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Morselli Gysi D, Barabási AL. Noncoding RNAs improve the predictive power of network medicine. Proc Natl Acad Sci U S A 2023; 120:e2301342120. [PMID: 37906646 PMCID: PMC10636370 DOI: 10.1073/pnas.2301342120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/09/2023] [Indexed: 11/02/2023] Open
Abstract
Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
- Department of Network and Data Science, Central European University, Budapest1051, Hungary
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2
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Ballarino M, Pepe G, Helmer-Citterich M, Palma A. Exploring the landscape of tools and resources for the analysis of long non-coding RNAs. Comput Struct Biotechnol J 2023; 21:4706-4716. [PMID: 37841333 PMCID: PMC10568309 DOI: 10.1016/j.csbj.2023.09.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.
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Affiliation(s)
- Monica Ballarino
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Manuela Helmer-Citterich
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
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3
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Kayabasi C, Gunduz C. The lncRNA expression profile signature of leukemia stem cells is altered upon PI3K/mTOR inhibition: an in vitro and in silico study. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2023; 43:99-115. [PMID: 37470414 DOI: 10.1080/15257770.2023.2236143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
Genetic and/or epigenetic alterations in hematopoietic stem cells (HSCs) contribute to leukemia stem cell (LSC) formation. We aimed to identify alterations in the lncRNA expression profile signature of LSCs upon inhibition of PI3K/Akt/mTOR signaling, which provides selective advantages to LSCs. We also aimed to elucidate the potential interaction networks and functions of differentially expressed lncRNAs (DELs). We suppressed PI3K/Akt/mTOR signaling in LSC and HSC cell-lines by specific PI3K/mTOR dual-inhibitor (VS-5584) and confirmed the inhibition by antibody-array. We defined DELs by qRT-PCR. Increased SRA, ZEB2-AS1, antiPeg11, DLX6-AS, SNHG4, and decreased H19, PCGEM1, CAR-Intergenic-10, L1PA16, IGF2AS, and SNHG5 levels (|log2fold-change|>5) were strictly associated with PI3K/Akt/mTOR pathway inhibition in LSC. We performed in silico analyses for DELs. ZEB2-AS1 was found to be specifically expressed in normal bone marrow and predominantly lower in leukemic cell-lines. Three sub-clusters were identified for DELs and they were associated with "abnormality of multiple cell lineages in the bone marrow." DELs were most highly enriched for "glucuronidation" Reactome pathway and "ascorbate and aldarate metabolism" and "inositol phosphate metabolism" KEGG pathways. Transcription factors, MBD4, NANOG, PAX6, RELA, CEBPB, and CEBPA were predicted to be associated with the DEL profile. SRA was predicted to interact with CREB1, RARA, and PPARA. The possible DELs' targets were predicted to form six ontological groups, be highly enriched for phosphoprotein, and be involved in "PPAR signaling pathway" and "ChREBP regulation by carbohydrates and cAMP." These results will help to elucidate the roles of lncRNAs in the mechanisms that provide selective advantages to leukemia stem cells.
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Affiliation(s)
- Cagla Kayabasi
- Department of Medical Biology, Ege University, Izmir, Turkey
| | - Cumhur Gunduz
- Department of Medical Biology, Ege University, Izmir, Turkey
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4
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Functional Relationships between Long Non-Coding RNAs and Estrogen Receptor Alpha: A New Frontier in Hormone-Responsive Breast Cancer Management. Int J Mol Sci 2023; 24:ijms24021145. [PMID: 36674656 PMCID: PMC9863308 DOI: 10.3390/ijms24021145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/11/2023] Open
Abstract
In the complex and articulated machinery of the human genome, less than 2% of the transcriptome encodes for proteins, while at least 75% is actively transcribed into non-coding RNAs (ncRNAs). Among the non-coding transcripts, those ≥200 nucleotides long (lncRNAs) are receiving growing attention for their involvement in human diseases, particularly cancer. Genomic studies have revealed the multiplicity of processes, including neoplastic transformation and tumor progression, in which lncRNAs are involved by regulating gene expression at epigenetic, transcriptional, and post-transcriptional levels by mechanism(s) that still need to be clarified. In breast cancer, several lncRNAs were identified and demonstrated to have either oncogenic or tumor-suppressive roles. The functional understanding of the mechanisms of lncRNA action in this disease could represent a potential for translational applications, as these molecules may serve as novel biomarkers of clinical use and potential therapeutic targets. This review highlights the relationship between lncRNAs and the principal hallmark of the luminal breast cancer phenotype, estrogen receptor α (ERα), providing an overview of new potential ways to inhibit estrogenic signaling via this nuclear receptor toward escaping resistance to endocrine therapy.
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Dong N, Mucke S, Khosla M. MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3081-3092. [PMID: 35594217 DOI: 10.1109/tcbb.2022.3176456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in recent years. A big challenge in building reliable machine learning models is that of data scarcity. In particular, existing approaches trained on the available small datasets, even when combined with precalculated handcrafted input features, often suffer from bad generalization and data leakage problems. We overcome the limitations of existing works by proposing a novel multitask graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (associations between miRNAs/diseases and protein-coding genes (PCGs), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multitask setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we conduct large-scale experiments on the standard benchmark datasets as well as on our proposed large independent testing sets and case studies. MuCoMiD obtains significantly higher Average Precision (AP) scores than all benchmarked models on three large independent testing sets, especially those with many new miRNAs, as well as in the detection of false positives. Thanks to its capability of learning directly from raw input information, MuCoMiD is easier to maintain and update than handcrafted feature-based methods, which would require recomputation of features every time there is a change in the original information sources (e.g., disease ontology, miRNA/disease-PCG associations, etc.). We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.
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Singh S, Shyamal S, Panda AC. Detecting RNA-RNA interactome. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1715. [PMID: 35132791 DOI: 10.1002/wrna.1715] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The last decade has seen a robust increase in various types of novel RNA molecules and their complexity in gene regulation. RNA molecules play a critical role in cellular events by interacting with other biomolecules, including protein, DNA, and RNA. It has been established that RNA-RNA interactions play a critical role in several biological processes by regulating the biogenesis and function of RNA molecules. Interestingly, RNA-RNA interactions regulate the biogenesis of diverse RNA molecules, including mRNAs, microRNAs, tRNAs, and circRNAs, through splicing or backsplicing. Structured RNAs like rRNA, tRNA, and snRNAs achieve their functional conformation by intramolecular RNA-RNA interactions. In addition, functional consequences of many intermolecular RNA-RNA interactions have been extensively studied in the regulation of gene expression. Hence, it is essential to understand the mechanism and functions of RNA-RNA interactions in eukaryotes. Conventionally, RNA-RNA interactions have been identified through diverse biochemical methods for decades. The advent of high-throughput RNA-sequencing technologies has revolutionized the identification of global RNA-RNA interactome in cells and their importance in RNA structure and function in gene expression regulation. Although these technologies revealed tens of thousands of intramolecular and intermolecular RNA-RNA interactions, we further look forward to future unbiased and quantitative high-throughput technologies for detecting transcriptome-wide RNA-RNA interactions. With the ability to detect RNA-RNA interactome, we expect that future studies will reveal the higher-order structures of RNA molecules and multi-RNA hybrids impacting human health and diseases. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.
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Affiliation(s)
- Suman Singh
- Institute of Life Sciences, Nalco Square, Bhubaneswar, India
- Regional Center for Biotechnology, Faridabad, India
| | | | - Amaresh C Panda
- Institute of Life Sciences, Nalco Square, Bhubaneswar, India
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7
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Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types. Noncoding RNA Res 2022; 7:98-105. [PMID: 35387279 PMCID: PMC8958468 DOI: 10.1016/j.ncrna.2022.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 01/01/2023] Open
Abstract
Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3′UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types. We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples. We show how more than 90% of these miRNA-mRNA interactions show a loss of regulation in the tumoral samples compared with their healthy counterparts. As expected, we found shared miRNA-mRNA dysregulated pairs among different tumors of the same tissue. However, anatomically different cancers also share multiple dysregulated interactions, suggesting that some cancer-related mechanisms are not tumor-specific. 2865 unique miRNA-mRNA pairs were identified across 13 cancer types, ≈ 40% of these pairs showed a loss of correlation in the tumoral samples in at least 2 out of the 13 analyzed cancers. Specifically, miR-200 family, miR-155 and miR-1 were identified, based on the computational analysis described below, as the miRNAs that potentially lose the highest number of interactions across different samples (only literature-based interactions were used for this analysis). Moreover, the miR-34a/ALDH2 and miR-9/MTHFD2 pairs show a switch in their correlation between healthy and tumor kidney samples suggesting a possible change in the regulation exerted by the miRNAs. Interestingly, the expression of these mRNAs is also associated with the overall survival. The disruption of miRNA regulation on its target, therefore, suggests the possible involvement of these pairs in cell malignant functions. The analysis reported here shows how the regulation of miRNA-mRNA interactions strongly differs between healthy and tumoral cells, based on the strong correlation variation between miRNA and its target that we obtained by analyzing the expression data of healthy and tumor tissue in highly reliable miRNA-target pairs. Finally, a go term enrichment analysis shows that the critical pairs identified are involved in cellular adhesion, proliferation, and migration.
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Kayabasi C, Caner A, Yilmaz Susluer S, Balci Okcanoglu T, Ozmen Yelken B, Asik A, Mutlu Z, Caliskan Kurt C, Goker Bagca B, Biray Avci C, Sahin F, Saydam G, Gunduz C. Comparative expression analysis of dasatinib and ponatinib-regulated lncRNAs in chronic myeloid leukemia and their network analysis. Med Oncol 2022; 39:29. [DOI: 10.1007/s12032-021-01629-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/13/2021] [Indexed: 10/19/2022]
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9
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Restoration of aged hematopoietic cells by their young counterparts through instructive microvesicles release. Aging (Albany NY) 2021; 13:23981-24016. [PMID: 34762598 PMCID: PMC8610119 DOI: 10.18632/aging.203689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/26/2021] [Indexed: 12/14/2022]
Abstract
This study addresses the potential to reverse age-associated morbidity by establishing methods to restore the aged hematopoietic system. Parabiotic animal models indicated that young secretome could restore aged tissues, leading us to establish a heterochronic transwell system with aged mobilized peripheral blood (MPB), co-cultured with young MPB or umbilical cord blood (UCB) cells. Functional studies and omics approaches indicate that the miRNA cargo of microvesicles (MVs) restores the aged hematopoietic system. The in vitro findings were validated in immune deficient (NSG) mice carrying an aged hematopoietic system, improving aged hallmarks such as increased lymphoid:myeloid ratio, decreased inflammation and cellular senescence. Elevated MYC and E2F pathways, and decreased p53 were key to hematopoietic restoration. These processes require four restorative miRs that target the genes for transcription/differentiation, namely PAX and phosphatase PPMIF. These miRs when introduced in aged cells were sufficient to restore the aged hematopoietic system in NSG mice. The aged MPBs were the drivers of their own restoration, as evidenced by the changes from distinct baseline miR profiles in MPBs and UCB to comparable expressions after exposure to aged MPBs. Restorative natural killer cells eliminated dormant breast cancer cells in vivo, indicating the broad relevance of this cellular paradigm - preventing and reversing age-associated disorders such as clearance of early malignancies and enhanced responses to vaccine and infection.
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10
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Paragkamian S, Thanati F, Iliopoulos I, Eliopoulos AG, Schneider R, Jensen LJ, Pafilis E, Pavlopoulos GA. OnTheFly 2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis. NAR Genom Bioinform 2021; 3:lqab090. [PMID: 34632381 PMCID: PMC8494211 DOI: 10.1093/nargab/lqab090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 09/09/2021] [Accepted: 09/20/2021] [Indexed: 02/06/2023] Open
Abstract
Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analyzing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly2.0, a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images. OnTheFly2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform named entity recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms. Multiple files can be analyzed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein-protein and protein-chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly2.0 currently supports 197 species and is available at http://bib.fleming.gr:3838/OnTheFly/ and http://onthefly.pavlopouloslab.info.
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Affiliation(s)
- Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari 16672, Greece
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari 16672, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari 16672, Greece
| | - Savvas Paragkamian
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003 Heraklion, Crete, Greece
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari 16672, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, Heraklion 71003, Crete, Greece
| | - Aristides G Eliopoulos
- Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Athens, 70013, Greece
| | - Reinhard Schneider
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Bioinformatics Core, Esch-sur-Alzette, L-4365, Luxembourg
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003 Heraklion, Crete, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari 16672, Greece
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11
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Philip M, Chen T, Tyagi S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Noncoding RNA 2021; 7:ncrna7020033. [PMID: 34201302 PMCID: PMC8293367 DOI: 10.3390/ncrna7020033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 12/15/2022] Open
Abstract
Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.
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Affiliation(s)
- Melcy Philip
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Tyrone Chen
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Sonika Tyagi
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
- Monash eResearch Centre, Monash University, Clayton, VIC 3800, Australia
- Department of Infectious Disease, Monash University (Alfred Campus), 85 Commercial Road, Melbourne, VIC 3004, Australia
- Correspondence:
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12
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Yildirim M, Oztay F, Kayalar O, Tasci AE. Effect of long noncoding RNAs on epithelial-mesenchymal transition in A549 cells and fibrotic human lungs. J Cell Biochem 2021; 122:882-896. [PMID: 33847014 DOI: 10.1002/jcb.29920] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 02/22/2021] [Accepted: 03/04/2021] [Indexed: 01/17/2023]
Abstract
Long noncoding RNAs (LncRNAs) regulate epithelial-mesenchymal transition (EMT). EMT involves myofibroblast differentiation and pulmonary fibrosis (PF). We aimed to determine the expression profiles of HOTAIR, CARLo-5, and CD99P1 LncRNAs in EMT-mediated myofibroblast differentiation in A549 cells and fibrotic human lungs and to explain their roles. A group of A549s was stimulated with transforming growth factor β (TGF-β; 5 ng/ml) to induce EMT. The remaining A549s were incubated with 20 μM FH535 after 24 h of TGF-β treatment to inhibit EMT. A549s were collected at 0, 24, 36, and 48 h. Expressions of three LncRNAs and protein/genes related to EMT, myofibroblast differentiation, and PF were assayed by quantitative reverse-transcription polymerase chain reaction and Western blot analysis in A549s and fibrotic human lungs. The targets of three LncRNAs were investigated by bioinformatics methods. TGF-β stimulation resulted in increased expressions of three LncRNAs, ACTA2, COL1A1, SNAI1, CTNNB1, TCF4, LEF1, α-SMA, and active-β-catenin, and decreased E-cadherin at 24, 36, and 48 h in A549s. FH535 treatment regressed these alterations. But it increased HOTAIR expression at 36 h and did not increase E-cadherin at 48 h. Fibrotic human lungs were characterized by increased expressions of HOTAIR, CARLo-5, CD99P1, and miR-214, decreased expressions of miR-148b, miR-218-1, miR-7-1, and the presence of CARLo-5 and CD99P1 in HDAC1-LncRNAs coprecipitation products, but not HOTAIR. Bioinformatic analysis showed the interactions of three LncRNAs with both proteins and at least 13 microRNAs related to EMT and PF. In conclusion, HOTAIR, CARLo-5, and CD99P1 can regulate EMT-mediated myofibroblast differentiation through interacting with proteins and miRNAs associated with EMT and PF. These LncRNAs can be considered as potential targets to decrease EMT for treating PF.
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Affiliation(s)
- Merve Yildirim
- Department of Biology, Science Faculty, Istanbul University, Istanbul, Turkey
| | - Fusun Oztay
- Department of Biology, Science Faculty, Istanbul University, Istanbul, Turkey
| | - Ozgecan Kayalar
- Department of Biology, Science Faculty, Istanbul University, Istanbul, Turkey.,School of Medicine, Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul, Turkey
| | - Ahmet Erdal Tasci
- Department of Thoracic Surgery, Lung Transplantation Center, Kartal Kosuyolu High Specialty Educational and Research Hospital, Istanbul, Turkey
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13
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Chen Y, Wu T, Zhu Z, Huang H, Zhang L, Goel A, Yang M, Wang X. An integrated workflow for biomarker development using microRNAs in extracellular vesicles for cancer precision medicine. Semin Cancer Biol 2021; 74:134-155. [PMID: 33766650 DOI: 10.1016/j.semcancer.2021.03.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/13/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023]
Abstract
EV-miRNAs are microRNA (miRNA) molecules encapsulated in extracellular vesicles (EVs), which play crucial roles in tumor pathogenesis, progression, and metastasis. Recent studies about EV-miRNAs have gained novel insights into cancer biology and have demonstrated a great potential to develop novel liquid biopsy assays for various applications. Notably, compared to conventional liquid biomarkers, EV-miRNAs are more advantageous in representing host-cell molecular architecture and exhibiting higher stability and specificity. Despite various available techniques for EV-miRNA separation, concentration, profiling, and data analysis, a standardized approach for EV-miRNA biomarker development is yet lacking. In this review, we performed a substantial literature review and distilled an integrated workflow encompassing important steps for EV-miRNA biomarker development, including sample collection and EV isolation, EV-miRNA extraction and quantification, high-throughput data preprocessing, biomarker prioritization and model construction, functional analysis, as well as validation. With the rapid growth of "big data", we highlight the importance of efficient mining of high-throughput data for the discovery of EV-miRNA biomarkers and integrating multiple independent datasets for in silico and experimental validations to increase the robustness and reproducibility. Furthermore, as an efficient strategy in systems biology, network inference provides insights into the regulatory mechanisms and can be used to select functionally important EV-miRNAs to refine the biomarker candidates. Despite the encouraging development in the field, a number of challenges still hinder the clinical translation. We finally summarize several common challenges in various biomarker studies and discuss potential opportunities emerging in the related fields.
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Affiliation(s)
- Yu Chen
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong
| | - Tan Wu
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong
| | - Zhongxu Zhu
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong
| | - Hao Huang
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong
| | - Liang Zhang
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong; Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong; Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong Province, China
| | - Ajay Goel
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Mengsu Yang
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong; Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong; Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong Province, China
| | - Xin Wang
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong; Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong; Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong Province, China.
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14
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Carter JM, Ang DA, Sim N, Budiman A, Li Y. Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer. Noncoding RNA 2021; 7:19. [PMID: 33803328 PMCID: PMC8005986 DOI: 10.3390/ncrna7010019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/28/2021] [Accepted: 03/05/2021] [Indexed: 02/06/2023] Open
Abstract
It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.
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Affiliation(s)
- Jean-Michel Carter
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore 637551, Singapore; (D.A.A.); (N.S.); (A.B.)
| | - Daniel Aron Ang
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore 637551, Singapore; (D.A.A.); (N.S.); (A.B.)
| | - Nicholas Sim
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore 637551, Singapore; (D.A.A.); (N.S.); (A.B.)
| | - Andrea Budiman
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore 637551, Singapore; (D.A.A.); (N.S.); (A.B.)
| | - Yinghui Li
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore 637551, Singapore; (D.A.A.); (N.S.); (A.B.)
- Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore 138673, Singapore
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15
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Pinkney HR, Wright BM, Diermeier SD. The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis. Noncoding RNA 2020; 6:E49. [PMID: 33339309 PMCID: PMC7768357 DOI: 10.3390/ncrna6040049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 02/07/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.
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Affiliation(s)
| | | | - Sarah D. Diermeier
- Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand; (H.R.P.); (B.M.W.)
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16
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Chen Q, Meng X, Liao Q, Chen M. Versatile interactions and bioinformatics analysis of noncoding RNAs. Brief Bioinform 2020; 20:1781-1794. [PMID: 29939215 DOI: 10.1093/bib/bby050] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/02/2018] [Indexed: 02/07/2023] Open
Abstract
Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.
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Affiliation(s)
- Qi Chen
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Xianwen Meng
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Qi Liao
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Ming Chen
- Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medical School of Ningbo University, Ningbo, Zhejiang, P. R. China
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17
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Lin Y, Liu T, Cui T, Wang Z, Zhang Y, Tan P, Huang Y, Yu J, Wang D. RNAInter in 2020: RNA interactome repository with increased coverage and annotation. Nucleic Acids Res 2020; 48:D189-D197. [PMID: 31906603 PMCID: PMC6943043 DOI: 10.1093/nar/gkz804] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/03/2019] [Accepted: 09/10/2019] [Indexed: 01/23/2023] Open
Abstract
Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.
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Affiliation(s)
- Yunqing Lin
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tianyuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zhao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuncong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Puwen Tan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yan Huang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan 528308, China
| | - Jia Yu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry & Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College (PUMC), Beijing 100730, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan 528308, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
- To whom correspondence should be addressed. Tel: +86 20 61648279; Fax: +86 20 61648279; or
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18
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Hochreuter MY, Altıntaş A, Garde C, Emanuelli B, Kahn CR, Zierath JR, Vienberg S, Barrès R. Identification of two microRNA nodes as potential cooperative modulators of liver metabolism. Hepatol Res 2019; 49:1451-1465. [PMID: 31408567 PMCID: PMC6972499 DOI: 10.1111/hepr.13419] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 07/03/2019] [Accepted: 07/02/2019] [Indexed: 12/19/2022]
Abstract
AIM Hepatic insulin resistance is a hallmark of type 2 diabetes and non-alcoholic fatty liver disease. Dysregulation of microRNA (miRNA) expression in insulin-resistant livers might coordinate impaired hepatic metabolic function. Here, we aimed to discover miRNAs and their downstream targets involved in hepatic insulin resistance. METHODS We determined miRNA expression profiles by small RNA sequencing of two mouse models of impaired hepatic insulin action: high-fat diet-induced obesity and liver-specific insulin receptor knockout. Conversely, we assessed the hepatic miRNA expression profile after treatment with the antidiabetic hormone, fibroblast growth factor 21 (FGF21). Ontology analysis of predicted miRNA gene targets was performed to identify regulated gene pathways. Target enrichment analysis and miRNA mimic overexpression in vitro were used to identify unified protein targets of nodes of regulated miRNAs. RESULTS We identified an array of miRNA species regulated by impaired liver insulin action or after fibroblast growth factor 21 treatment. Ontology analysis of predicted miRNA gene targets identified pathways controlling hepatic energy metabolism and insulin sensitivity. We identified a node of two miRNAs downregulated in the livers of liver-specific insulin receptor knockout mice, miR-883b and miR-205, which positively regulate the expression of transcription factor zinc finger E-box-binding homeobox 1 (ZBED1). We found another node of two miRNAs upregulated in the livers of fibroblast growth factor 21-treated mice, miR-155-3p and miR-1968-5p, which canonically downregulates the caveola component, polymerase I and transcript release factor (PTRF), a gene previously implicated in hepatic energy metabolism. CONCLUSIONS This study identifies two nodes of coregulated miRNAs that might coordinately control hepatic energy metabolism in states of insulin resistance.
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Affiliation(s)
- Mette Yde Hochreuter
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Ali Altıntaş
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Christian Garde
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Brice Emanuelli
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - C. Ronald Kahn
- Section of Integrative Physiology and MetabolismJoslin Diabetes Center and Harvard Medical SchoolBostonMassachusettsUSA
| | - Juleen R. Zierath
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark,Department of Molecular Medicine and SurgeryKarolinska InstitutetStockholmSweden,Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | | | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
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19
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Non-coding RNA regulatory networks. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194417. [PMID: 31493559 DOI: 10.1016/j.bbagrm.2019.194417] [Citation(s) in RCA: 245] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 08/13/2019] [Accepted: 08/13/2019] [Indexed: 02/06/2023]
Abstract
It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.
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20
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Martin S, Blankenship C, Rausch JW, Sztuba-Solinska J. Using SHAPE-MaP to probe small molecule-RNA interactions. Methods 2019; 167:105-116. [DOI: 10.1016/j.ymeth.2019.04.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/20/2019] [Accepted: 04/16/2019] [Indexed: 01/14/2023] Open
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21
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Gong J, Shao D, Xu K, Lu Z, Lu ZJ, Yang YT, Zhang QC. RISE: a database of RNA interactome from sequencing experiments. Nucleic Acids Res 2019; 46:D194-D201. [PMID: 29040625 PMCID: PMC5753368 DOI: 10.1093/nar/gkx864] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 09/29/2017] [Indexed: 12/12/2022] Open
Abstract
We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.
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Affiliation(s)
- Jing Gong
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Di Shao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Zhipeng Lu
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA
| | - Zhi John Lu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Yucheng T Yang
- Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095-1554, USA
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
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22
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Fleming DS, Miller LC. Differentially Expressed MiRNAs and tRNA Genes Affect Host Homeostasis During Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus Infections in Young Pigs. Front Genet 2019; 10:691. [PMID: 31428130 PMCID: PMC6687759 DOI: 10.3389/fgene.2019.00691] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/02/2019] [Indexed: 12/15/2022] Open
Abstract
Background: Porcine respiratory and reproductive syndrome virus (PRRSV) is a single-stranded RNA virus member that infects pigs and causes losses to the commercial industry reaching upward of a billion dollars annually in combined direct and indirect costs. The virus can be separated into etiologies that contain multiple heterologous low and highly pathogenic strains. Recently, the United States has begun to see an increase in heterologous type 2 PRRSV strains of higher virulence (HP-PRRSV). The high pathogenicity of these strains can drastically alter host immune responses and the ability of the animal to maintain homeostasis. Because the loss of host homeostasis can denote underlying changes in gene and regulatory element expression profiles, the study aimed to examine the effect PRRSV infections has on miRNA and tRNA expression and the roles they play in host tolerance or susceptibility. Results: Using transcriptomic analysis of whole blood taken from control and infected pigs at several time points (1, 3, 8 dpi), the analysis returned a total of 149 statistically significant (FDR ⫹ 0.15) miRNAs (n = 89) and tRNAs (n = 60) that were evaluated for possible pro- and anti-viral effects. The tRNA differential expression increased in both magnitude and count as dpi increased, with no statistically significant expression at 1 dpi, but increases at 3 and 8 dpi. The most abundant tRNA amino acid at 3 dpi was alanine, while glycine was the most abundant at 8 dpi. For the miRNAs, focus was put on upregulation that can inhibit gene expression. These results yielded candidates with potential anti- and pro-viral actions such as Ssc-miR-125b, which is predicted to limit PRRSV viral levels, and Ssc-miR-145-5p shown to cause alternative macrophage priming. The results also showed that both the tRNAs and miRNAs displayed expression patterns. Conclusions: The results indicated that the HP-PRRSV infection affects host homeostasis through changes in miRNA and tRNA expression and their subsequent gene interactions that target and influence the function of host immune, metabolic, and structural pathways.
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Affiliation(s)
- Damarius S Fleming
- ORAU/ORISE, Oak Ridge, TN, United States.,Virus and Prion Diseases of Livestock Research Unit, National Animal Disease Center, USDA, Agricultural Research Service, Ames, IA, United States
| | - Laura C Miller
- Virus and Prion Diseases of Livestock Research Unit, National Animal Disease Center, USDA, Agricultural Research Service, Ames, IA, United States
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23
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Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild AC, Tsay M, Lu R, Jurisica I. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res 2019; 46:D360-D370. [PMID: 29194489 PMCID: PMC5753284 DOI: 10.1093/nar/gkx1144] [Citation(s) in RCA: 356] [Impact Index Per Article: 71.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Accepted: 10/30/2017] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.
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Affiliation(s)
- Tomas Tokar
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | - Andrea E M Rossos
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | - Mark Abovsky
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | | | - Mike Tsay
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | - Richard Lu
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia
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24
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Russo F, Di Bella S, Vannini F, Berti G, Scoyni F, Cook HV, Santos A, Nigita G, Bonnici V, Laganà A, Geraci F, Pulvirenti A, Giugno R, De Masi F, Belling K, Jensen LJ, Brunak S, Pellegrini M, Ferro A. miRandola 2017: a curated knowledge base of non-invasive biomarkers. Nucleic Acids Res 2019; 46:D354-D359. [PMID: 29036351 PMCID: PMC5753291 DOI: 10.1093/nar/gkx854] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 09/13/2017] [Indexed: 12/13/2022] Open
Abstract
miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.
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Affiliation(s)
- Francesco Russo
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | | | - Federica Vannini
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Gabriele Berti
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Flavia Scoyni
- University of Eastern Finland, Kuopio, 72010, Finland
| | - Helen V Cook
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Alberto Santos
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.,Clinical Proteomics, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Giovanni Nigita
- Department of Cancer Biology and Genetics, The Ohio State University, OH 43210, USA
| | - Vincenzo Bonnici
- Department of Computer Science, University of Verona, Verona, 37134, Italy
| | - Alessandro Laganà
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-6574, USA
| | - Filippo Geraci
- Institute of Informatics and Telematics (IIT), National Research Council (CNR), Pisa, 56124, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Verona, 37134, Italy
| | - Federico De Masi
- Department of Bio and Health Informatics, DTU Bioinformatics, Technical University of Denmark, Lyngby, 2800, Denmark
| | - Kirstine Belling
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Lars J Jensen
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Søren Brunak
- Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Marco Pellegrini
- Institute of Informatics and Telematics (IIT), National Research Council (CNR), Pisa, 56124, Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
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25
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Bottini S, Pratella D, Grandjean V, Repetto E, Trabucchi M. Recent computational developments on CLIP-seq data analysis and microRNA targeting implications. Brief Bioinform 2019; 19:1290-1301. [PMID: 28605404 PMCID: PMC6291801 DOI: 10.1093/bib/bbx063] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Indexed: 01/18/2023] Open
Abstract
Cross-Linking
Immunoprecipitation associated to
high-throughput sequencing (CLIP-seq) is a technique used to
identify RNA directly bound to RNA-binding proteins across the entire transcriptome in
cell or tissue samples. Recent technological and computational advances permit the
analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive
network of RNA–protein interaction and to integrate it to other genome-wide analyses.
Therefore, the design and quality management of the CLIP-seq analyses are of critical
importance to extract clean and biological meaningful information from CLIP-seq
experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main
component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding
sites of miRNAs, thus providing insightful information about the role played by miRNA(s).
In this review, we summarize and discuss the most recent computational methods for
CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and
prediction with a regard toward human pathologies.
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Affiliation(s)
- Silvia Bottini
- Université Côte d'Azur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France
| | - David Pratella
- Université Côte d'Azur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France
| | - Valerie Grandjean
- Université Côte d'Azur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France
| | - Emanuela Repetto
- Université Côte d'Azur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France
| | - Michele Trabucchi
- Université Côte d'Azur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France
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26
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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27
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Zhang Y, Tao Y, Liao Q. Long noncoding RNA: a crosslink in biological regulatory network. Brief Bioinform 2019; 19:930-945. [PMID: 28449042 DOI: 10.1093/bib/bbx042] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Indexed: 01/17/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) had been defined as a novel class of functional RNAs longer than 200 nucleotides around a decade ago. It is widely acknowledged that lncRNAs play a significant role in regulation of gene expression, but the biological and molecular mechanisms are diverse and complex, and remain to be determined. Especially, the regulatory network of lncRNAs associated with other biological molecules is still a controversial matter, thus becoming a new frontier of the studies on transcriptome. Recent advance in high-throughput sequencing technologies and bioinformatics approaches may be an accelerator to lift the mysterious veil. In this review, we will outline well-known associations between lncRNAs and other biological molecules, demonstrate the diverse bioinformatics approaches applied in prediction and analysis of lncRNA interaction and perform a case study for lncRNA linc00460 to concretely decipher the lncRNA regulatory network.
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Affiliation(s)
- Yuwei Zhang
- Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China
| | - Yang Tao
- Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China
| | - Qi Liao
- Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China
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28
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Pletscher-Frankild S, Jensen LJ. Design, implementation, and operation of a rapid, robust named entity recognition web service. J Cheminform 2019; 11:19. [PMID: 30850898 PMCID: PMC6419787 DOI: 10.1186/s13321-019-0344-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 03/04/2019] [Indexed: 01/09/2023] Open
Abstract
Most BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision and recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. For about a decade, we have run named entity recognition (NER) web services, which are designed to be efficient, implemented using a multi-threaded queueing system to robustly handle many simultaneous requests, and hosted at a supercomputer facility. To participate in this new task, we extended the existing NER tagging service with support for the BeCalm API. The tagger suffered no downtime during the challenge and, as in earlier tests, proved to be highly efficient, consistently processing requests of 5000 abstracts in less than half a minute. In fact, the majority of this time was spent not on the NER task but rather on retrieving the document texts from the challenge servers. The latter was found to be the main bottleneck even when hosting a copy of the tagging service on a Raspberry Pi 3, showing that local document storage or caching would be desirable features to include in future revisions of the API standard.
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Affiliation(s)
- Sune Pletscher-Frankild
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Intomics A/S, Lyngby, Denmark
| | - Lars Juhl Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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29
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Politano G, Di Carlo S, Benso A. 'One DB to rule them all'-the RING: a Regulatory INteraction Graph combining TFs, genes/proteins, SNPs, diseases and drugs. Database (Oxford) 2019; 2019:baz108. [PMID: 31682269 PMCID: PMC6827393 DOI: 10.1093/database/baz108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/21/2019] [Accepted: 08/06/2019] [Indexed: 01/01/2023]
Abstract
In the last decade, genomics data have been largely adopted to sketch, study and better understand the complex mechanisms that underlie biological processes. The amount of publicly available data sources has grown accordingly, and several types of regulatory interactions have been collected and documented in literature. Unfortunately, often these efforts do not follow any data naming/interoperability/formatting standards, resulting in high-quality but often uninteroperable heterogeneous data repositories. To efficiently take advantage of the large amount of available data and integrate these heterogeneous sources of information, we built the RING (Regulatory Interaction Graph), an integrative standardized multilevel database of biological interactions able to provide a comprehensive and unmatched high-level perspective on several phenomena that take place in the regulatory cascade and that researchers can use to easily build regulatory networks around entities of interest.
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Affiliation(s)
| | - Stefano Di Carlo
- Control and Computer Engineering Department, Politecnico di Torino, Italy
| | - Alfredo Benso
- Control and Computer Engineering Department, Politecnico di Torino, Italy
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30
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Zhang W, Yue X, Tang G, Wu W, Huang F, Zhang X. SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. PLoS Comput Biol 2018; 14:e1006616. [PMID: 30533006 PMCID: PMC6331124 DOI: 10.1371/journal.pcbi.1006616] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 01/14/2019] [Accepted: 11/02/2018] [Indexed: 01/12/2023] Open
Abstract
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/. LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. In this paper, we propose a novel computational method “SFPEL-LPI” to predict lncRNA-protein interactions. SFPEL-LPI makes use of lncRNA sequences, protein sequences and known lncRNA-protein associations to extract features and calculate similarities for lncRNAs and proteins, and then combines them with a feature projection ensemble learning frame. SFPEL-LPI can predict unobserved interactions between lncRNAs and proteins, and also can make predictions for new lncRNAs (or proteins), which have no interactions with any proteins (or lncRNAs). SFPEL-LPI produces high-accuracy performances on the benchmark dataset when evaluated by five-fold cross validation, and outperforms state-of-the-art methods. The case studies demonstrate that SFPEL-LPI can find out novel associations, which are confirmed by literature. To facilitate the lncRNA-protein interaction prediction, we develop a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.
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Affiliation(s)
- Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, China
- School of Computer Science, Wuhan University, Wuhan, China
- * E-mail: , (WZ); (XZ)
| | - Xiang Yue
- Department of Computer Science and Engineering, The Ohio State University, Columbus, United States of America
| | - Guifeng Tang
- School of Computer Science, Wuhan University, Wuhan, China
| | - Wenjian Wu
- Electronic Information School, Wuhan University, Wuhan, China
| | - Feng Huang
- School of Computer Science, Wuhan University, Wuhan, China
| | - Xining Zhang
- School of Computer Science, Wuhan University, Wuhan, China
- * E-mail: , (WZ); (XZ)
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31
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Ju Y, Gong J, Yang YT, Zhang QC. Investigation of RNA-RNA Interactions Using the RISE Database. ACTA ACUST UNITED AC 2018; 64:e58. [PMID: 30408350 DOI: 10.1002/cpbi.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Yanyan Ju
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
| | - Jing Gong
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
| | - Yucheng T Yang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
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32
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Sinha S, Eisenhaber B, Jensen LJ, Kalbuaji B, Eisenhaber F. Darkness in the Human Gene and Protein Function Space: Widely Modest or Absent Illumination by the Life Science Literature and the Trend for Fewer Protein Function Discoveries Since 2000. Proteomics 2018; 18:e1800093. [PMID: 30265449 PMCID: PMC6282819 DOI: 10.1002/pmic.201800093] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 09/07/2018] [Indexed: 12/15/2022]
Abstract
The mentioning of gene names in the body of the scientific literature 1901-2017 and their fractional counting is used as a proxy to assess the level of biological function discovery. A literature score of one has been defined as full publication equivalent (FPE), the amount of literature necessary to achieve one publication solely dedicated to a gene. It has been found that less than 5000 human genes have each at least 100 FPEs in the available literature corpus. This group of elite genes (4817 protein-coding genes, 119 non-coding RNAs) attracts the overwhelming majority of the scientific literature about genes. Yet, thousands of proteins have never been mentioned at all, ≈2000 further proteins have not even one FPE of literature and, for ≈4600 additional proteins, the FPE count is below 10. The protein function discovery rate measured as numbers of proteins first mentioned or crossing a threshold of accumulated FPEs in a given year has grown until 2000 but is in decline thereafter. This drop is partially offset by function discoveries for non-coding RNAs. The full human genome sequencing does not boost the function discovery rate. Since 2000, the fastest growing group in the literature is that with at least 500 FPEs per gene.
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Affiliation(s)
- Swati Sinha
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Birgit Eisenhaber
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein ResearchFaculty of Health and Medical SciencesUniversity of CopenhagenDK-2200 CopenhagenDenmark
| | - Bharata Kalbuaji
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Frank Eisenhaber
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
- School of Computer Science and Engineering (SCSE)Nanyang Technological University (NTU)637553Singapore
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33
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Pan X, Jensen LJ, Gorodkin J. Inferring disease-associated long non-coding RNAs using genome-wide tissue expression profiles. Bioinformatics 2018; 35:1494-1502. [DOI: 10.1093/bioinformatics/bty859] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 08/28/2018] [Accepted: 10/04/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Xiaoyong Pan
- Department of Veterinary and Animal Sciences, Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, Denmark
| | - Lars Juhl Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, Denmark
| | - Jan Gorodkin
- Department of Veterinary and Animal Sciences, Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark
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34
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Pan X, Wenzel A, Jensen LJ, Gorodkin J. Genome-wide identification of clusters of predicted microRNA binding sites as microRNA sponge candidates. PLoS One 2018; 13:e0202369. [PMID: 30142196 PMCID: PMC6108476 DOI: 10.1371/journal.pone.0202369] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/01/2018] [Indexed: 12/21/2022] Open
Abstract
The number of discovered natural miRNA sponges in plants, viruses, and mammals is increasing steadily. Some sponges like ciRS-7 for miR-7 contain multiple nearby miRNA binding sites. We hypothesize that such clusters of miRNA binding sites on the genome can function together as a sponge. No systematic effort has been made in search for clusters of miRNA targets. Here, we, to our knowledge, make the first genome-wide target site predictions for clusters of mature human miRNAs. For each miRNA, we predict the target sites on a genome-wide scale, build a graph with edge weights based on the pairwise distances between sites, and apply Markov clustering to identify genomic regions with high binding site density. Significant clusters are then extracted based on cluster size difference between real and shuffled genomes preserving local properties such as the GC content. We then use conservation and binding energy to filter a final set of miRNA target site clusters or sponge candidates. Our pipeline predicts 3673 sponge candidates for 1250 miRNAs, including the experimentally verified miR-7 sponge ciRS-7. In addition, we point explicitly to 19 high-confidence candidates overlapping annotated genomic sequence. The full list of candidates is freely available at http://rth.dk/resources/mirnasponge, where detailed properties for individual candidates can be explored, such as alignment details, conservation, accessibility and target profiles, which facilitates selection of sponge candidates for further context specific analysis.
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Affiliation(s)
- Xiaoyong Pan
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Anne Wenzel
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Lars Juhl Jensen
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- * E-mail: (LJJ); (JG)
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
- * E-mail: (LJJ); (JG)
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35
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Gong J, Ju Y, Shao D, Zhang QC. Advances and challenges towards the study of RNA-RNA interactions in a transcriptome-wide scale. QUANTITATIVE BIOLOGY 2018. [DOI: 10.1007/s40484-018-0146-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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36
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Koci O, Logan M, Svolos V, Russell RK, Gerasimidis K, Ijaz UZ. An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights. PeerJ 2018; 6:e5047. [PMID: 30065857 PMCID: PMC6064635 DOI: 10.7717/peerj.5047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 05/31/2018] [Indexed: 12/20/2022] Open
Abstract
With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn’s Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag.
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Affiliation(s)
- Orges Koci
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Michael Logan
- Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, UK
| | - Vaios Svolos
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Richard K Russell
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Royal Hospital for Children, Glasgow, UK
| | - Konstantinos Gerasimidis
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Umer Zeeshan Ijaz
- Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, UK
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37
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Murugesan SN, Yadav BS, Maurya PK, Chaudhary A, Singh S, Mani A. Expression and network analysis of YBX1 interactors for identification of new drug targets in lung adenocarcinoma. J Genomics 2018; 6:103-112. [PMID: 29973960 PMCID: PMC6030768 DOI: 10.7150/jgen.20581] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Accepted: 08/31/2017] [Indexed: 12/27/2022] Open
Abstract
Y-Box Binding protein 1 (YBX-1) is known to be involved in various types of cancers. It's interactors also play major role in various cellular functions. Present work aimed to study the expression profile of the YBX-1 interactors during lung adenocarcinoma (LUAD). The differential expression analysis involved 57 genes from 95 lung adenocarcinoma samples, construction of gene network and topology analysis. A Total of 43 genes were found to be differentially expressed from which 17 genes were found to be down regulated and 26 genes were up-regulated. We observed that Polyadenylate-binding protein 1 (PABPC1), a protein involved in YBX1 translation, is highly correlated with YBX1. The interaction network analysis for a differentially expressed non-coding RNA Growth Arrest Specific 5 (GAS5) suggests that two proteins namely, Growth Arrest Specific 2 (GAS2) and Peripheral myelin protein 22 (PMP22) are potentially involved in LUAD progression. The network analysis and differential expression suggests that Collagen type 1 alpha 2 (COL1A2) can be potential biomarker and target for LUAD.
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Affiliation(s)
| | - Birendra Singh Yadav
- Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India-211004
| | - Pramod Kumar Maurya
- Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India-211004
| | - Amit Chaudhary
- Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India-211004
| | - Swati Singh
- Center of Bioinformatics, University of Allahabad, India-211002
| | - Ashutosh Mani
- Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India-211004
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Arunkumar G, Anand S, Raksha P, Dhamodharan S, Prasanna Srinivasa Rao H, Subbiah S, Murugan AK, Munirajan AK. LncRNA OIP5-AS1 is overexpressed in undifferentiated oral tumors and integrated analysis identifies as a downstream effector of stemness-associated transcription factors. Sci Rep 2018; 8:7018. [PMID: 29728583 PMCID: PMC5935738 DOI: 10.1038/s41598-018-25451-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 04/23/2018] [Indexed: 12/16/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) play an important role in the regulation of key cellular processes in early development and cancer. LncRNA Oip5-as1 facilitates stem cell self-renewal in mouse by sponging mmu-miR-7 and modulating NANOG level, yet its role in cancer is less understood. We analyzed OIP5-AS1 expression in oral tumors and in TCGA datasets. We observed overexpression of OIP5-AS1 in oral tumors (P < 0.001) and in tumors of epithelial origin from TCGA. OIP5-AS1 expression was strongly associated with undifferentiated tumors (P = 0.0038). In silico analysis showed miR-7 binding site is conserved in mouse and human OIP5-AS1. However, human NANOG 3'-UTR lost the binding site for hsa-miR-7a-3. Therefore, we screened for other miRNAs that can be sponged by OIP5-AS1 and identified six potential miRNAs and their downstream target genes. Expression analysis showed downregulation of miRNAs and upregulation of downstream target genes, particularly in undifferentiated tumors with high-level of OIP5-AS1 suggesting OIP5-AS1 could post-transcriptionally modulate the downstream target genes. Further, systematic epigenomic analysis of OIP5-AS1 promoter revealed binding motifs for MYC, NANOG and KLF4 suggesting that OIP5-AS1 could be transactivated by stemness-associated transcription factors in cancer. OIP5-AS1 overexpression in undifferentiated oral tumors may be suggestive of enhanced cancer stemness, and consequently, poor clinical outcome.
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Affiliation(s)
- Ganesan Arunkumar
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, 600 113, India
| | - Shankar Anand
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, 600 113, India
| | - Partha Raksha
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, 600 113, India
| | - Shankar Dhamodharan
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, 600 113, India
| | | | - Shanmugam Subbiah
- Center for Oncology, Royapettah Government Hospital & Kilpauk Medical College, Royapettah, Chennai, 600 014, India
| | - Avaniyapuram Kannan Murugan
- Department of Molecular Oncology, King Faisal Specialist Hospital and Research Center, Riyadh, 11211, Saudi Arabia
| | - Arasambattu Kannan Munirajan
- Department of Genetics, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, 600 113, India.
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Brogaard L, Larsen LE, Heegaard PMH, Anthon C, Gorodkin J, Dürrwald R, Skovgaard K. IFN-λ and microRNAs are important modulators of the pulmonary innate immune response against influenza A (H1N2) infection in pigs. PLoS One 2018; 13:e0194765. [PMID: 29677213 PMCID: PMC5909910 DOI: 10.1371/journal.pone.0194765] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 02/02/2018] [Indexed: 11/19/2022] Open
Abstract
The innate immune system is paramount in the response to and clearance of influenza A virus (IAV) infection in non-immune individuals. Known factors include type I and III interferons and antiviral pathogen recognition receptors, and the cascades of antiviral and pro- and anti-inflammatory gene expression they induce. MicroRNAs (miRNAs) are increasingly recognized to participate in post-transcriptional modulation of these responses, but the temporal dynamics of how these players of the antiviral innate immune response collaborate to combat infection remain poorly characterized. We quantified the expression of miRNAs and protein coding genes in the lungs of pigs 1, 3, and 14 days after challenge with swine IAV (H1N2). Through RT-qPCR we observed a 400-fold relative increase in IFN-λ3 gene expression on day 1 after challenge, and a strong interferon-mediated antiviral response was observed on days 1 and 3 accompanied by up-regulation of genes related to the pro-inflammatory response and apoptosis. Using small RNA sequencing and qPCR validation we found 27 miRNAs that were differentially expressed after challenge, with the highest number of regulated miRNAs observed on day 3. In contrast, the number of protein coding genes found to be regulated due to IAV infection peaked on day 1. Pulmonary miRNAs may thus be aimed at fine-tuning the initial rapid inflammatory response after IAV infection. Specifically, we found five miRNAs (ssc-miR-15a, ssc-miR-18a, ssc-miR-21, ssc-miR-29b, and hsa-miR-590-3p)-four known porcine miRNAs and one novel porcine miRNA candidate-to be potential modulators of viral pathogen recognition and apoptosis. A total of 11 miRNAs remained differentially expressed 14 days after challenge, at which point the infection had cleared. In conclusion, the results suggested a role for miRNAs both during acute infection as well as later, with the potential to influence lung homeostasis and susceptibility to secondary infections in the lungs of pigs after IAV infection.
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Affiliation(s)
- Louise Brogaard
- Section for Protein Science and Signaling Biology, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- * E-mail:
| | - Lars E. Larsen
- Division of Diagnostics and Scientific Advice–Virology, National Veterinary Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Peter M. H. Heegaard
- Section for Protein Science and Signaling Biology, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Christian Anthon
- Center for non-coding RNA in Technology and Health (RTH), Department of Veterinary and Animal Science, University of Copenhagen, Frederiksberg, Denmark
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health (RTH), Department of Veterinary and Animal Science, University of Copenhagen, Frederiksberg, Denmark
| | - Ralf Dürrwald
- Department of Infectious Diseases, Robert Koch Institute, Berlin, Germany
| | - Kerstin Skovgaard
- Section for Protein Science and Signaling Biology, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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Panni S, Prakash A, Bateman A, Orchard S. The yeast noncoding RNA interaction network. RNA (NEW YORK, N.Y.) 2017; 23:1479-1492. [PMID: 28701522 PMCID: PMC5602107 DOI: 10.1261/rna.060996.117] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 06/26/2017] [Indexed: 06/07/2023]
Abstract
This article describes the creation of the first expert manually curated noncoding RNA interaction networks for S. cerevisiae The RNA-RNA and RNA-protein interaction networks have been carefully extracted from the experimental literature and made available through the IntAct database (www.ebi.ac.uk/intact). We provide an initial network analysis and compare their properties to the much larger protein-protein interaction network. We find that the proteins that bind to ncRNAs in the network contain only a small proportion of classical RNA binding domains. We also see an enrichment of WD40 domains suggesting their direct involvement in ncRNA interactions. We discuss the challenges in collecting noncoding RNA interaction data and the opportunities for worldwide collaboration to fill the unmet need for this data.
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Affiliation(s)
- Simona Panni
- Università della Calabria, Dipartimento di Biologia, Ecologia e Scienze della Terra, Rende 87036, Italy
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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Junge A, Zandi R, Havgaard JH, Gorodkin J, Cowland JB. Assessing the miRNA sponge potential of RUNX1T1 in t(8;21) acute myeloid leukemia. Gene 2017; 615:35-40. [PMID: 28322996 DOI: 10.1016/j.gene.2017.03.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 03/01/2017] [Accepted: 03/15/2017] [Indexed: 01/08/2023]
Abstract
t(8;21) acute myeloid leukemia (AML) is characterized by a translocation between chromosomes 8 and 21 and formation of a distinctive RUNX1-RUNX1T1 fusion transcript. This translocation places RUNX1T1 under control of the RUNX1 promoter leading to a pronounced upregulation of RUNX1T1 transcripts in t(8;21) AML, compared to normal hematopoietic cells. We investigated the role of highly-upregulated RUNX1T1 under the hypothesis that it acts as competing endogenous RNA (ceRNA) titrating microRNAs (miRNAs) away from their target transcripts and thus contributes to AML formation. Using publicly available t(8;21) AML RNA-Seq and miRNA-Seq data available from The Cancer Genome Atlas (TCGA) project, we obtained a network consisting of 605 genes that may act as ceRNAs competing for miRNAs with the suggested RUNX1T1 miRNA sponge. Among the 605 ceRNA candidates, 121 have previously been implied in cancer development. Players in the integrin, cadherin, and Wnt signaling pathways affected by the RUNX1T1 sponge were overrepresented. Finally, among a set of 21 high interest RUNX1T1 ceRNAs we found multiple genes that have previously been linked to AML formation. In conclusion, our study offers a novel look at the role of the RUNX1-RUNX1T1 fusion transcript in t(8;21) AML beyond previously investigated genetic and epigenetic aberrations.
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Affiliation(s)
- Alexander Junge
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Denmark; Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
| | - Roza Zandi
- The Granulocyte Research Laboratory, Department of Hematology, National University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Jakob Hull Havgaard
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Denmark; Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
| | - Jan Gorodkin
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Denmark; Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark.
| | - Jack Bernard Cowland
- The Granulocyte Research Laboratory, Department of Hematology, National University Hospital, University of Copenhagen, Copenhagen, Denmark; Section of Haematology-Oncology, Department of Clinical Genetics, National University Hospital, University of Copenhagen, Copenhagen, Denmark.
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