Huan Gong1, Ming Zhang2, Yiwen Han3, Ying Zhang4, Jing Pang3, Yanyang Zhao3, Beidong Chen3, Wei Wu3, Ruomei Qi3, Tiemei Zhang5. 1. The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. gonghuan3861@bjhmoh.cn. 2. Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China. 3. The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. 4. School of Sport Science, Beijing Sport University, Beijing, People's Republic of China. 5. The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. tmzhang126@126.com.
Abstract
BACKGROUND: MicroRNAs play an important role in many fundamental biological and pathological processes. Defining the microRNAs profile underlying the processes by beneficial and detrimental lifestyles, including caloric restriction (CR), exercise and high-fat diet (HF), is necessary for understanding both normal physiology and the pathogenesis of metabolic disease. We used the microarray to detect microRNAs expression in livers from CR, EX and HF mice models. After predicted potential target genes of differentially expressed microRNAs with four algorithms, we applied GO and KEGG to analyze the function of predicted microRNA targets. RESULTS: We describe the overall microRNAs expression pattern, and identified 84 differentially expressed microRNAs changed by one or two or even all the three lifestyle modifications. The common and different enriched categories of gene function and main biochemical and signal transduction pathways were presented. CONCLUSIONS: We provided for the first time a comprehensive and thorough comparison of microRNAs expression profiles in liver among these lifestyle modifications. With this knowledge, our findings provide us with an overall vision of microRNAs in the molecular impact of lifestyle on health as well as useful clues for future and thorough research of the role of microRNAs.
BACKGROUND: MicroRNAs play an important role in many fundamental biological and pathological processes. Defining the microRNAs profile underlying the processes by beneficial and detrimental lifestyles, including caloric restriction (CR), exercise and high-fat diet (HF), is necessary for understanding both normal physiology and the pathogenesis of metabolic disease. We used the microarray to detect microRNAs expression in livers from CR, EX and HF mice models. After predicted potential target genes of differentially expressed microRNAs with four algorithms, we applied GO and KEGG to analyze the function of predicted microRNA targets. RESULTS: We describe the overall microRNAs expression pattern, and identified 84 differentially expressed microRNAs changed by one or two or even all the three lifestyle modifications. The common and different enriched categories of gene function and main biochemical and signal transduction pathways were presented. CONCLUSIONS: We provided for the first time a comprehensive and thorough comparison of microRNAs expression profiles in liver among these lifestyle modifications. With this knowledge, our findings provide us with an overall vision of microRNAs in the molecular impact of lifestyle on health as well as useful clues for future and thorough research of the role of microRNAs.
Authors: Sarah J Mitchell; Julio Madrigal-Matute; Morten Scheibye-Knudsen; Evandro Fang; Miguel Aon; José A González-Reyes; Sonia Cortassa; Susmita Kaushik; Marta Gonzalez-Freire; Bindi Patel; Devin Wahl; Ahmed Ali; Miguel Calvo-Rubio; María I Burón; Vincent Guiterrez; Theresa M Ward; Hector H Palacios; Huan Cai; David W Frederick; Christopher Hine; Filomena Broeskamp; Lukas Habering; John Dawson; T Mark Beasley; Junxiang Wan; Yuji Ikeno; Gene Hubbard; Kevin G Becker; Yongqing Zhang; Vilhelm A Bohr; Dan L Longo; Placido Navas; Luigi Ferrucci; David A Sinclair; Pinchas Cohen; Josephine M Egan; James R Mitchell; Joseph A Baur; David B Allison; R Michael Anson; José M Villalba; Frank Madeo; Ana Maria Cuervo; Kevin J Pearson; Donald K Ingram; Michel Bernier; Rafael de Cabo Journal: Cell Metab Date: 2016-06-14 Impact factor: 27.287