High-throughput discovery of genetic determinants of circadian misalignment

Abstract

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 PLOS Genetics | https://doi.org/10.1371/journal.pgen. Data Availability Statement:The data underlying the results presented in this study are available from the IMPC consortium (https://www. mousephenotype.org/help/api-access/) or Cambridge-suda genomic resource center (http:// 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. Author summarySynchronization to environmental changes such as day and night cycles and seasonal cycles is critical for survival. Organisms have therefore evolved a specialized circadian system to anticipate and adapt to daily changes in the environment. Loss of synchrony between the internal circadian clock and environment day and night changes is responsible for jet lag, but may also promote sleep disorders, metabolic disorders and many diseases. The availability of large amounts of mouse data from the International Mouse Phenotype Consortium provides new opportunities to identify novel genetic components of mouse behaviour and metabolism. In this study, we performed a high-throughput identification of genetic components of circadian misalignment by developing a machine learning-based algorithm. By analyzing the indirect calorimetry parameters from more than 2000 C57BL/6N mice and mice from 750 mutant lines, we identified 5 genes involved in circadian misalignment of activity and feeding behaviour. Further analyzing genetic knock-out mice for one of these genes, we were able to validate our screening method by functional studies. Our systemic analysis thus paves the way for searching the genetic determinants for circadian misalignment.

Simultaneous Assessment of Circadian Rhythms and Sleep in Mice Using Passive Infrared Sensors: A User's Guide

“…Critically, genetically modified mice provide an ever-expanding resource to understanding the molecular mechanisms underlying these biological processes. Repositories of genetically modified mice have been successfully utilized for large-scale screens for sleep and circadian phenotypes (Banks et al, 2018;Funato et al, 2016;Parsons et al, 2015;Potter et al, 2016;Zhang et al, 2020). In addition, methods such as gene editing allow specific human mutations to be studied in mice to establish their role in sleep or circadian rhythms (Kurien et al, 2019).…”

Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology

“…Construction of such spectral-libraries requires some effort, specialized equipment for fractionation and may not always be possible for samples with low amounts such as primary cells. Library-free approaches would greatly aid to simplify DIA workflows in the future and there are considerable efforts currently being invested into producing prediction tools for MS/MS spectra and retention time 62 .…”

An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling

“…Besides genomic data, many studies rely on indirect measurements like ambulatory recordings of wrist actigraphy and DLMO to predict circadian time and evaluate misalignments [ 175 , 176 ]. Several studies applied indirect and non-invasive measurements to develop a predictive model of phase [ 123 , 124 , 125 , 126 , 127 , 128 , 177 ]. Autoregressive moving average with exogenous inputs (ARMAX) model was developed using a supervised learning approach and predicted the DLMO phase using 24 h data obtained from ambulatory recordings [ 127 ].…”

“…Indirect calorimetry (IC) measurements can also serve to gain insights into circadian rhythms. This method provided detailed information on the energy metabolism of mice, it evaluated energy expenditure through indirect calorimetry by measuring oxygen consumption with an open flow respirometric system [ 177 ]. The International Mouse Phenotyping Consortium (IMPC) provides indirect calorimetric data generated from different knock-out mouse models.…”

“…“Syncscreener” was developed to predict phase using available calorimetric measurements from wild type and genetically altered mice. The algorithm could predict five genes associated with circadian misalignment among 750 mutant lines [ 177 ].…”