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Oldenburg A, Briand N, Sørensen AL, Cahyani I, Shah A, Moskaug JØ, Collas P. A lipodystrophy-causing lamin A mutant alters conformation and epigenetic regulation of the anti-adipogenic MIR335 locus. J Cell Biol 2017; 216:2731-2743. [PMID: 28751304 PMCID: PMC5584164 DOI: 10.1083/jcb.201701043] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 05/04/2017] [Accepted: 06/20/2017] [Indexed: 12/30/2022] Open
Abstract
Mutations in the Lamin A/C (LMNA) gene-encoding nuclear LMNA cause laminopathies, which include partial lipodystrophies associated with metabolic syndromes. The lipodystrophy-associated LMNA p.R482W mutation is known to impair adipogenic differentiation, but the mechanisms involved are unclear. We show in this study that the lamin A p.R482W hot spot mutation prevents adipogenic gene expression by epigenetically deregulating long-range enhancers of the anti-adipogenic MIR335 microRNA gene in human adipocyte progenitor cells. The R482W mutation results in a loss of function of differentiation-dependent lamin A binding to the MIR335 locus. This impairs H3K27 methylation and instead favors H3K27 acetylation on MIR335 enhancers. The lamin A mutation further promotes spatial clustering of MIR335 enhancer and promoter elements along with overexpression of the MIR355 gene after adipogenic induction. Our results link a laminopathy-causing lamin A mutation to an unsuspected deregulation of chromatin states and spatial conformation of an miRNA locus critical for adipose progenitor cell fate.
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Affiliation(s)
- Anja Oldenburg
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Nolwenn Briand
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anita L Sørensen
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Inswasti Cahyani
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Akshay Shah
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jan Øivind Moskaug
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway.,Norwegian Center for Stem Cell Research, Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway .,Norwegian Center for Stem Cell Research, Department of Immunology, Oslo University Hospital, Oslo, Norway
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52
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Gov E, Arga KY. Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer. Sci Rep 2017; 7:4996. [PMID: 28694494 PMCID: PMC5504034 DOI: 10.1038/s41598-017-05298-w] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 05/26/2017] [Indexed: 02/06/2023] Open
Abstract
Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.
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Affiliation(s)
- Esra Gov
- Department of Bioengineering, Marmara University, 34722, Goztepe, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, 34722, Goztepe, Istanbul, Turkey.
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53
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Cao YL, Jia YJ, Xing BH, Shi DD, Dong XJ. Plasma microRNA-16-5p, -17-5p and -20a-5p: Novel diagnostic biomarkers for gestational diabetes mellitus. J Obstet Gynaecol Res 2017. [PMID: 28621051 DOI: 10.1111/jog.13317] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Ya-Lei Cao
- Department of Gynaecology and Obstetrics; Cangzhou Center Hospital; Cangzhou China
| | - Yan-Ju Jia
- Department of Gynaecology and Obstetrics; Tianjin Central Hospital of Gynaecology Obstetrics; Tianjin China
| | - Bao-Heng Xing
- Department of Gynaecology and Obstetrics; Cangzhou Center Hospital; Cangzhou China
| | - Dan-Dan Shi
- Department of Gynaecology and Obstetrics; Cangzhou Center Hospital; Cangzhou China
| | - Xiu-Juan Dong
- Department of Gynaecology and Obstetrics; Cangzhou Center Hospital; Cangzhou China
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54
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Gov E, Kori M, Arga KY. RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective. Syst Biol Reprod Med 2017; 63:219-238. [PMID: 28574782 DOI: 10.1080/19396368.2017.1330368] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer. ABBREVIATIONS CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-β: transforming growth factor-β.
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Affiliation(s)
- Esra Gov
- a Department of Bioengineering , Marmara University , Istanbul , Turkey.,b Department of Bioengineering , Adana Science and Technology University , Adana , Turkey
| | - Medi Kori
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
| | - Kazim Yalcin Arga
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
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55
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Ayyildiz D, Gov E, Sinha R, Arga KY. Ovarian Cancer Differential Interactome and Network Entropy Analysis Reveal New Candidate Biomarkers. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2017; 21:285-294. [PMID: 28375712 DOI: 10.1089/omi.2017.0010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Ovarian cancer is one of the most common cancers and has a high mortality rate due to insidious symptoms and lack of robust diagnostics. A hitherto understudied concept in cancer pathogenesis may offer new avenues for innovation in ovarian cancer biomarker development. Cancer cells are characterized by an increase in network entropy, and several studies have exploited this concept to identify disease-associated gene and protein modules. We report in this study the changes in protein-protein interactions (PPIs) in ovarian cancer within a differential network (interactome) analysis framework utilizing the entropy concept and gene expression data. A compendium of six transcriptome datasets that included 140 samples from laser microdissected epithelial cells of ovarian cancer patients and 51 samples from healthy population was obtained from Gene Expression Omnibus, and the high confidence human protein interactome (31,465 interactions among 10,681 proteins) was used. The uncertainties of the up- or downregulation of PPIs in ovarian cancer were estimated through an entropy formulation utilizing combined expression levels of genes, and the interacting protein pairs with minimum uncertainty were identified. We identified 105 proteins with differential PPI patterns scattered in 11 modules, each indicating significantly affected biological pathways in ovarian cancer such as DNA repair, cell proliferation-related mechanisms, nucleoplasmic translocation of estrogen receptor, extracellular matrix degradation, and inflammation response. In conclusion, we suggest several PPIs as biomarker candidates for ovarian cancer and discuss their future biological implications as potential molecular targets for pharmaceutical development as well. In addition, network entropy analysis is a concept that deserves greater research attention for diagnostic innovation in oncology and tumor pathogenesis.
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Affiliation(s)
- Dilara Ayyildiz
- 1 Department of Bioengineering, Marmara University , Istanbul, Turkey .,2 Department of Biomedical Sciences and Biotechnology, University of Udine , Udine, Italy
| | - Esra Gov
- 1 Department of Bioengineering, Marmara University , Istanbul, Turkey .,3 Department of Bioengineering, Adana Science and Technology University , Adana, Turkey
| | - Raghu Sinha
- 4 Department of Biochemistry and Molecular Biology, Penn State College of Medicine , Hershey, Pennsylvania
| | - Kazim Yalcin Arga
- 1 Department of Bioengineering, Marmara University , Istanbul, Turkey
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56
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Gov E, Arga KY. Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network. IET Syst Biol 2016; 10:219-228. [PMID: 27879476 PMCID: PMC8687357 DOI: 10.1049/iet-syb.2016.0001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Revised: 04/14/2016] [Accepted: 04/27/2016] [Indexed: 01/26/2024] Open
Abstract
Transcriptional regulation of gene expression is an essential cellular process that is arranged by transcription factors (TFs), microRNAs (miRNA) and their target genes through a variety of mechanisms. Here, we set out to reconstruct a comprehensive transcriptional regulatory network of Homo sapiens consisting of experimentally verified regulatory information on miRNAs, TFs and their target genes. We have performed topological analyses to elucidate the transcriptional regulatory roles of miRNAs and TFs. When we thoroughly investigated the network motifs, different gene regulatory scenarios were observed; whereas, mutual TF-miRNA regulation (interactive cooperation) and hierarchical operation where miRNAs were the upstream regulators of TFs came into prominence. Otherwise, biological process specific subnetworks were also constructed and integration of gene and miRNA expression data on ovarian cancer was achieved as a case study to observe dynamic patterns of the gene expression. Meanwhile, both co-operation and hierarchical operation types were determined in active ovarian cancer and process-specific subnetworks. In addition, the analysis showed that multiple signals from miRNAs were integrated by TFs. Our results demonstrate new insights on the architecture of the human transcriptional regulatory network, and here we present some lessons we gained from deciphering the reciprocal interplay between miRNAs, TFs and their target genes.
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Affiliation(s)
- Esra Gov
- Department of Bioengineering, Marmara University, 34722 Goztepe, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, 34722 Goztepe, Istanbul, Turkey.
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57
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Kori M, Gov E, Arga KY. Molecular signatures of ovarian diseases: Insights from network medicine perspective. Syst Biol Reprod Med 2016; 62:266-82. [PMID: 27341345 DOI: 10.1080/19396368.2016.1197982] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor.
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Affiliation(s)
- Medi Kori
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
| | - Esra Gov
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
| | - Kazim Yalcin Arga
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
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58
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Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network. Oncotarget 2016; 7:37906-37919. [PMID: 27191267 PMCID: PMC5122359 DOI: 10.18632/oncotarget.9353] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 04/28/2016] [Indexed: 12/13/2022] Open
Abstract
Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases.
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59
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Integration of multiple biological features yields high confidence human protein interactome. J Theor Biol 2016; 403:85-96. [PMID: 27196966 DOI: 10.1016/j.jtbi.2016.05.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Revised: 05/01/2016] [Accepted: 05/11/2016] [Indexed: 01/05/2023]
Abstract
The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.
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60
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Sinha I, Karagoz K, Fogle RL, Hollenbeak CS, Zea AH, Arga KY, Stanley AE, Hawkes WC, Sinha R. “Omics” of Selenium Biology: A Prospective Study of Plasma Proteome Network Before and After Selenized-Yeast Supplementation in Healthy Men. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:202-13. [DOI: 10.1089/omi.2015.0187] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Indu Sinha
- Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | - Kubra Karagoz
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Rachel L. Fogle
- Department of Surgery, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | | | - Arnold H. Zea
- Stanley S, Scott Cancer Center and Department of Microbiology, Immunology, and Parasitology, LSU Health Sciences Center, New Orleans, Louisiana, USA
| | - Kazim Y. Arga
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Anne E. Stanley
- Department of Mass Spectrometry Core, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | - Wayne C. Hawkes
- United State Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, University of California Davis, California, USA
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania, USA
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Personalized medicine beyond genomics: alternative futures in big data—proteomics, environtome and the social proteome. J Neural Transm (Vienna) 2015; 124:25-32. [DOI: 10.1007/s00702-015-1489-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Accepted: 11/19/2015] [Indexed: 12/15/2022]
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