Machine Learning Helps Identify CHRONO as a Circadian Clock Component Ron C. Anafi1,2.*, Yool Lee3., Trey K. Sato3., Anand Venkataraman3, Chidambaram Ramanathan4, Ibrahim H. Kavakli5, Michael E. Hughes6, Julie E. Baggs7, Jacqueline Growe1,2, Andrew C. Liu4, Junhyong Kim8, John B. Hogenesch2,3* 1 Division of Sleep Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 2 Center for Sleep and Circadian Neurobiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 3 Department of Pharmacology and the Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 4 Department of Biological Sciences, University of Memphis, Memphis, Tennessee, United States of America, 5 Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey, 6 Department of Biology, University of Missouri–St. Louis, St. Louis, Missouri, United States of America, 7 Department of Pharmacology, Morehouse School of Medicine, Atlanta, Georgia, United States of America, 8 Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America Abstract Over the last decades, researchers have characterized a set of ‘‘clock genes’’ that drive daily rhythms in physiology and behavior. This arduous work has yielded results with far-reaching consequences in metabolic, psychiatric, and neoplastic disorders. Recent attempts to expand our understanding of circadian regulation have moved beyond the mutagenesis screens that identified the first clock components, employing higher throughput genomic and proteomic techniques. In order to further accelerate clock gene discovery, we utilized a computer-assisted approach to identify and prioritize candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experi- mental screen to characterize the top candidates. We found that several physically interact with known clock components in a mammalian two-hybrid screen and modulate in vitro cellular rhythms in an immortalized mouse fibroblast line (NIH 3T3). One candidate, Gene Model 129, interacts with BMAL1 and functionally represses the key driver of molecular rhythms, the BMAL1/CLOCK transcriptional complex. Given these results, we have renamed the gene CHRONO (computationally highlighted repressor of the network oscillator). Bi-molecular fluorescence complementation and co-immunoprecipitation demonstrate that CHRONO represses by abrogating the binding of BMAL1 to its transcriptional co-activator CBP. Most importantly, CHRONO knockout mice display a prolonged free-running circadian period similar to, or more drastic than, six other clock components. We conclude that CHRONO is a functional clock component providing a new layer of control on circadian molecular dynamics. Citation: Anafi RC, Lee Y, Sato TK, Venkataraman A, Ramanathan C, et al. (2014) Machine Learning Helps Identify CHRONO as a Circadian Clock Component. PLoS Biol 12(4): e1001840. doi:10.1371/journal.pbio.1001840 Academic Editor: Ueli Schibler, University of Geneva, Switzerland Received August 27, 2013; Accepted March 7, 2014; Published April 15, 2014 Copyright: ß 2014 Anafi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work is supported by the National Institute of Neurological Disorders and Stroke (1R01NS054794-06 to ACL and JBH), the Defense Advanced Research Projects Agency (DARPA-D12AP00025 to John Harer, Duke University), an American Sleep Medicine Foundation Grant to RCA, and support from the National Institute on Aging (2P01AG017628-11) and the National Heart, Lung, and Blood Institute (5K12HL090021-05). This project is also funded, in part, by the Penn Genome Frontiers Institute under a HRFF grant with the Pennsylvania Department of Health, which disclaims responsibility for any analyses, interpretations or conclusions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Abbreviations: BIFC, bi-molecular fluorescence complementation; Chrono, computationally highlighted repressor of the network oscillator; co-IP, co- immunoprecipitation; D:D, constant darkness; ECDF, empirical cumulative distribution function; FDR, false discovery rate; GFP, green florescent protein; H3–K9, histone H3 lysine 9; HEK 293T, Human Embryonic Kidney 293 cells containing the SV40 T-Antigen; IF, immunofluorescence; L:D, light:dark; NIH 3T3, immortalized mouse fibroblast cell line; NS, nonspecific; PML, promyelocytic leukemia; qPCR, quantitative PCR; SCN, suprachiasmatic nuclei; U2OS, human osteosarcoma cell line; YFP, yellow florescent protein; ZT, zeitgeber time. * E-mail: [email protected] (RCA); [email protected] (JBH) . These authors contributed equally to this work. Introduction Period and Cryptochrome gene families are prominent among these targets, and their products ultimately repress CLOCK/ Circadian rhythms are ubiquitous in daily life, coordinating the BMAL1 activity and their own transcription [4,5]. A second loop sleep–wake cycle along with oscillations in hormone secretion, regulates Bmal1 expression through the opposing actions of the blood pressure, and cognitive function [1,2]. While a central REV–ERB and ROR nuclear receptor protein families [6,7]. master-pacemaker is located in the suprachiasmatic nuclei (SCN) Circadian oscillations are in turn subject to multiple layers of of the hypothalamus, cell autonomous rhythms are generated control. The casein kinase I proteins (CSNK1D and CSNK1E) throughout the body. The CLOCK/BMAL1 transcriptional and the F-box and leucine-rich repeat proteins (FBXL3, FBXL21) complex lies at the core of the molecular clock. These proteins [8–10] regulate the nuclear accumulation and/or stability of clock bind E-box elements in the promoters of target genes [3]. The components, respectively. Moreover, recent evidence highlights PLOS Biology | www.plosbiology.org 1 April 2014 | Volume 12 | Issue 4 | e1001840 Machine Learning Helps Identify A New Clock Component Author Summary Results and Discussion Daily rhythms are ever-present in the living world, driving In order to identify novel ‘‘core clock genes,’’ we considered the sleep–wake cycle and many other physiological changes. physiologically relevant features that define core circadian com- In the last two decades, several labs have identified ‘‘clock ponents: (1) Core clock components cycle with a ,24-h period. (2) genes’’ that interact to generate underlying molecular Core clock gene mutation or knockdown affects circadian behav- oscillations. However, many aspects of circadian molecular ioral rhythms. (3) Core clock genes interact with other core clock physiology remain unexplained. Here, we used a simple genes. (4) Core clock genes are expressed in most tissues. (5) Core ‘‘machine learning’’ approach to identify new clock genes clock genes are phylogenically conserved between vertebrates and by searching the genome for candidate genes that share flies. clock-like features such as cycling, broad-based tissue RNA Importantly, as is demonstrated by our exemplar set of known expression, in vitro circadian activity, genetic interactions, clock genes (Figure 1A), none of these features are absolute require- and homology across species. Genes were ranked by their ments: The canonical circadian gene Clock, for example, does not similarity to known clock components and the candidates cycle robustly in the pituitary [25] or SCN [26]. Individual knock- were screened and validated for evidence of clock func- down of either the Nr1d1 or Nr1d2 genes has minimal phenotypic tion in vitro. One candidate, which we renamed CHRONO effect [27]. Rather, these features lie on a continuum, each lending (Gm129), interacted with the master regulator of the clock, BMAL1, disrupting its transcriptional activity. We found that some support to a given gene having a core circadian function. Chrono knockout mice had prolonged locomotor activity We used published, genome-wide datasets that provide infor- rhythms, getting up progressively later each day. Our mation on each of these features and developed simple, albeit experiments demonstrated that CHRONO interferes with imperfect, metrics to quantify each feature. These metrics were the ability of BMAL1 to recruit CBP, a bona fide histone designed to reward clock-like features. acetylase and key transcriptional coactivator of the circa- dian clock. Core Clock Metrics Cycling. In order to assess transcript cycling, we reanalyzed high-resolution time course microarray data for liver, pituitary, the importance of metabolic cofactors and histone modifiers (e.g., and NIH 3T3 cells [25]. As detailed in the Materials and Methods HDAC3, P300, CBP, SIRT1, and NAMPT) in modulating these section, we combined the p values obtained by evaluating cycling feedback loops. in each tissue to create a single general cycling metric (MCyc) for The understanding of circadian timekeeping has demonstrated each gene. Higher values of MCyc correspond