Journal Home > Volume 6 , Issue 2

Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

Genome-wide studies of time of day in the brain: Design and analysis

Show Author's information Gang Wu1Marc D. Ruben1Yinyeng Lee1Jiajia Li2Michael E. Hughes2John B. Hogenesch1( )
 Divisions of Human Genetics and Immunobiology, Center for Chronobiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way, Cincinnati, OH 45229, U.S.A.
 Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, MO 63310, U.S.A.

Abstract

Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.

Keywords: circadian, transcriptome, rhythmic analysis, brain

References(83)

[1]
MW Young, SA Kay. Time zones: a comparative genetics of circadian clocks. Nat Rev Genet. 2001, 2(9): 702-715.
[2]
PL Lowrey, JS Takahashi. Genetics of circadian rhythms in mammalian model organisms. In The Genetics of Circadian Rhythms. Amsterdam: Elsevier, 2011.
[3]
R Allada, BY Chung. Circadian organization of behavior and physiology in Drosophila. Annu Rev Physiol. 2010, 72: 605-624.
[4]
JA Mohawk, CB Green, JS Takahashi. Central and peripheral circadian clocks in mammals. Annu Rev Neurosci. 2012, 35: 445-462.
[5]
MH Hastings, AB Reddy, ES Maywood. A clockwork web: circadian timing in brain and periphery, in health and disease. Nat Rev Neurosci. 2003, 4(8): 649-661.
[6]
M Stratmann, U Schibler. Properties, entrainment, and physiological functions of mammalian peripheral oscillators. J Biol Rhythms. 2006, 21(6): 494-506.
[7]
E Slat, GM Freeman Jr, ED Herzog. The clock in the brain: neurons, glia, and networks in daily rhythms. Handb Exp Pharmacol. 2013(217): 105-123.
[8]
RJ Konopka, S Benzer. Clock mutants of drosophila melanogaster. Proc Natl Acad Sci U S A. 1971, 68(9): 2112-2116.
[9]
MH Vitaterna, DP King, AM Chang, et al. Mutagenesis and mapping of a mouse gene, Clock, essential for circadian behavior. Science. 1994, 264(5159): 719-725.
[10]
MP Antoch, EJ Song, AM Chang, et al. Functional identification of the mouse circadian Clock gene by transgenic BAC rescue. Cell. 1997, 89(4): 655-667.
[11]
DP King, Y Zhao, AM Sangoram, et al. Positional cloning of the mouse circadian clock gene. Cell. 1997, 89(4): 641-653.
[12]
R Allada, NE White, WV So, et al. A mutant Drosophila homolog of mammalian Clock disrupts circadian rhythms and transcription of period and timeless. Cell. 1998, 93(5): 791-804.
[13]
ZS Sun, U Albrecht, O Zhuchenko, et al. RIGUI, a putative mammalian ortholog of the Drosophila period gene. Cell. 1997, 90(6): 1003-1011.
[14]
H Tei, H Okamura, Y Shigeyoshi, et al. Circadian oscillation of a mammalian homologue of the Drosophila period gene. Nature. 1997, 389(6650): 512-516.
[15]
TA Bargiello, MW Young. Molecular genetics of a biological clock in Drosophila. Proc Natl Acad Sci U S A. 1984, 81(7): 2142-2146.
[16]
TA Bargiello, FR Jackson, MW Young. Restoration of circadian behavioural rhythms by gene transfer in Drosophila. Nature. 1984, 312(5996): 752-754.
[17]
P Reddy, WA Zehring, DA Wheeler, et al. Molecular analysis of the period locus in Drosophila melanogaster and identification of a transcript involved in biological rhythms. Cell. 1984, 38(3): 701-710.
[18]
WA Zehring, DA Wheeler, P Reddy, et al. P-element transformation with period locus DNA restores rhythmicity to mutant, arrhythmic Drosophila melanogaster. Cell. 1984, 39(2 Pt 1): 369-376.
[19]
MJ McDonald, M Rosbash. Microarray analysis and organization of circadian gene expression in Drosophila. Cell. 2001, 107(5): 567-578.
[20]
RA Akhtar, AB Reddy, ES Maywood, et al. Circadian cycling of the mouse liver transcriptome, as revealed by cDNA microarray, is driven by the suprachiasmatic nucleus. Curr Biol. 2002, 12(7): 540-550.
[21]
S Panda, MP Antoch, BH Miller, et al. Coordinated transcription of key pathways in the mouse by the circadian clock. Cell. 2002, 109(3): 307-320.
[22]
KF Storch, O Lipan, I Leykin, et al. Extensive and divergent circadian gene expression in liver and heart. Nature. 2002, 417(6884): 78-83.
[23]
HR Ueda, WB Chen, A Adachi, et al. A transcription factor response element for gene expression during circadian night. Nature. 2002, 418(6897): 534-539.
[24]
ME Hughes, KC Abruzzi, R Allada, et al. Guidelines for genome-scale analysis of biological rhythms. J Biol Rhythms. 2017, 32(5): 380-393.
[25]
M Hatori, S Gill, LS Mure, et al. Lhx1 maintains synchrony among circadian oscillator neurons of the SCN. Elife. 2014, 3: e03357.
[26]
WG Pembroke, A Babbs, KE Davies, et al. Temporal transcriptomics suggest that twin-peaking genes reset the clock. Elife. 2015, 4: e10518.
[27]
M Hughes, L Deharo, SR Pulivarthy, et al. High- resolution time course analysis of gene expression from pituitary. Cold Spring Harb Symp Quant Biol. 2007, 72: 381-386.
[28]
R Zhang, NF Lahens, HI Ballance, et al. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc Natl Acad Sci USA. 2014, 111(45): 16219-16224.
[29]
M Ruben, MD Drapeau, D Mizrak, et al. A mechanism for circadian control of pacemaker neuron excitability. J Biol Rhythms. 2012, 27(5): 353-364.
[30]
E Nagoshi, K Sugino, , et al. Dissecting differential gene expression within the circadian neuronal circuit of Drosophila. Nat Neurosci. 2010, 13(1): 60-68.
[31]
JM Keil, A Qalieh, KY Kwan. Brain transcriptome databases: a user’s guide. J Neurosci. 2018, 38(10): 2399-2412.
[32]
Z Wang, M Gerstein, M Snyder. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10(1): 57-63.
[33]
ME Hughes, L DiTacchio, KR Hayes, et al. Harmonics of circadian gene transcription in mammals. PLoS Genet. 2009, 5(4): e1000442.
[34]
SY Krishnaiah, G Wu, BJ Altman, et al. Clock regulation of metabolites reveals coupling between transcription and metabolism. Cell Metab. 2017, 25(5): 1206.
[35]
SA Wen, DY Ma, M Zhao, et al. Spatiotemporal single-cell analysis of gene expression in the mouse suprachiasmatic nucleus. Nat Neurosci. 2020, in press, .
[36]
JJ Li, GR Grant, JB Hogenesch, et al. Considerations for RNA-seq analysis of circadian rhythms. Meth Enzymol. 2015, 551: 349-367.
[37]
A Pizarro, K Hayer, NF Lahens, et al. CircaDB: a database of mammalian circadian gene expression profiles. Nucleic Acids Res. 2012, 41(D1): D1009-D1013.
[38]
VR Patel, K Eckel-Mahan, P Sassone-Corsi, et al. CircadiOmics: integrating circadian genomics, transcriptomics, proteomics and metabolomics. Nat Methods. 2012, 9(8): 772-773.
[39]
SJ Li, K Shui, Y Zhang, et al. CGDB: a database of circadian genes in eukaryotes. Nucleic Acids Res. 2017, 45(D1): D397-D403.
[40]
XF Li, LS Shi, K Zhang, et al. CirGRDB: a database for the genome-wide deciphering circadian genes and regulators. Nucleic Acids Res. 2018, 46(D1): D64-D70.
[41]
MS Robles, J Cox, M Mann. In-vivo quantitative proteomics reveals a key contribution of post- transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genet. 2014, 10(1): e1004047.
[42]
A Kauffmann, R Gentleman, W Huber. ArrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009, 25(3): 415-416.
[43]
LG Wang, SQ Wang, W Li. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012, 28(16): 2184-2185.
[44]
PY Hsu, SL Harmer. Global profiling of the circadian transcriptome using microarrays. In Methods in Molecular Biology. New York, NY: Springer New York, 2014.
[45]
A Dobin, CA Davis, F Schlesinger, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013, 29(1): 15-21.
[46]
S Anders, PT Pyl, W Huber. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015, 31(2): 166-169.
[47]
B Li, CN Dewey. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12: 323.
[48]
NL Bray, H Pimentel, P Melsted, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016, 34(5): 525-527.
[49]
CJ Doherty, SA Kay. Circadian control of global gene expression patterns. Annu Rev Genet. 2010, 44: 419-444.
[50]
ME Hughes, JB Hogenesch, K Kornacker. JTK_ CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J Biol Rhythms. 2010, 25(5): 372-380.
[51]
R Yang, Z Su. Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation. Bioinformatics. 2010, 26(12): i168-i174.
[52]
RD Yang, C Zhang, Z Su. LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data. Bioinformatics. 2011, 27(7): 1023-1025.
[53]
PF Thaben, PO Westermark. Detecting rhythms in time series with RAIN. J Biol Rhythms. 2014, 29(6): 391-400.
[54]
JA Perea, A Deckard, SB Haase, et al. SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data. BMC Bioinformatics. 2015, 16: 257.
[55]
AL Hutchison, M Maienschein-Cline, AH Chiang, et al. Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. PLoS Comput Biol. 2015, 11(3): e1004094.
[56]
AL Hutchison, R Allada, AR Dinner. Bootstrapping and empirical Bayes methods improve rhythm detection in sparsely sampled data. J Biol Rhythms. 2018, 33(4): 339-349.
[57]
Y Ren, CI Hong, S Lim, et al. Finding clocks in genes: a Bayesian approach to estimate periodicity. Biomed Res Int. 2016, 2016: 3017475.
[58]
F Agostinelli, N Ceglia, B Shahbaba, et al. What time is it? Deep learning approaches for circadian rhythms. Bioinformatics. 2016, 32(19): 3051.
[59]
JJ Hughey, T Hastie, AJ Butte. ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system. Nucleic Acids Res. 2016, 44(8): e80.
[60]
G Wu, RC Anafi, ME Hughes, et al. MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics. 2016, 32(21): 3351-3353.
[61]
H De Los Santos, EJ Collins, C Mann, et al. ECHO: an application for detection and analysis of oscillators identifies metabolic regulation on genome-wide circadian output. Bioinformatics. 2020, 36(3): 773-781.
[62]
A Deckard, RC Anafi, JB Hogenesch, et al. Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data. Bioinformatics. 2013, 29(24): 3174-3180.
[63]
G Wu, J Zhu, J Yu, et al. Evaluation of five methods for genome-wide circadian gene identification. J Biol Rhythms. 2014, 29(4): 231-242.
[64]
EF Glynn, J Chen, AR Mushegian. Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms. Bioinformatics. 2006, 22(3): 310-316.
[65]
A Avizienis, L Chen. On the Implementation of N-version Programming for Software Fault Tolerance during Execution. In Proceedings of COMPSAC 77. 1977:149-155.
[66]
M Carlucci, A Kriščiūnas, HH Li, et al. DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity. Bioinformatics. 2019: btz834.
[67]
PF Thaben, PO Westermark. Differential rhythmicity: detecting altered rhythmicity in biological data. Bioinformatics. 2016, 32(18): 2800-2808.
[68]
R Parsons, R Parsons, N Garner, et al. CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms. Bioinformatics. 2020, 36(4): 1208-1212.
[69]
JM Singer, JJ Hughey. LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data. J Biol Rhythms. 2019, 34(1): 5-18.
[70]
JM Singer, DY Fu, JJ Hughey. Simphony: simulating large-scale, rhythmic data. PeerJ. 2019, 7: e6985.
[71]
G Wu, J Zhu, FH He, et al. Gene and genome parameters of mammalian liver circadian genes (LCGs). PLoS One. 2012, 7(10): e46961.
[72]
Y Benjamini, Y Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc: Ser B Methodol. 1995, 57(1): 289-300.
[73]
A Subramanian, P Tamayo, VK Mootha, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102(43): 15545-15550.
[74]
DW Huang, BT Sherman, QN Tan, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35(suppl_2): W169-W175.
[75]
MV Kuleshov, MR Jones, AD Rouillard, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016, 44(W1): W90-W97.
[76]
R Zhang, AA Podtelezhnikov, JB Hogenesch, et al. Discovering biology in periodic data through phase set enrichment analysis (PSEA). J Biol Rhythms. 2016, 31(3): 244-257.
[77]
MJ Deery, ES Maywood, JE Chesham, et al. Proteomic analysis reveals the role of synaptic vesicle cycling in sustaining the suprachiasmatic circadian clock. Curr Biol. 2009, 19(23): 2031-2036.
[78]
A Videnovic, PC Zee. Consequences of circadian disruption on neurologic health. Sleep Med Clin. 2015, 10(4): 469-480.
[79]
JZ Li, BG Bunney, F Meng, et al. Circadian patterns of gene expression in the human brain and disruption in major depressive disorder. Proc Natl Acad Sci USA. 2013, 110(24): 9950-9955.
[80]
CY Chen, RW Logan, TZ Ma, et al. Effects of aging on circadian patterns of gene expression in the human prefrontal cortex. Proc Natl Acad Sci USA. 2016, 113(1): 206-211.
[81]
ML Seney, K Cahill, JF 3rd Enwright, et al. Diurnal rhythms in gene expression in the prefrontal cortex in schizophrenia. Nat Commun. 2019, 10(1): 3355.
[82]
MD Ruben, JB Hogenesch, DF Smith. Sleep and circadian medicine: time of day in the neurologic clinic. Neurol Clin. 2019, 37(3): 615-629.
[83]
JJ Li, RY Yu, F Emran, et al. Achilles-mediated and sex-specific regulation of circadian mRNA rhythms in drosophila. J Biol Rhythms. 2019, 34(2): 131-143.
File
BSA_2020_9050005-S001.pdf (152.9 KB)
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 13 January 2020
Accepted: 27 February 2020
Published: 31 August 2020
Issue date: June 2020

Copyright

© The authors 2020.

Acknowledgements

This review was inspired by the "Statistical Methods for Time Series Analysis of Rhythms" (SMTSAR) workshop on Society for Research on Biological Rhythms (SRBR) meeting held in 2016 (https://github.com/gangwug/SRBR_SMTSARworkshop2016) and 2018 (https://github.com/ gangwug/SRBR_SMTSARworkshop2018). We thank Tanya Leise for reading through the manuscript and supporting the SMTSAR workshop. We thank Tiago de Andrade, Robert E. Schmidt, Lauren J. Francey, David F. Smith, and organizers of SRBR meetings for supporting the SMTSAR workshop. We also thank all SMTSAR workshops attendees for valuable discussion. This work is supported by the National Institute of Neurological Disorders and Stroke (5R01NS054794-13 to JBH and Andrew Liu), the National Heart, Lung, and Blood Institute (5R01HL138551-02 to Eric Bittman and JBH), and the National Cancer Institute (1R01CA227485-01A1 to Ron Anafi and JBH).

Rights and permissions

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).

Return