CoChat
Sign Up
Cited by 121Genomics and Chromatin DynamicsChromatin Remodeling and CancerPeroxisome Proliferator-Activated Receptors

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Transcription factor assisted loading and enhancer dynamics dictate the hepatic fasting response

Open access
Goldstein, Ido, Baek, Songjoon, Presman, Diego M., Paakinaho, Ville, Swinstead, Erin E., Hager, Gordon L.·2017·Genome Research
DOI 10.1101/gr.212175.116PMC PMC5340970
Open this paper in CoChat — free accountOpen PDF

Abstract

Fasting elicits transcriptional programs in hepatocytes leading to glucose and ketone production. This transcriptional program is regulated by many transcription factors (TFs). To understand how this complex network regulates the metabolic response to fasting, we aimed at isolating the enhancers and TFs dictating it. Measuring chromatin accessibility revealed that fasting massively reorganizes liver chromatin, exposing numerous fasting-induced enhancers. By utilizing computational methods in combination with dissecting enhancer features and TF cistromes, we implicated four key TFs regulating the fasting response: glucocorticoid receptor (GR), cAMP responsive element binding protein 1 (CREB1), peroxisome proliferator activated receptor alpha (PPARA), and CCAAT/enhancer binding protein beta (CEBPB). These TFs regulate fuel production by two distinctly operating modules, each controlling a separate metabolic pathway. The gluconeogenic module operates through assisted loading, whereby GR doubles the number of sites occupied by CREB1 as well as enhances CREB1 binding intensity and increases accessibility of CREB1 binding sites. Importantly, this GR-assisted CREB1 binding was enhancer-selective and did not affect all CREB1-bound enhancers. Single-molecule tracking revealed that GR increases the number and DNA residence time of a portion of chromatin-bound CREB1 molecules. These events collectively result in rapid synergistic gene expression and higher hepatic glucose production. Conversely, the ketogenic module operates via a GR-induced TF cascade, whereby PPARA levels are increased following GR activation, facilitating gradual enhancer maturation next to PPARA target genes and delayed ketogenic gene expression. Our findings reveal a complex network of enhancers and TFs that dynamically cooperate to restore homeostasis upon fasting.

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 · 10.1101/gr.212175.116

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Fasting substantially affects the hepatic chromatin landscape, exposing “fasting-induced enhancers.” (A) Genome browser tracks of the Cyp4a14 locus depicting increases in DNase I hypersensitivity, H3K27 acetylation, and RNA levels upon fasting (24 h; three animals were assayed in each group). (B) Volcano plot of all hepatic DNase I hypersensitive sites. DHS sites within the shaded areas show a significant change in accessibility upon fasting (twofold or greater, adjusted P-value ≤0.05). DHS sites overlapping with fasting-induced H3K27ac sites are depicted by a red X. These DHS sites tend themselves to be enhanced by fasting (mostly to the right). DHS sites overlapping with fasting-repressed H3K27ac sites are depicted by a blue X. These DHS sites tend themselves to be decreased by fasting (mostly to the left). (C,D) DHS (C) and H3K27ac (D) ChIP-seq tag density at fasting-induced enhancers in the fed and fasted states. (E) The number of DHS sites in the vicinity of fasting-induced genes (n = 1055) or unresponsive genes (random set, n = 1055) was plotted as a function of distance from gene transcriptional start site (TSS; plotted in 10-kb bins).
Figure 1. Fasting substantially affects the hepatic chromatin landscape, exposing “fasting-induced enhancers.” (A) Genome browser tracks of the Cyp4a14 locus depicting increases in DNase I hypersensitivity, H3K27 acetylation, and RNA levels upon fasting (24 h; three animals were assayed in each group). (B) Volcano plot of all hepatic DNase I hypersensitive sites. DHS sites within the shaded areas show a significant change in accessibility upon fasting (twofold or greater, adjusted P-value ≤0.05). DHS sites overlapping with fasting-induced H3K27ac sites are depicted by a red X. These DHS sites tend themselves to be enhanced by fasting (mostly to the right). DHS sites overlapping with fasting-repressed H3K27ac sites are depicted by a blue X. These DHS sites tend themselves to be decreased by fasting (mostly to the left). (C,D) DHS (C) and H3K27ac (D) ChIP-seq tag density at fasting-induced enhancers in the fed and fasted states. (E) The number of DHS sites in the vicinity of fasting-induced genes (n = 1055) or unresponsive genes (random set, n = 1055) was plotted as a function of distance from gene transcriptional start site (TSS; plotted in 10-kb bins).CC BY-NC · PubMed Central
Figure 2. An unbiased method for detecting differences in footprinting depth and motif-flanking accessibility reveals the key TFs that bind at fasting-induced enhancers following fasting. (A,B) The number of DNase I cuts (i.e., “cut count”) is collected at and around a motif (±100 bp from motif center) in all motif occurrences within DHS sites. This cut count profile is then compared between the fed and fasted conditions, and a delta value is given for each motif (x-axis in the scatter plot). Then, a log ratio between the observed and expected (due to DNase I cut bias) cut counts at the motif is calculated. This ratio is normalized to provide a reliable footprint depth value that is unaffected by surrounding hypersensitivity (Methods). The delta value between footprint depth in the fasted and fed conditions is then given for each motif (y-axis in the scatter plot). The bigger the size of the circle marking the motif, the deeper the footprint is in the fasted state. Cut count data for B were pooled from three replicates. When single replicates were used, the observed pattern was similar (Supplemental Fig. S1D). (C) Individual normalized footprint depth aggregate plots for the CEBP, CRE, and GRE motifs. Footprint depth is illustrated with horizontal dashed lines: (red) fasted; (blue) fed. (D) Scatter plot depicting changes in footprint depth and hypersensitivity of fasting-related motifs in total liver DHS sites compared to fasting-induced enhancers. (E) Extent of CEBPB, CREB1, and GR binding (measured by ChIP-seq tag density) at fasting-responsive enhancers in liver following fasting (24 h).
Figure 2. An unbiased method for detecting differences in footprinting depth and motif-flanking accessibility reveals the key TFs that bind at fasting-induced enhancers following fasting. (A,B) The number of DNase I cuts (i.e., “cut count”) is collected at and around a motif (±100 bp from motif center) in all motif occurrences within DHS sites. This cut count profile is then compared between the fed and fasted conditions, and a delta value is given for each motif (x-axis in the scatter plot). Then, a log ratio between the observed and expected (due to DNase I cut bias) cut counts at the motif is calculated. This ratio is normalized to provide a reliable footprint depth value that is unaffected by surrounding hypersensitivity (Methods). The delta value between footprint depth in the fasted and fed conditions is then given for each motif (y-axis in the scatter plot). The bigger the size of the circle marking the motif, the deeper the footprint is in the fasted state. Cut count data for B were pooled from three replicates. When single replicates were used, the observed pattern was similar (Supplemental Fig. S1D). (C) Individual normalized footprint depth aggregate plots for the CEBP, CRE, and GRE motifs. Footprint depth is illustrated with horizontal dashed lines: (red) fasted; (blue) fed. (D) Scatter plot depicting changes in footprint depth and hypersensitivity of fasting-related motifs in total liver DHS sites compared to fasting-induced enhancers. (E) Extent of CEBPB, CREB1, and GR binding (measured by ChIP-seq tag density) at fasting-responsive enhancers in liver following fasting (24 h).CC BY-NC · PubMed Central
Figure 3. The transcriptional response to fasting in liver is comprised of two TF modules. (A) Nascent RNA levels of fasting-induced genes in primary hepatocytes following a 3-h treatment with different combinations of glucagon (gluc), corticosterone (cort), and WY-14643 (wy). (B) Time course of fasting-induced genes shows an early induction of gluconeogenic genes and a later induction of FAO/ketogenic genes in liver. (C) Glucose production in primary hepatocytes following treatment with different combinations of glucagon, corticosterone, and WY-14643. (D) Nascent RNA levels of Ppara following corticosterone treatment (3 h) in primary hepatocytes. (E) Nascent RNA levels of Pdk4 in primary hepatocytes following 1, 3, and 8 h of treatment with different combinations of WY-14643 and corticosterone: (*) statistical significance (P ≤ 0.05) compared to a nontreated sample (NT) in each time point. (F) Time course of serum corticosterone and β-hydroxybutyrate levels during fasting: (*) statistical significance (P ≤ 0.05) compared to control values (fed). (n.s.) Not significant.
Figure 3. The transcriptional response to fasting in liver is comprised of two TF modules. (A) Nascent RNA levels of fasting-induced genes in primary hepatocytes following a 3-h treatment with different combinations of glucagon (gluc), corticosterone (cort), and WY-14643 (wy). (B) Time course of fasting-induced genes shows an early induction of gluconeogenic genes and a later induction of FAO/ketogenic genes in liver. (C) Glucose production in primary hepatocytes following treatment with different combinations of glucagon, corticosterone, and WY-14643. (D) Nascent RNA levels of Ppara following corticosterone treatment (3 h) in primary hepatocytes. (E) Nascent RNA levels of Pdk4 in primary hepatocytes following 1, 3, and 8 h of treatment with different combinations of WY-14643 and corticosterone: (*) statistical significance (P ≤ 0.05) compared to a nontreated sample (NT) in each time point. (F) Time course of serum corticosterone and β-hydroxybutyrate levels during fasting: (*) statistical significance (P ≤ 0.05) compared to control values (fed). (n.s.) Not significant.CC BY-NC · PubMed Central
Figure 4. Corticosterone increases the number of CREB1 binding sites and CREB1 binding intensity. (A) Nascent RNA levels of Ppargc1a in primary hepatocytes following 1, 3, and 8 h of treatment with different combinations of glucagon (gluc) and corticosterone (cort): (*) statistical significance (P ≤ 0.05) compared to a nontreated sample (NT) in each time point. (B) Genome browser tracks of the Tat locus depicting increases in CREB1 binding following glucagon treatment (1 h) and cotreatment with corticosterone (1 h) as well as increased accessibility following either fasting or GR activation by dexamethasone (dex). Adrenalectomized mice were treated with dex (1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013). (C,D) Cotreatment with glucagon and corticosterone leads to an increase in the number (C) and intensity (D) of CREB1 binding sites. (E,F) Corticosterone-increased CREB1 binding sites show more CREB1 binding than corticosterone-unaffected sites (measured by ChIP-seq tag density). (**) Statistical significance (P ≤ 0.0001).
Figure 4. Corticosterone increases the number of CREB1 binding sites and CREB1 binding intensity. (A) Nascent RNA levels of Ppargc1a in primary hepatocytes following 1, 3, and 8 h of treatment with different combinations of glucagon (gluc) and corticosterone (cort): (*) statistical significance (P ≤ 0.05) compared to a nontreated sample (NT) in each time point. (B) Genome browser tracks of the Tat locus depicting increases in CREB1 binding following glucagon treatment (1 h) and cotreatment with corticosterone (1 h) as well as increased accessibility following either fasting or GR activation by dexamethasone (dex). Adrenalectomized mice were treated with dex (1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013). (C,D) Cotreatment with glucagon and corticosterone leads to an increase in the number (C) and intensity (D) of CREB1 binding sites. (E,F) Corticosterone-increased CREB1 binding sites show more CREB1 binding than corticosterone-unaffected sites (measured by ChIP-seq tag density). (**) Statistical significance (P ≤ 0.0001).CC BY-NC · PubMed Central
Figure 5. GR binds at corticosterone-increased binding sites and increases their accessibility. (A,B) Corticosterone-increased CREB1 binding sites show more GR binding than corticosterone-unaffected sites (measured by ChIP-seq tag density). (**) Statistical significance (P ≤ 0.0001). (C) The number of GR peaks in the vicinity of cort-unaffected or cort-increased CREB1 peaks was plotted as a function of distance from CREB1 peak center (plotted in 50-bp bins). (D,E) Corticosterone-increased CREB1 binding sites show more DNase I hypersensitivity than corticosterone-unaffected sites in livers from adrenalectomized mice treated with dexamethasone (dex, 1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013) (measured by DNase-seq tag density). (**) Statistical significance (P ≤ 0.0001). (F,G) GR increases DNase I hypersensitivity at fasting-induced enhancers (measured by DNase-seq tag density). Adrenalectomized mice were treated with dexamethasone (dex, 1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013). (**) Statistical significance (P ≤ 0.0001).
Figure 5. GR binds at corticosterone-increased binding sites and increases their accessibility. (A,B) Corticosterone-increased CREB1 binding sites show more GR binding than corticosterone-unaffected sites (measured by ChIP-seq tag density). (**) Statistical significance (P ≤ 0.0001). (C) The number of GR peaks in the vicinity of cort-unaffected or cort-increased CREB1 peaks was plotted as a function of distance from CREB1 peak center (plotted in 50-bp bins). (D,E) Corticosterone-increased CREB1 binding sites show more DNase I hypersensitivity than corticosterone-unaffected sites in livers from adrenalectomized mice treated with dexamethasone (dex, 1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013) (measured by DNase-seq tag density). (**) Statistical significance (P ≤ 0.0001). (F,G) GR increases DNase I hypersensitivity at fasting-induced enhancers (measured by DNase-seq tag density). Adrenalectomized mice were treated with dexamethasone (dex, 1 mg/kg) for 1 h, DNase-seq data were generated and described in Grøntved et al. (2013). (**) Statistical significance (P ≤ 0.0001).CC BY-NC · PubMed Central
Figure 6. GR increases the number and residence time of CREB1 molecules. (A) Summary of single-molecule tracking data. The percentage of the unbound, fast-bound, and slow-bound fractions as well as each fraction's average residence time is depicted under different treatments: (fsk) forskolin; (dex) dexamethasone. (B) Box plot depicting dwell time distributions of CREB1 molecules under different treatments. (*) Statistical significance (P ≤ 0.05) compared to indicated sample. (n.s.) Not significant. (C) A model for hepatic TF dynamics during fasting. The liver is exposed to glucagon in early fasting, leading to CREB1 activation by phosphorylation. At midterm fasting, GR is activated by increasing levels of corticosterone leading to two trajectories: First, GR assists the loading of CREB1 onto gluconeogenic enhancers resulting in synergized gluconeogenic gene expression and glucose production; and second, GR induces the gene level of PPARA which, as fasting persists, promotes a FAO/ketogenic gene program.
Figure 6. GR increases the number and residence time of CREB1 molecules. (A) Summary of single-molecule tracking data. The percentage of the unbound, fast-bound, and slow-bound fractions as well as each fraction's average residence time is depicted under different treatments: (fsk) forskolin; (dex) dexamethasone. (B) Box plot depicting dwell time distributions of CREB1 molecules under different treatments. (*) Statistical significance (P ≤ 0.05) compared to indicated sample. (n.s.) Not significant. (C) A model for hepatic TF dynamics during fasting. The liver is exposed to glucagon in early fasting, leading to CREB1 activation by phosphorylation. At midterm fasting, GR is activated by increasing levels of corticosterone leading to two trajectories: First, GR assists the loading of CREB1 onto gluconeogenic enhancers resulting in synergized gluconeogenic gene expression and glucose production; and second, GR induces the gene level of PPARA which, as fasting persists, promotes a FAO/ketogenic gene program.CC BY-NC · 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

Show all 51 references

Cited by

Sorted by citations

See all 121

Related

Metadata

Authors
Goldstein, I., Baek, S., Presman, D. M., Paakinaho, V., Swinstead, E. E., & Hager, G. L.
Affiliations
National Institutes of Health (US)
ORCID
Venue
Genome Research
Publisher
Cold Spring Harbor Laboratory
Published
2016-12-28
Volume
27
Issue
3
Pages
427-439
ISSN
  • 1088-9051 (ISSN-L)
  • 1549-5469
DOI
10.1101/gr.212175.116
PMC
PMC5340970
OpenAlex
W2566710076
Cited by
121
References
51
Citations by year
2026: 11 · 2025: 9 · 2024: 17 · 2023: 16 · 2022: 15 · 2021: 13 · 2020: 14 · 2019: 11 · 2018: 6 · 2017: 9
Type
posted content
Language
en
Open access
Licence
cc-by-nc
Locations
  • Genome Research (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Americanae (AECID Library) (submittedVersion, cc-by-nc-sa)
  • HighWire Press Open Archive (submittedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-sa)
Funders
  • National Institutes of Health
  • National Cancer Institute
  • Center for Cancer Research
  • Sigrid Jusélius Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Peroxisome Proliferator-Activated Receptors
Keywords
fasting response, hepatic glucose production, ketogenesis, transcription factors, enhancer dynamics, chromatin accessibility, glucocorticoid receptor, CREB1, PPARα, CEBPB, single-molecule tracking, hepatic metabolism
MeSH terms
Animals, Binding Sites, Chromatin, Enhancer Elements, Genetic, Fasting, Glucose, Ketones, Male, Mice, Inbred C57BL, Receptors, Glucocorticoid, Transcriptional Activation, Cyclic AMP Response Element-Binding Protein, Hepatocytes, CCAAT-Enhancer-Binding Protein-beta, Chromatin Assembly and Disassembly, PPAR alpha, Mice

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