colon, it is called colorectal cancer. Most of colorectal cancer causing genes are potential targets for the miRNA (microRNA) that bind to 3′UTR (untranslated regions) of mRNA and inhibit translation. Mutations occurring in miRNA binding regions can alter the miRNA, mRNA combination, and can alter gene expression drastically. We hypothesized that 3′UTR mutation in miRNA binding site could alter the miRNA, mRNA interaction, thereby altering gene expression. Altered gene expression activity could promote tumorigenesis in colon. Therefore, we formulated a systematic in silico procedure that integrates data from various databases, followed rigorous selection criteria, and identified mutations that might alter the expression levels of cancer causing genes. Further we performed expression analysis to shed light on the potential tissues that might be affected by mutation, enrichment analysis to find the metabolic functions of the gene, and network analysis to highlight the important interactions of cancer causing genes with other genes to provide insight that complex network will be disturbed upon mutation. We provide in silico evidence for the effect of these mutations in colorectal cancer.
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Thody, Joshua, Vincent Moulton, and Irina Mohorianu. "PAREameters: a tool for computational inference of plant miRNA–mRNA targeting rules using small RNA and degradome sequencing data." Nucleic Acids Research 48, no. 5 (2020): 2258–70. http://dx.doi.org/10.1093/nar/gkz1234.
Повний текст джерелаАнотація:
Abstract MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA–mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.
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Xie, Peng, Yu Liu, Yanda Li, Michael Q. Zhang, and Xiaowo Wang. "MIROR: a method for cell-type specific microRNA occupancy rate prediction." Mol. BioSyst. 10, no. 6 (2014): 1377–84. http://dx.doi.org/10.1039/c3mb70610a.
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Pérez-Cremades, Daniel, Ana B. Paes, Xavier Vidal-Gómez, Ana Mompeón, Carlos Hermenegildo, and Susana Novella. "Regulatory Network Analysis in Estradiol-Treated Human Endothelial Cells." International Journal of Molecular Sciences 22, no. 15 (2021): 8193. http://dx.doi.org/10.3390/ijms22158193.
Повний текст джерелаАнотація:
Background/Aims: Estrogen has been reported to have beneficial effects on vascular biology through direct actions on endothelium. Together with transcription factors, miRNAs are the major drivers of gene expression and signaling networks. The objective of this study was to identify a comprehensive regulatory network (miRNA–transcription factor–downstream genes) that controls the transcriptomic changes observed in endothelial cells exposed to estradiol. Methods: miRNA/mRNA interactions were assembled using our previous microarray data of human umbilical vein endothelial cells (HUVEC) treated with 17β-estradiol (E2) (1 nmol/L, 24 h). miRNA–mRNA pairings and their associated canonical pathways were determined using Ingenuity Pathway Analysis software. Transcription factors were identified among the miRNA-regulated genes. Transcription factor downstream target genes were predicted by consensus transcription factor binding sites in the promoter region of E2-regulated genes by using JASPAR and TRANSFAC tools in Enrichr software. Results: miRNA–target pairings were filtered by using differentially expressed miRNAs and mRNAs characterized by a regulatory relationship according to miRNA target prediction databases. The analysis identified 588 miRNA–target interactions between 102 miRNAs and 588 targets. Specifically, 63 upregulated miRNAs interacted with 295 downregulated targets, while 39 downregulated miRNAs were paired with 293 upregulated mRNA targets. Functional characterization of miRNA/mRNA association analysis highlighted hypoxia signaling, integrin, ephrin receptor signaling and regulation of actin-based motility by Rho among the canonical pathways regulated by E2 in HUVEC. Transcription factors and downstream genes analysis revealed eight networks, including those mediated by JUN and REPIN1, which are associated with cadherin binding and cell adhesion molecule binding pathways. Conclusion: This study identifies regulatory networks obtained by integrative microarray analysis and provides additional insights into the way estradiol could regulate endothelial function in human endothelial cells.
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Yang, Zongxing, Jin Yang, Juan Wang, et al. "Identify Potential Regulators in HIV-1 Latency by Joint microRNA and mRNA Analysis." Cellular Physiology and Biochemistry 36, no. 2 (2015): 569–84. http://dx.doi.org/10.1159/000430121.
Повний текст джерелаАнотація:
Background/Aims: The main obstacle to cure HIV infection is the existence of long-lasting latent reservoirs. Many efforts have been made to understand basal mechanisms of HIV-1 latency, in which miRNAs play an important role. However, integrated analysis of miRNA and mRNA expression in HIV-1 latency is lacking. Methods and Results: Global miRNA and mRNA expression was determined by microarrays and quantitative reverse transcription PCR in well-characterized HIV-1 latently and actively infected cells, respectively. Interactions of miRNA-mRNA, mRNA-mRNA, and transcription factor-miRNA pairs were assembled into the function network. Our results show that transcription regulation related genes were mostly enriched in HIV-1 latently infected cells. Gene set enrichment analysis revealed nuclear transport related pathways were up-regulated in the latency group. Network dynamic analysis highlighted many gene-pairs sharing the largest changes in different HIV-1 infection state. 83.33% miRNA-target pairs were validated against database, and RHOB related genes constitute the interface between HIV-1 latency and replication state. Conclusion: We show for the first time a joint miRNA and mRNA expression profile related to a HIV-1 latency phenotype, outline a dynamic network of potential regulators involving in HIV-1 latency or replication state, and gain new insights into the source messages for affecting HIV-1 latency.
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Pushkin, A. A., E. A. Dzenkova, N. N. Timoshkina, and D. Yu Gvaldin. "Data analysis of high-throughput sequencing and microarray to identify key signatures of microribonucleic acids in glioblastoma." Research and Practical Medicine Journal 8, no. 3 (2021): 21–33. http://dx.doi.org/10.17709/2410-1893-2021-8-3-2.
Повний текст джерелаАнотація:
Purpose of the study. This research was devoted to study of mRNA and miRNA expression patterns in glioglastomas using The Cancer Genome Atlas (TCGA) data, to search for genetic determinants that determine the prognosis of patient survival and to create of interaction networks for glioblastomas.Materials and methods. Based on the data of the open TCGA database groups of glioblastomas and conventionally normal brain tissue samples were formed. Survival gene and miRNA expression data were extracted for each sample. After the data stratification by groups the differential expression analysis and search the genes affecting patient survival was carried out. The enrichment analysis by functional affiliation and an interactome analysis were performed.Results. A total of 156 glioblastoma samples with mRNA sequencing data, 571 samples with microarray microRNA analysis data, and 15 control samples were analyzed. Networks of mRNA-miRNA interactions were built and expression profiles of genes and miRNAs characteristic of glioblastomas were developed. We have determined the genes which aberrant level is associated with survival and shown the pairwise DEG and DE of microRNA correlations.Conclusion. The microRNA-mRNA regulatory pairs identified for glioblastomas can stimulate the development of new therapeutic approaches based on subtype-specific regulatory mechanisms of oncogenesis.
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Luo, Zijun, Robert Azencott, and Yi Zhao. "Modeling miRNA-mRNA interactions: fitting chemical kinetics equations to microarray data." BMC Systems Biology 8, no. 1 (2014): 19. http://dx.doi.org/10.1186/1752-0509-8-19.
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Fu, Xiaonan, Pengcheng Liu, George Dimopoulos, and Jinsong Zhu. "Dynamic miRNA-mRNA interactions coordinate gene expression in adult Anopheles gambiae." PLOS Genetics 16, no. 4 (2020): e1008765. http://dx.doi.org/10.1371/journal.pgen.1008765.
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Lee, A., J. L. Lovecchio, J. Parasmeswaran, et al. "Integrated network analysis of miRNA–mRNA interactions in ovarian cancer outcomes." Gynecologic Oncology 137 (April 2015): 110–11. http://dx.doi.org/10.1016/j.ygyno.2015.01.274.
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Geaghan, Michael P., Joshua R. Atkins, Alan M. Brichta, et al. "Alteration of miRNA-mRNA interactions in lymphocytes of individuals with schizophrenia." Journal of Psychiatric Research 112 (May 2019): 89–98. http://dx.doi.org/10.1016/j.jpsychires.2019.02.023.
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Bi, Zhao, and Bin Xue. "Consensus datasets of mouse miRNA-mRNA interactions from multiple online resources." Data in Brief 14 (October 2017): 143–47. http://dx.doi.org/10.1016/j.dib.2017.07.035.
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Balakrishnan, Ilango, Xiaodong Yang, Joseph Brown, et al. "Genome-Wide Analysis of miRNA-mRNA Interactions in Marrow Stromal Cells." STEM CELLS 32, no. 3 (2014): 662–73. http://dx.doi.org/10.1002/stem.1531.
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Kozar, Ines, Demetra Philippidou, Christiane Margue, Lauren A. Gay, Rolf Renne, and Stephanie Kreis. "Cross-Linking Ligation and Sequencing of Hybrids (qCLASH) Reveals an Unpredicted miRNA Targetome in Melanoma Cells." Cancers 13, no. 5 (2021): 1096. http://dx.doi.org/10.3390/cancers13051096.
Повний текст джерелаАнотація:
MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA–target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3′ miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA–target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA–target interactions, which have so far been omitted in the cancer field.