Functional Study of miR-27a in Human Hepatic Stellate Cells by Proteomic Analysis: Comprehensive View and a Role in Myogenic Tans-Differentiation Yuhua Ji 1 , Jinsheng Zhang 2 , Wenwen Wang 3 , Juling Ji 3 * 1 Key Laboratory of Neuroregeneration, Nantong University, Nanton, China, 2 Department of Pathology, Shanghai Medical College, Fudan University, Shanghai, PR China, 3 Department of Pathology, Medical School of Nantong University, Nantong, PR China Abstract We previous reported that miR-27a regulates lipid metabolism and cell proliferation during hepatic stellate cells (HSCs) activation. To further explore the biological function and underlying mechanisms of miR-27a in HSCs, global protein expression affected by overexpression of miR-27a in HSCs was analyzed by a cleavable isotope-coded affinity tags (cICAT) based comparative proteomic approach. In the present study, 1267 non-redundant proteins were identified with unique accession numbers (score $1.3, i.e. confidence $95%), among which 1171 were quantified and 149 proteins (12.72%) were differentially expressed with a differential expression ratio of 1.5. We found that up-regulated proteins by miR-27a mainly participate in cell proliferation and myogenesis, while down-regulated proteins were the key enzymes involved in de novo lipid synthesis. The expression of a group of six miR-27a regulated proteins was validated and the function of one miR-27a regulated protein was further validated. The results not only delineated the underlying mechanism of miR-27a in modulating fat metabolism and cell proliferation, but also revealed a novel role of miR-27a in promoting myogenic tans- differentiation during HSCs activation. This study also exemplified proteomics strategy as a powerful tool for the functional study of miRNA. Citation: Ji Y, Zhang J, Wang W, Ji J (2014) Functional Study of miR-27a in Human Hepatic Stellate Cells by Proteomic Analysis: Comprehensive View and a Role in Myogenic Tans-Differentiation. PLoS ONE 9(9): e108351. doi:10.1371/journal.pone.0108351 Editor: Yao Liang Tang, Georgia Regents University, United States of America Received May 19, 2014; Accepted August 19, 2014; Published September 29, 2014 Copyright: ß 2014 Ji et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its supporting information files. Funding: This research is supported by grants from the Natural Science Foundation of China (NSFC, http://www.nsfc.gov.cn/publish/portal1/), No. 81141048 and 30900563 to JJL, No. 81272027 to JYH, Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents from Jiangsu Provincial Department of Education (http://english.jsjyt.gov.cn/) to JJL, a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province No. 13KJA180005 to JYH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: jijuling@ntu.edu.cn Introduction microRNAs (miRNAs) regulate gene expression post-transcrip- tionally by binding primarily to the 39untranslated region (39UTR) of their target mRNAs, resulting in mRNA destabilization or translational repression[1]. Genes encoding 2042 mature human miRNAs have so far been identified (miRBase v.19) [2] and miRNAs are predicted to regulate the expression of up to 60% of human protein-encoding genes [3]. The best way to understand the biological function of a miRNA is to identify the genes that it regulates. Several bioinformatics methods have been developed for miRNA target prediction, including TargetScan (www.targetscan. org), miRanda (www.microrna.org), TarBase (diana.cslab.ece.n- tua.gr), PicTar (pictar.mdcberlin. de) et al. However since the mechanism of miRNA target recognition is still not fully understood, target gene prediction is not accurate and sometimes over predict [4]. In addition, a single miRNA can target hundreds of proteins and a single protein can be influenced by multiple miRNAs [5]. Thus comprehensive understanding of the pheno- typic effects of miRNAs at the cellular level is currently difficult. The use of quantitative proteomic strategies to characterize targets of miRNAs has opened new avenues to miRNA biology study [6]. The method of cleavable isotope-coded affinity tags (cICAT) coupling with nano LC-MS/MS is a quantitative proteomic approach that enables rapid, comprehensive and reliable analysis of the proteomes of two comparable samples [7]. More importantly, compared with other quantitative proteo- mic strategies, cICAT based approach could greatly reduce the sample complexity, therefore those low abundance proteins could be readily identified. We have previously reported that miR-27a,b suppresses fat accumulation and promotes cell proliferation during hepatic stellate cells (HSCs) activation [8]. Thereafter, miR-27 has been evidenced to act as negative regulator of adipocyte differentiation [9] or lipid metabolism [10], and positive regulator of cell proliferation [11] by several groups. It has also been regarded as an oncogene in some malignant tumor [12,13]. To further explore the possible functions and underlying mechanism of miR-27a during HSCs activation, human stellate cell line LX2/miR-27a stable transfectants was established and validated. Global protein expression profiles were compared between LX2/miR-27a and PLOS ONE | www.plosone.org 1 September 2014 | Volume 9 | Issue 9 | e108351