High-throughput discovery of genetic determinants of circadian misalignment

English version

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: https://doi.org/10.1371/journal.pgen.1008577

Souhrn

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 Slc7a11^tm1a/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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