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The Journal of Clinical Endocrinology & Metabolism Vol. 89, No. 5 2000-2014
Copyright © 2004 by The Endocrine Society

Special Feature

In Vivo Epinephrine-Mediated Regulation of Gene Expression in Human Skeletal Muscle

Nathalie Viguerie, Karine Clement, Pierre Barbe, Melanie Courtine, Arriel Benis, Dominique Larrouy, Blaise Hanczar, Veronique Pelloux, Christine Poitou, Yadh Khalfallah, Gregory S. Barsh, Claire Thalamas, Jean-Daniel Zucker and Dominique Langin

Unité de Recherches sur les Obésités, Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 586 (N.V., P.B., D.Lar., D.Lan.), Institut Louis Bugnard, Centre Hospitalier Universitaire de Toulouse, Université Paul Sabatier, 31403 Toulouse, France; INSERM Avenir and EA3502 Université Paris 6 (K.C., V.P., C.P.), Service de Médecine et Nutrition, Hôtel-Dieu, 75004 Paris, France; Laboratoire d’Informatique Médicale et de Bioinformatique (M.C., A.B., B.H., J.-D.Z.), Faculté de Médecine de Bobigny, Université Paris Nord, 93017 Bobigny, France; INSERM, Unité 449 (Y.K.), Faculté de Médecine R. Laennec, 69372 Lyon, France; Department of Pediatrics and Genetics (G.S.B.), Howard Hugues Medical Institute, Beckman Center, Stanford University School of Medicine, Stanford, California 94305; and Centre d’Investigation Clinique Inserm-Hôpitaux de Toulouse (C.T.), Hôpital Purpan, 31059 Toulouse, France

Address all correspondence and requests for reprints to: Dominique Langin, Unité de Recherche sur les Obésités, Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 586, Institut Louis Bugnard, Bâtiment L3, Centre Hospitalier Universitaire Rangueil, 31403 Toulouse Cedex 4, France. E-mail: langin{at}toulouse.inserm.fr.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results and Discussion
 Conclusion
 References
 
The stress hormone epinephrine produces major physiological effects on skeletal muscle. Here we determined skeletal muscle mRNA expression profiles before and during a 6-h epinephrine infusion performed in nine young men. Stringent statistical analysis of data obtained using 43,000 cDNA element microarrays showed that 1206 and 474 genes were up- and down-regulated, respectively. Microarray data were validated using reverse transcription quantitative PCR. Gene classification was performed through data mining of Gene Ontology annotations, cluster analysis of regulated genes among 14 human tissues, and correlation analysis of mRNA and clinical parameter variations. Evidence of an autoregulatory control was provided by the regulation of key genes of the cAMP-dependent transcription pathway. Genes with known functional cAMP response elements were regulated by the hormone. The impact on metabolism was illustrated by coordinated regulations of genes involved in carbohydrate and protein metabolisms. Epinephrine had a profound effect on genes involved in immunity and inflammatory response, a previously unappreciated aspect of catecholamine action. Information on 526 mRNAs corresponded to genes of unknown function. These data define the molecular signatures of epinephrine action in human skeletal muscle. They may contribute to the understanding of skeletal muscle alterations observed in pathological conditions characterized by sympathetic nervous system overdrive.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results and Discussion
 Conclusion
 References
 
CATECHOLAMINES ARE INVOLVED in a host of physiological functions, in particular those integrating responses to stress signals. Historically, catecholamines have been associated with the fight-or-flight response. Norepinephrine is the major neurotransmitter in the peripheral sympathetic nervous system, whereas epinephrine is the primary hormone secreted by the adrenal medulla in humans. As part of the response to stress, release of both the neurotransmitter and hormone may be stimulated. Their effect on target tissues is mediated by 6{alpha}- and 3ß-adrenoceptor subtypes (1). The variety of receptors and signal transduction pathways combined with differential tissue distribution accounts for the diversity of biological responses. Epinephrine acts both on {alpha}- and ß-adrenoceptors. Intravenously it evokes an increase in blood pressure that is explained by a direct myocardial stimulation through ß-adrenoceptors and a vasoconstriction in many vascular beds through {alpha}-adrenoceptors. However, blood flow is markedly increased in skeletal muscle through the powerful ß2-adrenoceptor-mediated vasodilation. The hormone also influences a number of important metabolic processes (2). It decreases the uptake of glucose in peripheral tissues, partly through an inhibition of insulin secretion. It stimulates glycogenolysis in several organs and has a well-characterized effect on adipose tissue lipolysis that allows modulation of plasma free fatty acid levels (3). Epinephrine stimulates energy expenditure in humans. This effect is mediated by ß1- and ß2-adrenoceptors (4). The sympathetically mediated thermogenesis can be explained by a moderate increase in myocardial energy expenditure; an increase in adipose tissue lipolysis; and an increase in substrate oxidation, most notably in skeletal muscle. Catecholamines have also a profound effect on protein metabolism in skeletal muscle (5). Treatment with ß2-adrenergic agonists induces hypertrophy of skeletal muscle in livestock and humans due to an increased rate of protein synthesis and a reduced rate of protein breakdown.

Skeletal muscle is equipped uniquely with the ß2-adrenoceptor that accounts for the effect of epinephrine on the tissue. Through activation of adenylyl cyclase, stimulation of the Gs protein-coupled receptor leads to an increase of intracellular cAMP and activation of protein kinase A (PKA). This signaling cascade, one of the most versatile and multifunctional, is responsible for the modulation of numerous processes including gene transcription. Phosphorylation of the cAMP response element binding protein (CREB) stimulates cellular gene transcription of target genes (6, 7). The stoichiometry of CREB phosphorylation correlates well with intensity of stimulus and the level of gene activation. The CREB family of transcription factors, i.e. CREB, cAMP response element modulator (CREM) and activating transcription factor 1, belong to the basic domain-leucine zipper class. CREB dimers bind to the cAMP response element (CRE) often represented by the consensus palindrome TGACGTCA. The CRE is usually located in the proximal promoter region. Binding of CREB promotes recruitment of RNA polymerase II complexes. Instrumental in the activation of transcription is the bipartite CREB transactivation domain, which is composed of the constitutive Q2 domain and the kinase-inducible domain. Transcriptional repression involves dynamic dephosphorylation of the activators or involvement of the CREM isoform inducible cAMP early repressor (ICER). ICER expression is strongly induced by cAMP and ICER itself is able to repress its own expression. These dynamic and versatile processes allow fine-tuning in the control of gene expression by catecholamines.

Considerable knowledge has been gained on the molecular mechanisms of cAMP-dependent modulation of gene transcription. However, a limited number of target genes have been identified. Because skeletal muscle is an important site of action for epinephrine, we wished to analyze global transcriptional modifications induced by short-term exposure to the hormone. To that hand, we used a microarray with 43,000 cDNAs that represent a large fraction of the human transcriptome and measured the changes before and after a 6-h infusion with epinephrine in young men. We report here that epinephrine directly modulates the mRNA levels of 1680 genes in human skeletal muscle. Most of the genes are novel targets of epinephrine. They belong to functional classes that explain the biological and metabolic effect of epinephrine.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results and Discussion
 Conclusion
 References
 
Subjects

Nine healthy male Caucasian volunteers (22–32 yr old) who had not been submitted to any pharmacological or nutritional protocols before the study were recruited. All had stable weight during the previous 3 months. Their body mass index was 23.5 ± 0.5 kg/m2 (range 20.8–25.7 kg/m2). Selection of subjects was based on a screening evaluation consisting of detailed medical history, physical examination, dietary assessment, complete blood count, urine analysis, resting electrocardiogram, blood pressure measurements, and several blood chemistry analyses. The subjects were on their usual diet before the study, and none were engaged in heavy physical activity training. The study protocol was approved by the Comité Consultatif de Protection des Personnes Toulouse 1 (Ethics Committee), and written informed consents for all subjects were obtained. Investigations were performed at the Clinical Investigation Center of Toulouse University Hospitals.

Experimental protocol

An initial screening visit was performed 7 d before the beginning of the experimental protocol to check the inclusion criteria. Body composition was assessed in fasting condition by dual-energy x-ray absorptiometry performed with a total body scanner (DPX, software 3.6, Lunar Radiation Corp., Madison, WI) enabling quantification of fat mass, lean body mass, and total bone mineral content the day before the experimental day (8). The same investigations were performed before and after epinephrine infusion. After an overnight fast, a catheter was inserted at 0800 h into the left antecubital vein for blood sampling and kept patent with isotonic saline. After a 1-h resting period in a supine position, oxygen consumption (VO2) and carbon dioxide production were monitored over 45 min by using an open-circuit ventilated-canopy system (Deltatrac II monitor, Datex Instrumentarium Corp., Helsinki, Finland) calibrated with a reference gas. Resting metabolic rate was derived from VO2 and carbon dioxide production during the last 30-min period by using indirect calorimetry (9). After resting metabolic rate measurement, three 10-min-interval blood samples were drawn for determinations of basal hormonal and metabolic parameters. Then a percutaneous biopsy of the vastus lateralis muscle was obtained by using Weil Blakesley pliers (10). Approximately 3 ml 1% lidocaine was injected into the skin before the biopsy.

The procedure involved a 5-mm incision through the skin and muscle sheath 15–20 cm above the knee. Average 50-mg (wet weight) muscle samples were obtained, immediately frozen in liquid nitrogen, and stored at –80 C until analysis. Then epinephrine infusion was performed through the iv catheter placed in the right arm by using an autosyringe infusion pump. Epinephrine with isotonic saline as vehicle was infused iv at 0.04 µg/kg·min during 6 h. One-hour-interval blood samples were drawn from the catheter placed in the left arm for determinations of hormonal and metabolic parameters during the infusion. During the baseline period and epinephrine infusion, the heart rate was continuously recorded by using a standard three-lead electrocardiogram, and systolic and diastolic blood pressures were evaluated every 10 min by using a Dinamap device. Resting energy expenditure was measured during the last 30 min of each 60-min period. Six hours after the beginning of epinephrine infusion, skeletal muscle biopsies were performed on the controlateral side. The infusion was stopped after completion of the biopsy.

Biochemical determinations

Plasma catecholamines were assayed by HPLC by using electrochemical (amperometric) detection as previously described (11). The detection limit was 20 pg/sample for the two catecholamines. Day-to-day and within-run variabilities were 4 and 3%, respectively. Glycerol was determined in plasma by using an ultrasensitive radiometric method (12); the intraassay and interassay variabilities were 5.0 and 9.2%, respectively. Plasma glucose was assayed with a glucose oxidase technique (Biotrol, Paris, France); the intraassay and interassay variabilities were 1.5 and 5.1%, respectively. Nonesterified fatty acids were assayed with an enzymatic method (Unipath, Dardilly, France); the intraassay and interassay variabilities were 1.1 and 1.6%, respectively. Plasma insulin was measured by using a Biinsulin immunoradiometric assay kit from Sanofi Diagnostics Pasteur (Marne-La-Coquette, France); the intra-assay and interassay variabilities were 2.7 and 5.8%, respectively. Phenotypical values are given as mean ± SEM. One-way ANOVA for repeated measures was used for comparisons of the metabolic and hormonal parameters before and during the epinephrine infusion with time as the factor of analysis, followed by a Bonferroni-Dunnett post hoc test with baseline values as the control. P < 0.05 was the threshold of significance.

Determination of mRNA levels

Total RNA was extracted using the RNA STAT-60 isolation reagent (Tel-Test, Friendswood, TX). Total RNA quantity and quality were assessed using the Agilent 2100 bioanalyzer and RNA 6000 labChip kit (Agilent Technologies, Massy, France). Reverse transcription (RT) was performed using Thermoscript reverse transcriptase (Invitrogen, Carlsbad, CA) and random hexamers with 500 ng total RNA for each sample from the nine patients. cDNA (10 ng) was used as template for real-time PCR. Real-time PCR was performed on GeneAmp 7000 sequence detection system (Applied Biosystems, Foster City, CA) as previously described (13). A standard curve for each primer pair was obtained using serial dilutions of human skeletal muscle cDNA. We used 18S ribosomal RNA as control to normalize gene expression using the Ribosomal RNA Control TaqMan assay kit (Applied Biosystems).

RNA amplification

Amplified RNA (aRNA) were prepared using the Message Amp aRNA kit (Ambion, Cambridgeshire, UK) (14, 15). Briefly, 400 ng total RNA were used for an RT reaction containing a T7 promoter sequence primer. After second-strand cDNA synthesis, in vitro transcription was performed overnight resulting in an approximately 1000-fold yield in aRNA. aRNA quantity and quality were assessed using the Agilent 2100 bioanalyzer.

Probe labeling and microarray hybridization

Fluorescent probes were synthesized from 3 µg aRNA using the CyScribe First Strand cDNA labeling kit (Amersham Biosciences, Orsay, France). Cy3 and Cy5 probes from one subject were purified and concentrated using Microcon YM-30 column (Millipore, Bedford, MA) after the addition of human cot-1 DNA (Invitrogen), yeast tRNA and poly(dA)DNA (Sigma, St. Louis, MO). After denaturation, the probe was added to the array in a 3.4x saline sodium citrate (SSC) 0.3% sodium dodecyl sulfate buffer that was covered by a glass coverslip. The slide was then placed in a sealed humidified hybridization chamber for 16 h hybridization at 65 C. Slides were washed twice in 2x SSC 0.1% sodium dodecyl sulfate, 1x SSC, and then 0.5x SSC. The arrays were immediately scanned using a GenePix 4000A scanner (Axon Instruments, Foster City, CA). Images were analyzed using GenePix pro 3 software (Axon Instruments).

Analysis of microarray data

Data files generated by Genepix (Axon Instruments) were entered into the Stanford Microarray Database (genome-www5.stanford.edu/MicroArray/SMD/). After a filtering procedure omitting manually flagged elements (i.e. bad-quality spots) and spots with an average intensity less than 2.5-fold above the background, 39,141 spots were recovered. A uniform scale factor was applied to all measured intensities to normalize signal intensities between both images. The log2 Cy5/Cy3 ratios (epinephrine/control) were extracted for the nine experiments. Before calculations, the data from at least eight over nine experiments were normalized using locally weighted linear regression (lowess) (www.tigr.org/software/tm4/midas.html) (16). Data were analyzed using the significance analysis of microarray (SAM) procedure (www-stat. stanford.edu/~tibs/SAM/), a validated statistical nonparametric technique for identifying differentially expressed genes across high-density microarrays (13, 17). A one-class analysis was performed and additional criteria were imposed to increase confidence that the estimated changes observed for one cDNA reflect real differences in gene expression. The additional analysis consisted in the identification of other clones representing the same gene (i.e. the same UniGene number) as the one sorted using SAM. For each gene, a Student’s t test was performed for each replicates.

To analyze the function of the known genes, we used Gene Ontology (GO) annotations (18) (www.geneontology.org) and a newly in-house developed data mining tool (our unpublished data). To determine whether epinephrine-regulated genes shared common patterns of expression, cluster analysis of the transcripts was performed among 14 different human tissues using a hierarchical clustering method (rana. lbl.gov/EisenSoftware.htm) (19). To combine analyses of gene expression and clinical parameter data, we developed a metaalgorithm on SPSS-Clementine (www.spss.com/spssbi/clementine/index.htm) able to select individual gene expression ratio and clinical parameter (our unpublished data). Clementine is a data-mining tool that helps to discover and predict relationships in data sets. The work is structured around a data stream corresponding to selected treatments applied to the data from their sources to their destinations (models or new data such as statistics). We calculated Spearman correlations to assess the degree of relationship between variations in mRNA levels and changes in clinical parameters. The computational process results in two-dimensional graphics in which the gene identification numbers are shown on the y-axis, variations in gene expression are shown on the x-axis, and a color gradient represents the variations in the clinical parameter.


   Results and Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results and Discussion
Conclusion
 References
 
Effect of epinephrine on cardiovascular and metabolic parameters

Nine healthy male volunteers received a 6-h infusion of epinephrine at 0.04 µg/kg·min. The infusion induced a 30-fold increase in plasma epinephrine levels without modifications of plasma norepinephrine levels (Table 1Go). The epinephrine levels obtained during the infusion correspond to physical exercise at maximal intensity (20) and pathological states like pheochromocytoma, shock, or major stress. The increase in heart rate was significant at 3 and 5 h. Systolic and diastolic blood pressures were not modified by the infusion. The resting metabolic rate adjusted for lean body mass was significantly increased by about 20%. Skeletal muscle has been shown to account for 40% of epinephrine-induced thermogenesis (21). As an index of the stimulation of adipose tissue lipolysis by catecholamines (3), plasma glycerol and nonesterified fatty acid levels were increased during epinephrine infusion. The effect on nonesterified fatty acid concentrations was characterized by a peak at 1 h followed by a sustained elevation of plasma levels, compared with basal values albeit at lower levels than at 1 h. The peak may be related to the surge in fatty acids released by adipose tissue. At subsequent time points, fatty acid concentrations reflect the continuously stimulated lipolysis balanced by an increase in fatty acid use. This increase is not the result of a change in the ratio between lipid and carbohydrate oxidation because the respiratory quotient was not modified. With the increased metabolic rate, both glucide and lipid oxidations were induced. The increase in blood levels of glucose reflects the stimulation of hepatic glycogenolysis (2). As a consequence, the elevated glycemia induces the accompanying increase in plasma insulin levels. When compared with the effect of fasting, the increase in plasma insulin levels is lower than expected for the observed variation in glycemia. This less-than-expected induction is probably due to the well-known direct inhibitory action of catecholamines on insulin secretion (1).


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  		TABLE 1. Time course of catecholamine plasma concentrations and of various metabolic parameters during the 6-h epinephrine infusion at 0.04 µg/kg·min in nine healthy male volunteers

 
Changes in mRNA expression induced by epinephrine

aRNA from skeletal muscle was used to hybridize high-density microarrays with 42,878 cDNAs. Accurate measurements of net spot intensities were recovered for 37,642 cDNAs. High-intensity signals for at least eight over nine experiments were obtained for 16,739 cDNAs (43%), which were selected for further analysis. Using a conventional t test with P = 0.01, one would expect to identify 170 genes by chance. To control for multiple testing, we used SAM, a permutation-based method that determines a false discovery rate (FDR), i.e. the probability that a given gene identified as differentially expressed is a false positive (17). The procedure resulted in the selection of 2082 cDNAs differentially expressed with a FDR of 0.1%. The very low FDR corresponds to a median number of falsely called genes of 2. This FDR is much lower than the FDR of 15%, which we reported in a previous study on thyroid hormone (13). The improved confidence in the present study probably comes from the increased number of subjects analyzed in microarray experiments (nine vs. five) but also from a normalization procedure that uses lowess. Lowess is a method that can remove the dependence between Cy5 or Cy3 intensities and the log2(ratio) values (16) and thereby avoid spurious results derived from genes with low levels of expression. Our study design also contributes to the quality of the results. Longitudinal studies of hormone effect on skeletal muscle gene expression produced significant data with control for multiple testing despite changes in gene expression that are relatively modest (Ref. 13 , 22 and present study). Transversal studies comparing gene expression profiles in skeletal muscle of diabetic and nondiabetic subjects failed to identify significant genes when multiple comparison is taken into account (23, 24). The variability between individuals is probably less critical in longitudinal studies as each individual is its own control.

Among the 2082 cDNAs, 1107 were represented by a unique clone on the microarray. Eight hundred seventy cDNAs had replicates among the 37,642 cDNAs. We tested whether the expressed values of the log2 ratio of replicates was 0. Four hundred two genes were eliminated using the Student’s t test (P < 0.05). The final list (supplemental Table 1Go, published on The Endocrine Society’s Journals Online web site, http://jcem.endojournals.org) comprised 1680 differentially expressed genes, 1206 being up-regulated (mean fold change 1.37) and 474 down-regulated (mean fold change 0.71). It must be stressed that this selection procedure is highly stringent because each mRNA variants of a gene produced by alternative promoters, splicing, or polyadenylation sites have the same UniGene number but are not necessarily identically regulated.

Validation of microarray data

We first checked whether, in our experimental conditions, mRNA amplification did not induce a distortion in mRNA representation. mRNA levels of three genes was quantified using RT-real-time PCR (RT-qPCR) on both total and aRNA from six patients. The correlation was very high (r = 0.99) indicating, as previously reported (14, 15), that aRNA is representative of mRNA in total RNA preparations. To confirm data obtained from microarray hybridizations and statistical analysis, we measured changes in mRNA levels for 14 genes, including three up-regulated, four down-regulated, and seven unaffected genes sorted by SAM. These genes are representative of various biological processes. Table 2Go shows the comparison between the fold changes in mRNA levels measured with microarray hybridization using aRNA and the fold changes calculated using RT-qPCR starting from total RNA. The estimation of the gene expression ratios by the microarray experiments highly correlated with the data obtained by RT-qPCR (r = 0.97). Statistical results were discordant for two genes, secreted protein acidic and rich in cysteine and voltage-dependent anion channel 1. The secreted protein acidic and rich in cysteine gene was selected by SAM in microarray experiments and exhibited borderline significance (P < 0.1) in RT-qPCR experiments. On the contrary, voltage-dependent anion channel 1 mRNA levels, which were different using RT-qPCR, were not found differential with the microarray data. This latter result shows that the stringent statistical procedure aimed at decreasing the number of false positives discards genes that are differentially expressed.


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  		TABLE 2. Comparison of fold changes in mRNA levels determined using microarray and RT-real time PCR

 
Global view at mRNA expression changes induced by epinephrine

The majority of the genes (70%) was up-regulated. Most of them may use a classical transcriptional activation process mediated by an increase in intracellular cAMP level and activation of members of the CREB family, i.e. CREB, CREM, and activating transcription factor 1. However, the transcriptional activity of activator protein-2, nuclear factor {kappa}B, and some nuclear receptors is modulated by cAMP (6). These factors may thereby contribute to the positive gene regulation. Nearly 30% of the genes were down-regulated. The mechanisms underlying cAMP-mediated transcriptional repression remain obscure. One pathway could involve muscle-specific members of the basic helix-loop-helix family such as MyoD and Myf5, which are inhibited by PKA (25). Phosphorylation and activation of transcriptional (co)repressors is another possibility. In yeast, PKA-mediated phosphorylation of the CREB repressor Sko1p induces the nuclear translocation and thereby the activity of the factor (26). Moreover, despite the short duration of the experiment, one cannot exclude that some of the regulations are secondary to changes in hormonal or metabolic parameters (e.g. glucose or fatty acids) known to influence gene transcription.

We developed a metaalgorithm to combine gene expression data and clinical parameters. Ranking of variations in mRNA and epinephrine levels according to Spearman correlation coefficients allowed a selection of the regulated genes most closely associated with changes in epinephrine concentrations. A graphical representation (Fig. 1AGo) is shown for the top 15 genes up (0.6 <rs <0.8) and down-regulated (–0.8 <rs <–0.5). The tight association between the parameters suggests that these genes, of which 18 have yet- unknown functions, are direct targets of epinephrine and constitute candidates for the exploration of the mechanisms of transcriptional regulation. To sort the regulated genes into functional categories, we used GO annotations (www.geneontology.org) and an in-house recently developed data mining tool. Figure 2Go shows the relative impact of the hormone on genes defining representative functions and its effect on gene induction or repression. GO annotations were available for 12,474 genes represented on the microarray and 976 regulated genes.



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  		FIG. 1. Graphical representation of changes in gene expression and clinical parameters. A, Gene expression vs. epinephrine levels. Up- (left panel) and down-regulated (right panel) genes with the 15 highest Spearman correlation coefficients are shown. B, Gene expression vs. resting metabolic rate. Up-regulated genes with the 20 highest Spearman correlation coefficients are shown. Each line corresponds to one gene identified by UniGene number. Variation in mRNA levels is shown on the x-axis. Each dot represents one subject. The color of the dot corresponds to the extent of the variation in epinephrine levels ({Delta}epinephrine/epinephrine) for A and resting metabolic rate ({Delta}resting metabolic rate/resting metabolic rate) for B. In B, the gene with the highest correlation coefficient (rs = 0.93) is boxed.

 


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  		FIG. 2. Impact of epinephrine on genes grouped in functional categories. GO annotations (www.geneontology.org) were used to depict the main functions modified by epinephrine with an in-house-developed program. GO numbers are indicated for each function. Blue bar shows the number of microarray genes at a GO level (shown in inset) divided by the number of annotated microarray genes. Purple bar shows the number of regulated genes at a GO level (shown in inset) divided by the number of regulated and annotated genes. Black bar shows the number of regulated genes at a GO level (shown in inset) divided by the number of microarray genes at the same GO level (shown in inset). Red and green bars represent the importance of a GO level among regulated genes. Red bar is calculated as follows: (up-regulated genes at a GO level/all up-regulated genes)/[(up-regulated genes at a GO level/all up-regulated genes) + (down-regulated genes at a GO level/all down-regulated genes)]. Green bar is a similar representation for down-regulated genes. The number of up- and down-regulated genes with annotations are shown as insets within red and green bars, respectively. Percentages of up- and down-regulated genes with annotations are also shown.

 
The main functional categories at level 3 of the biological process tree were metabolism, cell growth and maintenance, and cell communication, i.e. cell-cell and cell-extracellular matrix interactions. Up-regulated genes were overrepresented in the response to external stimulus and response to stress categories (86 and 93%, respectively). For 526 cDNAs, available information indicated expressed sequence tags or genes encoding hypothetical proteins of unknown function. The genes represent novel putative target genes of catecholamines. Comparison of mRNA expression patterns for epinephrine-regulated genes in 14 tissues (27) showed a cluster containing 65 genes highly expressed in the cardiac muscle (Fig. 3AGo). Among them, 21 genes have also been shown to be expressed in skeletal muscle (source.stanford.edu and telethon.bio.unipd.it/GETProfiles/) (28). The cluster is likely to represent a set of genes typical of striated muscles. Catecholamines and thyroid hormones both increase resting metabolic rate, an effect that is mediated in part by action on skeletal muscle (21, 29). We therefore compared epinephrine-regulated genes with the 381 up-regulated and two down-regulated genes that we have recently shown as being controlled by T3 in human skeletal muscle (supplemental Table 2Go) (13). Twelve and 21 genes had concordant and discordant (up under T3 and down under epinephrine) regulations, respectively. The p85{alpha}regulatory subunit of phosphatidylinositol 3-kinase, a key enzyme of insulin signaling, is up-regulated to the same extent by both types of hormones in line with their common antagonistic effect of insulin action. However, the comparison shows that the overlap in terms of hormonal gene regulation is limited. It is therefore likely that the transcriptional adaptations leading to similar physiological effects (e.g. on energy expenditure, substrate oxidation, and protein synthesis) impact distinct genetic networks.




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  		FIG. 3. Two-dimensional clustering of 14 tissue experiments and transcripts that showed variation during epinephrine infusion. The set of genes were selected from the data matrix provided by the hybridization of 14 human tissues to a common reference pool. Experiments and responsive genes were grouped by hierarchical clustering after centering the log2 ratios on the mean for all experiments. Each row represents a single gene and each column an experimental sample. For each sample, the ratio of the abundance of the transcripts of each gene to the mean abundance across all experiments is represented by the color of the corresponding cell in the matrix file. Green boxes represent transcript levels lower than the mean. Red boxes are transcript levels higher than the mean, black boxes transcript level equal to the mean, and gray lines are missing data. Each node of the gene dendrogram was analyzed, and we focused on sets of genes clustered by functions. The upper dendrogram shows similarities in the expression pattern between tissues. A, Genes highly expressed in heart. Genes boxed in green have been shown to be expressed in skeletal muscle. B, Genes highly expressed in leukocytes and lymphocyte-producing tissues. Genes boxed in green encode secreted peptides.

 
cAMP-dependent signal transduction pathway

Epinephrine is acting on skeletal muscle through the Gs-coupled ß2-adrenergic receptor and stimulates cAMP production. We examined the effect of the hormone on expression of the genes involved in the cAMP signaling pathway (Fig. 4Go). At the level of adenylyl cyclase, the enzyme that converts ATP into cAMP, there was a down-regulation of adenylyl cyclase-associated protein 2, a protein that links the adenylyl cyclase protein complex to the actin cytoskeleton. Several phosphodiesterases (PDEs), which degrade intracellular cAMP and hence play a crucial role in the modulation of cAMP signaling, were regulated by epinephrine. The genes for cAMP-specific PDE4B, the cGMP-stimulated PDE2A and the cGMP-specific PDE6A and PDE6G, were all up-regulated (30). PDE4 and PDE2 contribute to cAMP hydrolysis, whereas PDE6, which is expressed in retinal rod but also in skeletal muscle, hydrolyzes cGMP. The PDE4D interacting protein, also called myomegalin, was down-regulated. Expressed in skeletal muscle, myomegalin functions as an anchor to localize PDE4D to the Golgi/centrosomal region (31). This coordinated up-regulation may constitute a feedback mechanism to control cAMP levels but also reveals the cross-talk between cAMP and cGMP pathways. PKA exerts a pivotal role in the cAMP signaling pathway through the phosphorylation of proteins involved in numerous cellular processes. Several families of proteins interact with PKA and modify its location, and hence biological role, within the cell. The ß-inhibitor of PKA, which inactivates PKA through binding to the catalytic subunit in the nucleus and transfer to the cytoplasm, was down-regulated. Conversely, expression of the PKA anchor protein 4, which binds to the regulatory subunit, was induced. Seven subunits of protein serine/threonine phosphatases were regulated during epinephrine infusion. On the five genes encoding catalytic or regulatory subunits of protein phosphatase 1 and 2A, which may play an important role in the control of CREB (de)phosphorylation (6), all but one, a regulatory subunit, showed decreased mRNA expression.



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  		FIG. 4. Effect of epinephrine on the expression of genes of the cAMP signal transduction cascade. Filled circles and open ellipses represent genes down- and up-regulated, respectively. Fold changes are included, and, if available, detailed data on subunit regulations are given. Gs, Stimulatory G protein; ACAP, adenylyl cyclase-associated protein 2; PDE2/4, phosphodiesterases 2A and 4B; AKAP, PKA anchor protein 4; ßIPKA, PKA catalytic subunit inhibitor-ß; CRI1, CREB binding protein/EP300 inhibitory protein 1; PP1/2A, protein phosphatases 1 and 2A (cat., catalytic; reg., regulatory subunits).

 
Modulation of transcription by the cAMP signaling pathway is mainly ensured by the CREB family of transcription factors. Consistent with the presence of CRE in its promoter, CREB expression was up-regulated by epinephrine. A crucial phase in the transcriptional activation is the recruitment by CREB of the coactivator CREB binding protein (32). CREB binding protein inhibitory protein 1 was down-regulated by epinephrine. This inhibitor is highly expressed in skeletal muscle in which it inhibits MyoD-dependent transcription (33). We also observed an induction of CREM/ICER mRNA. CREM/ICER is an early response gene whose burst of expression is transient due to a negative autoregulatory loop (34). By analogy with other organs, CREM/ICER induction in skeletal muscle may cause a refractory period for cAMP-mediated activation of some genes.

The overall picture that may be deduced from the changes in the cAMP-dependent signaling cascade is a combination of attenuation and stimulation. Attenuation of cAMP effect is illustrated by PDE and CREM/ICER up-regulation. Reinforcement of cAMP action may be contributed through positive effect on CREB expression and down-regulation of serine/threonine phosphatases.

CREs and cAMP-regulated genes

CREB mediates the activation of cAMP-responsive genes by binding as a dimer to the consensus palindrome TGACGTCA. Variant functional CREs containing the half-site TGACG have been identified but may be less active. Using recently published surveys, we wished to determine whether genes regulated by epinephrine in skeletal muscle had been previously identified as genes with functional CRE. Mayr and Montminy (32) have produced a list of cAMP-regulated genes containing consensus sites for CREB binding. Using a hidden Markov model trained on known CREB sites, Conkright et al. (35) performed a genome-wide analysis of target genes. Conservation between human and mouse sequences was used to diminish spurious occurrence of CREs. The list covered genes with a wide range of functions and tissue specificity. However, many of the identified genes were expressed in neurons. Comparison of the two lists of genes with our data identified 12 genes (Table 3Go). All were up-regulated during epinephrine infusion in accordance with published data. Besides CREB and CREM/ICER, the genes include the dual specificity phosphatase 1, which is a known CREB target gene, also identified in the genome-wide analysis (35), and three transcription factors (aryl hydrocarbon receptor, Pit 1, and PBX2).


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  		TABLE 3. Target genes containing CREs

 
Furthermore, the characterization of genes up-regulated by epinephrine may constitute an initial step in the analysis of transcriptional mechanisms of regulation. The expressions of induced genes shown on the left panel of Fig. 1AGo are tightly linked to epinephrine levels. These genes may be good candidates to characterize functional CREs. These comparisons illustrate the potential of bridging information from in vivo hormonal effect on gene expression and molecular mechanisms of gene transcription.

Energy metabolism

Carbohydrates are stored as glycogen whose catabolism provides a direct source of energy for the exercising muscle. Thirty genes involved in carbohydrate metabolism are regulated (24 up and six down) (Table 4Go). Several target genes were involved in glycogen metabolism including five of the six down-regulated genes (Fig. 5Go). Two key genes of glycogen synthesis were repressed. In liver and skeletal muscle, uridine 5'-diphosphate (UDP)-glucose is a direct precursor of glycogen. UDP-glucose pyrophosphorylase 2 transfers a glucose moiety from glucose-1-phosphate to Mg-uridine 5'-triphosphate and forms UDP-glucose. The energy of the phospho-glycosyl bond of UDP-glucose is used by glycogen synthase to catalyze the incorporation of glucose into glycogen. The glucan (1,4-{alpha}-) branching enzyme 1, a monomeric enzyme highly expressed in liver and muscle, allows ramification of the glycogen macromolecule. The glycogen synthase kinase 3ß was up-regulated. This enzyme is a cAMP-dependent serine/threonine kinase inactivating glycogen synthase (36). Regarding glycogen breakdown, the brain isoform of the glycogen phosphorylase was induced under epinephrine infusion, whereas the mRNA for the cAMP-activated phosphorylase ß-kinase was decreased. Defects in phosphorylase ß-kinase are the cause of phosphorylase kinase deficiency of liver and muscle, a glycogen storage disorder (37). A catalytic and regulatory subunit of protein phosphatase 1 was also negatively regulated. Protein phosphatase 1-mediated dephosphorylation relays insulin action in the inhibition of glycogenolysis and activation of glycogen synthesis (38). These regulations concur to an increase in glycogenolysis and a decrease in glycogen synthesis.


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  		TABLE 4. Genes participating in carbohydrate metabolism regulated by epinephrine in human skeletal muscle

 


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  		FIG. 5. Effect of epinephrine on the expression of genes involved in carbohydrate metabolism. The transcriptional pattern suggests that glucose use is increased under epinephrine. Filled and open ellipses represent genes down- and up-regulated, respectively. Fold changes are included, and, if available, detailed data on subunit regulations (phosphoglucomutase subunits 1, 3, and 5) are given. FBPase-1, Fructose-1,6-bisphosphatase; PFK-1, 6-phosphofructo-1-kinase; PDH, pyruvate dehydrogenase; PDK2, pyruvate dehydrogenase kinase, isoenzyme 2; PFK-2/FBPase-2, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase; PPP1CB, protein phosphatase 1 catalytic subunit-ß isoform; PPP1R1A, protein phosphatase 1 regulatory (inhibitor) subunit 1A; branching enzyme, glucan (1,4-{alpha}) branching enzyme 1.

 
Fructose-2,6-bisphosphate has a central role in glucose metabolism. It plays antagonistic actions between glycolytic and gluconeogenic pathways (39). The 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase is one of the most highly up-regulated gene of the present study. The corresponding protein is a homodimeric bifunctional enzyme involved in the precise regulation of the levels of fructose 2,6-bisphosphate, which is a potent allosteric activator of 6-phosphofructo-1-kinase and reduces the activity of the key regulatory enzyme of gluconeogenesis, fructose-1,6-bisphosphatase. The steady-state concentration of fructose-2,6-bisphosphate is determined by the balance between these opposing reactions. Adenovirus-induced overexpression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase in mouse liver has been shown to result in reduced activity of glucose-6-phosphatase and stimulation of glucokinase, thereby stimulating the glycolytic rate (40). Furthermore, two genes encoding proteins involved in galactose metabolism were up-regulated, the UDP-galactose transporter UGT1 and the UDP-glucose ceramide glucosyltransferase-like 2. Derived from lactose, galactose is converted to UDP-galactose before glucose metabolism. Altogether, these regulations are in favor of an increase in glycolysis.

A critical point in lipid metabolism is the fatty acid entry into the cell. We found that two genes encoding long chain fatty acid transporters highly expressed in skeletal muscle are regulated. The fatty acid transporter 4 gene was induced, whereas the fatty acid translocase FAT/CD36 gene was down-regulated. These opposite regulations may sign a change in the characteristics of fatty acid entry into skeletal muscle and transport between intracellular compartments (41). Lipid metabolism genes also comprised several apolipoproteins [apolipoprotein (apo)B, apoC1, apoC2, apoM, apoL1], which were up-regulated (supplemental Table 1Go).

Few genes participating in the mitochondrial respiratory chain were regulated by epinephrine. Two mRNA-encoding proteins involved in the transfer of electrons from nicotinamide adenine dinucleotide, reduced form (NADH) to the respiratory chain were up-regulated, a subunit of the NADH dehydrogenase (ubiquinone) 1 and a subunit of the NADH dehydrogenase (ubiquinone) flavoprotein 1. A component of the ubiquinol-cytochrome c reductase complex, the Rieske iron-sulfur polypeptide 1, and the ß-polypeptide of the H+ transporting mitochondrial F1 complex ATP synthase were down-regulated. The role of skeletal muscle uncoupling proteins is still elusive (42). They may play a role in energy metabolism through uncoupling of ATP synthesis from VO2, regulation of mitochondrial fatty acid metabolism, and control of reactive oxygen species production. Expression of uncoupling proteins 2 and 3 was not altered by epinephrine as confirmed by quantitative RT-qPCR measurements (Table 2Go). However, an induction of uncoupling protein 4, which is up-regulated during cold exposure in brain (43), was observed. PGC-1{alpha} is a transcriptional coactivator that plays an important role in the integration of responses to thermogenic stimuli (44). It is induced in rodent skeletal muscle after sympathetic nervous system activation. We found no change in mRNA expression, which may indicate a difference in PGC-1{alpha} gene regulation between rodent and human skeletal muscle (Table 2Go).

Correlations between changes in gene expression and variations in energy expenditure

Compared with thyroid hormone (13, 45), the transcriptional effect of catecholamine on the respiratory chain is moderate. It seems therefore likely that catecholamine action on skeletal muscle metabolic rate uses different pathways. An indication of potential genes involved in energy metabolism may be provided by the comparison between variations in gene expression and changes in resting metabolic rate (Fig. 1BGo). Ranking of genes classified in metabolism from GO annotations using Spearman correlation coefficients revealed 20 genes up-regulated with rs greater than 0.8. The list includes eight transcription factors that may play a role in the regulation of energy expenditure. The gene (Hs.1588) with the highest correlation coefficient (rs = 0.93) encodes the 4-aminobutyrate aminotransferase, an enzyme responsible for the catabolism of {gamma}-aminobutyric acid, which is highly expressed in skeletal muscle. The protein may be indirectly involved in energy metabolism. Moreover, it may constitute a molecular marker of energy expenditure regulation as its mRNA variations parallel changes in resting metabolic rate (see box in Fig. 1BGo).

Protein degradation

Catecholamines have an anabolic effect on skeletal muscle protein metabolism, which may be due to a stimulation of protein synthesis and an inhibitory action on proteolysis (5). Few genes involved in protein synthesis were regulated by epinephrine. This result indicates that the main effect of epinephrine is an inhibition of protein degradation (46, 47). However, it cannot be ruled out that the activation of protein synthesis genes may require a longer time. Intracellular proteolysis occurs via a lysosomal Ca2+-dependent and a nonlysosomal ATP-dependent pathway. The latter system degrades most endogenous proteins through covalent linking of proteins to the ubiquitin system and then rapid degradation by the proteasome machinery. It is striking that most of the down-regulated genes involved in protein breakdown participated in the ubiquitin system. Three genes encode members of the F-box protein family. The F-box proteins constitute one of the four subunits of the SKP1-cullin-F-box ubiquitin protein ligase complex. This protein plays a unique role in the phosphorylation-dependent ubiquitination. In addition, the ring-box 1 mRNA expression was reduced. The gene encodes a protein that heterodimerizes with cullin-1 to catalyze ubiquitin polymerization. The mRNA level of ubiquitin protein ligase E3A was decreased by epinephrine. Ubiquitin ligases play a crucial role in skeletal muscle atrophy and ß-adrenergic agonists are well known to reduce muscle wasting (48). Such regulations underline the molecular basis for an anticatabolic impact of catecholamines.

Regulation of genes involved in defense response

It is well established that inflammation activates the sympathetic nervous system. However, it is also increasingly recognized that catecholamines modulate the immune system and inflammatory response (49). Figure 2Go reveals that most genes involved in the response to stress (level 3 of biological process GO annotations) were up-regulated. To refine the classification, epinephrine-regulated genes were selected for the defense response (level 5 of biological process GO annotations). All but one was induced (Supplementary Table 3Go). On 96 genes, 35 encode secreted proteins. A coordinated regulation of genes encoding chemokines with Cys-Cys motif is apparent because 10 members (CCL1, 4, 5, 7, 11, 13, 14, 18, 19, and 20) were positively regulated on a total of 17 genes of the family represented on the array. Cys-X-Cys chemokines (CXCL4 and 10) were also up-regulated. These secreted cytokines are involved in immunoregulatory and inflammatory processes. They are chemotactic factors that possess various specificities to attract monocytes, lymphocytes, basophils, eosinophils, and neutrophils. Other secreted peptides with comparable functions were also up-regulated such as IL-16, defensin 4, the TNF ligand superfamily member 7, and the endothelial monocyte-activating polypeptide. Defense against pathogens is also ensured by the complement system. Epinephrine induced mRNA expression of components of the classical (1, 2, 5, and 8ß) and alternative (B and D) pathways as well as associated or related peptides (C4 binding protein-ß and vitronectin). Pleiotropic effect on the immune system was also illustrated by the up-regulation of interleukin receptors (IL1R accessory protein, IL2R{gamma}, IL4R{alpha}, and IL6R{alpha}). To get a different view at the effect of epinephrine on cell defense mechanisms, we analyzed the pattern of epinephrine-regulated mRNA expression in leukocytes and tissues producing lymphocytes (Fig. 3BGo). The cluster comprised 69 genes highly expressed in these tissues. This analysis raised the possibility that some of the regulated genes are expressed in resident or newly recruited mononuclear cells as shown in the hindlimb muscle of the mdx mouse, an animal model of Duchenne muscular dystrophy (50). Analyses of gene function and tissue distribution bring independent evidence that epinephrine has a profound impact on the inflammatory response in skeletal muscle.

These regulations are reminiscent of alterations in gene expression observed in response to skeletal muscle injuries. An induction of cytokine-related genes and especially of chemokine and chemokine receptors is observed in regenerating muscle after injection of the snake venom, cardiotoxin (51). Such a pattern is also found in the hindlimb muscle of mdx mice, which also show an up-regulation of complement system genes (52). Trauma of skeletal muscle is observed during excessive training. Development of an immune/inflammatory response may explain the overtraining syndrome (53). Our data suggest that the sympathetic nervous system could be an important regulator of the installation and maintenance of the inflammatory state. The data also may have significance for diseases with dysfunction of the sympathetic nervous system. Cardiac cachexia is a serious complication of chronic heart failure characterized by weakness and fatigue due to reduced skeletal muscle mass and impaired muscle quality (54). The cachectic patients have higher levels of plasma catecholamines than other patients with chronic heart failure and show inflammatory and immune reactions. The regulation of the expression of genes involved in these pathways by catecholamines may constitute one of the pathogenic mechanisms.


   Conclusion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results and Discussion
 Conclusion
References
 
In this paper, we have characterized the mRNA expression profile in response to epinephrine in vivo. Sorting of the regulated genes into functional classes, determination of common expression patterns in human tissues, and combined analysis of individual variations in gene expression and clinical parameters defined the molecular signatures in human skeletal muscle. Epinephrine modulated the expression of genes involved in the cAMP-mediated transcription pathway, thereby promoting autoregulatory mechanisms. In line with the known physiological effects of the hormone, modulation of genes involved in glycogen and glucose metabolism, and protein catabolism was observed. The study also revealed novel targets, especially genes involved in the immune system and inflammatory response. The data provide a molecular basis for the physiological action of epinephrine and the pathological alterations induced by sympathetic nervous system dysfunction.


   Acknowledgments
 
We thank the expert technical assistance of Marie-Adeline Marques (Centre d’Investigation Clinique, Toulouse) and Jean-José Maoret (Institut Louis Bugnard Molecular Biology Core Facility, Toulouse). We also thank Dr. Wim Saris (Maastricht University) for critical reading of the manuscript.


   Footnotes
 
This work was supported by grants from INSERM (CIC-Merck Clinical Research Program) (to D.Lan. and P.B.), Action Thématique Concertée INSERM en Nutrition (to D.Lan. and K.C.), and Fondation Claude Bernard (to K.C.).

Abbreviations: apo, Apolipoprotein; aRNA, amplified RNA; CRE, cAMP response element; CREB, cAMP response element-binding protein; CREM, CRE modulator; FDR, false discovery rate; GO, Gene Ontology; ICER, inducible cAMP early repressor; NADH, nicotinamide adenine dinucleotide, reduced form; PDE, phosphodiesterase; PKA, protein kinase A; RT, reverse transcription; RT-qPCR, RT-quantitative real-time PCR; SAM, significance analysis of microarray; SSC, saline sodium citrate; UDP, uridine 5'-diphosphate; VO2, oxygen consumption.

Received October 6, 2003.

Accepted December 22, 2003.


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