Sleep and circadian disorders
Research Interests:
The influence of sleep and circadian biology on physiology and disease.
Key Words:
Sleep, circadian rhythms, bioinformatics, machine learning, mathematical modeling, neurodegeneration, cancer
Description of Research:
Our lab uses techniques from machine learning, engineering, and systems biology to understand how sleep and molecular rhythms influence physiology in the brain and body.
Work in mice, fish, and flies has shown that daily rhythms modulate the expression of thousands of transcripts, proteins, and metabolites. The influence of sleep and wake, while less well characterized, appears similarly profound.
The translation of these findings to medicine has been limited by our inability to collect time course tissue samples from people, especially from sick patients. Even when genes or proteins are found to cycle with time-of-day or sleep/wake, it is often a daunting challenge to understand how these many changes interact to influence physiology.
We have developed methods to extract rhythmic signals from large human tissue databases. In this way we are trying to “fill in the gaps” regarding human circadian molecular physiology – and the changes that occur with illness. We are also trying to better characterize the importance of these rhythms and understand the different ways that they can be disrupted.
Our lab is particularly focused on understanding how molecular rhythms might be disrupted in two very different, but critically important diseases: cancer and neurodegeneration.
Rotation Projects:
Please contact Ron about possible projects.
Selected Publications
Anafi RC, Francey LJ, Hogenesch JB, Kim J: CYCLOPS reveals human transcriptional rhythms in health and disease. Proceedings of the National Academy of Sciences of the United States of America 114(20): 5312-5317, May 2017.
Li SY, Hammarlund JA, Wu G, Lian JW, Howell SJ, Clarke RB, Adamson AD, Gonçalves CF, Hogenesch JB, Anafi RC, Meng QJ.: Tumor circadian clock strength influences metastatic potential and predicts patient prognosis in luminal A breast cancer. Proc Natl Acad Sci U S A 121: e2311854121, Feb 2024.
Anafi RC, Kayser MS, Raizen DM: Exploring phylogeny to find the function of sleep. Nature Reviews. Neuroscience 20(2): 109-116, Feb 2019.
Ruben MD, Wu G, Smith DF, Schmidt RE, Francey LJ, Lee YY, Anafi RC, Hogenesch JB: A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine. Science Translational Medicine 10(458): eaat8806, Sep 2018.
Ben-Zvi D, Meoli L, Abidi WM, Nestoridi E, Panciotti C, Castillo E, Pizarro P, Shirley E, Gourash WF, Thompson CC, Munoz R, Clish CB, Anafi RC, Courcoulas AP, Stylopoulos N: Time-Dependent Molecular Responses Differ between Gastric Bypass and Dieting but Are Conserved Across Species. Cell Metabolism 28(2): 310-323.e6, Aug 2018.
Foteinou PT, Venkataraman A, Francey LJ, Anafi RC, Hogenesch JB, Doyle FJ: Computational and experimental insights into the circadian effects of SIRT1. Proceedings of the National Academy of Sciences of the United States of America 115(45): 11643-11648, Nov 2018.
Krishnaiah SY, Wu G, Altman B, Growe J, Rhoades SD, Coldren F, Venkataraman A, Olarerin-George AO, Francey LJ, Mukherjee S, Girish S, Selby CP, Cal S, Er U, Sianati B, Sengupta A, Anafi RC, Kavakli IH, Sancar A, Baur JA, Dang CV, Hogenesch JB, Weljie AM: Clock regulation of metabolites reveals coupling between transcription and metabolism. Cell Metabolism 25(4): 961-974.e4, Apr 2017.
Arnardottir ES, Nikonova EV, Shockley KR, Podtelezhnikov AA, Anafi RC, Tanis KQ, Maislin G, Stone DJ, Renger JJ, Winrow CJ, Pack AI: Blood-gene expression reveals reduced circadian rhythmicity in individuals resistant to sleep deprivation. Sleep 37(10): 1589-1600, Oct 2014.
Zhang R, Podtelezhnikov AA, Hogenesch JB, Anafi RC: Discovering biology in periodic data through Phase Set Enrichment Analysis (PSEA). Journal of Biological Rhythms 31(3): 244-257, Jun 2016.
Anafi RC, Lee Y, Sato TK, Venkataraman A, Ramanathan C, Kavakli IH, Hughes ME, Baggs JE, Growe J, Liu AC, Kim J, Hogenesch J: Machine learning helps identify CHRONO as a circadian clock component. PLoS Biology 12(4): e1001840, Apr 2014.
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Last updated: 09/17/2024
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