Translational Profiling of Clock Cells Reveals Circadianly Synchronized Protein Synthesis

University of Geneva, Switzerland
PLoS Biology (Impact Factor: 9.34). 11/2013; 11(11):e1001703. DOI: 10.1371/journal.pbio.1001703
Source: PubMed


Genome-wide studies of circadian transcription or mRNA translation have been hindered by the presence of heterogeneous cell populations in complex tissues such as the nervous system. We describe here the use of a Drosophila cell-specific translational profiling approach to document the rhythmic "translatome" of neural clock cells for the first time in any organism. Unexpectedly, translation of most clock-regulated transcripts-as assayed by mRNA ribosome association-occurs at one of two predominant circadian phases, midday or mid-night, times of behavioral quiescence; mRNAs encoding similar cellular functions are translated at the same time of day. Our analysis also indicates that fundamental cellular processes-metabolism, energy production, redox state (e.g., the thioredoxin system), cell growth, signaling and others-are rhythmically modulated within clock cells via synchronized protein synthesis. Our approach is validated by the identification of mRNAs known to exhibit circadian changes in abundance and the discovery of hundreds of novel mRNAs that show translational rhythms. This includes Tdc2, encoding a neurotransmitter synthetic enzyme, which we demonstrate is required within clock neurons for normal circadian locomotor activity.

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Available from: Yanmei Huang
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    • "Profiling has proven to be increasingly valuable in studies of the translation process, for example, in the discovery of novel open reading frames (ORFs), the determination of elongation rates, the identification of sites of ribosome pausing and in the study of protein folding (for review, see Morris 2009; Weiss and Atkins 2011; Michel and Baranov 2013; Ingolia 2014; Jackson and Standart 2015). It also has broad application in the analysis of global gene expression and has been exploited in studies of infectious diseases (Stern-Ginossar et al. 2012, 2015; Liu et al. 2013; Arias et al. 2014; Caro et al. 2014; Jensen et al. 2014; Muzzey et al. 2014; Vasquez et al. 2014; Yang et al. 2015), cell growth, differentiation and development (Brar et al. 2012; Huang et al. 2013; Lee et al. 2013; Stadler and Fire, 2013; Stumpf et al. 2013; Subramaniam et al. 2013; Baudin-Baillieu et al. 2014; Brubaker et al. 2014; Duncan and Mata 2014; Gonzalez et al. 2014; Hendriks et al. 2014; Katz et al. 2014; Kronja et al. 2014; Schrader et al. 2014; Vaidyanathan et al. 2014; de Klerk et al. 2015), apoptosis (Wiita et al. 2013), mitochondrial gene expression and disease (Rooijers et al. 2013; Williams et al. 2014), cell stress (Gerashenko et al. 2012; Labunskyy et al. 2014; Zid and O'Shea 2014; Sidrauski et al. 2015), cell toxicity (Haft et al. 2014), and cell evolution (Artieri and Fraser 2014; McManus et al. 2014). "
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    ABSTRACT: Ribosome profiling is a technique that permits genome-wide, quantitative analysis of translation and has found broad application in recent years. Here we describe a modified profiling protocol and software package designed to benefit more broadly the translation community in terms of simplicity and utility. The protocol, applicable to diverse organisms, including organelles, is based largely on previously published profiling methodologies, but uses duplex-specific nuclease (DSN) as a convenient, species-independent way to reduce rRNA contamination. We show that DSN-based depletion compares favorably with other commonly used rRNA depletion strategies and introduces little bias. The profiling protocol typically produces high levels of triplet periodicity, facilitating the detection of coding sequences, including upstream, downstream, and overlapping open reading frames (ORFs) and an alternative ribosome conformation evident during termination of protein synthesis. In addition, we provide a software package that presents a set of methods for parsing ribosomal profiling data from multiple samples, aligning reads to coding sequences, inferring alternative ORFs, and plotting average and transcript-specific aspects of the data. Methods are also provided for extracting the data in a form suitable for differential analysis of translation and translational efficiency.
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    • "TRAP has been applied to cell-types in various mouse tissues including brain (Doyle et al., 2008; Heiman et al., 2008; Schmidt et al., 2012; Ainsley et al., 2014), heart (Fang et al., 2013; Zhou et al., 2013), liver (Wilkins et al., 2014), and kidney (Liu et al., 2014). In addition to mice, TRAP has been applied in other species such as drosophila and zebrafish (Thomas et al., 2012; Huang et al., 2013; Tryon et al., 2013). TRAP has the advantages of being more high-throughput than LCM without requiring the dissociation of neurons from intact tissue as needed for FACS. "
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