High-throughput discovery of genetic determinants of circadian misalignment

Autoři: Tao Zhang aff001;  Pancheng Xie aff001;  Yingying Dong aff001;  Zhiwei Liu aff001;  Fei Zhou aff001;  Dejing Pan aff001;  Zhengyun Huang aff001;  Qiaocheng Zhai aff001;  Yue Gu aff001;  Qingyu Wu aff002;  Nobuhiko Tanaka aff004;  Yuichi Obata aff004;  Allan Bradley aff005;  Christopher J. Lelliott aff005;  ;  Lauryl M. J. Nutter aff006;  Colin McKerlie aff006;  Ann M. Flenniken aff006;  Marie-France Champy aff007;  Tania Sorg aff007;  Yann Herault aff007;  Martin Hrabe De Angelis aff008;  Valerie Gailus Durner aff008;  Ann-Marie Mallon aff010;  Steve D. M. Brown aff010;  Terry Meehan aff011;  Helen E. Parkinson aff011;  Damian Smedley aff012;  K. C. Kent Lloyd aff013;  Jun Yan aff014;  Xiang Gao aff014;  Je Kyung Seong aff015;  Chi-Kuang Leo Wang aff016;  Radislav Sedlacek aff009;  Yi Liu aff017;  Jan Rozman aff008;  Ling Yang aff001;  Ying Xu aff001
Působiště autorů: Cambridge-Suda Genomic Resource Center, Jiangsu Key Laboratory of Neuropsychiatric Diseases, Medical college of Soochow University, Suzhou, Jiangsu, China aff001;  Cyrus Tang Hematology Center, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China aff002;  State Key Laboratory of Radiation Medicine and Prevention, Medical college of Soochow University, Suzhou, China aff003;  RIKEN BioResource Center, Tsukuba, Japan aff004;  The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom aff005;  The Centre for Phenogenomics, Toronto, Canada aff006;  CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), Illkirch, France aff007;  German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany aff008;  Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Vestec, Czech Republic aff009;  Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, United Kingdom aff010;  European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom aff011;  School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom aff012;  School of Medicine and Mouse Biology Program, University of California, Davis, California, United States of America aff013;  SKL of Pharmaceutical Biotechnology and Model Animal Research Center, Collaborative Innovation Center for Genetics and Development, Nanjing Biomedical Research Institute, Nanjing University, Nanjing, China aff014;  College of Veterinary Medicine, Seoul National University, and Korea Mouse Phenotyping Center, Seoul, Republic of Korea aff015;  National Laboratory Animal Center, National Applied Research Laboratories (NARLabs), Taipei, Taiwan aff016;  Department of Physiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America aff017;  German Center for Diabetes Research (DZD), Neuherberg, Germany aff018
Vyšlo v časopise: High-throughput discovery of genetic determinants of circadian misalignment. PLoS Genet 16(1): e32767. doi:10.1371/journal.pgen.1008577
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008577


Circadian systems provide a fitness advantage to organisms by allowing them to adapt to daily changes of environmental cues, such as light/dark cycles. The molecular mechanism underlying the circadian clock has been well characterized. However, how internal circadian clocks are entrained with regular daily light/dark cycles remains unclear. By collecting and analyzing indirect calorimetry (IC) data from more than 2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC), we show that the onset time and peak phase of activity and food intake rhythms are reliable parameters for screening defects of circadian misalignment. We developed a machine learning algorithm to quantify these two parameters in our misalignment screen (SyncScreener) with existing datasets and used it to screen 750 mutant mouse lines from five IMPC phenotyping centres. Mutants of five genes (Slc7a11, Rhbdl1, Spop, Ctc1 and Oxtr) were found to be associated with altered patterns of activity or food intake. By further studying the Slc7a11tm1a/tm1a mice, we confirmed its advanced activity phase phenotype in response to a simulated jetlag and skeleton photoperiod stimuli. Disruption of Slc7a11 affected the intercellular communication in the suprachiasmatic nucleus, suggesting a defect in synchronization of clock neurons. Our study has established a systematic phenotype analysis approach that can be used to uncover the mechanism of circadian entrainment in mice.

Klíčová slova:

Animal behavior – Circadian oscillators – Circadian rhythms – Chronobiology – Machine learning algorithms – Mice – Phenotypes – Random variables


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