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Analysis of gene regulatory networks in the mammalian circadian rhythm.

Yan J, Wang H, Liu Y, Shao C - PLoS Comput. Biol. (2008)

Bottom Line: Here we try to address these questions by integrating all available circadian microarray data in mammals.We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes.Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

View Article: PubMed Central - PubMed

Affiliation: CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Shanghai, China. junyan@picb.ac.cn

ABSTRACT
Circadian rhythm is fundamental in regulating a wide range of cellular, metabolic, physiological, and behavioral activities in mammals. Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past, the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown. Here we try to address these questions by integrating all available circadian microarray data in mammals. We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues. Comparisons across mouse, rat, rhesus macaque, and human showed that the circadian phases of known key circadian genes were delayed for 4-5 hours in rat compared to mouse and 8-12 hours in macaque and human compared to mouse. A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data. We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes. The analysis of the network structure revealed the paths through which light, food, and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals. Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

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Hierarchical clustering of 21 circadian microarray datasets based on global circadian phase dissimilarities.Datasets are denoted by first author names and tissue types.
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pcbi-1000193-g002: Hierarchical clustering of 21 circadian microarray datasets based on global circadian phase dissimilarities.Datasets are denoted by first author names and tissue types.

Mentions: We clustered the 21 circadian phase datasets using hierarchical clustering. The datasets from the same tissue or biologically closely related tissues were clustered together, suggesting that the differences in circadian phases between tissues resulted from their biological differences (Figure 2). To ensure that these differences between tissues were also reproducible between experiments, we used circular ANOVA to identify the circadian oscillating genes shared between two tissues but associated with significantly different circadian phases between these tissues. There were 12 circadian oscillating genes shared between two SCN datasets and at least two liver datasets. Among them, Per1, Per2, Nr1d2, and Avpr1a showed a significant (p<0.01) advance of about 6 hours in their circadian phases in SCN datasets compared to liver datasets, whereas Dnajb1, Hmgb3, Hsp110, and Pdcd4 showed no significant differences in their circadian phases between SCN and liver (Figure 3). To test if such differences also exist between SCN and whole brain tissues, we also compared SCN with 3 whole brain datasets. There were 12 circadian oscillating genes shared between two SCN datasets and at least two whole brain datasets. Per2, Nr1d2, and Tuba8 again showed a significant advance of about 6 hours in their circadian phases in SCN datasets compared to whole brain datasets, whereas Hmgb3, Hsp110, Sgk, and Fabp7 showed no significant differences in their circadian phases between SCN and whole brain. Further examination validated that the known key circadian genes including Per1, Per2, Cry1, Arntl, Nr1d1, and Nr1d2 all showed around 6 hour advances in circadian phases between SCN and non-SCN tissues in general, whereas heat shock proteins showed consistent circadian phases across all tissues. There were 15 circadian oscillating genes shared between 3 heart datasets including whole heart, atria, and ventricle and at least 3 liver datasets. Comparing the heart datasets with the liver datasets, Bhlhb2 (p<0.001) and Tspan4 (p = 0.006) had circadian phase 5–6 hours earlier in heart than liver whereas Dscr1 (p = 0.002) had circadian phase 8 hours later in heart than liver. Other known key circadian genes such as Per1/Per2, Arntl, and Nr1d1/Nr1d2 showed consistent circadian phases between heart and liver. Comparing the whole brain datasets with the liver datasets, Tfrc, St3gal5, and Tspan4 had circadian phases more than 4 hours earlier in whole brain than liver, whereas Hist1h1c, Tsc22d1, Myo1b, Litaf, and BC004004 had circadian phases more than 4 hours later in whole brain than liver.


Analysis of gene regulatory networks in the mammalian circadian rhythm.

Yan J, Wang H, Liu Y, Shao C - PLoS Comput. Biol. (2008)

Hierarchical clustering of 21 circadian microarray datasets based on global circadian phase dissimilarities.Datasets are denoted by first author names and tissue types.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2543109&req=5

pcbi-1000193-g002: Hierarchical clustering of 21 circadian microarray datasets based on global circadian phase dissimilarities.Datasets are denoted by first author names and tissue types.
Mentions: We clustered the 21 circadian phase datasets using hierarchical clustering. The datasets from the same tissue or biologically closely related tissues were clustered together, suggesting that the differences in circadian phases between tissues resulted from their biological differences (Figure 2). To ensure that these differences between tissues were also reproducible between experiments, we used circular ANOVA to identify the circadian oscillating genes shared between two tissues but associated with significantly different circadian phases between these tissues. There were 12 circadian oscillating genes shared between two SCN datasets and at least two liver datasets. Among them, Per1, Per2, Nr1d2, and Avpr1a showed a significant (p<0.01) advance of about 6 hours in their circadian phases in SCN datasets compared to liver datasets, whereas Dnajb1, Hmgb3, Hsp110, and Pdcd4 showed no significant differences in their circadian phases between SCN and liver (Figure 3). To test if such differences also exist between SCN and whole brain tissues, we also compared SCN with 3 whole brain datasets. There were 12 circadian oscillating genes shared between two SCN datasets and at least two whole brain datasets. Per2, Nr1d2, and Tuba8 again showed a significant advance of about 6 hours in their circadian phases in SCN datasets compared to whole brain datasets, whereas Hmgb3, Hsp110, Sgk, and Fabp7 showed no significant differences in their circadian phases between SCN and whole brain. Further examination validated that the known key circadian genes including Per1, Per2, Cry1, Arntl, Nr1d1, and Nr1d2 all showed around 6 hour advances in circadian phases between SCN and non-SCN tissues in general, whereas heat shock proteins showed consistent circadian phases across all tissues. There were 15 circadian oscillating genes shared between 3 heart datasets including whole heart, atria, and ventricle and at least 3 liver datasets. Comparing the heart datasets with the liver datasets, Bhlhb2 (p<0.001) and Tspan4 (p = 0.006) had circadian phase 5–6 hours earlier in heart than liver whereas Dscr1 (p = 0.002) had circadian phase 8 hours later in heart than liver. Other known key circadian genes such as Per1/Per2, Arntl, and Nr1d1/Nr1d2 showed consistent circadian phases between heart and liver. Comparing the whole brain datasets with the liver datasets, Tfrc, St3gal5, and Tspan4 had circadian phases more than 4 hours earlier in whole brain than liver, whereas Hist1h1c, Tsc22d1, Myo1b, Litaf, and BC004004 had circadian phases more than 4 hours later in whole brain than liver.

Bottom Line: Here we try to address these questions by integrating all available circadian microarray data in mammals.We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes.Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

View Article: PubMed Central - PubMed

Affiliation: CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Shanghai, China. junyan@picb.ac.cn

ABSTRACT
Circadian rhythm is fundamental in regulating a wide range of cellular, metabolic, physiological, and behavioral activities in mammals. Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past, the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown. Here we try to address these questions by integrating all available circadian microarray data in mammals. We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues. Comparisons across mouse, rat, rhesus macaque, and human showed that the circadian phases of known key circadian genes were delayed for 4-5 hours in rat compared to mouse and 8-12 hours in macaque and human compared to mouse. A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data. We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes. The analysis of the network structure revealed the paths through which light, food, and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals. Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

Show MeSH
Related in: MedlinePlus