CoChat
Sign Up
Cited by 143Circadian rhythm and melatoninMetabolism, Diabetes, and CancerAdipose Tissue and Metabolism

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

Transcriptomic analyses reveal rhythmic and CLOCK-driven pathways in human skeletal muscle

Open access
Perrin, Laurent, Loizides-Mangold, Ursula, Chanon, Stéphanie, Gobet, Cédric, Hulo, Nicolas, Isenegger, Laura, Weger, Benjamin D, Migliavacca, Eugenia, Charpagne, Aline, Betts, James A, Walhin, Jean-Philippe, Templeman, Iain, Stokes, Keith, Thompson, Dylan, Tsintzas, Kostas, Robert, Maud, Howald, Cedric, Riezman, Howard, Feige, Jerome N, Karagounis, Leonidas G, Johnston, Jonathan D, Dermitzakis, Emmanouil T, Gachon, Frédéric, Lefai, Etienne, Dibner, Charna·2018·eLife
DOI 10.7554/elife.34114PMC PMC5902165
Open this paper in CoChat — free account

Abstract

Circadian regulation of transcriptional processes has a broad impact on cell metabolism. Here, we compared the diurnal transcriptome of human skeletal muscle conducted on serial muscle biopsies in vivo with profiles of human skeletal myotubes synchronized in vitro. More extensive rhythmic transcription was observed in human skeletal muscle compared to in vitro cell culture as a large part of the in vivo mRNA rhythmicity was lost in vitro. siRNA-mediated clock disruption in primary myotubes significantly affected the expression of ~8% of all genes, with impact on glucose homeostasis and lipid metabolism. Genes involved in GLUT4 expression, translocation and recycling were negatively affected, whereas lipid metabolic genes were altered to promote activation of lipid utilization. Moreover, basal and insulin-stimulated glucose uptake were significantly reduced upon CLOCK depletion. Our findings suggest an essential role for the circadian coordination of skeletal muscle glucose homeostasis and lipid metabolism in humans.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7554/elife.34114

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Rhythmic gene expression in human skeletal muscle. (A) Heat map showing genes rhythmic at the pre-mRNA and mRNA level (R-I.R-E: upper panel), at the pre-mRNA level only (R-I: middle panel), and at the mRNA level only (R-E: lower panel). Standardized relative gene expression is indicated in green (low) and red (high) and ordered by their respective phase. (B) mRNA half-life proxy by exon/intron ratio showing lower stability for genes with rhythmic mRNA (R–E) profiles. (C) Amplitude distribution of genes that are rhythmic only at the mRNA level (R-E, blue), the pre-mRNA level (R-I, red), or rhythmic for both (R-I.R-E). Genes with higher amplitude of transcription at the pre-mRNA level have a higher probability to be rhythmic at the mRNA level (R-I.R-E). (D) Number of genes in each group in relation to the -log10 BH corrected p-value; dashed line indicates threshold of 0.05. (E) Phase distribution at the pre-mRNA and mRNA level for the three groups described in (A). (F) Phase distribution for genes activated by acute muscle exercise (red), inflammation (blue), or both (green). (G) Temporal expression of core clock components, and (H) key muscle transcription factors. N = 10 human muscle biopsy donors. (I) Phase distribution of predicted rhythmic DNA motif activity.
Figure 1. Rhythmic gene expression in human skeletal muscle. (A) Heat map showing genes rhythmic at the pre-mRNA and mRNA level (R-I.R-E: upper panel), at the pre-mRNA level only (R-I: middle panel), and at the mRNA level only (R-E: lower panel). Standardized relative gene expression is indicated in green (low) and red (high) and ordered by their respective phase. (B) mRNA half-life proxy by exon/intron ratio showing lower stability for genes with rhythmic mRNA (R–E) profiles. (C) Amplitude distribution of genes that are rhythmic only at the mRNA level (R-E, blue), the pre-mRNA level (R-I, red), or rhythmic for both (R-I.R-E). Genes with higher amplitude of transcription at the pre-mRNA level have a higher probability to be rhythmic at the mRNA level (R-I.R-E). (D) Number of genes in each group in relation to the -log10 BH corrected p-value; dashed line indicates threshold of 0.05. (E) Phase distribution at the pre-mRNA and mRNA level for the three groups described in (A). (F) Phase distribution for genes activated by acute muscle exercise (red), inflammation (blue), or both (green). (G) Temporal expression of core clock components, and (H) key muscle transcription factors. N = 10 human muscle biopsy donors. (I) Phase distribution of predicted rhythmic DNA motif activity.CC BY · PubMed Central
Figure 1—figure supplement 1. Temporal gene expression in human skeletal muscle. (A) Temporal expression of genes involved in vesicular trafficking; (B) Temporal expression of genes involved in glucose uptake; (C) Temporal expression of genes involved in lipid metabolism analyzed by RNA-seq. N = 10 human muscle biopsy donors.
Figure 1—figure supplement 1. Temporal gene expression in human skeletal muscle. (A) Temporal expression of genes involved in vesicular trafficking; (B) Temporal expression of genes involved in glucose uptake; (C) Temporal expression of genes involved in lipid metabolism analyzed by RNA-seq. N = 10 human muscle biopsy donors.CC BY · PubMed Central
Figure 2. Disruption of the circadian oscillator impacts on functional gene expression hSKM. Differential gene expression analysis between hSKM bearing a disrupted (siCLOCK) or intact (siControl) circadian clock. Comparison of the median gene expression at all analyzed circadian time points between the two groups. A total of 16,776 genes were detected by RNA-seq as expressed above the threshold of counts per million (CPM) >3. (A) Core clock genes; (B) 15,446 genes remained unchanged (dark blue), and 1330 genes exhibited a significantly different expression level upon siCLOCK-mediated clock disruption (light blue), with 588 being up-regulated (orange) and 742 down-regulated (grey) (FDR <0.05 and p-value <0.05). Altered genes comprised those related to glycerophospholipid and triglyceride metabolism, storage and transport (C) inositol phosphate metabolism (D) and sphingolipid metabolism and storage (E). (F) Relative levels of PC, PE, PI, PS, Cer GlcCer, SM, CL and TAG, analyzed by mass spectrometry based lipidomics in hSKM cells transfected with either siControl (orange bar) or siCLOCK (blue bar). Total phosphatidylcholine (PC) and glycosylceramide (GlcCer) levels are significantly decreased or increased, respectively, upon siCLOCK transfection. Data are mean ± SEM, N = 4 (# p-value <0.05). (*) for FDR <0.05, (**) for FDR <0.01, (***) for FDR <0.001.
Figure 2. Disruption of the circadian oscillator impacts on functional gene expression hSKM. Differential gene expression analysis between hSKM bearing a disrupted (siCLOCK) or intact (siControl) circadian clock. Comparison of the median gene expression at all analyzed circadian time points between the two groups. A total of 16,776 genes were detected by RNA-seq as expressed above the threshold of counts per million (CPM) >3. (A) Core clock genes; (B) 15,446 genes remained unchanged (dark blue), and 1330 genes exhibited a significantly different expression level upon siCLOCK-mediated clock disruption (light blue), with 588 being up-regulated (orange) and 742 down-regulated (grey) (FDR <0.05 and p-value <0.05). Altered genes comprised those related to glycerophospholipid and triglyceride metabolism, storage and transport (C) inositol phosphate metabolism (D) and sphingolipid metabolism and storage (E). (F) Relative levels of PC, PE, PI, PS, Cer GlcCer, SM, CL and TAG, analyzed by mass spectrometry based lipidomics in hSKM cells transfected with either siControl (orange bar) or siCLOCK (blue bar). Total phosphatidylcholine (PC) and glycosylceramide (GlcCer) levels are significantly decreased or increased, respectively, upon siCLOCK transfection. Data are mean ± SEM, N = 4 (# p-value <0.05). (*) for FDR <0.05, (**) for FDR <0.01, (***) for FDR <0.001.CC BY · PubMed Central
Figure 2—figure supplement 1. Study design. Primary human myotubes were transfected with 20 nM of non-targeting siRNA (siControl) or with same amount of siCLOCK at day 6 of differentiation. 24 hr following siRNA transfection, in vitro synchronization by 10 µM forskolin pulse during 1 hr was conducted for all the dishes, except for those collected at time point 0 hr. Cells were harvested in duplicate dishes for each time point and for each donor, every 2 hr during 48 hr. Extracted RNA samples were subjected to RNA sequencing analysis or to real-time quantitative PCR (RT-qPCR).
Figure 2—figure supplement 1. Study design. Primary human myotubes were transfected with 20 nM of non-targeting siRNA (siControl) or with same amount of siCLOCK at day 6 of differentiation. 24 hr following siRNA transfection, in vitro synchronization by 10 µM forskolin pulse during 1 hr was conducted for all the dishes, except for those collected at time point 0 hr. Cells were harvested in duplicate dishes for each time point and for each donor, every 2 hr during 48 hr. Extracted RNA samples were subjected to RNA sequencing analysis or to real-time quantitative PCR (RT-qPCR).CC BY · PubMed Central
Figure 2—figure supplement 2. siRNA-mediated CLOCK knockdown leads to a flattening of the Bmal1-luc circadian oscillation amplitude in hSKM. Human primary myoblasts were transduced with lentiviral particles expressing Bmal1-luc and differentiated into myotubes. siControl or siCLOCK transfection was performed 24 hr prior synchronization with forskolin. (A) CLOCK mRNA was measured by RT-qPCR in samples used for RNA-seq and collected at time points 0 hr (before synchronization), 24 hr and 48 hr. Data were normalized to the mean of 9S and HPRT. CLOCK expression was reduced by 82 ± 4% (mean of the three time ± SEM, N = 2, done in duplicates; (***) p-value <0.001) in siCLOCK-transfected cells. (B) Bmal1-luc bioluminescence profile shows an important reduction of amplitude in siCLOCK-transfected myotubes compared to controls. Representative Bmal1-luc oscillation profiles are shown for non-transfected (light blue line), siControl (black line), and siCLOCK (red line) transfected myotubes. Bmal1-luc oscillation profiles were recorded in duplicates (two experiments, one donor per experiment).
Figure 2—figure supplement 2. siRNA-mediated CLOCK knockdown leads to a flattening of the Bmal1-luc circadian oscillation amplitude in hSKM. Human primary myoblasts were transduced with lentiviral particles expressing Bmal1-luc and differentiated into myotubes. siControl or siCLOCK transfection was performed 24 hr prior synchronization with forskolin. (A) CLOCK mRNA was measured by RT-qPCR in samples used for RNA-seq and collected at time points 0 hr (before synchronization), 24 hr and 48 hr. Data were normalized to the mean of 9S and HPRT. CLOCK expression was reduced by 82 ± 4% (mean of the three time ± SEM, N = 2, done in duplicates; (***) p-value <0.001) in siCLOCK-transfected cells. (B) Bmal1-luc bioluminescence profile shows an important reduction of amplitude in siCLOCK-transfected myotubes compared to controls. Representative Bmal1-luc oscillation profiles are shown for non-transfected (light blue line), siControl (black line), and siCLOCK (red line) transfected myotubes. Bmal1-luc oscillation profiles were recorded in duplicates (two experiments, one donor per experiment).CC BY · PubMed Central
Figure 3. Basal and insulin-induced glucose uptake by hSKM is downregulated in the absence of a functional circadian clock. (A) CLOCK mRNA was measured in hSKM cells transfected with siControl or siCLOCK by RT-qPCR and normalized to the mean of 9S and HPRT. CLOCK expression was reduced by 91 ± 2% (mean ± SEM, N = 4; (***) p-value <0.001) in siCLOCK-transfected cells. Protein levels of CLOCK (B) and 14-3-3θ (C) were assessed by western blot. Left panel: representative western blot; right panel protein quantification over all monoplicates (mean ± SEM, N = 5). CLOCK and 14-3-3θ protein levels were reduced by 75 ± 2%, and 28 ± 8%, respectively. (D) Glucose uptake rates (in pmol/mg.min) measured in the absence (basal) or presence (insulin) of insulin (1 hr, 100 nM) in siControl or siCLOCK-transfected cells. Note significant reduction of basal (31 ± 3%) and insulin-stimulated glucose uptake (28 ± 3%). Data are mean ± SEM of four independent experiments using myotubes from four different donors (same as for A-C). (*) p-value <0.05, (**) p-value <0.01, (***) p-value <0.001.
Figure 3. Basal and insulin-induced glucose uptake by hSKM is downregulated in the absence of a functional circadian clock. (A) CLOCK mRNA was measured in hSKM cells transfected with siControl or siCLOCK by RT-qPCR and normalized to the mean of 9S and HPRT. CLOCK expression was reduced by 91 ± 2% (mean ± SEM, N = 4; (***) p-value <0.001) in siCLOCK-transfected cells. Protein levels of CLOCK (B) and 14-3-3θ (C) were assessed by western blot. Left panel: representative western blot; right panel protein quantification over all monoplicates (mean ± SEM, N = 5). CLOCK and 14-3-3θ protein levels were reduced by 75 ± 2%, and 28 ± 8%, respectively. (D) Glucose uptake rates (in pmol/mg.min) measured in the absence (basal) or presence (insulin) of insulin (1 hr, 100 nM) in siControl or siCLOCK-transfected cells. Note significant reduction of basal (31 ± 3%) and insulin-stimulated glucose uptake (28 ± 3%). Data are mean ± SEM of four independent experiments using myotubes from four different donors (same as for A-C). (*) p-value <0.05, (**) p-value <0.01, (***) p-value <0.001.CC BY · PubMed Central
Figure 3—figure supplement 1. TBC1D4/AS160 protein levels are not affected by siCLOCK. Representative western blot for TBC1D4/AS160 in hSKM cells transfected with either siControl or siCLOCK for 72 hr. Quantification of TBC1D4/AS160 protein levels shows no statistically significant difference of upon CLOCK disruption. Data are mean ± SEM (N = 5, monoplicates).
Figure 3—figure supplement 1. TBC1D4/AS160 protein levels are not affected by siCLOCK. Representative western blot for TBC1D4/AS160 in hSKM cells transfected with either siControl or siCLOCK for 72 hr. Quantification of TBC1D4/AS160 protein levels shows no statistically significant difference of upon CLOCK disruption. Data are mean ± SEM (N = 5, monoplicates).CC BY · PubMed Central
Figure 4. Temporal gene expression analysis in human skeletal myotubes bearing a disrupted or functional circadian clock. (A) Total of 12,985 genes were identified by RNA-seq as expressed above log2 RPKM >0. Genes were subjected to the circadian analysis, adapted for high-resolution datasets over 48 hr. Genes were categorized as rhythmic or non-rhythmic (NR) (left diagram) and rhythmic genes (994) were grouped into 15 models (right panel). Genes that were non-rhythmic in either one of the 15 models (11,991 genes) are represented in model 16. (B) Heat maps for genes assigned to models 1 to 4. Relative expression is indicated in green (low) and red (high). (C) Individual temporal expression profiles of core clock genes ARNTL, NR1D1, NR1D2, CRY1, CRY2, PER1, PER2 and PER3 in siControl or siCLOCK-transfected cells. (D) Upper panel: Representative examples for genes assigned to models 1–4. Lower panel: Circadian amplitude quantification of siControl and siCLOCK in models 1–4.
Figure 4. Temporal gene expression analysis in human skeletal myotubes bearing a disrupted or functional circadian clock. (A) Total of 12,985 genes were identified by RNA-seq as expressed above log2 RPKM >0. Genes were subjected to the circadian analysis, adapted for high-resolution datasets over 48 hr. Genes were categorized as rhythmic or non-rhythmic (NR) (left diagram) and rhythmic genes (994) were grouped into 15 models (right panel). Genes that were non-rhythmic in either one of the 15 models (11,991 genes) are represented in model 16. (B) Heat maps for genes assigned to models 1 to 4. Relative expression is indicated in green (low) and red (high). (C) Individual temporal expression profiles of core clock genes ARNTL, NR1D1, NR1D2, CRY1, CRY2, PER1, PER2 and PER3 in siControl or siCLOCK-transfected cells. (D) Upper panel: Representative examples for genes assigned to models 1–4. Lower panel: Circadian amplitude quantification of siControl and siCLOCK in models 1–4.CC BY · PubMed Central
Figure 4—figure supplement 1. Comparison of rhythmic transcript profiles between the two donors. (A) More rhythmic transcripts are found in donor two compared to donor one in both siControl and siCLOCK condition either due to the genetic differences between both donors or due to a better response to the forskolin pulse used for synchronization. (B) Circadian amplitude quantification for the summary of models 1–4 in siControl and siCLOCK. (C) Examples of genes qualified as rhythmic in model 1–4.
Figure 4—figure supplement 1. Comparison of rhythmic transcript profiles between the two donors. (A) More rhythmic transcripts are found in donor two compared to donor one in both siControl and siCLOCK condition either due to the genetic differences between both donors or due to a better response to the forskolin pulse used for synchronization. (B) Circadian amplitude quantification for the summary of models 1–4 in siControl and siCLOCK. (C) Examples of genes qualified as rhythmic in model 1–4.CC BY · PubMed Central
Figure 4—figure supplement 2. Temporal profiles of core clock transcript expression. (A) Expression profile of NPAS2, TEF and BHLHE41 assessed by RNA-seq. (B) ARNTL (BMAL1), NR1D1 (REVERBα) and PER3 measured by RT-qPCR. mRNA levels were analyzed every 4 hr over 48 hr for each donor to confirm the RNA-seq results. Data were normalized to the mean of 9S and HPRT. For donor one data represent monoplicates, whereas for donor two data were done in duplicates. Donor 1: circle; donor 2: triangle; siControl: black line; siCLOCK: red line.
Figure 4—figure supplement 2. Temporal profiles of core clock transcript expression. (A) Expression profile of NPAS2, TEF and BHLHE41 assessed by RNA-seq. (B) ARNTL (BMAL1), NR1D1 (REVERBα) and PER3 measured by RT-qPCR. mRNA levels were analyzed every 4 hr over 48 hr for each donor to confirm the RNA-seq results. Data were normalized to the mean of 9S and HPRT. For donor one data represent monoplicates, whereas for donor two data were done in duplicates. Donor 1: circle; donor 2: triangle; siControl: black line; siCLOCK: red line.CC BY · PubMed Central
Figure 4—figure supplement 3. Genes involved in cell cycle regulation exhibit circadian expression profile in hSKM. CCNB1, CDC6, CDC7, CDC20, CDK1, E2F1, E2F7, MCM2, MCM5, MCM7, PLK1, PRC1, RBL1/p107 and TPX2 expression profiles obtained by RNA-seq. All genes shown were classified in model 1 except for E2F1 and MCM5 (model 3).
Figure 4—figure supplement 3. Genes involved in cell cycle regulation exhibit circadian expression profile in hSKM. CCNB1, CDC6, CDC7, CDC20, CDK1, E2F1, E2F7, MCM2, MCM5, MCM7, PLK1, PRC1, RBL1/p107 and TPX2 expression profiles obtained by RNA-seq. All genes shown were classified in model 1 except for E2F1 and MCM5 (model 3).CC BY · PubMed Central
Figure 5. Comparison between the circadian transcriptome of human skeletal muscle and human primary myotubes. (A) Scatter plot, representing the amplitude of expression in relation to the corrected p-value for genes that were rhythmic in vivo (human muscle biopsies). Genes that were also rhythmic in vitro (hSKM, models 1–4) are colored in red. Blue dots represent genes with a p-value <0.01 and log2 amp >0.5. (B) Phase distribution plot of genes rhythmic in muscle biopsies and primary myotubes shows enrichment at the 04:00 time point. (C) Examples of genes, involved in glucose homeostasis and muscle regeneration, that are rhythmic in vivo and in vitro (RNA-seq data, N = 10).
Figure 5. Comparison between the circadian transcriptome of human skeletal muscle and human primary myotubes. (A) Scatter plot, representing the amplitude of expression in relation to the corrected p-value for genes that were rhythmic in vivo (human muscle biopsies). Genes that were also rhythmic in vitro (hSKM, models 1–4) are colored in red. Blue dots represent genes with a p-value <0.01 and log2 amp >0.5. (B) Phase distribution plot of genes rhythmic in muscle biopsies and primary myotubes shows enrichment at the 04:00 time point. (C) Examples of genes, involved in glucose homeostasis and muscle regeneration, that are rhythmic in vivo and in vitro (RNA-seq data, N = 10).CC BY · PubMed Central
Figure 6. Schema summarizing impact of clock disruption on muscle metabolic function. Clock disruption leads to impaired insulin sensitivity and decrease in glucose uptake (1), causes a dysregulation of genes involved in vesicle trafficking (2) and impacts lipid metabolism and lipid metabolite oscillations (3) as reported in Loizides-Mangold et al. (2017).
Figure 6. Schema summarizing impact of clock disruption on muscle metabolic function. Clock disruption leads to impaired insulin sensitivity and decrease in glucose uptake (1), causes a dysregulation of genes involved in vesicle trafficking (2) and impacts lipid metabolism and lipid metabolite oscillations (3) as reported in Loizides-Mangold et al. (2017).CC BY · PubMed Central
Author response image 1. Comparison of our in vivo vastus lateralis rhythmic dataset with mouse skeletal muscle rhythmic data (Dyar et al., 2014; Zhang et al., 2014). Blue = our in vivo RNAseq data; red = (Dyar et al., 2014) soleus; green = (Zhang et al., 2014). The numbers presented in the Venn diagram correspond to the total number of rhythmically expressed genes for each segment (exons only). For the human in vivo dataset (our work), this corresponds to R–E + R–I.R–E, with the cutoff described in the manuscript (Figure 1). For (Dyar et al., 2014) (microarray) and (Zhang et al., 2014) (microarray) rhythmic genes were identified using a Benjamini–Hochberg q–value <0.2 in the JTK_Cycle algorithm.
Author response image 1. Comparison of our in vivo vastus lateralis rhythmic dataset with mouse skeletal muscle rhythmic data (Dyar et al., 2014; Zhang et al., 2014). Blue = our in vivo RNAseq data; red = (Dyar et al., 2014) soleus; green = (Zhang et al., 2014). The numbers presented in the Venn diagram correspond to the total number of rhythmically expressed genes for each segment (exons only). For the human in vivo dataset (our work), this corresponds to R–E + R–I.R–E, with the cutoff described in the manuscript (Figure 1). For (Dyar et al., 2014) (microarray) and (Zhang et al., 2014) (microarray) rhythmic genes were identified using a Benjamini–Hochberg q–value <0.2 in the JTK_Cycle algorithm.CC BY · PubMed Central
Author response image 2.
Author response image 2. CC BY · PubMed Central
Author response image 3. Examples of the gene expression profiles from our database qualified as circadian by JTK_CYCLE.
Author response image 3. Examples of the gene expression profiles from our database qualified as circadian by JTK_CYCLE.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 1

  1. Bergstrom, 1962, Muscle Electrolytes in Man - Determined by Neutron Activation Analysis on Needle Biopsy Specimens - Study on Normal Subjects, Kidney Patients, and Patients with Chronic Diarrhoea, Scandinavian Journal of Clinical and Laboratory Investigation, 14

Cited by

Sorted by citations

See all 143

Related

See all

Metadata

Authors
Perrin, L., Loizides-Mangold, U., Chanon, S., Gobet, C., Hulo, N., Isenegger, L., Weger, B. D., Migliavacca, E., Charpagne, A., Betts, J. A., Walhin, J.-P., Templeman, I., Stokes, K., Thompson, D., Tsintzas, K., Robert, M., Howald, C., Riezman, H., Feige, J. N., Karagounis, L. G., Johnston, J. D., Dermitzakis, E. T., Gachon, F., Lefai, E., & Dibner, C.
Affiliations
  • University of Geneva (CH)
  • Division of Endocrinology, Diabetes, Hypertension and Nutrition, Department of Internal Medicine Specialties, University Hospital of Geneva, Geneva, Switzerland
  • Inserm (FR)
  • École Polytechnique Fédérale de Lausanne (CH)
  • Nestlé Institute of Health Sciences, Lausanne, Switzerland
  • University of Bath (GB)
  • University of Nottingham (GB)
  • Hôpital Edouard Herriot (FR)
  • Institute of Genetics and Genomics of Geneva, Geneva, Switzerland
  • Nestlé (Switzerland) (CH)
  • University of Surrey (GB)
  • CarMeN Laboratory, INSERM U1060, Oullins, France
Corresponding author
Dibner, Charna
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2018-04-16
Volume
7
Pages
30
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.34114
PMC
PMC5902165
OpenAlex
W2797820730
Cited by
139
References
108
Citations by year
2026: 12 · 2025: 14 · 2024: 24 · 2023: 15 · 2022: 17 · 2021: 16 · 2020: 24 · 2019: 12 · 2018: 5
FWCI
8.71
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
  • Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
  • Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
Funders
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Sinergia Grant No. CRSII3-154405, 31003A-166700)
  • Fondation Privée HUG
  • Fondation Ernst et Lucie Schmidheiny
  • Société Académique de Genève
  • Biotechnology and Biological Sciences Research Council
Topics
  • Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
  • Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
Keywords
circadian rhythm, circadian clock, skeletal muscle, rhythmic gene expression, myotubes, in vitro culture, GLUT4, glucose uptake, insulin signaling, lipid metabolism, glucose homeostasis
MeSH terms
Glucose, Humans, Muscle, Skeletal, Gene Expression Profiling, Lipid Metabolism, Metabolic Networks and Pathways, CLOCK Proteins, Circadian Clocks

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review