Machine Learning Helps Identify CHRONO as a Circadian Clock Component

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 experimental 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. Author Summary Daily rhythms are ever-present in the living world, driving the sleep-wake cycle and many other physiological changes. In the last two decades, several labs have identified clock genes that interact to generate underlying molecular oscillations. However, many aspects of circadian molecular physiology remain unexplained. Here, we used a simple machine learning approach to identify new clock genes by searching the genome for candidate genes that share clock-like features such as cycling, broad-based tissue RNA expression, in vitro circadian activity, genetic interactions, and homology across species. Genes were ranked by their similarity to known clock components and the candidates were screened and validated for evidence of clock function in vitro. One candidate, which we renamed CHRONO (Gm129), interacted with the master regulator of the clock, BMAL1, disrupting its transcriptional activity. We found that Chrono knockout mice had prolonged locomotor activity rhythms, getting up progressively later each day. Our experiments demonstrated that CHRONO interferes with the ability of BMAL1 to recruit CBP, a bona fide histone acetylase and key transcriptional coactivator of the circadian clock.