Abstract
Krüppel-like factor 9 (Klf9) is a feedforward regulator of glucocorticoid receptor (GR) signaling. Here we show that in zebrafish klf9 is expressed with GR-dependent oscillatory dynamics in synchrony with fkbp5, a GR target that encodes a negative feedback regulator of GR signaling. We found that fkbp5 transcript levels are elevated in klf9–/– mutants and that Klf9 associates with chromatin at the fkbp5 promoter, which becomes hyperacetylated in klf9–/– mutants, suggesting that the GR regulates fkbp5 via an incoherent feedforward loop with klf9. As both the GR and Fkbp5 are known to regulate metabolism, we asked how loss of Klf9 affects metabolic rate and gene expression. We found that klf9–/– mutants have a decreased oxygen consumption rate (OCR) and upregulate glycolytic genes, the promoter regions of which are enriched for potential Klf9 binding motifs. Our results suggest that Klf9 functions downstream of the GR to regulate cellular glucocorticoid responsivity and metabolic homeostasis.
Introduction
Glucocorticoids, the terminal hormonal output of the vertebrate hypothalamus-pituitary-adrenal (HPA) axis, function to maintain homeostasis by regulating diverse aspects of physiology, including metabolism and the immune system. Endogenous glucocorticoids (cortisol in humans and fish, corticosterone in rodents) are crucial for mounting physiological responses to stimuli that are either predictable (e.g., day/night cycles, feeding schedule) or unexpected (e.g., during the fight/flight response to acute threat). Basal circulating glucocorticoid (GC) levels oscillate with a circadian rhythm, peaking in the hours just before an animal’s active phase (early morning in diurnal species, evening in nocturnal species). This circadian fluctuation is produced by amplitude modulation of shorter ultradian (i.e., ∼hourly) pulses of hormone secretion (; ; ). Ultradian GC pulses cause cycles of glucocorticoid receptor (GR) activation and association with chromatin that produce ultradian pulses of transcription (). Acute stressors also activate the HPA axis and pulsed GC release, and the magnitude of the physiological response depends on whether the stressor occurs during the rising or falling phase of the ultradian rhythm ().
Because the GR is ubiquitously expressed and binds thousands of genomic sites in a given tissue (), changes in circulating GC levels produce broad changes in gene expression, and hence in physiology and behavior. Temporal gene regulation is one way in which organisms optimize usage of resources (; ; ; ), and conditions that disrupt GC dynamics—e.g., chronic/repeated stress, interference with circadian cues, or Cushing’s Disease (hypercortisolemia)—are associated with multi-systemic disorders, including immune, psychological and metabolic syndromes (). We have previously reported that treatment of zebrafish larvae with chronic 1 μM cortisol leads to aberrant immune gene expression as well as long-term effects on the dynamics of the hypothalamus-pituitary-interrenal (HPI, equivalent to the mammalian HPA) axis and immune gene expression (, ).
GC signaling dynamics are controlled by negative feedback at both systemic and intracellular levels. Systemically, negative feedback of circulating GC on the pituitary is key to the generation of ultradian GC pulses (, ; ). Within cells, a negative feedback loop exists between the GR and its co-chaperone Fkbp5: transcription of fkbp5 is activated by the GR and translated Fkbp5 inactivates the GR by sequestering it in the cytoplasm, increasing resistance to further activation by GC (; ). Elevated FKBP5 expression contributes to HPA dysfunction and associated diseases (; ; ; ), including diabetes and obesity (; ). However, our knowledge of fkbp5 transcriptional regulation is incomplete.
Krüppel-like Factors (KLFs) are zinc-finger transcription factors that co-regulate nuclear receptor signaling () and fine-tune transcription through competitive binding to regulatory DNA (). Klf9 is a GR target that is ubiquitously expressed, including in the brain (; ; ) wherein it directs a maladaptive response to chronic stress (), and in the liver where its overexpression contributes to hyperglycemia (). GR-klf9 feedforward regulation was documented in human skin and murine macrophages (; ), and by our lab in zebrafish (), a model organism well-suited for studies of GC signaling (; ; ). Here we extend our previous report that Klf9 mediates transcriptomic effects of chronic GC exposure by asking if Klf9 contributes to the regulation of fkbp5 and metabolism, key targets of GC signaling. We found that klf9 and fkbp5 are synchronously expressed with GR-dependent dynamics that differ from those of other GC-responsive genes; that fkbp5 activity is elevated in klf9–/– mutants; and that Klf9 protein interacts with chromatin at the fkbp5 promoter. We also show that metabolism is altered in klf9–/– mutants, as indicated by decreased oxygen consumption rate (OCR) and upregulation of glycolytic genes.
Materials and Methods
Zebrafish Strains, Husbandry, and Embryo Treatments
The AB strain was used for all genetic modifications. Husbandry and procedures were carried out as described previously () and in the Supplementary Methods. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the MDI Biological Laboratory, and all methods were performed in accordance with the relevant guidelines and regulations.
The Klf9-AMtag line was created using CRISPR-Cas9 and homology-directed repair as described in the Supplementary Methods, using a donor sequence encoding the AM epitope tag (Supplementary Figure 10) recognized by the Active Motif anti-AM antibody (Active Motif, Carlsbad, CA).
Analysis of Gene Expression
Quantitative reverse transcription and polymerase chain reaction (qRT-PCR) was carried out as described previously () and in the Supplementary Methods. For time-course experiments, each timepoint sample consisted of 3–6 larvae cultured in an individual Petri dish, and the dishes were arranged in the incubator so as to minimize disturbance during sampling. Sinusoid models were fit to time-course expression data using the equation y = m + a∗exp(-b∗x)∗sin(c∗(x-d)), where m = MESOR, a = amplitude, b = decay rate, c = 2∗pi∗frequency, and d = phase shift. Non-linear least squares regression was performed using either the nls function in the R Stats package or in Microsoft Excel using the Solver function (); both methods generated the same solutions. Initial values for constants were chosen to generate a reasonable qualitative fit before iterative fitting programs were run. When generating random data sets and model fits (see section “Results”), initial constants were set to the best fit of data combined from both conditions (either VEH- and CORT-treated, or wildtype and klf9–/–, depending on the experiment).
RNA-seq data were obtained from our previous study (). The generation and analysis of the data are described in the Supplementary Methods.
The NanoString PlexSet assay for 24 probe sets (21 genes of interest plus 3 reference genes) was performed according to the manufacturer’s instructions (NanoString Technologies, Seattle, WA) and as described in the Supplementary Methods. Probe sets were designed by NanoString with direction and approval of the authors (see Supplementary Table 1). Normalized counts were exported from the NanoString nSolver program (), and heat maps were generated from Z-transformed log2 counts data using the heatmap.2 function in gplots 3.1.1 package () in R. Hierarchical clustering was determined using the hclust function in the R stats package and the “complete” method with distance between rows calculated by the dist function and Euclidean method.
Cortisol Measurement
Cortisol was assayed using the Neogen Cortisol ELISA kit, as described previously () and in the Supplementary Methods.
Chromatin Immunoprecipitation
Chromatin Immunoprecipitation (ChIP) was performed as described by and in the Supplementary Methods.
Oxygen Consumption Rate
Oxygen consumption rate was analyzed using an XF96e Extracellular Flux Analyzer (Agilent Technologies) as described in the Supplementary Methods. The first data point is typically inconsistent in these runs and was therefore excluded from the analysis. The remaining 11 data points were imported to GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, United States) and plotted as a function of time (Supplementary Figure 12A). Linear regression of time-series data was used to determine the slope (rate of change in OCR over time) and Y-intercept, which is indicative of basal metabolic rate. Statistical significance between treatment groups for the slopes and Y-intercepts were calculated by two-factor ANOVA (treatment and genetic background as the two variable, Supplementary Figure 12B) followed by Šídák’s multiple comparisons test (GraphPad Prism 8.0). A possible confound that we cannot exclude is that CORT and VEH treatment solutions could have differences in oxygen solubility.
Results
Klf9 and fkbp5 Share Synchronous and Glucocorticoid Receptor-Dependent Temporal Expression Dynamics
Treatment of zebrafish embryos with chronic 1 μM cortisol (CORT) produces a modest but significant elevation in whole-body cortisol and GR activity at 5 days post-fertilization (dpf) (). The CORT-treated larvae upregulate immune-related genes, an effect dependent on both the GR and Klf9 (), and give rise to adults in which fkbp5 and klf9 transcripts are persistently elevated in blood cells on average, although not in every instance (). We hypothesized that such inconsistency could be an artifact of measuring dynamic gene expression with insufficient temporal resolution, and therefore undertook high-density time-course sampling to measure fkbp5 and klf9 expression in 5 dpf larvae, by which stage the HPI axis is fully developed () and both genes are actively transcribed (Supplementary Figure 1). Although bulk analysis of larval gene expression lacks spatial resolution, we reasoned that such an analysis would be informative since both fkbp5 and klf9 are ubiquitously expressed (; Zhang et al., 2017), and transcription in peripheral zebrafish tissues is highly entrained to circadian rhythms via GC signaling (; ). For the time-courses, care was taken not to disturb larvae during sampling (see section “Materials and Methods”) to minimize stress-responsive expression. The results revealed that klf9 and fkbp5 are expressed with synchronous oscillations on both the circadian and ultradian time scales (Figure 1A and Supplementary Figure 1C). Transcript levels of both genes peaked just prior to zeitgeber time 0 (ZT 0, corresponding to lights-on in the incubator), falling sharply thereafter. The data for both genes could be fit by non-linear regression to sinusoidal models, with wavelengths of 8.2 and 9.5 h for fkbp5 and klf9 respectively (ANOVA p < 0.0001, Supplementary Figure 2).
FIGURE 1
Since the 10-h sampling window was too narrow to draw conclusions about circadian periodicity, we measured expression of klf9, fkbp5 and the circadian gene per1a over a 24-h period. While per1a had a prototypical 24-h oscillation, klf9 and fkbp5 had a shorter periodicity and lower amplitude oscillation (Supplementary Figure 3). Nevertheless, transcript levels of all three genes peaked synchronously just before ZT 0 and dropped precipitously thereafter (Supplementary Figure 3). We interpret this as coordination of GR activity with the circadian clock, as whole-larva cortisol levels show a similar diurnal drop (Supplementary Figure 4) and GCs drive circadian cell cycle and metabolic rhythms in zebrafish larvae (; ). We verified the GR-dependence and diurnal dynamics of klf9 and fkbp5 activity by comparing transcript levels in wild-type larvae and GR–/– mutants at ZT −0.25 and ZT 3. In the GR–/– mutants, both genes were lowly and flatly expressed, being significantly under-expressed at the wildtype (WT) expression peak of ZT −0.25 and remaining below WT at the 3.25 ZT expression nadir (Figure 1B).
To determine if the temporal dynamics shared by klf9 and fkbp5 are common to GC-responsive genes in general we re-measured one replicate (40 samples of pooled larvae) of our time-course using the NanoString platform and a probe set (Supplementary Table 1) representing 21 genes known to be direct GR targets and/or that we have experimentally identified as consistently responding to CORT treatment, including klf9 and fkbp5 (). Hierarchical clustering of these NanoString data distinguished two main time-dependent clusters of genes (Figure 1C). The tight correlation between klf9 and fkbp5 was underscored by their clustering together on their own branch, which in turn clustered with other genes whose expression peaked in early morning hours, including circadian regulators per1a and nr1d2a, as well as rhcga and ncf1. Conversely, for the second main cluster, which included the GR target tsc22d3 as well as several immune genes, expression was low in the early morning when endogenous CORT is high, then rose after ZT 0.
We next assessed the effect of chronic CORT exposure, shown by our previous studies to elevate klf9 transcript levels at the 5 dpf mid-morning (∼ZT 3) timepoint when samples were collected (; ). Overall, the treatment resulted in a significant elevation of klf9 and fkbp5 transcripts across all timepoints (p < 0.005, paired t-tests), albeit with instances of lower levels at some timepoints (Figure 2A). To rigorously test whether the exposure significantly increased the average expression of these genes we also computationally generated 1,000 time series in which either VEH or CORT measurements were randomly selected at each timepoint in the experimental data. We then fit sinusoid models to each randomized time series. Each sinusoid equation includes five constants (see section “Materials and Methods”), including the Midline Estimating Statistic Of Rhythm (MESOR, a rhythm-adjusted mean value). For both fkbp5 and klf9, the MESORs of models fit to either VEH or CORT experimental data were located at opposite extremes of approximately normal distributions of MESORs from all randomized models (p < 0.02, Figure 2B), providing confirmation that chronic CORT exposure produced a statistically significant increase in klf9 and fkbp5 transcript levels.
FIGURE 2
Finally, to determine how the response of klf9 and fkbp5 to chronic CORT compares to that of the other GR targets in our NanoString panel we calculated the expression ratio of CORT- to VEH-treated samples at each timepoint for each gene. The CORT response of klf9 and fkbp5 was correlated, clustering separately from that of all other genes and manifesting a stronger effect than most (Figure 2C). The immune genes mpeg1.2 and irg1l responded similarly in magnitude, but with different timing (rising rather than falling after ZT 0). The overall effect of the treatment across all genes was a subtle increase in transcript levels (Figure 2D).
Krüppel-Like Factor 9 Negatively Regulates fkbp5 and Binds fkbp5 Promoter-Proximal Chromatin
We performed additional time-courses to compare fkbp5 mRNA levels in WT and klf9–/– larvae. Absent Klf9, the diurnal oscillation in fkbp5 activity was conserved, but fkbp5 activity was elevated on average (Figure 3A, p = 0.007, paired t-test), and sinusoid models fit to both WT and klf9–/– time-course data indicated a significant elevation of the fkbp5 MESOR in the mutants (Figure 3B and Supplementary Figure 5). In addition, the circa-dawn peak of fkbp5 activity was delayed by ∼35 min in klf9–/– larvae, occurring ∼30 min before lights-on in WT but several minutes after lights-on in mutants, and the time from peak to nadir was subsequently compressed from ∼3 h in WT to ∼2 h in mutants (Supplementary Figure 6). Combining chronic CORT treatment with the klf9–/– mutation cumulatively increased fkbp5 transcript levels, which were further increased by inhibiting Fkbp5 activity with FK506 (as expected due to loss of Fkbp5-mediated inhibition of the GR), although the magnitude of the latter effect was lower in klf9–/– larvae (Supplementary Figure 7).
FIGURE 3
The qRT-PCR data described above suggested that Klf9 negatively regulates fkbp5. To determine if Klf9 physically interacts with fkbp5, we performed ChIP-qPCR using primers encompassing putative Klf9 binding motifs identified via JASPAR in the fkbp5 promoter region (Figure 3C and Supplementary Figure 8). A commercial anti-Klf9 antibody recovered significantly more fkbp5 promoter region DNA than did a non-specific IgG, and this signal was reduced in klf9–/– mutants (Supplementary Figure 9). However, the commercial antibody recognizes an amino acid sequence that is conserved in zebrafish Klf13. We therefore constructed a new zebrafish CRISPR line in which a C-terminal AM epitope tag was introduced into the endogenous klf9 locus (Supplementary Figure 10 and Supplementary Methods), allowing us to perform Klf9-specific ChIP with the anti-AM antibody. This antibody produced a fkbp5 ChIP-qPCR signal specific to chromatin from the AM-tagged line (Figure 3D). Together these data indicate that Klf9 interacts physically with the fkbp5 promoter region.
Because our previous gene ontology analysis suggested that Klf9 regulates steroid metabolism (), we also asked whether the elevation of fkbp5 transcripts observed in klf9–/– larvae might be due to mutants having higher endogenous cortisol levels. We did not, however, detect any difference in either baseline cortisol concentration or diurnal variation in 5 dpf klf9–/– larvae, and in fact the cortisol response to an acute stressor was if anything decreased in mutants (Supplementary Figure 4). Thus, the elevated fkbp5 activity in klf9–/– larvae is unlikely to be due to systemically elevated cortisol.
While Klf9 has been implicated in both transcriptional activation and repression in different contexts (), it functions predominantly as a repressor in mouse hippocampus, where it binds promoter-proximal DNA enriched for circadian E-Box motifs (). HOMER motif enrichment analysis of 149 genes consistently upregulated by chronic CORT () revealed that 86% of those genes had promoter-proximal KLF and circadian E-Box motifs (while only 46% had glucocorticoid response elements, Supplementary Figure 11 and Supplementary Table 2), with fkbp5 having E-boxes at +154 and −1,840 bp. As the N-terminal domain of mammalian Klf9 interacts with the Sin3a histone de-acetylation complex (Zhang et al., 2001), we hypothesized Klf9 might repress fkbp5 via de-acetylation of histone H3 at the fkbp5 promoter. Using an antibody to H3K14ac and ChIP-qPCR, we found a ∼threefold increase in fkbp5 promoter DNA recovery in klf9–/– larvae compared with WT (p < 0.05, Figure 3E). This provides additional evidence that Klf9 directly represses fkbp5, forming a circuit predicted to regulate GR activity dynamics through a combination of Fkbp5-mediated negative feedback and Klf9-mediated incoherent feed-forward regulatory logic (Figure 3F).
Loss of klf9 Decreases Oxygen Consumption Rate and Alters Metabolic Gene Expression
The GR, Fkbp5, and Klf9 have all been previously implicated in the regulation of metabolism. To ask if loss of Klf9 affects metabolism in developing zebrafish, we first measured the OCR of 1 dpf embryos and found a 10% decrease in klf9–/– mutants compared to WT (Figure 4A and Supplementary Figure 12). We also found a significant interaction between genotype and chronic CORT treatment, which decreased OCR in WT but not mutant larvae, although OCR was still significantly decreased in the mutants compared to WT under chronic CORT (Figure 4A and Supplementary Figure 12).
FIGURE 4
For additional insight we interrogated our published RNA-seq dataset comparing WT and klf9–/– larvae () by performing a focused principal component analysis (PCA) on two gene lists (Supplementary Table 3) comprising targets of Hif1 and AMPK signaling, which respectively regulate glycolysis and oxidative metabolism. These gene lists were previously shown to accurately reflect changes in metabolic phenotype of cancer cells, which rely more heavily on glycolysis as they become more malignant (i.e., the Warburg Effect) (). The first principal components (PC) of both datasets (respectively, accounting for 28 and 27% of the variance) represented the effect of chronic CORT treatment, and plotting them against each other reveals correlation between the two pathways (Supplementary Figure 13A). Top genes contributing to AMPK PC1 included insulin regulator mafa at the VEH pole, while the CORT-treated pole was weighted by the GC target g6pca, two members of the stress-responsive gadd45 family, and acadm, which encodes a catalyst of mitochondrial beta-oxidation. Klf9–/– mutant samples showed a shift away from the CORT pole of AMPK PC1, suggesting less influence of CORT on expression of these genes in mutants. Plotting PC1 vs. PC2 within either the Hif1 or AMPK dataset segregated the samples by treatment and genotype (Supplementary Figure 13B). Hif1 PC2 (12% of variance) segregated WT from klf9–/– mutant samples; examples of genes at either end of this axis included the Hif1a regulator egln3, upregulated in mutants (Figure 4B), and eprs1, downregulated in mutants. Lactate dehydrogenase (ldha), a noted Hif1 target (; ), was also upregulated in klf9–/– mutants (Figure 4B). Wild-type samples spanned AMPK PC2 (19% of variance), whereas klf9–/– samples were restricted to one pole (Supplementary Figure 13B), indicating less variation in expression in mutants of these genes, which included pck1 and pck2.
We previously reported that genes involved in glycolysis and pyruvate metabolism were reciprocally regulated in klf9–/– and GR–/– mutants, being upregulated in the former and downregulated in the latter (). KEGG pathway analysis of the full set of genes significantly affected in klf9–/– larvae under all conditions (FDR q < 0.05, Supplementary Table 4) showed enrichment for genes involved in glycolysis/gluconeogenesis as well as the adjacent pentose phosphate pathway (PPP) (Figure 4C, FDR < 0.01). We further parsed this by separate Gene Ontology comparisons between WT and klf9–/– larvae in VEH or chronic CORT conditions. Processes up-regulated in CORT-treated klf9–/– vs. CORT-treated WT larvae again included glycolysis and pyruvate metabolism (Supplementary Figure 14 and Supplementary Table 4), while processes upregulated in mutants under VEH conditions were instead largely involved in innate immunity as reported previously (). These data suggest that chronic CORT exposure activates a metabolic stress response that unmasks the role of Klf9 in regulating genes involved in glycolysis and related processes. We used HOMER to look for enriched transcription factor binding motifs within 2,000 bp of the transcription start site (TSS) of genes upregulated in mutants treated with CORT. The consensus motif for Klf9 was the highest scoring motif on the list (Supplementary Figure 14), and seven of the top ten were highly similar binding motifs for other KLF factors that Klf9 could also be expected to bind (). Notably, except for fbp2, all the glycolytic and PPP genes overexpressed in klf9–/– mutants had potential KLF sites (Figure 4C). In contrast, genes downregulated in mutants showed no enrichment for KLF motifs, and were instead enriched for motifs for IRFs, HNF4a, CREB5, and the retinoic acid receptor. These data suggest that Klf9 functions predominantly as a repressor as previously reported (), leading us to hypothesize that it regulates metabolism in part by repressing glycolytic genes, with a predicted effect of shunting flux through the PPP (Figure 4D). Further studies are required to test this hypothesis, using ChIP to assay Klf9 binding to putative KLF sites in metabolic genes, and analysis of metabolites to test predicted effects of klf9 mutation.
Discussion
We have shown that in zebrafish the GC-responsive genes klf9 and fkbp5 are expressed with synchronous oscillatory dynamics that are distinct from those of other GC-responsive genes we examined. FKBP5 is a gene of high biomedical interest given its role in GR regulation, maladaptation to stress, and mental and metabolic health (; Zimmer et al., 2020). Klf9 has also been linked to a maladaptive stress response (), and our results suggest that the GR, klf9, and fkbp5 comprise a “hardwired” genetic circuit. FKBP5 was previously reported to be repressed by KLF9 in human epidermis (), and we show here that loss of Klf9 function in zebrafish leads to elevated fkbp5 transcript levels and hyperacetylation of chromatin at the fkbp5 promoter, and that Klf9 physically associates with that chromatin, supporting that fkbp5 is a target of Klf9-mediated repression. The proposed GR-klf9-fkbp5 regulatory circuit (Figure 3F) combines negative feedback and incoherent feedforward loops, motifs predicted to facilitate adaptation to repeated or chronic stimuli (; ; ). Further study is necessary to test this hypothesis and explore its ramifications for specific cell and tissue types under different conditions affecting GC signaling, and to determine how the transcript dynamics reported here relate to protein dynamics.
The evidence reported here and in our previous study () that klf9 both responds to and regulates GC signaling, together with our observation that klf9–/– mutants have reduced OCR at 1 dpf and increased expression of glycolytic genes at 5 dpf, suggests that klf9 is instrumental in regulating metabolism and metabolic responses to cortisol (; ; ). Repression of glycolytic genes by Klf9 fits with its reported inhibition of growth and regeneration (; ) and regulation of stem-cell metabolism () and could also contribute to its role in tumor suppression (; Zhong et al., 2018), given the reliance of cancer cells on glycolysis. Shunting flux through the PPP is predicted to increase NADPH, which fits with the known role of Klf9 as a regulator of redox homeostasis (Zucker et al., 2014). It is important to emphasize, however, that we do not yet know the mechanisms underlying the effects of either the klf9–/– mutation or chronic CORT on the OCR in 1 dpf embryos. A previous study reported that morpholino knockdown of klf9 disrupts erythropoiesis in zebrafish (Zhang et al., 2017), suggesting decreased OCR in klf9–/– larvae could result from a deficit in red blood cells. However, in that study primitive hematopoiesis was unaffected, and no differences observed prior to 2 dpf (whereas we measured OCR at 1 dpf), which makes that explanation unlikely. While our gene expression results suggest that loss of klf9 function produces a metabolic shift toward glycolysis, those results were obtained in 5 dpf larvae, and may not be directly relatable to the differences in OCR observed at 1 dpf, given the significant development that occurs between 1 and 5 dpf. Our results are nonetheless consistent with evidence of cell autonomous metabolic regulation in mice, where over-expression of klf9 increased oxygen consumption and the number of mitochondria in cultured adipocytes (). Similarly, elimination of fkbp5 elevates resting metabolic rate while protecting against obesity and increasing glucose tolerance (). FKBP5 expression and blood glucose/insulin resistance are positively correlated in humans (), and a mutation causing elevated FKBP5 is associated with reduced weight loss following bariatric surgery ().
Induction of hepatic klf9 by dexamethasone promotes gluconeogenesis and hyperglycemia via PPAR activation, while klf9 knockout animals display hypoglycemia (). PPAR promotes PPP flux as well as de novo lipogenesis (). Interestingly, we found that promoters of genes under-expressed in klf9–/– mutants are enriched for sequence motifs recognized by the retinoic acid receptor, known to dimerize with PPAR (). Lipid signaling may be an indirect means by which Klf9 regulates gene expression, which may account for the enrichment for IRF motifs among genes upregulated by CORT treatment in WT but not klf9–/– larvae () as lipid signaling regulates interferon levels (; ). The Klf9-dependency of GC-induced immune gene expression () may be secondary to metabolic differences in klf9–/– mutants, as reactive oxygen species, disruption of glycolytic flux, and the accompanying drop in NADH promote inflammasome activation (; ). Further studies are required to test these possibilities.
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Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE144885.
Ethics statement
The animal study was reviewed and approved by the Institutional Animal Use and Care Committee, MDI Biological Laboratory.
Author contributions
IG and JC: conceptualization, writing—original draft, and visualization. IG, RB, NJ, and JC: methodology and writing—review and editing. IG, JG, and RB: investigation. NJ: resources. JC and NJ: supervision. JC: project administration and funding acquisition. All authors contributed to the article and approved the submitted version.
Funding
Funding and infrastructure support for this work was provided by grants from the National Institutes of Health (R03-HD099468, P20-GM104318, and P20-GM103423) and the MDI Biological Laboratory.
Acknowledgments
We thank Joel Graber (MDI Biological Laboratory) for helpful discussions and advice on the analysis of gene expression data.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2021.727037/full#supplementary-material
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Summary
Keywords
glucocorticoid receptor, Krüppel-like factor 9, fkbp5, cortisol, metabolism, zebrafish, RNA-seq, gene expression
Citation
Gans IM, Grendler J, Babich R, Jayasundara N and Coffman JA (2021) Glucocorticoid-Responsive Transcription Factor Krüppel-Like Factor 9 Regulates fkbp5 and Metabolism. Front. Cell Dev. Biol. 9:727037. doi: 10.3389/fcell.2021.727037
Received
17 June 2021
Accepted
15 September 2021
Published
06 October 2021
Volume
9 - 2021
Edited by
Venkaiah Betapudi, United States Department of Homeland Security, United States
Reviewed by
Frank A. Simmen, University of Arkansas for Medical Sciences, United States; Joyce Lloyd, Virginia Commonwealth University, United States; Akira Muto, Toho University, Japan; Mathilakath Vijayan, University of Calgary, Canada
Updates
Copyright
© 2021 Gans, Grendler, Babich, Jayasundara and Coffman.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: James A. Coffman, jcoffman@mdibl.org
This article was submitted to Cellular Biochemistry, a section of the journal Frontiers in Cell and Developmental Biology
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