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1 Transcription Factor Assisted Loading and Enhancer
2 Dynamics Dictate the Hepatic Fasting Response
3
4 Authors: Ido Goldstein1, Songjoon Baek1, Diego M. Presman1*, Ville Paakinaho1*, Erin E. Swinstead1,
5 Gordon L. Hager1
6
7 Affiliations: 1Lab of Receptor Biology and Gene Expression, The National Cancer Institute, The National
8 institutes of Health, Bethesda, Maryland, USA, 20892
9
10 * These authors contributed equally to this study
11
12 Corresponding authors:
13 Gordon L. Hager 14 Lab of Receptor Biology and Gene Expression 15 NCI Center for Cancer Research (NCI/CCR) 16 Bldg. 41 Room B602 17 Bethesda, MD 20892 18 Phone: 301-496-9867, Fax: 301-496-4951 19 ([email protected]) 20 21 Ido Goldstein 22 Lab of Receptor Biology and Gene Expression 23 NCI Center for Cancer Research (NCI/CCR) 24 Bldg. 41 Room B307 25 Bethesda, MD 20892 26 Phone: 301-435-2488, Fax: 301-496-4951 27 ([email protected]) 28
29 Running title: Enhancer-TF dynamics dictate the fasting response
30 Keywords: fasting, transcription factors, chromatin, gluconeogenesis, ketogenesis.
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1 Abstract
2 Fasting elicits transcriptional programs in hepatocytes leading to glucose and ketone production. This
3 transcriptional program is regulated by many transcription factors (TFs). To understands how this
4 complex network regulates the metabolic response to fasting we aimed at isolating the enhancers and
5 TFs dictating it. Measuring chromatin accessibility revealed that fasting massively reorganizes liver
6 chromatin, exposing numerous fasting-induced enhancers. By utilizing computational methods in
7 combination with dissecting enhancer features and TF cistromes, we implicated four key TFs regulating
8 the fasting response: glucocorticoid receptor (GR), cAMP responsive element binding protein 1 (CREB1),
9 peroxisome proliferator activated receptor alpha (PPARA) and CCAAT/enhancer binding protein beta
10 (CEBPB). These TFs regulate fuel production by two distinctly-operating modules, each controlling a
11 separate metabolic pathway. The gluconeogenic module operates through assisted loading whereby GR
12 doubles the number of sites occupied by CREB1 as well as enhances CREB1 binding intensity and
13 increases accessibility of CREB1 binding sites. Importantly, this GR-assisted CREB1 binding was enhancer-
14 selective and did not affect all CREB1-bound enhancers. Single-molecule tracking revealed that GR
15 increases the number and DNA residence time of a portion of chromatin-bound CREB1 molecules. These
16 events collectively result in rapid synergistic gene expression and higher hepatic glucose production.
17 Conversely, the ketogenic module operates via a GR-induced TF cascade whereby PPARA levels are
18 increased following GR activation, facilitating gradual enhancer maturation next to PPARA target genes
19 and delayed ketogenic gene expression. Our findings reveal a complex network of enhancers and TFs
20 that dynamically cooperate to restore homeostasis upon fasting.
21
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1 Introduction
2 Transcriptional activation is facilitated by transcription factor (TF) binding at DNA regulatory
3 elements (i.e. enhancers), eventually increasing rate of gene transcription (Shlyueva et al. 2014). Active
4 enhancers show three key characteristics: (i) an increase in specific histone marks such as H3K27
5 acetylation (Creyghton et al. 2010) (ii) enrichment of TF binding events, and (iii) increased chromatin
6 accessibility, measured by hypersensitivity to DNase digestion followed by sequencing (DNase-seq)
7 (Shlyueva et al. 2014). Much of the accessible chromatin structure of enhancers is established during
8 differentiation, allowing cell-type specific gene expression (Thurman et al. 2012). However, accessibility
9 of enhancers and enhancer marks also dynamically change in terminally differentiated tissues to
10 accommodate signal-dependent gene expression (Grontved et al. 2013; Ostuni et al. 2013; Grontved et
11 al. 2015). Therefore, the established enhancer landscape of the tissue is not static but is rather dynamic
12 and partly mediated by signal-activated TFs affecting chromatin accessibility (Voss and Hager 2014).
13 It is well established that some TF-bound DNA elements are relatively protected from DNase
14 digestion, presumably due to physical hindrance of DNase by the TF (Galas and Schmitz 1978), leading to
15 the presence of ‘TF footprints’ . These footprints are identified genome-wide in DNase hypersensitive
16 sites (DHS) by calculating the relative decrease in DNase digestion at TF motifs (Hesselberth et al. 2009;
17 Sung et al. 2016). Thus, in addition to characterizing enhancers genome-wide, DNase-seq can provide a
18 map of TF footprints within enhancers in an unbiased manner (i.e. not necessitating prior knowledge of
19 an involved TF). Although the identification of TF footprints genome-wide is a valuable and promising
20 technique, it is not amenable for all TFs as some of them do not leave a footprint (Sung et al. 2014).
21 The prevalent dogma assumes continuous TF binding from the point of upstream TF activation
22 to the completion of gene induction, a time period ranging from minutes to hours. Widely used
23 techniques such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) have propagated
24 this concept (Spitz and Furlong 2012). With a static model in mind, it was assumed that many TFs bind
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1 enhancers simultaneously and reside there for long periods of time. Yet, evidence from single-cell
2 imaging experiments have accumulated to show many TFs reside on DNA for only a few seconds, cycling
3 on and off repeatedly following activation (McNally et al. 2000; Bosisio et al. 2006; Sharp et al. 2006).
4 Recently, single-molecule tracking (SMT) allowing imaging of single TFs in real-time was applied to
5 examine TF residence time (Mueller et al. 2013). The handful of TFs tracked to date all exhibit DNA
6 residence times in the ranges of seconds (Chen et al. 2014; Izeddin et al. 2014; Morisaki et al. 2014;
7 Swinstead et al. 2016). This important aspect of TF biology has critical ramifications regarding gene
8 expression. By frequently exchanging with DNA, TFs continuously re-asses their environment and can
9 respond quickly to changes in activating signals, enabling the TF to achieve a fine-tuned transcriptional
10 response (Stavreva et al. 2009).
11 Dynamic binding endows TFs with the ability to indirectly facilitate the binding of other TFs. Two
12 TFs which bind the same DNA element were shown not only to lack competition for DNA binding, but
13 rather augment each other’s binding through increasing DNA accessibility, a phenomenon termed
14 ‘dynamic assisted loading’ (Voss et al. 2011). Such an assisted loading relationship between TFs has been
15 described in several systems (Biddie et al. 2011; Grontved et al. 2013; Miranda et al. 2013; Madsen et al.
16 2014; Soccio et al. 2015; Zhu et al. 2015; Swinstead et al. 2016).
17 Although ample evidence point to the dynamic nature of enhancers and TF activity, how these
18 events come into play following a physiological signal (e.g. hormones, nutritional status) in intact tissues
19 is unexplored. The hepatic response to fasting is an optimal process to study TF dynamics in a
20 physiologically-relevant system for several reasons. (i) The upstream hormonal and metabolic signals
21 affecting the fasting response are well-defined (Corssmit et al. 2001). (ii) The fasting response is heavily
22 regulated at the transcriptional level. TFs from various TF families and classes were documented to
23 regulate fasting-related genes: forkhead box proteins (FoxO1, FoxO3, FoxO4, FoxO6, FoxA2), cAMP
24 responsive element binding proteins (CREB1, CREBH), CCAAT enhancer binding proteins (CEBPA, CEBPB),
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1 nuclear receptors (glucocorticoid receptor – GR, peroxisome proliferator activated receptor alpha –
2 PPARA, thyroid receptor, hepatocyte nuclear factor 4 alpha, retinoic acid receptor, estrogen-related
3 receptor). For a complete list we refer the readers to a recent review (Goldstein and Hager 2015). (iii)
4 Fuel production during fasting is highly dynamic and is temporally organized. The process of
5 gluconeogenesis (i.e. de novo synthesis of glucose) plays a more prominent role in early fasting while
6 ketogenesis (i.e. the production of ketone bodies from fatty-acid oxidation-derived acetyl CoA) becomes
7 the major fuel-producing pathway as fasting continues (Cahill 2006). Thus, during fasting transcriptional
8 regulation occurs that dynamically adjusts to changes in metabolic needs (Bahar Halpern et al. 2015).
9 The TFs involved in regulating the fasting response have been studied mostly with a focus on a
10 single TF’s role. A systematic view of the TF network controlling the fasting response and the dynamics
11 of this network is lacking. Thus, identity of the principal TFs that drive the fasting response, the crosstalk
12 between them, and how they cooperate during fasting is largely unknown. Finally, the regulatory
13 mechanism driving the temporal organization of fuel production (gluconeogenesis first with ketogenesis
14 increasing later) is unexplored.
15
16 Results
17 Fasting massively reorganizes hepatic chromatin accessibility to expose ‘fasting-induced enhancers’
18 To globally evaluate the changes in chromatin landscape following fasting and to map fasting-
19 responsive enhancers in mouse liver, we assessed chromatin accessibility using DNase-seq. We found
20 substantial changes in chromatin accessibility for 3,955 DHS sites (Fig. 1A-B), mostly occurring at
21 intergenic regions (Supplemental Fig. S1A). Specifically, there were 1,481 fasting-induced DHS sites and
22 2,474 fasting-repressed DHS sites following 24 hours of food deprivation (Fig. 1B). We examined
23 whether DNase hypersensitivity patterns are correlated with changes in H3K27 acetylation, a histone
24 modification marking active enhancers (Creyghton et al. 2010). We plotted all fasting-responsive
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1 H3K27ac sites that overlap with DHS sites, fasting-induced H3K27ac sites are shown in red while fasting-
2 repressed H3K27ac sites are shown in blue. Nearly all fasting-induced H3K27ac sites overlapped with
3 fasting-induced DHS, the same pattern was observed for fasting-repressed sites (Fig. 1B). To
4 quantitatively measure a possible correlation between both enhancer features in terms of signal
5 strength, we plotted the overlapping fasting-induced DHS/H3K27ac sites and found the two enhancer
6 marks to be well correlated (Fig. S1B). Moreover, during fasting, H3K27ac is higher in fasting-induced
7 DHS sites compared to fasting-repressed DHS sites (Fig. 1C-D). Such broad changes in accessibility
8 suggest extensive alterations in TF occupancy and binding dynamics at these sites during fasting,
9 possibly leading to a highly modulated transcriptional program.
10 To examine the effect of these alterations in chromatin landscape on gene expression, we
11 measured the changes in hepatic transcriptome following fasting using RNA-seq (Supplemental Table
12 S1). We found that fasting-induced DHS sites are proximally located to fasting-induced genes (Fig. 1E,
13 Supplemental Table S2) and that the DHS signal next to fasting-induced genes is increased following
14 fasting compared to fasting-repressed genes (Fig. S1C). Collectively, the data reveal a network of
15 thousands of loci in mouse liver that contain classic enhancer features (DHS, H3K27ac and proximity to
16 regulated genes). Chromatin accessibility and histone marks are dynamically altered in these loci in
17 response to fasting. Therefore, the loci with increased accessibility were termed ‘fasting-induced
18 enhancers’, the reciprocal loci were termed ‘fasting-repressed enhancers’ (Fig. 1B, shaded areas).
19
20 Footprint depth of specific TFs, the accessibility proximal to them and their binding at fasting-induced
21 enhancers increase upon fasting
22 Having mapped the enhancer regions involved in the fasting response, we sought to identify TFs
23 that regulate fasting-related transcription. Many TFs are known to regulate the fasting response.
24 However, it is not clear which factors play a major role in the response and which factors have a
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1 supporting role (Goldstein and Hager 2015). Thus, instead of focusing on TFs from prior knowledge, we
2 utilized an unbiased method to identify fasting-related TFs. We measured two parameters indicative of
3 TF function: footprint depth at TF motifs and motif-flanking chromatin accessibility. An increase in
4 footprint depth following fasting suggests increased TF binding leading to increased protection from
5 DNase digestion (Gusmao et al. 2016), whereas increased flanking accessibility suggests TF-mediated
6 opening of chromatin (Sherwood et al. 2014) (Fig. 2A).
7 The difference between the fed and fasted conditions in terms of flanking accessibility and
8 footprint depth for all known TF motifs in all DHS sites was determined (Fig. 2B). As expected, the
9 footprint depth (y axis) of most TF motifs as well as the flanking accessibility/hypersensitivity around
10 them (x axis) did not change following fasting and the majority of TF motifs accumulated around the
11 origin. However, four groups of outliers were observed (for statistical characterization of outliers see
12 Supplemental Methods and Supplemental Table S3). A group comprised of CEBP and CEBP-like motifs
13 showed increased footprint depth upon fasting, with no increase in accessibility (‘Group A’). A second
14 group containing GRE and GRE-like motifs showed increased accessibility upon fasting with no change in
15 footprint depth (‘Group B’). A third group comprised of CRE/Jun/ATF motifs (all are highly similar)
16 showed an increase both in footprint depth and accessibility upon fasting (‘Group C’). Individual plots
17 depicting changes in footprint depth (normalized to changes in flanking accessibility) at representative
18 motifs from Groups A-C are shown in Fig. 2C. The CEBP and CRE motifs show deeper footprints in the
19 fasted condition while the GRE motif shows no apparent footprint as previously reported (Sung et al.
20 2014; Sung et al. 2016).
21 Lastly, a group of STAT motifs showed a reciprocal pattern to Group C whereby both footprint
22 depth and accessibility were decreased upon fasting (‘Group D’, Fig. 2B). The decrease in both footprint
23 depth and flanking accessibility around STAT motifs suggests a role for STAT proteins in repressing
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1 fasting-related pathways and is consistent with a recent report of STAT3-dependent suppression of
2 gluconeogenesis (Nie et al. 2009).
3 We next explored the possibility that footprint depth and flanking accessibility are more
4 prominently increased at fasting-induced enhancers compared to total DHS sites. Indeed, at each
5 relevant motif, the footprint became deeper and the surrounding more accessible at fasting-induced
6 enhancers compared to total DHS sites (Fig. 2D). An increase in these parameters suggests amplified
7 binding of associated TFs at those motifs upon fasting. To explore this hypothesis, we performed ChIP-
8 seq following fasting of the three major TFs known to bind these motifs in liver and to be involved in the
9 fasting response – CREB1, GR and CCAAT/enhancer binding protein beta (CEBPB). We compared TF
10 binding at fasting-altered enhancers in the fed and fasted states. Binding of all three TFs was
11 significantly enriched at fasting-induced enhancers compared to fasting-repressed enhancers.
12 Importantly, the three factors showed markedly increased binding at these enhancers following fasting
13 as compared to the fed state (Fig. 2E). In addition to increased binding of CREB1 at fasting-induced
14 enhancers, we performed ChIP-seq for the fasting-activated form of CREB1 (Ser133-phosphorylated
15 CREB1, or pCREB1, Fig. S2A). Similarly to CREB1, we found pCREB1 to bind favorably at fasting-induced
16 enhancers during fasting (Supplemental Fig. S2B).
17 To examine whether the increases in footprint depth and flanking accessibility are indeed
18 directly associated with TF binding, we partitioned genome-wide motif occurrences to TF-bound motifs
19 and unbound motifs (in which a ChIP-seq peak was not called). Flanking accessibility as well as footprint
20 depth at the relevant motifs increased in TF-bound motifs compared to total motifs. In contrast, all
21 unbound motifs presented only marginal increase in the two parameters following fasting and most
22 unbound motifs were not identified as outliers according to our analysis (Supplemental Fig. S2C,
23 Supplemental Table S3).
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1 In line with the findings in Fig. 2, de-novo motif analysis revealed that the most highly-enriched
2 TF binding motif in fasting-repressed enhancers was STAT. Accordingly, the most highly-enriched TF
3 binding motifs in fasting-induced enhancers were CEBP, CREB1 and GR, together with the binding motif
4 for PPARA (Supplemental Fig. S2D). The lack of a footprint at PPAR motifs and only mild changes in
5 accessibility around the motif (Fig. 2B, S2E) explains its absence from the four outlier groups described
6 in Fig. 2B. Nonetheless, PPARA did show increased binding at fasting-induced enhancers compared to
7 fasting-repressed enhancers (Supplemental Fig. S2F). Taken together, these data indicate that a
8 particular, context-dependent set of motifs show a deeper footprint in response to fasting which is
9 associated with increased TF binding. However, some TFs do not leave a detectable footprint but still
10 bind specific regions in a context-dependent manner (i.e. during fasting), leading to chromatin opening
11 at their vicinity. These findings reveal a complex transcriptional response to fasting comprising of several
12 key TFs, raising the possibility of spatial and/or temporal crosstalk between these fasting-related TFs.
13
14 Gene regulation during fasting is governed by two TF modules
15 CREB1 and GR are activated by the major fasting-related hormones glucagon and corticosterone
16 respectively, while PPARA is activated by fatty-acids, whose levels increase in hepatocytes during
17 fasting. CEBPB is mostly considered a critical factor without which the fasting response is significantly
18 impaired but which is constitutively active in contrast to the other three TFs (Goldstein and Hager 2015).
19 Thus, downstream analyses aiming at deciphering the possible crosstalk between the key TFs would
20 focus on the three signal-activated TFs. We first evaluated gene expression following these fasting-
21 related signals. We treated primary mouse hepatocytes with combinations of glucagon, corticosterone
22 and a PPARA agonist (WY-14643) and examined fasting-induced genes from our RNA-seq analysis
23 related to gluconeogenesis, FAO and ketogenesis (Supplemental Table S1). In the event where one TF
24 cooperates with another TF in increasing transcription we would expect a synergistic effect, i.e. an
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1 increase in transcription in the combined treatment which is greater than the sum of each treatment
2 alone. In contrast, when two TFs independently regulate transcription, the combined treatment should
3 amount only to the sum of two single treatments. Glucagon was able to induce known gluconeogenic
4 genes; this induction was synergized by corticosterone but was either unaffected (Ppargc1a) or even
5 slightly reduced (Tat, Pck1) by PPARA. In contrast, GR and PPARA had a synergistic effect on
6 FAO/ketogenic genes with no substantial effect on these targets by glucagon (Fig. 3A, Supplemental Fig.
7 S3A).
8 To evaluate these two expression patterns in mice, animals were fasted for varying durations
9 and gene expression levels were examined. Induction of gluconeogenic genes was evident as soon as 4
10 hours following food removal in line with early activation of gluconeogenesis (Cahill 2006). In contrast,
11 expression of FAO/ketogenic genes only increased at prolonged fasting (24 hrs) when ketones are
12 produced (Fig. 3B, 3F). To correlate these gene expression patterns with fuel production, we measured
13 glucose produced by primary hepatocytes in the presence of fasting-related signals. As expected,
14 glucagon increased glucose production. Corticosterone was not able to increase glucose production
15 alone but significantly augmented it in the presence of glucagon. Activating PPARA had no effect on
16 glucose production under any circumstance (Fig. 3C). These observations are in line with the above
17 findings showing the involvement of GR and CREB1, but not PPARA, in a gluconeogenic gene program.
18 These data suggest that the transcriptional response to fasting is governed by two TF modules. One
19 module is mediated by CREB1-GR crosstalk, resulting in synergistic expression of gluconeogenic genes at
20 short fasting periods. The other module, brought about by GR-PPARA crosstalk, leads to increased
21 FAO/ketogenic gene expression as fasting persists and gluconeogenic precursors are depleted.
22 One possible route by which the GR-PPARA module operates is through a ‘TF cascade’. Namely,
23 one TF increases the level of the second TF thereby indirectly augmenting its activity. This option
24 seemed plausible due to GR being able to induce the expression of the gene encoding PPARA in primary
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1 hepatocytes (Fig. 3D), in agreement with previous studies (Lemberger et al. 1994; Steineger et al. 1994).
2 Furthermore, GR bound at a fasting-induced enhancer within the Ppara gene body upon food removal,
3 suggesting direct regulation of Ppara by GR during fasting (Supplemental Fig. S3B). Evidence to support
4 the TF cascade model came from a time-course experiment wherein the transcript levels of
5 FAO/ketogenic genes were measured. Nascent transcripts rather than mature RNA levels were
6 measured to reflect transcriptional activation and not post-transcriptional events (to achieve this we
7 amplified regions spanning intron-exon junctions). We determined that the levels of FAO/ketogenic
8 genes increase as early as 1 hr of PPARA activation. At this time point, co-treatment with corticosterone
9 did not augment induction. However, following 3 hours of co-treatment, transcript levels increased
10 significantly whereas the levels of transcript in the single treatment condition began to decrease. This
11 pattern was even more pronounced after 8 hours of treatment and in several examined genes it was
12 only apparent at that time point (Fig. 3E, Supplemental Fig. S3C). The expression pattern identified here
13 is consistent with a TF cascade model in which GR gradually affects PPARA activity only at later time
14 points, after PPARA gene induction and protein synthesis have been initiated. While the primary
15 induction in the single treatment wanes quickly, the continuous increase in PPARA in the presence of
16 corticosterone prolongs the effect of WY-14643. However, we cannot exclude that some of the
17 increased PPARA activity is due to corticosterone-dependent increase in endogenous PPARA ligands.
18 The timeline of this TF cascade is strengthened by evidence from measurements in fasted mice.
19 Corticosterone levels started to increase at mid-term fasting (10 hrs), consistent with previous reports
20 (Champy et al. 2004). Conversely, the main ketone body, β-hydroxybutyrate, was not increased until 24
21 hours of fasting accompanied by a further increase in corticosterone and induction of PPARA transcript
22 levels (Fig. 3F, Supplemental Table S1). Thus, corticosterone levels rose several hours prior to
23 ketogenesis and prior to the increase in the levels of PPARA, the major ketogenic TF. We then evaluated
24 enhancer activity using H3K27ac ChIP-seq through the course of fasting. The increase in H3K27ac signal
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1 at fasting-induced enhancers harboring a PPARA motif became more apparent and significant at long-
2 term fasting (24 hrs) compared with mid-term fasting (10 hrs), suggesting increased enhancer activity in
3 prolonged fasting (Supplemental Fig. S3D). Accordingly, the increase in H3K27ac signal next to PPARA
4 target genes markedly increased 24 hours following food removal, with only modest increases at 10
5 hours of fasting (Supplemental Fig. S3E). Taken together, the findings presented in Fig. 3 delineate the
6 progression of the transcriptional response to fasting from a CREB1-GR-based gluconeogenic program to
7 a GR-PPARA FAO/ketogenic program.
8
9 GR assists the loading of CREB1 at multiple genomic loci to augment gene expression
10 To examine the modus operandi of the CREB1-GR module we performed a time-course
11 experiment measuring the nascent transcript levels of CREB1-GR synergistic genes following glucagon
12 alone or in combination with corticosterone. The rapid gene induction (1 hr following glucagon
13 treatment) resembled the pattern observed in the PPARA target gene in the single treatment (Fig. 3E).
14 However, in stark contrast to the delayed synergistic effect seen in the GR-PPARA module, we observed
15 a rapid synergistic effect between glucagon and corticosterone. Transcript levels synergistically rose 1 hr
16 following treatment. In some cases, transcript levels began to decrease already by 3 hrs (Fig. 4A,
17 Supplemental Fig. S4A). This observation suggests that CREB1 and GR directly cooperate to regulate
18 transcription. To examine the nature of that cooperation we performed ChIP-seq of CREB1 in primary
19 hepatocytes following short (1 hr) single and dual treatments. As expected, glucagon-dependent CREB1
20 binding events were observed next to Tat, a CREB1-regulated gene (Fig. 4B). Remarkably, co-treatment
21 with corticosterone (which leads to synergized Tat expression as shown in Fig. 3A) led to a major
22 increase in CREB1 binding upstream of Tat. Moreover, these sites (as well as others) became more
23 accessible following fasting, suggesting similar events take place in the liver during fasting (Fig. 4B,
24 Supplemental Fig. S4B). Similarly to gene expression and glucose production patterns, corticosterone did
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1 not significantly affect CREB1 binding in the absence of glucagon (Supplemental Fig. S4C). To determine
2 whether this is a global phenomenon we performed several genome-wide analyses. First, comparing the
3 total number of CREB1 binding sites between the single and dual treatments revealed that the dual
4 treatment doubled CREB1 binding events (3452 sites in glucagon-treated cells vs. 7081 sites in the dual
5 treatment group, Fig. 4C). In addition to the absolute number of sites, co-treatment led to a 2-fold
6 increase in CREB1 binding intensity at 39% of sites whereas only a negligible 0.3% of sites showed
7 decreased CREB1 binding (Fig. 4D). Furthermore, corticosterone-increased CREB1 sites generally show
8 more CREB1 binding than corticosterone-independent sites, affirming the augmenting effect of
9 corticosterone on CREB1 binding (Fig. 4E-F).
10 Notably, some CREB1-bound loci were unaltered by corticosterone. In accordance, glucagon-
11 induced gluconeogenic genes adjacent to these sites were unaltered by corticosterone (Supplemental
12 Fig. S5A-C). This suggests that GR affects glucagon-dependent transcription only on a subset of
13 enhancers where it assists CREB1 loading, and does not indiscriminately augment CREB1 DNA binding
14 activity. To address that possibility, we performed ChIP-seq of GR in primary hepatocytes. Indeed, GR
15 co-localized with CREB1 at corticosterone-increased CREB1 sites at the Tat locus (Fig. 4B). Globally, GR
16 binding was significantly increased in the vicinity of the 3678 CREB1 sites augmented by corticosterone
17 (i.e. the sites unique to the dual treatment). This was evident both in terms of binding intensity (Fig. 5A-
18 B) and in the number of GR binding events adjacent to CREB1 sites (Fig. 5C). We also found that in liver,
19 corticosterone-increased CREB1 sites are opened following GR activation by dexamethasone (a synthetic
20 GR agonist) more readily than unaffected sites as measured by DNase-seq (Fig. 4B, 5D-E). Linking this
21 effect of GR to fasting, we observed that fasting-induced enhancers also become more accessible by
22 dexamethasone (Fig. F-G).
23 Furthermore, we isolated corticosterone-increased CREB1 sites with the highest GR ChIP-seq
24 signal (n = 1230) and analyzed them for motif enrichment. In further support of an assisted loading
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1 model, the top motif was the CRE (p=10-161) with the GRE ranking second (p=10-152) compared to either
2 total CREB1 binding sites (n = 7130) where CRE was the top motif (p=10-642) and GRE only ranking 24th
3 (p=10-78) or to a random set of enhancers (n = 1230) where GRE was not enriched altogether. These
4 observations further attest to GR’s role in opening fasting-induced enhancers and facilitating CREB1
5 binding and are in agreement with the finding that among all known motifs, the highest change in
6 accessibility following fasting was documented around the GR motif (Fig. 2B).
7 Next, we measured transcript levels in the presence of an inhibitor of GR trans-activation
8 (RU486). The synergistic effect corticosterone has on glucagon-induced genes (Tat, Ppargc1a, Pck1) was
9 completely abolished upon RU486 treatment. Conversely, RU486 did not affect glucagon-induced genes
10 that show no corticosterone-dependent increase in CREB1 binding or synergistic gene expression (G6pc,
11 Nr4a1. Supplemental Fig. S5D). Thus the synergistic effect of corticosterone is mediated by GR and not
12 mineralocorticoid receptor, a different corticosterone-activated TF which is not inhibited by RU486
13 (Cadepond et al. 1997).
14 Collectively, the data suggests a mode of action whereby GR augments CREB1 binding through
15 increasing enhancer accessibility and allowing synergistic gene expression. This dynamic assisted loading
16 mechanism is likely independent of physical interaction between the two TFs because it is enhancer-
17 selective and does not encompass the entirety of CREB1 binding sites. Moreover, GR-dependent
18 increases in chromatin accessibility are abundant in fasting-induced enhancers, causally linking GR to
19 chromatin rearrangements during fasting.
20
21 GR increases the number of bound CREB1 molecules and their DNA residence time
22 The above observations clearly point to a facilitating effect of GR on CREB1 binding, CREB1-
23 dependent gene expression, and glucose production. To examine the real-time dynamics of this
24 relationship, we tracked CREB1 movement at the single-molecule level. We were unable to image single
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1 molecules in primary hepatocytes due to technical reasons (see Supplemental Methods). Thus, we
2 utilized HepG2 cells, which are of hepatic origin and preserve many hepatic-related features including
3 glucagon- and corticosterone-induced gene expression profiles. Employing Halo-Tag-fused CREB1 and
4 the JF549 dye [which covalently binds to the HaloTag (Grimm et al. 2015)], we identified and tracked
5 single CREB1 molecules using highly inclined laminated optical sheet (HILO) illumination (Tokunaga et al.
6 2008). Quantitative analysis of large track sets (Supplemental Fig. S6A) revealed that about half of CREB1
7 molecules are chromatin-bound at a given time. Within that bound population of molecules we
8 observed a continuum of exponentially-distributed DNA residence times. A single exponential model
9 was insufficient to fit the data, while a two component model fits the data with high precision
10 (Supplemental Fig. S6B). This indicates that the DNA-bound population of molecules is divided into two
11 mathematically-distinguished sub-populations, or two fractions: a short-lived (Tns, fast bound) and a
12 longer-lived (Ts, slow bound) fraction. Previous reports suggest that the slow fraction of molecules
13 represents specific binding events associated with enhancers or promoters, while the fast fraction
14 defines non-specific binding to chromatin or a DNA scanning mechanism (Chen et al. 2014; Morisaki et
15 al. 2014; Swinstead et al. 2016).
16 To examine a potential effect of GR on the binding properties of CREB1, we co-treated cells with
17 forskolin (a CREB1 agonist) and dexamethasone and tracked single CREB1 molecules in live cells. These
18 agonists are commonly used in liver cell lines to achieve maximal activity of these two TFs (Goldstein et
19 al. 2013). Indeed, forskolin led to CREB1 phosphorylation as well as to induction of CREB1 target genes
20 while dexamethasone led to GR nuclear localization and induction of GR target genes (Fig. S6C-E). Both
21 the residence time of CREB1 molecules on DNA and the amount of slow-bound molecules increased
22 upon forskolin treatment. Remarkably, co-treatment with dexamethasone led to a further increase in
23 both DNA residence time and the DNA-bound fraction of CREB1 molecules (Fig. 6A-B, Supplemental Fig.
24 S6A-B, Supplemental Movies S1-S3). These real-time observations portray GR as a facilitating factor to
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1 productive CREB1 binding and are in agreement with our findings showing an assisted loading
2 mechanism for the two TFs.
3
4 Discussion
5 Mammals have adapted to periods of nutrient deprivation by activating fuel-producing
6 pathways such as gluconeogenesis and ketogenesis. Both pathways are supported by ATP produced in
7 liver by FAO which also provides precursors for ketogenesis (Goldstein and Hager 2015). A major
8 conclusion drawn from our observations is the significant contribution of enhancer and TF dynamics to
9 these frequently-occurring metabolic events. Upon fasting, a global effect on chromatin landscape was
10 evident with thousands of sites altering their accessibility to TFs, resulting in a prominent effect on the
11 transcriptome. This finding expands beyond the common tendency to focus only on the induction of a
12 few gluconeogenic genes (mainly Pck1 and G6pc) as the main events in fasting-related transcription.
13 We employed several unbiased genomic methodologies to isolate key fasting-related TFs from
14 the chromatin landscape data. We developed a program for detecting differences in TF footprint depth
15 and motif-flanking accessibility. Successful attempts at deriving TF activity from footprint depth
16 (Gusmao et al. 2016) or flanking accessibility (Sherwood et al. 2014) were reported. Our algorithm
17 combines these two attributes to provide a bi-variate prediction for TF activity. Using this program, we
18 found motifs with increased protection from DNase (i.e. deeper footprint) and higher flanking
19 accessibility upon fasting. Footprint detection is a powerful approach because it does not rely on prior
20 knowledge of a specific TF or of a subset of genomic loci. Moreover, this method measures biological
21 properties that are directly indicative of TF function. However, the main pitfall is that not all specifically-
22 bound TFs protect motif DNA from DNase digestion (Sung et al. 2014). Thus, the program we developed
23 also measures changes in motif-flanking accessibility which are thought to result from secondary events
24 following TF binding (e.g. recruitment of chromatin-modifying machinery and histone modifying
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17
1 enzymes (Voss and Hager 2014)). Indeed, using this program, we were able to detect GR, a key fasting-
2 related TF that does not leave a footprint. Combining these two TF-derived parameters provides a motif-
3 wide map of key TFs without relying on prior knowledge of TFs involved in a given pathway.
4 Additionally, we found enriched motifs within fasting-induced enhancers. Motif enrichment is
5 also a useful approach but it is weakened by the need to analyze only a subset of genomic loci (e.g.
6 fasting-induced enhancers) rather than use the entire enhancer landscape as was done in the presented
7 algorithm. These two complementary approaches independently detected three key TFs - CEBPB, CREB1
8 and GR, focusing our attention on them. We also included PPARA (which was only detected through
9 motif enrichment) for further analyses due to its well established role in the hepatic fasting response.
10 These unbiased approaches provided evidence for the involvement of these TFs in the global response
11 to fasting. Attention to these factors is further supported by their strong tendency to bind fasting-
12 induced enhancers and their documented role in certain aspects of the fasting response (Goldstein and
13 Hager 2015).
14 Employing an ex-vivo approach to examine fasting-induced genes, we found two transcription-
15 regulating modules dominated by GR-CREB1 crosstalk (in the gluconeogenic module) and GR-PPARA
16 crosstalk (in the FAO/ketogenic module). The FAO/ketogenic module operates through a TF cascade
17 whereby GR induced the PPARA gene. This leads to a gradual increase in ketogenic enhancer activity and
18 late induction of ketogenic genes, resulting in activation of ketogenesis only in prolonged fasting. In
19 contrast, the gluconeogenic module functions through a rapid and dynamic assisted loading mechanism.
20 Upon activation by corticosterone, GR binds near CREB1 binding sites, making the enhancer chromatin
21 structure more accessible. This results in increased CREB1 binding, synergistic gene induction and higher
22 glucose production.
23 Direct physical interaction between TFs is the commonly-offered model for synergistic gene
24 expression. Our findings would argue against this model. Synergistic gene expression was only evident in
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1 a subset of gluconeogenic genes, and GR-dependent CREB1 assisted loading was only apparent in a
2 subset of CREB1 binding sites. In line with the genomic data, the SMT findings show that activation of GR
3 affected only a fraction of CREB1 molecules rather than shifting the binding kinetics of the entire
4 population of CREB1 molecules. The fact that many CREB1 molecules are unaffected by GR explains the
5 mild (but significant nonetheless) effect of GR on CREB1 real-time dynamics. Taken together, we provide
6 evidence from molecular, biochemical, genomic and live-cell imaging approaches to describe a dynamic
7 assisted loading mechanism resulting in synergistic gene expression and culminating in increased
8 glucose production.
9 From a metabolic standpoint, our findings delineate the transcriptional regulatory events
10 underlying temporally-organized fuel production during fasting. Glucagon is secreted promptly after the
11 decrease of blood glucose levels, leading to CREB1 activation (Altarejos and Montminy 2011). Upon
12 subsequent secretion of corticosterone at mid-term fasting, GR is activated and augments a CREB1-
13 dependent gluconeogenic gene program. Additionally, GR induces PPARA expression and boosts a
14 PPARA-dependent ketogenic gene program which is at play during longer fasting periods (Fig. 6C). In
15 summary, our findings shed light on the intricate regulation of fuel production by hormonal signals and
16 reveal the major role of enhancer and TFs dynamics in homeostatic gene regulation.
17
18 Methods
19 Animals and fasting procedure
20 Male C57BL/6 mice (8-12 weeks old) purchased from Charles River were maintained according to NIH
21 guidelines under 12 hr light-dark cycles (lights on at 6:00AM). In fasting experiments food was removed
22 on 6:00AM. Excluding fasting time-course experiments, the fed group was fed ad libitum. For collection
23 of liver tissue, mice sacrificed by cervical dislocation.
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1 Fasting time course experiments: each group contained at least six mice, error bars in qPCR represent
2 s.e.m. between biological replicates (the s.e.m. is calculated for the normalized average ct value of two
3 technical replicates per mouse). Control mice were fasted and then re-fed for 2 hrs. For collection of
4 liver tissue, mice sacrificed by cervical dislocation. For collection of blood metabolites/hormones mice
5 (n=6, error bars represent s.d.) were anesthetized using isoflurane, blood was extracted by cardiac
6 puncture. Blood left to clot for 30 min. at room temperature followed by incubation at 4oC O/N. Serum
7 isolated after centrifugation (4oC, 20,000 rcf, 1 min). Glucose was measured from 2 μl of serum using
8 OneTouch glucometer (BD). β-hydroxybutyrate was measured from 3 μl of serum using β-
9 hydroxybutyrate assay kit (Sigma, MAK041). Corticosterone was measured from 15 μl of serum using RIA
10 (Ani-Lytics, Gaithersburg, Maryland).
11 Isolation of primary mouse hepatocytes, reagents and western blot procedure are described in the
12 Supplemental Methods.
13 DNase hypersensitivity assay
14 DNase-seq was essentially performed as previously described (Grontved et al. 2012). Briefly, isolated
15 nuclei from fresh livers were treated with DNase and DNA fragments of 100-500 bp from chromatin
16 digested with 40 U/ml DNase were purified using sucrose gradients. DNA was precipitated and
17 reconstituted in nuclease free H2O. Each group contained three mice.
18 Hepatic glucose production
19 One day following isolation, primary hepatocytes (1.7x105 cells/well in 12-well plates) were incubated
20 with ‘starvation media’ (DMEM w/o glucose, pyruvate, glutamine or phenol red. Gibco, A14430-01) for
21 24 hrs. Then, starvation media containing pyruvate (5 mM) and glutamine (5 mM) was added along with
22 glucagon, corticosterone and WY14643 as described in the Figure legend. Following 24 hrs, 40 µl of
23 media was sampled and glucose was measured using the glucose oxidase colorimetric method according
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1 to manufacturer’s instructions (Sigma, GAGO20). Experiment was replicated three independent times.
2 Error bars represent s.d. of three technical replicates.
3 Sequencing, initial data analysis and peak calling
4 Chromatin immunoprecipitation is described in the Supplemental Methods. Sequence reads (50-mer)
5 were generated for ChIP-seq and DNase-seq experiments on the Illumina HiSeq 2000 and Illumina
6 NextSeq 500 platforms at the Advanced Technology Center (ATC), National Cancer Institute (NCI)
7 (Rockville, MD, USA) and the tags were uniquely aligned to the mouse reference genome (NCBI37/mm9
8 assembly). Regions of enriched tags known as “hotspots" (i.e. peaks) were determined using the
9 algorithms and methods previously described with minor modification
10 (http://sourceforge.net/projects/dnase2hotspots) (Baek et al. 2012). Replicate concordant hotspots
11 have been calculated between replicates. For each individual replicate, hotspot regions were called with
12 a minimal threshold (z-score > 2), and initial replicate concordant regions were defined as the
13 intersection of hotspots between replicates. Then, the tags from each replicate are combined and
14 hotspots from the pooled tags are determined using a false discovery rate (FDR) of 0%. Finally, replicate
15 concordant hotspots were defined as the intersection of the FDR-thresholded set of hotspots from the
16 merged data and the initial replicate concordant regions.
17 Tag Density Profiles (used for genome browser screen shots)
18 We constructed a tag density profile of the data by extending each mapped read to the 150-bp length
19 into the 3' direction relative to that strand and counted the distribution of tag counts over the genome.
20 The scale factor is given by 10 million / the total number of non-mitochondrial reads. By multiplying the
21 scale factor, the normalized tag density profiles were obtained.
22 Differential DHS and H3K27ac sites
23 Differentially regulated DHS and H3K27ac sites were identified using DESeq (Anders and Huber 2010)
24 from three biological replicates, fold change ≥ 2, adjusted p value ≤ 0.05 (Supplemental Table S5).
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1 Normalization was performed using the standard normalization provided in the DESeq Bioconductor
2 package (version 1.22.1) by calling the estimateSizeFactors and sizeFactors functions, based on the
3 hypothesis that majority of genes/sites are not differentially expressed, a scaling factor is computed for
4 each lane and raw read counts were multiplied by the associated scaling factor.
5 Analysis of corticosterone-affected CREB1 binding sites
6 Two replicates of glucagon-treated cells and dual treated cells (glucagon + corticosterone) were used for
7 CREB1 ChIP-seq (peak maximal densities were well correlated between replicates as determined with
8 linear regression: R2=0.8217 and 0.8586 respectively). Corticosterone-affected CREB1 sites were
9 determined by a 2-fold change or higher in the site’s maximal density value in the gluc+cort condition
10 compared to the gluc condition (Supplemental Table S4). CREB1 binding sites with maximal tag density
11 lower than 30 were excluded.
12 Differentially regulated genes
13 RNA isolation and quantitative real-time PCR (qPCR) are detailed in the Supplemental Methods.
14 Significant change of RNA expression was analyzed by Cufflinks and CuffDiff (Trapnell et al. 2010;
15 Trapnell et al. 2013) using three replicates (fold change cutoff ≥ 1.5, q value ≤ 0.05, Supplemental Table
16 S1).
17 Scatter Plots
18 - DHS/H3K27ac scatter plot: commonly-enriched sites of DHS and H3K27ac were defined as DHS hotspot
19 sites which are located within 1kb of a H3K27ac site. The values of X axis are the maximum tag counts in
20 commonly enriched DHS sites and the corresponding Y axis values are the maximum tag count of the
21 nearest H3K27ac site. Tag counts were normalized to 10 million reads. R2 value was calculated by
22 Spearman correlation.
23 - CREB1 binding scatter plot: the fold change (glucagon+cort/glucagon) in maximal tag density is plotted
24 for each CREB1 hotspot.
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1 Footprint depth calculation, flanking accessibility and the resulting scatter plot are described in the
2 Supplemental Methods.
3 Volcano Plot
4 The X axis shows the log2 fold change between the fasted and fed conditions of the tag count of the
5 sites. The Y axis shows the negative log10 transformed adjusted p-values of the t-test. DHS sites
6 overlapping H3K27ac sites are color-coded: red – DHS sites overlapping with a fasting-induced H3K27ac
7 site, blue – DHS sites overlapping with a fasting-repressed H3K27ac site. Overlap parameters: the
8 maximal density of a H3K27ac site is 1 kb or closer to the maximal density of a DHS site.
9 Motif analysis, genome annotation and binned genomic distance analysis
10 Analyses performed using HOMER (http://homer.salk.edu/homer/). Analysis described in Supplemental
11 Fig. S2 is de-novo motif analysis, all enriched motifs are presented. Known motif analysis described in
12 Fig. 5 was done on three groups of sites:
13 (1) A group of corticosterone-increased CREB1 sites overlapping with GR binding sites with the highest
14 GR signal (maximal tag density >=10) (n = 1230, ‘Assisted loading cluster’, Supplemental Table S4).
15 (2) Total CREB1 sites (n = 7130)
16 (3) A random set of DHS sites (n = 1230).
17 The distance of GR peaks from CREB1 peaks and DHS sites from TSS was also done using HOMER
18 Box plots and aggregation plots
19 Box plots represent sequenced tag density (per bp) around (+/-100 bp) the center of fasting-altered DHS
20 sites or ChIP-seq peak or around (+/- 50 kb) fasting-altered genes (generated using HOMER). The mean
21 is denoted by a ‘+’ sign. SMT box plots represent the distribution of individual dwell times for each
22 single-molecule in the slow long-lived fraction. The distribution is used to calculate the residence time of
23 CREB1. Outliers, representing molecules with residence time over the one and a half times the
24 interquartile range of the box plot, are not shown.
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1 Aggregation plots represent average sequenced tag density (10 bp bins) around (+/-500 bp) the center
2 of fasting-altered DHS sites or ChIP-seq peak (generated using HOMER).
3 Single molecule tracking and the resulting box plot are described in the Supplemental Methods.
4 Statistics
5 Asterisks denote statistical significance (p ≤ 0.05) compared to control or to indicated sample as
6 determined by an unpaired, two-tailed t-test (n.s. = not significant). p-value in the SMT dwell time
7 comparison represents a two-sample Kolmogorov Smirnov test defined by the brackets.
8 Published data sets
9 The GR-dependent DHS data raw data was obtained from GEO (GSE46047) (Grontved et al. 2013).
10 PPARA ChIP-seq raw data obtained from GEO (GSE35262) (Boergesen et al. 2012).
11 Study approval
12 All animal procedures were approved by the Animal Users and Care Committee, the National Cancer
13 Institute, National Institutes of Health.
14
15 Data access
16 All high throughput sequencing data are summarized in Supplemental Table S6 and have been
17 submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under
18 accession number GSE72087
19
20 Acknowledgements:
21 We thank Lars Grontved, Tom Johnson, Lyuba Varticovski, Tom Misteli and Irwin Arias for their
22 comments and help. This work was supported by the Intramural Research Program of the National
23 Institutes of Health (NIH), the National Cancer Institute (NCI) and the Center for Cancer Research (CCR).
24 V.P. was supported, in part, by the Sigrid Jusélius Foundation.
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1
2 Author contributions:
3 I.G. conceived and performed experiments, performed bioinformatic analyses and wrote the
4 manuscript. S.B. performed bioinformatic analyses. D.M.P. and V.P. performed SMT experiments and
5 analyses. E.E.S. helped in bioinformatic analyses. G.L.H helped in experimental design and wrote the
6 manuscript.
7
8 References:
9 Altarejos JY, Montminy M. 2011. CREB and the CRTC co-activators: sensors for hormonal and metabolic 10 signals. Nat RevMolCell Biol 12: 141-151. 11 Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol 11: 12 R106. 13 Baek S, Sung MH, Hager GL. 2012. Quantitative analysis of genome-wide chromatin remodeling. 14 Methods MolBiol 833: 433-441. 15 Bahar Halpern K, Tanami S, Landen S, Chapal M, Szlak L, Hutzler A, Nizhberg A, Itzkovitz S. 2015. Bursty 16 gene expression in the intact mammalian liver. Mol Cell 58: 147-156. 17 Biddie SC, John S, Sabo PJ, Thurman RE, Johnson TA, Schiltz RL, Miranda TB, Sung MH, Trump S, 18 Lightman SL et al. 2011. Transcription factor AP1 potentiates chromatin accessibility and 19 glucocorticoid receptor binding. MolCell 43: 145-155. 20 Boergesen M, Pedersen TA, Gross B, van Heeringen SJ, Hagenbeek D, Bindesboll C, Caron S, Lalloyer F, 21 Steffensen KR, Nebb HI et al. 2012. Genome-wide profiling of liver X receptor, retinoid X 22 receptor, and peroxisome proliferator-activated receptor alpha in mouse liver reveals extensive 23 sharing of binding sites. Mol Cell Biol 32: 852-867. 24 Bosisio D, Marazzi I, Agresti A, Shimizu N, Bianchi ME, Natoli G. 2006. A hyper-dynamic equilibrium 25 between promoter-bound and nucleoplasmic dimers controls NF-kB-dependent gene activity. 26 EMBO J 25: 798-810. 27 Cadepond F, Ulmann A, Baulieu EE. 1997. RU486 (mifepristone): mechanisms of action and clinical uses. 28 Annual review of medicine 48: 129-156. 29 Cahill GF, Jr. 2006. Fuel metabolism in starvation. Annual review of nutrition 26: 1-22. 30 Champy MF, Selloum M, Piard L, Zeitler V, Caradec C, Chambon P, Auwerx J. 2004. Mouse functional 31 genomics requires standardization of mouse handling and housing conditions. Mammalian 32 genome : official journal of the International Mammalian Genome Society 15: 768-783. 33 Chen J, Zhang Z, Li L, Chen BC, Revyakin A, Hajj B, Legant W, Dahan M, Lionnet T, Betzig E et al. 2014. 34 Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156: 1274- 35 1285. 36 Corssmit EP, Romijn JA, Sauerwein HP. 2001. Review article: Regulation of glucose production with 37 special attention to nonclassical regulatory mechanisms: a review. Metabolism: clinical and 38 experimental 50: 742-755.
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
25
1 Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton 2 GM, Sharp PA et al. 2010. Histone H3K27ac separates active from poised enhancers and predicts 3 developmental state. ProcNatlAcadSciUSA 107: 21931-21936. 4 Galas DJ, Schmitz A. 1978. DNAse footprinting: a simple method for the detection of protein-DNA 5 binding specificity. Nucleic Acids Res 5: 3157-3170. 6 Goldstein I, Hager GL. 2015. Transcriptional and chromatin regulation during fasting - The genomic era. 7 Trends Endocrinol Metab 26: 699-710. 8 Goldstein I, Yizhak K, Madar S, Goldfinger N, Ruppin E, Rotter V. 2013. p53 promotes the expression of 9 gluconeogenesis-related genes and enhances hepatic glucose production. Cancer Metab 1: 9. 10 Grimm JB, English BP, Chen J, Slaughter JP, Zhang Z, Revyakin A, Patel R, Macklin JJ, Normanno D, Singer 11 RH et al. 2015. A general method to improve fluorophores for live-cell and single-molecule 12 microscopy. Nat Methods. 13 Grontved L, Bandle R, John S, Baek S, Chung HJ, Liu Y, Aguilera G, Oberholtzer C, Hager GL, Levens D. 14 2012. Rapid genome-scale mapping of chromatin accessibility in tissue. Epigenetics Chromatin 5: 15 1-12. 16 Grontved L, John S, Baek S, Liu Y, Buckley JR, Vinson C, Aguilera G, Hager GL. 2013. C/EBP maintains 17 chromatin accessibility in liver and facilitates glucocorticoid receptor recruitment to steroid 18 response elements. EMBO J 32: 1568-1583. 19 Grontved L, Waterfall JJ, Kim DW, Baek S, Sung MH, Zhao L, Park JW, Nielsen R, Walker RL, Zhu YJ et al. 20 2015. Transcriptional activation by the thyroid hormone receptor through ligand dependent 21 receptor recruitment and chromatin remodeling. Nat Commun 6: 7048. 22 Gusmao EG, Allhoff M, Zenke M, Costa IG. 2016. Analysis of computational footprinting methods for 23 DNase sequencing experiments. Nat Methods 13: 303-309. 24 Hesselberth JR, Zhang Z, Sabo PJ, Chen X, Sandstrom R, Reynolds.A.P, Thurman RE, Neph S, Kuehn MS, 25 Noble WS et al. 2009. Global mapping of protein-DNA interactions in vivo by digital genomic 26 footprinting. NatMethods 6: 283-289. 27 Izeddin I, Recamier V, Bosanac L, Cisse II, Boudarene L, Dugast-Darzacq C, Proux F, Benichou O, Voituriez 28 R, Bensaude O et al. 2014. Single-molecule tracking in live cells reveals distinct target-search 29 strategies of transcription factors in the nucleus. Elife 3: e02230. 30 Lemberger T, Staels B, Saladin R, Desvergne B, Auwerx J, Wahli W. 1994. Regulation of the peroxisome 31 proliferator-activated receptor alpha gene by glucocorticoids. J Biol Chem 269: 24527-24530. 32 Madsen MS, Siersbaek R, Boergesen M, Nielsen R, Mandrup S. 2014. Peroxisome Proliferator-Activated 33 Receptor gamma and C/EBPalpha Synergistically Activate Key Metabolic Adipocyte Genes by 34 Assisted Loading. Mol Cell Biol 34: 939-954. 35 McNally JG, Mueller WG, Walker D, Wolford RG, Hager GL. 2000. The glucocorticoid receptor: Rapid 36 exchange with regulatory sites in living cells. Science 287: 1262-1265. 37 Miranda TB, Voss TC, Sung MH, Baek S, John S, Hawkins M, Grontved L, Schiltz RL, Hager GL. 2013. 38 Reprogramming of the chromatin landscape: interplay of the estrogen and glucocorticoid 39 receptors at the genomic level. Cancer Res 73: 5130-5139. 40 Morisaki T, Muller WG, Golob N, Mazza D, McNally JG. 2014. Single-molecule analysis of transcription 41 factor binding at transcription sites in live cells. Nat Commun 5: 4456. 42 Mueller F, Stasevich TJ, Mazza D, McNally JG. 2013. Quantifying transcription factor kinetics: at work or 43 at play? Crit Rev Biochem Mol Biol 48: 492-514. 44 Nie Y, Erion DM, Yuan Z, Dietrich M, Shulman GI, Horvath TL, Gao Q. 2009. STAT3 inhibition of 45 gluconeogenesis is downregulated by SirT1. Nat Cell Biol 11: 492-500. 46 Ostuni R, Piccolo V, Barozzi I, Polletti S, Termanini A, Bonifacio S, Curina A, Prosperini E, Ghisletti S, 47 Natoli G. 2013. Latent enhancers activated by stimulation in differentiated cells. Cell 152: 157- 48 171.
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
26
1 Sharp ZD, Mancini MG, Hinojos CA, Dai F, Berno V, Szafran AT, Smith KP, Lele TT, Ingber DE, Mancini MA. 2 2006. Estrogen-receptor-alpha exchange and chromatin dynamics are ligand- and domain- 3 dependent. J Cell Sci 119: 4101-4116. 4 Sherwood RI, Hashimoto T, O'Donnell CW, Lewis S, Barkal AA, van Hoff JP, Karun V, Jaakkola T, Gifford 5 DK. 2014. Discovery of directional and nondirectional pioneer transcription factors by modeling 6 DNase profile magnitude and shape. Nat Biotechnol 32: 171-178. 7 Shlyueva D, Stampfel G, Stark A. 2014. Transcriptional enhancers: from properties to genome-wide 8 predictions. Nat RevGenet 15: 272-286. 9 Soccio RE, Chen ER, Rajapurkar SR, Safabakhsh P, Marinis JM, Dispirito JR, Emmett MJ, Briggs ER, Fang B, 10 Everett LJ et al. 2015. Genetic Variation Determines PPARgamma Function and Anti-diabetic 11 Drug Response In Vivo. Cell 162: 33-44. 12 Spitz F, Furlong EE. 2012. Transcription factors: from enhancer binding to developmental control. 13 NatRevGenet 13: 613-626. 14 Stavreva DA, Wiench M, John S, Conway-Campbell BL, McKenna MA, Pooley JR, Johnson TA, Voss TC, 15 Lightman SL, Hager GL. 2009. Ultradian hormone stimulation induces glucocorticoid receptor- 16 mediated pulses of gene transcription. NatCell Biol 11: 1093-1102. 17 Steineger HH, Sorensen HN, Tugwood JD, Skrede S, Spydevold O, Gautvik KM. 1994. Dexamethasone and 18 insulin demonstrate marked and opposite regulation of the steady-state mRNA level of the 19 peroxisomal proliferator-activated receptor (PPAR) in hepatic cells. Hormonal modulation of 20 fatty-acid-induced transcription. Eur J Biochem 225: 967-974. 21 Sung M-H, Baek S, Hager GL. 2016. Genome-wide footprinting: ready for prime time? Nat Meth 13: 222- 22 228. 23 Sung MH, Guertin MJ, Baek S, Hager GL. 2014. DNase footprint signatures are dictated by factor 24 dynamics and DNA sequence. MolCell 56: 275-285. 25 Swinstead Erin E, Miranda Tina B, Paakinaho V, Baek S, Goldstein I, Hawkins M, Karpova Tatiana S, Ball D, 26 Mazza D, Lavis Luke D et al. 2016. Steroid Receptors Reprogram FoxA1 Occupancy through 27 Dynamic Chromatin Transitions. Cell 165: 593-605. 28 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang 29 H, Vernot B et al. 2012. The accessible chromatin landscape of the human genome. Nature 489: 30 75-82. 31 Tokunaga M, Imamoto N, Sakata-Sogawa K. 2008. Highly inclined thin illumination enables clear single- 32 molecule imaging in cells. Nat Methods 5: 159-161. 33 Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. 2013. Differential analysis of gene 34 regulation at transcript resolution with RNA-seq. Nat Biotechnol 31: 46-53. 35 Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. 36 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and 37 isoform switching during cell differentiation. Nat Biotechnol 28: 511-515. 38 Voss TC, Hager GL. 2014. Dynamic regulation of transcriptional states by chromatin and transcription 39 factors. NatRevGenet 15: 69-81. 40 Voss TC, Schiltz RL, Sung MH, Yen PM, Stamatoyannopoulos JA, Biddie SC, Johnson TA, Miranda TB, John 41 S, Hager GL. 2011. Dynamic exchange at regulatory elements during chromatin remodeling 42 underlies assisted loading mechanism. Cell 146: 544-554. 43 Zhu B, Gates LA, Stashi E, Dasgupta S, Gonzales N, Dean A, Dacso CC, York B, O'Malley BW. 2015. 44 Coactivator-Dependent Oscillation of Chromatin Accessibility Dictates Circadian Gene Amplitude 45 via REV-ERB Loading. Mol Cell 60: 769-783. 46
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1 Figure legends:
2 Fig. 1: Fasting majorly affects hepatic chromatin landscape, exposing ‘fasting-induced enhancers’.
3 A. Genome browser tracks of the Cyp4a14 locus depicting increases in DNase hypersensitivity, H3K27
4 acetylation and RNA levels upon fasting (24 hrs) (3 animals were assayed in each group).
5 B. Volcano plot of all hepatic DNase hypersensitive sites. DHS sites within the shaded areas show a
6 significant change in accessibility upon fasting (≥ 2 fold, adjusted p-value ≤ 0.05). DHS sites overlapping
7 with fasting-induced H3K27ac sites are depicted by red X. These DHS sites tend themselves to be
8 enhanced by fasting (mostly in the right-hand side area). DHS sites overlapping with fasting-repressed
9 H3K27ac sites are depicted by blue X. These DHS sites tend themselves to be decreased by fasting
10 (mostly in the left-hand side area).
11 C+D. DHS (C) and H3K27ac (D) ChIP-seq tag density at fasting-induced enhancers in the fed and fasted
12 states.
13 E. The number of DHS sites in the vicinity of fasting-induced genes (n = 1055) or unresponsive genes
14 (random set, n= 1055) was plotted as a function of distance from gene transcriptional start site (TSS,
15 plotted in 10kb bins).
16
17 Fig. 2: An unbiased method for detecting differences in footprinting depth and motif-flanking
18 accessibility reveals the key TFs that bind at fasting-induced enhancers following fasting
19 A+B. The number of DNase cuts (i.e. ‘cut count’) is collected at and around a motif (+/- 100 bp from
20 motif center) in all motif occurrences within DHS sites. This cut count profile is then compared between
21 the fed and fasted conditions and a delta value is given for each motif (x axis in scatter plot). Then, a log
22 ratio between the observed and expected (due to DNase cut bias) cut counts at the motif is calculated.
23 This ratio is normalized to provide a reliable footprint depth value which is unaffected by surrounding
24 hypersensitivity (for details see Methods). The delta value between footprint depth in the fasted and fed
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1 conditions is then given for each motif (y axis in scatter plot). The bigger the size of the circle marking
2 the motif the deeper the footprint is in the fasted state. Cut count data for B were pooled from three
3 replicates. When single replicates were used, the observed pattern was similar (Fig. S1D).
4 C. Individual normalized footprint depth aggregate plots for the CEBP, CRE and GRE motifs. Footprint
5 depth is illustrated with horizontal dashed lines (red for fasted, blue for fed).
6 D. Scatter plot depicting changes in footprint depth and hypersensitivity of fasting-related motifs in total
7 liver DHS sites compared to fasting-induced enhancers.
8 E. Extent of CEBPB, CREB1 and GR binding (measured by ChIP-seq tag density) at fasting-responsive
9 enhancers in liver following fasting (24 hrs).
10
11 Fig. 3: The transcriptional response to fasting in liver is comprised of two TF modules
12 A. Nascent RNA levels of fasting-induced genes in primary hepatocytes following a 3 hr treatment with
13 different combinations of glucagon (gluc), corticosterone (cort) and WY-14643 (wy).
14 B. Time course of fasting-induced genes shows an early induction of gluconeogenic genes and a later
15 induction of FAO/ketogenic genes in liver.
16 C. Glucose production in primary hepatocytes following treatment with different combinations of
17 glucagon (gluc), corticosterone (cort) and WY-14643 (wy).
18 D. Nascent RNA levels of Ppara following corticosterone treatment (3 hrs) in primary hepatocytes.
19 E. Nascent RNA levels of Pdk4 in primary hepatocytes following 1,3 and 8 hrs of treatment with different
20 combinations of WY-14643 (wy) and corticosterone (cort). Asterisks denote statistical significance (p ≤
21 0.05) compared to a non-treated sample (NT) in each time point.
22 F. Time course of serum corticosterone and β-hydroxybutyrate levels during fasting. Asterisks denote
23 statistical significance (p ≤ 0.05) compared to control values (fed).
24
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
29
1 Fig. 4: corticosterone increases the number of CREB1 binding sites and CREB1 binding intensity
2 A. Nascent RNA levels of Ppargc1a in primary hepatocytes following 1,3 and 8 hrs of treatment with
3 different combinations of glucagon (gluc) and corticosterone (cort). Asterisks denote statistical
4 significance (p ≤ 0.05) compared to a non-treated sample (NT) in each time point.
5 B. Genome browser tracks of the Tat locus depicting increases in CREB1 binding following glucagon
6 treatment (1 hr) and co-treatment with corticosterone (1 hr) as well as increased accessibility following
7 either fasting or GR activation by dexamethasone (dex). Adrenalectomized mice were treated with dex
8 (1mg/kg) for 1 hr, DNase-seq data generated and described in (Grontved et al. 2013)
9 C+D. Co-treatment with glucagon and corticosterone leads to an increase in the number (C) and
10 intensity (D) of CREB1 binding sites.
11 E+F Corticosterone-increased CREB1 binding sites show more CREB1 binding than corticosterone-
12 unaffected sites (measured by ChIP-seq tag density).
13
14 Fig. 5: GR binds at corticosterone-increased binding sites and increases their accessibility.
15 A+B. Corticosterone-increased CREB1 binding sites show more GR binding than corticosterone-
16 unaffected sites (measured by ChIP-seq tag density).
17 C. The number of GR peaks in the vicinity of cort-unaffected or cort-increased CREB1 peaks was plotted
18 as a function of distance from CREB1 peak center (plotted in 50 bp bins).
19 D+E. Corticosterone-increased CREB1 binding sites show more DNase hypersensitivity than
20 corticosterone-unaffected sites in livers from adrenalectomized mice treated with dexamethasone (dex,
21 1mg/kg) for 1 hr, DNase-seq data generated and described in (Grontved et al. 2013) (measured by
22 DNase-seq tag density).
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
30
1 F+G. GR increases DNase hypersensitivity at fasting-induced enhancers (measured by DNase-seq tag
2 density). Adrenalectomized mice were treated with dexamethasone (dex, 1mg/kg) for 1 hr, DNase-seq
3 data generated and described in (Grontved et al. 2013).
4
5 Fig. 6: GR increases the number and residence time of CREB1 molecules
6 A. Summary of single-molecule tracking data. The percent of the unbound, fast-bound and slow-bound
7 fractions as well as each fraction’s average residence time is depicted under different treatments (fsk =
8 forskolin, dex = dexamethasone).
9 B. Boxplot depicting dwell time distributions of CREB1 molecules under different treatments.
10 C. A model for hepatic TF dynamics during fasting. The liver is exposed to glucagon in early fasting,
11 leading to CREB1 activation by phosphorylation. At mid-term fasting GR is activated by increasing levels
12 of corticosterone leading to two trajectories: (1) GR assists the loading of CREB1 onto gluconeogenic
13 enhancers resulting in synergized gluconeogenic gene expression and glucose production. (2) GR induces
14 the gene level of PPARA which, as fasting persists, promotes a FAO/ketogenic gene program.
15
16
C A DHS sequenced RNA H3K27ac DNase tags per site per bp fasted fasted fasted fed fed fed 10 12 0 2 4 6 8 -2 induced enhancer center(kb) 850 - 850 - 40 - 40 - 80 - 80 - distance from fasting- 0 _ 1 _ 1 _ 1 _ 1 _ 0 _ 20 kb -1 Cyp4a14 Downloaded from 0 1 fed fasted genome.cshlp.org 2 onOctober6,2021-Publishedby
H3K27ac ChIP sequenced D tags per site per bp B 0 1 2 3 4 -2 induced enhancer center(kb) -log10 p-value (fasted DHS / fed DHS) 10.0 7.5 0.0 2.5 5.0 distance from fasting- fasting-repressed -1 −4 2,474 fasting-decreased DHSs (fold>2, p<0.05) log2 foldchange(fasted enhancers X 0 Cold SpringHarborLaboratoryPress X X X −2 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 1 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X fed fasted X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 2 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X DHS X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X E X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X fasting-induced X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
number of fasting-induced X X X X X X X X X X X X X X X X X X X X X X X X X X enhancers X X X X X X X /fed X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 2 X X X X X X DHS sites X X X X X X X X X X X X X X X X X 100 150 X X X X X X X 50 X X X X X 0 DHS X X X X
0-10 X ) distance from 4
11-20 1,481 fasting-enhanced DHSs (fold>2, p<0.05) fasting- DHS
21-30 sitewithoverlapping enhanced fasting-induced unresponsive
TSS (kb) 31-40 K27acsite
41-50 TSS TSS Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
A B Group A DNase I hypersensitive site Cebpa deeper Cebp footprint in fasting Group C TF Aare higher Jundm2 0.2 Hlf accessibility Atf1 Atf1 Cebp:Ap1 Cut DNA in fasting JunD fragments c-Jun:Cre Creb1
Pax7 HNF6 Cre 0.1 Pax7 Nfil3 Sr y SoDbx1x21Pou3f1 Pou2f1 Pax6 Pou3f4So x14 Lmx1b Ho xb7Zfp187 log Ho xa4 So x17 Barx2 Pdx1 Tlx22 Ar id5aHlx1 Cphx Group B Ho xd8Ho xc6 Hnf1 Pou1f1 Lhx5Lhx3 Nkx6-1Lhx1 Alx3 Hdx Hnf1a Pou4f3Dbx2 Nkx6-1Dlx1 Barx1Ddit3:Cebpa Ho xa10Ho xb8Ho xa6 PhoEn1Dlx2x2a Pax2 Ho xc8 HoEsx1Nkx2-5xc4 Arx Lmx1a Nkx6-3Pou3f2 Lhx9 PProp1ou3f3 ratio SoHox1 xa7 HoHoxa7Hoxc5Msx1xd12 Pax7HO 4 XA2 GRE Lhx3 Car t1 Gmeb1 Pax7 Vsx1Foxd3Hmx3 Lhx2 Ho xd13PhoHox2bxb4 Jun:Ap1Ap1 TBP Ho xb6Hmx2 Zfp128 HoVax2xc10 Lbx2 P300 Barhl2Dlx3Dlx4Isl2 Meo Nobox1Pbx1Pbx1x Bbx Uncx4.1Cdx2Otp PHmx1ax4 Ho xd10 Ho xb13Ho xd1 Lhx6FONkx1-2XI1 Bsx Hif1b Pitx2HoHoxd11xc11TBPRax Klf4 Max Hnf1b Rho x6 SoHoGsh2Cdx2Sox8xa1Hox5Homezxa3 Brca1 HoFHooxl1xc9Hoxc13xb5SoBarhl1Prrx1x13En2Cutl1 Pou6f1 PR Ar id3a HoGscxc9So x12 FPrrx2oxq1 TLX1::NFIC Ar Obo x5 Ho xd3 Sho x2 Nrf2 Ap1 Klf4 GRE Obo x5 Otx2Ho xa11Alx4 SO X9Hlxb9 Isgf3g So x18Zfp105 CHRFOIrx4XAHo1:ARxa2 Nf1 Ap1:IsreNf-E2 Mef2aPou2f1OboHoCdx1xb9x6Otx1Msx3Foxk1MafkIpf1Maff Myb l1 Myc:MaxRfx4 Eklf fasted SrfHoCarxa5PFot1ou6f1xj1 Ho xb4 Max Klf7Usf2 Obo x3Hbp1Irx6Irx2 SoFox30Rhoxh1 x11SoGfiLhx6x17 Rreb1 NRIrf43C1 HoFoxa9xa2 Lhx4Nf1 Og2xBach1 NfyFosl2a bHLHE40Atf3 Irf1 Vax1 Irx3 Hltf Tcf3Ap1-Irf4Ap1:IsreSix1X-boMafbx Ap1 Nrf1 Nkx3-1 Irf4 4 FO XA1Ho Irx3xc12Irx5 Lhx3RhoTcf3 x11 Irf4 Batf Nfy OCT4-SO X2-TCF-NANOGFO XL1 SRFSooYxj3x7 Msx2Tcf7MybSo x6 NFE2L2 Batf Gata3-pr imar yDmbx1Irf8NF1:FO XAHO1XA9 Tcf7l2HoTcf4xb3RXRA::VDR Rfxdc2 Usf1 Ho xa3 Pitx1 Lef1 Tcfl2 Myc Zfp161Usf1 Irf5 GA TA-DRMef2c4FO XA1Foxa2 Foxp1 n-Myc MycBhlhb2 Obo x2FO XFSo2 x4 Dlx5 Rfx3 Sp1 NFkB-p53-p50 Irf2 Oct2 GA TFA3oxa2GA TA-DRP8ax6 Rfx5 Pax5 Egr2 Nrf1 ARE T1isre Gata6 CRXSo x11 SrfEvx1Bm yb MybRfx1 Sp4 Oct4 HOOboIrf4PPXAAXx1ou5f153:FKHRCrxFO-fusionSoSoXDx2x4SoCutl11 x2 Ror a Rfx1Nur77 Hif-1aMyc Ho xa13 FOGbx1XA1 PGfi1bax4 Tlx?Gbx2 Isx NFkB-p65-Rel NFkB-p65-p50 Gata2Gata1Nf atEvi1Zscan4aRor a RorcEomes MybNkx1-1NR4A2 n-MycEgr1Egr1 NFkB-p65 Irf6 Six1Foxo3 NR2F1 TbetLhx8Tbx20Zbtb7bMafb So x15Hand1::TcfFOSix2Zscan4cXCTitf1Tgif2TBP1 e2a Hic1 RxrRara Mtf1a Pou2f2 RunxEmx2 Zbtb12Meis1So x3 Pbx3 Esrr a Esrrb Zfp740 Pou2f3 Bapx1 Six6 Pkno x1 NR1H2:Rxr a Nr2f2Mizf Egr1 4 TNkx3-1AL1::NfTCFSrebp1aGfy-Stafat:Ap13Six3 Zfp410Osr2PknoNr5a2Nr5a2T x2RargMaf a Pax5 Max E2f3E2f2Egr1 HmboNf atc2x1 Irf3Gata4Tcf1 Prdm1Nkx3-1Nkx2-5Smad3 Spdef Pax5 Sp1 0.0 CTCFNkx2-5-SatelliteElementRNkx2-4TUNXead12 FoPitx3xo1Olig2FxrSmad3 MafkMafHnf4aTbx5 Hif1a:ArGabpaEtv6Etv1nt Zbtb33 Sp100 RelA CARunxrG Smad2Tgif1Six4 ESR2 Etv4Etv5 Zfp281 Tal1::Gata1Tcf2IsreNkx2-2Six6Six6 p53Osr1Hnf4aNanog Srebp24 Smad4Myf6Err a Hnf4aSpdefEts1 5 Elk1Etv3Elk4 NFkB-p65 NFkB Nkx2-6Runx 4 Tbo x:SmadPbx1Atoh1PPARG VDREsrrbEhf GabpaTr4 4 Nkx3-2 Elf3Spz1 YY1E2f4 Mef2a Gata5Tcf1Gata1 Nkx2-3PU.1:IrfNFKBFEV1 p53 PPESRARG1 ::RXRAFli1PpareElk3E2f7Elk4Elk4 Stat6 Ets1:Irf4RunxPrdm14EBNARxr1:Rar :Dr5 Erg Rel Nkx3-1 GA TA:EomesSCL En1 Ews:Fli1E2f1E2A:Zbtb3RestPU.1Elf4Gli3Ets1 Rxr a Plagl1 Nr2e3 NFkB-p50,p52Ets1:E-boSpibZfp691x NHLHPrdm91 Elf2E2f1 Fli1Elk1 NFkB-cRel-p50Nkx2-9 Evx2 Ets1:RunxPax8Tcf e2aInsm1EhfEtv1 Glis2Plag1ZfxTf ap2aElk1 Tead ZNFElf5143|NeuroD1STARAF-halfsitep63p53 p53 Elf3 SpicMyf5 Sfpi1Znf143Hif2a Gabpa Tcfcp2l1Rest:NrsfZfp423SCLERG Ascl2 Gabpa Mzf1E2f1E2f6 Ets1Sfpi1 Ap2alpha Elf1 Tead4 Tcfcp2l1MyElf5oGE2A Ewsr1:Fli1Gcm1 Ets1 PUHEBTcf12.1Ap2gammaMy? Ebf1oD BORIS Zic2 EWS:ERG-fusion CTCF Zic1 Tcf ap2bTcf ap2e Zic3 NFkB-cRel SPDEF Ebf1Zfx PPARE Tf ap2a Tcf ap2c Myf CTCF fed Group D Stat3 Stat6 Ebf1 footprinting depth delta -(fasted-fed) Bcl6 Bcl6 Stat3 PU.1 Stat1 Stat3 Number of cuts per bp Stat3 -0.1 Stat5 Stat4 +Il23 TF binding Stat1 motif element -0.4 -0.2 0.0 flanking hypersensitivity delta (fasted-fed) * 16 fasting- C D E induced 8 enhancers 0.0 fasting- total DHSs repressed -0.5 0.4 4 fasting Cebp enhancers CEBP -1.0 enhancers Cebpa 2 fed -1.5 log2 CEBPB tag density footprinting depth fasted -40 -20 0 20 40 Cebp:Ap1 1 T (bp) TTCG GGTAA A AT C A C GCCT G AT fed fasted AA 0.3 Jundm2 8 0.0 JunD * -0.2 Atf1 4 -0.4 CREB1 c-Jun:Cre -0.6 0.2 -0.8 2 footprinting depth -40 -20 0 20 40 Cre (bp)
A CG T log2 CREB1 tag density G TA CCT GC G C AT TT C GT CTGA TCAGA Creb1 1 0.2 fed fasted 0.0 footprinting depth delta -(fasted-fed) 0.1 8 -0.2 * -0.4 GR -0.6 4 -0.8 footprinting depth -40 -20 0 20 40 0.0 0.5 1.0 1.5 (bp) A T C G C A G T C G G G C C AC AT AT GT TG ACT AGT CA flanking hypersensitivity 2
delta (fasted-fed) log2 GR tag density fed 1 fasted fed fasted Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
A isolated hepatocytes B mouse liver C Tat Tat
n.s n.s
) 100 40 5 * l m
* / * * 80 30 n.s 4 g n.s n * 3 ( 60 *
e * 20 s 40
2 o c
10 u 20 1 l g 0 0 relative expression 0 relative expression module NT gluc cort wy gluc gluc cort gluc 0 4 6 8 10 12 24 NT gluc cort wy gluc gluc cort gluc + + + + + + + + cort wy wy cort fasting duration (hr) cort wy wy cort GR + + wy wy 1- D 8 Ppara Ppargc1a n.s Ppargc1a 30 15 * 6 * CREB * n.s * 20 * 10 * 4 * *
10 5 2 relative expression
relative expression 0 0 0 NT gluc cort wy gluc gluc cort gluc relative expression 0 4 6 8 10 12 24 NT cort + + + + cort wy wy cort + fasting duration (hr) Hmgcs2 wy n.s * E 15 Hmgcs2 30 Pdk 8 * * * n.s n o 10 6 i s
s 20
e NT 4 r p
* x wy
5 e
cort 2 e v
i t 10 * wy+cort a l relative expression 0 *
0 e r NT gluc cort wy gluc gluc cort gluc relative expression 0 4 6 8 10 12 24 * + + + + cort wy wy cort A module A + fasting duration (hr) wy 0 0 2 4 6 8 10 20 Pdk4 Pdk4 treatment duration (hr)
PPAR 6 - n.s * 15 * F b GR corticosterone 4 800 5 -hydroxybutyrate (mM) * b-hydroxybutyrate 10 n.s * 4 2 600 5 * 3
relative expression 0 0 400 NT gluc cort wy gluc gluc cort gluc relative expression + + + + 0 4 6 8 10 12 24 2 cort wy wy cort fasting duration (hr) + 200 wy 1
NT cort gluc + cort cort + wy corticosterone (ng/ml) 0 0 gluc wy gluc + wy gluc + cort + wy 0 5 10 15 20 25 30 fasting duration (hr) fasting duration Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
A B 300 - Tat Ppargc1a NT 150 * 1 _ NT gluc 300 - n CREB1 o
i gluc s ChIP s 100 cort
e 1 _
r *
p gluc+ 300 -
x gluc+cort e
e cort v i t 50 1 _ a l 80 - e r * GR gluc+ * ChIP cort 0 1 _ 0 2 4 6 8 10 156 - treatment duration (hr) fed C 1 _ 156 - CREB1 DNase binding fasted sites 3678 1 _ 166 - 3403 ctrl 49 gluc 0 _ gluc + 166 - cort dex 0 _
10 kb
D 10 cort-increased sites: E F 39%, 2787/7130 8 gluc 128 9 gluc ** 64 8 +cort 6 1 tag 7 32
gluc+cort 6 4 1 tag density 16
CREB 5 cort- 8 4 1 ChIP sequenced 1 ChIP decreased 2 CREB log2 density 3 sites: 0.3%, 4 tags per site bp 23/7130 log2 2 CREB 0 2 0 1 2 3 4 5 6 7 8 9 10 -0.4 -0.2 0.0 0.2 0.4 0.6 gluc gluc Distance from peak center (kb) +cort log2 CREB tag density gluc Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
900 A B 64 C cort-unaffected 6 cort-unaffected ** 800 cort-increased cort-increased 700 d p
e 32
b 600 c
r n e e 4 500 p u
q tag density e
t 16 400 e i s s
r 300 GR P I e h p
2 200
C 8 s
g log2 number of GR peaks
R 100 a t G 0 4 0 -0.4 -0.2 0.0 0.2 0.4 0.6 0-50 51-100 101-150 151-200 201-250 Distance from peak center (kb) fected unaf increased Distance from CREB1 peak center (bp)
D E F G 128 ** 64 15 30 **
p 64 cort-unaffected ctrl b d
p r cort-increased 32 e dex b d c e
r
e 10 n p
c e 32 20 e e n p u t
i tag density e q e tag density s u t
e i 16 r q s s
e
e 16 r p S s
DHS 5 e DHS H s p S
10 g D H s 8 a t
g 8 D a log2 t log2 0 4 0 4 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 Distance from fasting- ctrl dex Distance from peak center (kb) fected induced enhancer center (kb)
cort-unaf cort-increased Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
A HaloTag-CREB1 B NT Residence Time * 43.1% 1.27 ± 0.05 sec 25 n.s. 6.2% 7.37 ± 0.95 sec * 50.7% 20
Residence Time )
fsk c
44.5% 1.32 ± 0.05 sec e
s
( 15
6.9% 7.51 ± 0.86 sec e
m
i
48.6% t
l l 10
e
w
fsk + dex Residence Time D 44.4% 1.34 ± 0.06 sec 5 7.6% 8.34 ± 0.94 sec 48.0% 0 Unbound Fast fraction NT fsk fsk +dex Slow fraction HaloTag-CREB1
C Glucagon Corticosterone
Fasting- induced assisted TF enhancers loading cascade
p
Gluconeogenesis Ketogenesis
Fasting Duration Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press
Transcription factor assisted loading and enhancer dynamics dictate the hepatic fasting response
Ido Goldstein, Songjoon Baek, Diego M Presman, et al.
Genome Res. published online December 28, 2016 Access the most recent version at doi:10.1101/gr.212175.116
Supplemental http://genome.cshlp.org/content/suppl/2017/02/15/gr.212175.116.DC1 Material
P
Accepted Peer-reviewed and accepted for publication but not copyedited or typeset; accepted Manuscript manuscript is likely to differ from the final, published version.
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