- Open Access
ARN: analysis and prediction by adipogenic professional database
© The Author(s). 2016
- Received: 9 May 2016
- Accepted: 14 July 2016
- Published: 8 August 2016
Adipogenesis is the process of cell differentiation by which mesenchymal stem cells become adipocytes. Extensive research is ongoing to identify genes, their protein products, and microRNAs that correlate with fat cell development. The existing databases have focused on certain types of regulatory factors and interactions. However, there is no relationship between the results of the experimental studies on adipogenesis and these databases because of the lack of an information center. This information fragmentation hampers the identification of key regulatory genes and pathways. Thus, it is necessary to provide an information center that is quickly and easily accessible to researchers in this field. We selected and integrated data from eight external databases based on the results of text-mining, and constructed a publicly available database and web interface (URL: http://184.108.40.206/arn/), which contained 30873 records related to adipogenic differentiation. Then, we designed an online analysis tool to analyze the experimental data or form a scientific hypothesis about adipogenesis through Swanson’s literature-based discovery process. Furthermore, we calculated the “Impact Factor” (“IF”) value that reflects the importance of each node by counting the numbers of relation records, expression records, and prediction records for each node. This platform can support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways.
Adipose tissue is an important site for lipid storage, energy homeostasis, and whole-body insulin sensitivity. It is important to understand the mechanisms involved in adipose tissue development. Growth of adipose tissue is the result of differentiation of new fat cells from precursor cells . It is obvious that adipogenesis is not a single gene trait, but is determined by a number of genes and their encoded proteins . Therefore, researchers need a professional comprehensive knowledge database including related genes, proteins, properties, biological processes, and environmental factors in accordance with their determined or predicted relations in the literature to assist researchers in understanding adipogenic differentiation from the perspective of systems biology.
After obtaining a large amount of data and information related to fat, a key element is linking the extracted information together to form new facts or hypotheses to be explored further by more conventional means of experimentation . Swanson developed and implemented a novel tool to mine the existing knowledge base for unreported or underreported relationships, and highlighted previously published but neglected hypotheses, a process known as literature-based discovery . This process functions by connecting two seemingly unrelated findings . This and implemented a novel tool to mine the existing knowledge and easily accessible to researchers in this field. Conclusive proof, the discovery is, in itself, very helpful to uncover previously unknown relationships . Furthermore, it can help investigators access context and mine knowledge that might not be revealed using a traditional search.
Records in ARN
Information mining and manual review
For the literature search, we established a set of queries by entering 47 key genes in adipogenesis  with simultaneous input contexts ‘adipo* differen*’, which is short for “adipocyte differentiation”. The query set was submitted one at a time to PubMed by Agilent Literature Search. The resulting documents were retrieved, parsed into sentences, and analyzed for known interaction terms such as ‘binding’ or ‘activate’. Agilent Literature Search uses a lexicon set to define gene names (concepts) and aliases, drawn from Entrez Gene, and interaction terms (verbs) of interest. An association was extracted from every sentence containing at least two concepts and one verb. Associations were then converted into interactions with corresponding sentences and source hyperlinks, and added to a Cytoscape network . The last download of abstracts was executed on 29 October 2015. In total, 9908 PubMed abstracts were obtained and served as the initial corpus for further processing.
The literature mining method has problems including ‘term variation’ and ‘term ambiguity’ . Term variation originates from the ability of a natural language to express a single concept in a number of ways. For example, in biomedicine, there are many synonyms for proteins, enzymes, and genes. Having six or seven synonyms for a single concept is not unusual in this domain . In the ARN database, we unified a gene as the official gene symbol. Term ambiguity occurs when the same term is used to refer to multiple concepts. For example, the term “fat” can be a noun or an adjective for “obese”. The two terms are often used in biomedical literature. Searching for “fat” in PubMed returned 187888 results. We found that fat was also used to name a gene or as a universal symbol. Therefore, it was necessary to carry out a manual examination of the results of literature mining to delete the wrong results. During this process, we removed most of the 9908 PubMed abstracts, and only 1449 remained.
Information processing and analysis
Screening the data of four external databases
Design of the analysis tool
Our interest in text-based scientific discovery led us to the development of the ARN-Analysis tool. Because we envision text-based discovery as a human-centered activity, our goal has been to codify a practical tool that assists a biomedical researcher in formulating and initially testing hypotheses .
As shown in Fig. 2 and Additional file 1: Table S1, the information is structured in the ARN database. Therefore, the discovery question is user generated on which subject the user wants to obtain new knowledge. Additionally, the filtering and selection of interesting B- or C-concepts is user dependent. Interesting in this case means interesting according to the current knowledge and goals of the user. It is the user who will have to make an interpretation of the computer-suggested list of possible results. Finally, the intersection of two or more result sets can be obtained by the user, which is likely to be hypotheses.
In this formula, IF (i) is the effect of node i on the differentiation of fat. Ri is the number of relationships of node i, Rmax is the number of relationships of node r-max that has the most relations; Ei is the number of expression records of node i. Emax is the number of expression records of node e-max that has the most expression records; Pi is the number of prediction records of node i. Pmax is the number of prediction records of node p-max that has the most prediction records. All values are updated with the database, so the information they contain is comprehensive and timely.
Basic information of the ARN database
Currently, the database contains 3054 nodes (genes and microRNAs), 1807 relation records, 1141 summary records, 10675 expression records, and 43 review images associated with adipogenesis according to 1457 papers. Among the 3054 nodes in the ARN database, we determined 12869 possible relationships sourced from miRGate, TRRUST, BioGRID and PAZAR.
The database can be searched using a web interface (http://220.127.116.11/arn/)  with three possible input forms depending on the user’s research focus. For gene searches, Entrez GeneID and official gene symbols are accepted. MicroRNAs require the names of mature microRNA sequences (e.g., mirn143). The literature requires the PubMed PMID (see Additional file 2: Handbook of ARN, Example 1). We provide the node, maps, literature, and expression pages for different kinds of information. Users can select their requested entry and the results page is displayed.
Correlations between databases
ARN-Analysis is a professional analysis tool for the study of adipogenesis
Changes in microRNA expression of white and brown adipose tissues in cold-induced mice
Scoring function of the ARN database
IF values of the top 50 nodes in the ARN database
Target control of adipogenesis genes
Deficiencies of the ARN database
In the process of adding the prediction relations to the ARN database through the external database, we found that the table structure of “Prediction” in Fig. 2 is inadequate. It lacks the relevant tags of the information source database, which obscures the specific sources of the prediction relations. In the future, we will correct this problem by upgrading the platform. In addition, for the IF calculation formula of each node at present, we determined the weights of Ri, Ei and Pi as 1/3. However, with continuous updating and improvement of the platform, the optimal weight of each influencing factor remains to be explored further.
The precursors of adipocytes, mesenchymal stem cells (MSCs), can also differentiate into osteoblasts, chondrocytes, and myoblasts. Understanding the factors that govern MSC differentiation has significant implications in diverse areas of human health from obesity to osteoporosis . Therefore, we would like to add them to our network in the future. Moreover, recently, long-chain non-encoding RNA (lncRNA) was found to be involved in the regulation of adipogenic differentiation [42, 43]. These data must be added as soon as they are available. Furthermore, information on the institutions in the papers will soon be added. We are certain that this addition will promote the exchange of ideas, project cooperation, and resource sharing between institutions. We plan to update the database monthly to provide state-of-the-art knowledge and keep track of improvements in the field. All recently added data will be displayed separately on the corresponding page.
The ARN database will serve as a platform for information and hypothesis generation for the research community, which will facilitate uncovering the complexity of adipogenesis-related mechanisms, pathways, and processes.
Project name: ARNdbProject. Home page: http://18.104.22.168/arn/. Operating system(s): Platform independent. Other requirements: Microsoft SQL Server,. NET and HTML5 for the Web interface. For interactive data visualization, we applied D3.
IF, Impact Factor; ARN, Adipogenic Regulation Network; TRRUST, transcriptional regulatory relationships unravelled by sentence-based text-mining; MSCs, mesenchymal stem cells; lncRNA, long-chain non-encoding RNA
The authors appreciate the contribution of Mr. Jiang wei-qiang for his help in constructing the database. The authors would also like to thank the generous technical supports from staff of National Beef Cattle Improvement Center for this study.
This work was supported by the National “86” Program of China (2013AA102505, 2011AA100307-02), the National Science and Technology Support Project of China (2011BAD28B04-03), the GMO New Varieties Major Project of China (2011ZX08007-002), the National Beef and Yak Industrial Technology System of China (CARS-38), the National Natural Science Foundation of China (31272411), and the Scientific and Technological Innovation Program of Shaanxi Province in China (2014KTZB02-02-1).
Availability of data and materials
All the contents in the ARN database are in Additional file 1.
Project home page: http://22.214.171.124/arn/
LSZ conceived the project and provided final approval of the manuscript version to be published; YH made substantial contributions to conception and design, acquisition of data, and analysis and interpretation of data; YH and LW were involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript.
All authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Hausman DB, DiGirolamo M, Bartness TJ, Hausman GJ, Martin RJ. The biology of white adipocyte proliferation. Obesity Rev. 2001;2:239–54.View ArticleGoogle Scholar
- Sarjeant K, Stephens JM. Adipogenesis. Cold Spring Harb Perspect Biol. 2012;4(9):a008417.View ArticlePubMedPubMed CentralGoogle Scholar
- Garten Y, Coulet A, Altman RB. Recent progress in automatically extracting information from the pharmacogenomic literature. Pharmacogenomics. 2010;11(10):1467–89. doi:10.2217/pgs.10.136.View ArticlePubMedPubMed CentralGoogle Scholar
- Swanson DR. 2011 literature-based resurrection of neglected medical discoveries. J Biomed Discov Collab. 2010;6:34–47.View ArticleGoogle Scholar
- Marc Weeber Klein H, de Jong-van den Berg LTW, Vos R. Using concepts in Literature-Based Discovery: Simulating Swanson’s Raynaud–Fish Oil and Migraine–Magnesium Discoveries. J Am Soc Inf Sci Technol. 2001;52:548–57.View ArticleGoogle Scholar
- Hur J, Sullivan KA, Schuyler AD, Hong Y, Pande M, States DJ, Jagadish HV, Feldman EL. Literature-based discovery of diabetes- and ROS-related targets. BMC Med Genomics. 2010;3:49.View ArticlePubMedPubMed CentralGoogle Scholar
- Cristancho AG, Lazar MA. Forming functional fat: a growing understanding of adipocyte differentiation. Nat Rev Mol Cell Biol. 2011;1211:722–34.View ArticleGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;210:2366–82.View ArticleGoogle Scholar
- Spasic I, Ananiadou S, McNaught J, Kumar A. Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform. 2005;63:239–51.View ArticleGoogle Scholar
- Rojas I, Bernardi L, Ratsch E, Kania R, Wittig U, Saric J. A database system for the analysis of biochemical pathways. In Silico Biol. 2002;2(2):75–86.PubMedGoogle Scholar
- Weeber M, Klein H, Aronson AR, Mork JG, de Jong-van den Berg LT, Vos R. Text-based discovery in biomedicine: the architecture of the DAD-system. Proc AMIA Symp. 2000:903-7. PMID:11080015, and PMCID: PMC2243779.Google Scholar
- The Adipogenesis Regulation Network database. http://126.96.36.199/arn/. Accessed 15 Jan 2016.
- Kim YJ, Hwang SJ, Bae YC, Jung JS. MiR-21 regulates adipogenic differentiation through the modulation of TGF-beta signaling in mesenchymal stem cells derived from human adipose tissue. Stem Cells. 2009;27(12):3093–102. doi:10.1002/stem.235.PubMedGoogle Scholar
- Tao C, Huang S, Wang Y, Wei G, Zhang Y, Qi D, Wang Y, Li K. Changes in white and brown adipose tissue microRNA expression in cold-induced mice. Biochem Biophys Res Commun. 2015;4633:193–9.View ArticleGoogle Scholar
- Trajkovski M, Lodish H. MicroRNA networks regulate development of brown adipocytes. Trends Endocrinol Metab. 2013;249:442–50.View ArticleGoogle Scholar
- Liu X, Tamada K, Kishimoto R, Okubo H, Ise S, Ohta H, Ruf S, Nakatani J, Kohno N, Spitz F, et al. Transcriptome profiling of white adipose tissue in a mouse model for 15q duplication syndrome. Genom Data. 2015;5:394–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Shore AM, Karamitri A, Kemp P, Speakman JR, Graham NS, Lomax MA. Cold-induced changes in gene expression in brown adipose tissue, white adipose tissue and liver. PLoS One. 2013;87:e68933.View ArticleGoogle Scholar
- Barth N, Langmann T, Schölmerich J, Schmitz G, Schäffler A. Identification of regulatory elements in the human adipose most abundant gene transcript-1 (apM-1) promoter: role of SP1/SP3 and TNF-alpha as regulatory pathways. Diabetologia. 2002;45(10):1425–33.PubMedGoogle Scholar
- Deckmann K, Rörsch F, Steri R, Schubert-Zsilavecz M, Geisslinger G, Grösch S. Dimethylcelecoxib inhibits mPGES-1 promoter activity by influencing EGR1 and NF-kB. Biochem Pharmacol. 2010;809:1365–72.View ArticleGoogle Scholar
- Li N, Muthusamy S, Liang R, Sarojini H, Wang E. Increased expression of miR-34a and miR-93 in rat liver during aging, and their impact on the expression of Mgst1 and Sirt1. Mech Ageing Dev. 2011;1323:75–85.View ArticleGoogle Scholar
- Madonna S, Scarponi C, Sestito R, Pallotta S, Cavani A, Albanesi C. The IFN-gamma-dependent suppressor of cytokine signaling 1 promoter activity is positively regulated by IFN regulatory factor-1 and Sp1 but repressed by growth factor independence-1b and Krüppel-like factor-4, and it is dysregulated in psoriatic keratinocytes. J Immunol. 2010;185(4):2467–81. doi:10.4049/jimmunol.1001426.View ArticlePubMedGoogle Scholar
- Gao J, Liu YY, D'Souza RM, Barabási AL. Target control of complex networks. Nat Commun. 2014;5:5415.View ArticlePubMedPubMed CentralGoogle Scholar
- Jin Q, Wang C, Kuang X, Feng X, Sartorelli V, Ying H, Ge K, Dent SY. Gcn5 and PCAF regulate PPARγ and Prdm16 expression to facilitate brown adipogenesis. Mol Cell Biol. 2014;3419:3746–53.View ArticleGoogle Scholar
- Cho YW, Hong S, Jin Q, Wang L, Lee JE, Gavrilova O, Ge K. Histone methylation regulator PTIP is required for PPARgamma and C/EBPalpha expression and adipogenesis. Cell Metab. 2009;101:27–39.View ArticleGoogle Scholar
- Qiang L, Wang L, Kon N, Zhao W, Lee S, Zhang Y, Rosenbaum M, Zhao Y, Gu W, Farmer SR, Accili D. Brown remodeling of white adipose tissue by SirT1-dependent deacetylation of Pparγ. Cell. 2012;1503:620–32.View ArticleGoogle Scholar
- Chen YH, Yeh FL, Yeh SP, Ma HT, Hung SC, Hung MC, Li LY. Myocyte enhancer factor-2 interacting transcriptional repressor MITR is a switch that promotes osteogenesis and inhibits adipogenesis of mesenchymal stem cells by inactivating peroxisome proliferator-activated receptor gamma-2. J Biol Chem. 2011;28612:10671–80.View ArticleGoogle Scholar
- Takada I, Kouzmenko AP, Kato S. Wnt and PPARgamma signaling in osteoblastogenesis and adipogenesis. Nat Rev Rheumatol. 2009;58:442–7.View ArticleGoogle Scholar
- Tong Q, Dalgin G, Xu H, Ting CN, Leiden JM, Hotamisligil GS. Function of GATA transcription factors in preadipocyte-adipocyte transition. Science. 2000;2905489:134–8.View ArticleGoogle Scholar
- Banerjee SS, Feinberg MW, Watanabe M, Gray S, Haspel RL, Denkinger DJ, Kawahara R, Hauner H, Jain MK. The Krüppel-like factor KLF2 inhibits peroxisome proliferator-activated receptor-gamma expression and adipogenesis. J Biol Chem. 2003;2784:2581–4.View ArticleGoogle Scholar
- Mori T, Sakaue H, Iguchi H, Gomi H, Okada Y, Takashima Y, Nakamura K, Nakamura T, Yamauchi T, Kubota N, Kadowaki T, Matsuki Y, Ogawa W, Hiramatsu R, Kasuga M. Role of Krüppel-like factor 15 KLF15 in transcriptional regulation of adipogenesis. J Biol Chem. 2005;28013:12867–75.View ArticleGoogle Scholar
- Zhao Y, Zhang YD, Zhang YY, Qian SW, Zhang ZC, Li SF, Guo L, Liu Y, Wen B, Lei QY, Tang QQ, Li X. p300-dependent acetylation of activating transcription factor 5 enhances C/EBPβ transactivation of C/EBPαduring 3 T3-L1 differentiation. Mol Cell Biol. 2014;343:315–24.View ArticleGoogle Scholar
- Pi J, Leung L, Xue P, Wang W, Hou Y, Liu D, Yehuda-Shnaidman E, Lee C, Lau J, Kurtz TW, Chan JY. Deficiency in the nuclear factor E2-related factor-2 transcription factor results in impaired adipogenesis and protects against diet-induced obesity. J Biol Chem. 2010;28512:9292–300.View ArticleGoogle Scholar
- Gupta RK, Arany Z, Seale P, Mepani RJ, Ye L, Conroe HM, Roby YA, Kulaga H, Reed RR, Spiegelman BM. Transcriptional control of preadipocyte determination by Zfp423. Nature. 2010;4647288:619–23.View ArticleGoogle Scholar
- Lee H, Kim HJ, Lee YJ, Lee MY, Choi H, Lee H, Kim JW. Krüppel-like factor KLF8 plays a critical role in adipocyte differentiation. PLoS One. 2012;712:e52474.View ArticleGoogle Scholar
- Zhang JF, Fu WM, He ML, Xie WD, Lv Q, Wan G, Li G, Wang H, Lu G, Hu X, Jiang S, Li JN, Lin MC, Zhang YO, Kung HF. MiRNA-20a promotes osteogenic differentiation of human mesenchymal stem cells by co-regulating BMP signaling. RNA Biol. 2011;85:829–38.View ArticleGoogle Scholar
- Sun J, Wang Y, Li Y, Zhao G. Downregulation of PPARγ by miR-548d-5p suppresses the adipogenic differentiation of human bone marrow mesenchymal stem cells and enhances their osteogenic potential. J Transl Med. 2014;12:168.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee EK, Lee MJ, Abdelmohsen K, Kim W, Kim MM, Srikantan S, Martindale JL, Hutchison ER, Kim HH, Marasa BS, Selimyan R, Egan JM, Smith SR, Fried SK, Gorospe M. miR-130 suppresses adipogenesis by inhibiting peroxisome proliferator-activated receptor gamma expression. Mol Cell Biol. 2011;314:626–38.View ArticleGoogle Scholar
- Kim SY, Kim AY, Lee HW, Son YH, Lee GY, Lee JW, Lee YS, Kim JB. miR-27a is a negative regulator of adipocyte differentiation via suppressing PPARgamma expression. Biochem Biophys Res Commun. 2010;3923:323–8.View ArticleGoogle Scholar
- Hu E, Kim JB, Sarraf P, Spiegelman BM. Inhibition of adipogenesis through MAP kinase-mediated phosphorylation of PPARgamma. Science. 1996;2745295:2100–3.View ArticleGoogle Scholar
- Lemkul JA, Lewis SN, Bassaganya-Riera J, Bevan DR. Phosphorylation of PPARγ Affects the Collective Motions of the PPARγ-RXRα-DNA Complex. PLoS One. 2015;105:e0123984.View ArticleGoogle Scholar
- Fakhry M, Hamade E, Badran B, Buchet R, Magne D. Molecular mechanisms of mesenchymal stem cell differentiation towards osteoblasts. World J Stem Cells. 2013;5(4):136–48. doi:10.4252/wjsc.v5.i4.136.View ArticlePubMedPubMed CentralGoogle Scholar
- Sun L, Goff LA, Trapnell C, Alexander R, Lo KA, Hacisuleyman E, Sauvageau M, Tazon-Vega B, Kelley DR, Hendrickson DG, Yuan B, Kellis M, Lodish HF, Rinn JL. Long noncoding RNAs regulate adipogenesis. Proc Natl Acad Sci U S A. 2013;1109:3387–92.View ArticleGoogle Scholar
- Chen J, Cui X, Shi C, Chen L, Yang L, Pang L, Zhang J, Guo X, Wang J, Ji C. Differential lncRNA expression profiles in brown and white adipose tissues. Mol Genet Genomics. 2015;2902:699–707.View ArticleGoogle Scholar
- The PubMed Database. http://www.ncbi.nlm.nih.gov/pubmed. Accessed 20 Oct 2015.
- The Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining Database. http://www.grnpedia.org/trrust/. Accessed 15 Jan 2016.
- Han H, Shim H, Shin D, Shim JE, Ko Y, Shin J, Kim H, Cho A, Kim E, Lee T, et al. TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep. 2015;5:11432. doi:10.1038/srep11432.View ArticlePubMedPubMed CentralGoogle Scholar
- The Public database of Transcription Factor and Regulatory Sequence Annotation. http://www.pazar.info/. Accessed 15 Jan 2016.
- Portales-Casamar E, Arenillas D, Lim J, Swanson MI, Jiang S, McCallum A, Kirov S, Wasserman WW. The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucleic Acids Res. 2009;37(Database issue):D54–60. doi:10.1093/nar/gkn783.View ArticlePubMedGoogle Scholar
- The miRGate database. http://mirgate.bioinfo.cnio.es/miRGate/. Accessed 15 Jan 2016.
- Andrés-León E, González Peña D, Gómez-López G, Pisano DG. miRGate: a curated database of human, mouse and rat miRNA-mRNA targets. Database (Oxford). 2015 Apr 8;2015:bav035. doi: 10.1093/database/bav035.
- The Biological General Repository for Interaction Datasets. http://thebiogrid.org/. Accessed 15 Jan 2016.
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34(Database issue):D535–9.View ArticlePubMedGoogle Scholar
- Weeber M, Klein H, de Jong-van den Berg LTW, et al. Using concepts in literature-based discovery: simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries[J]. J Am Soc Inf Sci Technol. 2001;527:548–57.View ArticleGoogle Scholar