- 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://126.96.36.199/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://188.8.131.52/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://184.108.40.206/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://220.127.116.11/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
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