1Human Genomics Laboratory, Pennington Biomedical Research Center; 2Molecular Genetics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana; 3Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
Submitted 4 October 2004 ; accepted in final form 27 January 2005
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ABSTRACT |
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microarray; MINMOD Millennium; exercise training
Exercise improves insulin action (14, 18, 19), and weight loss associated with regular exercise prevents the onset of diabetes (26, 54). Increased expression of the insulin-responsive glucose transport proteins has been observed in response to exercise training and correlated with improved insulin action in skeletal muscle (20, 23, 44). Seven days of exercise have been shown to increase SI (9, 22). Exercise training has also been associated with increased oxidative capacity of skeletal muscle (7, 39, 48, 59). Even though skeletal muscle in older individuals seems to retain the ability to increase glucose transport into muscle with endurance training (9), recent data indicate that an age-associated decline in mitochondrial function contributes to the insulin resistance observed in the elderly (38, 50). It is not known whether exercise training-associated improvements in glucose uptake are limited primarily by increased expression of glucose transporters or by the improved muscle oxidative potential. Improvements in SI with exercise training may be related to coordinated changes in activity of proteins involved in insulin signal transduction, fuel partitioning and metabolism, and cytoarchitecture of skeletal muscle.
How gene expression is modulated by acute and chronic exercise is important for the understanding of the mechanisms by which exercise training improves insulin action in skeletal muscle. The effects of exercise training on the expression of several genes involved in insulin signaling [insulin receptor substrate-1 (IRS-1, IRS-2), phosphatidylinositol 3-kinase (PI3K), mitogen-activated protein kinase (MAPK), AMP-activated protein kinase (AMPK)], glucose transport (GLUT1, GLUT4), glycogen metabolism [glycogen synthase (GS), glycogen synthase kinase-3 (GSK-3)], glycolysis [hexokinase II (HKII), phosphofructokinase (PFK)], mitochondrial genes (COX4, ND4), mitochondrial biogenesis [peroxisome proliferator-activated receptor- coactivator-1 (PGC-1), nuclear respiratory factor-1 (NRF-1), NRF-2, transcription factor A, mitochondrial (TFAM)], and fatty acid oxidation [carnitine palmitoyltransferase I (CPT I), uncoupling protein 3 (UCP3), NADH6] (13, 19, 50, 57, 60) have been investigated. These studies suggest that exercise training entrains a complex program of transcriptional changes in target tissues that has been associated with improved insulin sensitivity and glucose metabolism.
Microarray technology offers a powerful tool to characterize changes in transcript levels on a large scale. The array technology has been helpful in defining a set of insulin-regulated genes in human skeletal muscle of healthy individuals during a 3-h hyperinsulinemic euglycemic clamp (45). Several studies have provided a catalog of genes differentially expressed in skeletal muscle under diverse clinical conditions, such as in the comparison of healthy nondiabetic subjects, individuals with T2DM (36), obese insulin-sensitive and obese insulin-resistant subjects (58), and old individuals with T2DM having good or poor glycemic control (52). However, none of these studies has focused on gene expression in skeletal muscle of healthy, previously sedentary individuals in response to an endurance exercise training program, taking into account their changes in SI. There is considerable variability for the changes in SI in response to exercise training, and several factors may account for this phenomenon. We (6) recently showed that, among 596 participants from the HERITAGE Family Study, only 58% increased their SI by 10% or more with a standardized 20-wk training program. These changes in SI were not associated with changes in body weight, waist circumference, or physical fitness. In the current study, we sought to determine what differences in skeletal muscle gene expression patterns were present before and after 20 wk of endurance exercise training in high and low SI responders by using a subsample from the HERITAGE Family Study subjects.
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RESEARCH DESIGN AND METHODS |
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Measurement of glucose, insulin, and SI. Fasting plasma glucose and insulin levels were determined at baseline and after the 20-wk exercise training program. The frequently sampled intravenous glucose tolerance test (FSIVGTT) as described by Walton et al. (55) was administered in the morning after an overnight fast of 12 h. Data were collected at baseline and posttraining 2436 h after an exercise session as described elsewhere (1). From the FSIVGTT data, SI was derived using the recently released MINMOD Millennium software (4). SI is a measure of insulin action and corresponds to the ability of a given increment in plasma insulin to accelerate glucose uptake and suppress glucose production (11).
Experimental study design. Muscle biopsies were taken from the middle of the vastus lateralis muscle by the percutaneous needle biopsy technique (51). Each biopsy was partitioned into two pieces: one was immediately frozen in liquid nitrogen and stored at 80°C until total RNA preparation, and the other was frozen in isopentane cooled by liquid nitrogen and used for histochemistry and enzyme activity assays.
Total RNA preparation. Frozen tissue samples were crushed in liquid nitrogen, and total RNA was extracted using the Tri Reagent (Molecular Research Center, Cincinnati, OH), followed by purification with Qiagen columns from the RNeasy kit (Qiagen, Valencia, CA). The concentration and quality of the RNA were determined spectrophotometrically. Once the integrity of the RNA was confirmed in a nondenaturing agarose gel, RNA pools were prepared. Given the limited amount of material available, it was necessary to use RNA pools for the present study. In addition, using RNA pools provided an advantage by decreasing the variability that tissue heterogeneity might introduce in the gene expression profiles, reducing the "polymorphic noise" present in humans and minimizing the effect of sex differences. Furthermore, we were able to accumulate enough RNA in the pools while still maintaining individual samples for validation of differentially expressed genes by use of quantitative (Q)RT-PCR. Therefore, equal amounts of the individual samples were used to generate four RNA pools: baseline HSIR, baseline LSIR, posttraining HSIR, and posttraining LSIR. The samples from HSIR and LSIR were analyzed, comparing pretraining and posttraining RNA pools.
Array generation. High-density oligonucleotide microarrays were generated by the Pennington Biomedical Research Center Genomics Core Microarray Facility. Briefly, the array was printed using a library of 18,861 human oligonucleotides (Sigma-Genosys, The Woodlands, TX) representing 17,260 unique genes (or transcripts) based on LEADS cluster analyses (Compugen, San Jose, CA). The 6070mer oligonucleotides were diluted in 50% DMSO and spotted onto poly-L-lysine-coated slides using an OmniGrid Microarrayer equipped with a Stealth SPH32 Micro Spotting Pin Matrix and SMP4 Micro Spotting Pins (Telechem International, Sunnyvale, CA).
RNA amplification and array detection. Microarray analysis was performed in triplicate, comparing pooled RNA from the HSIR and LSIR groups to investigate the differences in gene transcript profiles prior to exercise training (pretraining arrays) deposited in the GEO database as Series GSE1718 [NCBI GEO] , with sample ID nos. GSM29507 [NCBI GEO] , GSM29523 [NCBI GEO] and GSM29529 [NCBI GEO] . We also compared the HSIR and LSIR groups after exercise training (posttraining arrays) in a second set of triplicate arrays deposited in the GEO database as Series GSE1718 [NCBI GEO] with sample ID nos. GSM29533 [NCBI GEO] , GSM29535 [NCBI GEO] , and GSM29536 [NCBI GEO] . Dye switching was prepared in one slide of each set to account for incorporation differences between Cy3 and Cy5. RNA was labeled and detected using a tyramide signal amplification (TSA) labeling and detection kit (Perkin-Elmer, Boston, MA). The TSA method (TSATM, NEN Life Science Products, Boston, MA) is highly sensitive and allows for the use of small amounts (2 µg) of total RNA. The TSA method has been shown to provide consistent and reproducible signal amplification across arrays (25). Probe labeling and array hybridization were performed as described in the instruction manual (MICROMAX TSA Labeling and Detection Kit). After hybridization to probes, slides were scanned using a GSI Lumonics ScanArray 5000 (Packard Biochip Technologies, Meriden, CT) and expression data were analyzed with QuantArray V3.0 software (Packard Bioscience, Meriden, CT).
Array normalization. A uniform scale factor was applied to normalize signal intensities between Cy5 and Cy3. Locally weighted regression and scatter plot smoothing (LOWESS) was applied to account for dye variation between slides and for signal intensity effects. For a given experiment, we assumed that most genes on our slides were present in equal amounts, such that their intensity ratios were equal to 1 (or 0 if data were in log form). However, because there are dye variations across a single slide, as well as between slides, a correction must be applied so that we get an average ratio of 1. LOWESS is one method to do this. LOWESS normalization is based on the concept that any function can be well approximated in a small neighborhood by a low-order polynomial (in our case, we use a linear fit). The LOWESS method fits a curve to a log plot of intensity values and subtracts the fit values from the actual values to get the normalized values. An in-house software program was used to perform the analysis (details of the algorithm can be found at http://bioinfo.pbrc.edu). By use of the LOWESS method, several genes with significant differences in expression across the microarray experiments were identified. The gene expression data were analyzed comprehensively using the software applications of Spotfire (DecisionSite, Somerville, MA) and GeneSpring (Silicon Genetics, Redwood City, CA). The nomenclature adopted for referencing gene names, symbols, and other descriptions is that of the Nomenclature Working Group (http://archive.uwcm.ac.uk/uwcm/mg/docs//mut_nom.html).
QRT-PCR. We selected five genes from the microarray experiments to be validated by QRT-PCR. The genes were selected because they exhibited a minimum of 50% difference (P < 0.05) between HSIR and LSIR groups either at baseline or in the posttraining experiments. These genes were TTN, pyruvate dehydrogenase kinase-4 (PDK4), V-SKI avian sarcoma viral oncogen homolog (SKI), C-terminal binding protein-1 (CTBP1), and four-and-a-half LIM domains 1 (FHL1). The QRT-PCR studies were performed using TaqMan probe and primer sets to measure the levels of mRNA (Applied Biosystems, Foster City, CA). The four RNA pools from the microarray experiments were also used for the QRT-PCR validation. Total RNA was reverse transcribed using the Reverse Transcription Reagents Kit (Applied Biosystems), according to the manufacturer's instructions, for the two-step RT-PCR methodology. Briefly, cDNA was generated from 150 ng of total RNA of each pool as the template, and a reference sample cDNA was obtained from a commercially available skeletal muscle total RNA source to generate the standard curves (Ambion, Austin, TX). PCR was performed using an ABI PRISM 7900 SDS instrument (Applied Biosystems). The primer-probe sets were either selected from the gene expression system database (Assays-on Demand, Applied Biosystems) or designed using the Primer Express software (Applied Biosystems) and are available upon request. Reactions were carried out in a 384-well plate format with 12 µl of reaction volume. Two concentrations of each cDNA pool were used in triplicate reactions. We also used a 1:50 dilution of the cDNA pools for normalization of the gene signals with 18S rRNA by using the TaqMan PreDeveloped Assay Ribosomal RNA Control Reagents Kit (Applied Biosystems). A standard curve was generated with serial dilutions of the cDNA reference sample and applied to each primer and probe set. The cycle threshold value for each sample was then used to calculate relative expression. The expression level of each gene was normalized to 18S rRNA. Data are reported as arbitrary units. Differential gene expression between the LSIR and the HSIR groups as measured by QRT-PCR was analyzed using a one-tailed Student's t-test, since we hypothesized that the differences between the groups would follow the same pattern as that measured by the oligonucleotide array analysis.
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RESULTS |
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Pretraining arrays. At baseline, relatively few transcripts were >1.4-fold more abundant in the HSIR compared with the LSIR (n = 42). Table 2 lists the HSIR overexpressed transcripts encoding proteins involved in glycolysis, nitrogen metabolism, signal transduction, transcription regulation, and muscle contraction and development and several other transcripts with unknown or unclassified function. In addition, five transcripts that were less abundant in the HSIR compared with the LSIR group (ratio <0.7) are involved in cell growth/maintenance (1) and tRNA processing (1) or have an unknown function (3) (Table 3).
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The differences between the HSIR and LSIR groups in the posttraining arrays for the TTN gene were also validated by QRT-PCR. We found a 2.6-fold difference in TTN transcript levels between the HSIR and LSIR groups after exercise training (Fig. 1). This difference is smaller than the average 4.7-fold difference observed in the posttraining array experiments but within the 2.6 to 6.9-fold range observed across the triplicate experiments.
The levels of FHL1, SKI, and CTBP1 in the sedentary state were not different between the HSIR and LSIR groups by QRT-PCR, as we had found in the pretraining arrays (Fig. 1). However, after exercise training, the QRT-PCR results revealed significantly higher FHL1 and SKI levels in the HSIR compared with the LSIR group, as observed in the posttraining arrays. The 1.7-fold difference in CTBP1 observed in the posttraining arrays in the HSIR vs. LSIR group could not been replicated by the QRT-PCR. Instead, we observed only a 20% difference in CTBP1 levels favoring the HSIR group after exercise training.
Therefore, differences in gene expression between HSIR and LSIR groups, either in the pre- or posttraining states, were confirmed by QRT-PCR in four of the five genes identified by microarray analysis as differentially expressed.
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DISCUSSION |
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The genes differentially expressed between HSIR and LSIR groups in the posttraining arrays likely include a distinctive set of genes involved in the divergent SI response to the training program. Moreover, because there were no differences in mean SI at baseline, the genes differentially expressed in the pretraining arrays between HSIR and LSIR groups are likely to represent another set of genes that contribute to SI response to the exercise training program.
Several microarray studies have provided a catalog of differentially expressed skeletal muscle genes under diverse clinical conditions associated with insulin resistance such as TD2M, obesity, and aging (36, 50, 52, 58). A direct comparison of these reports with our study is, however, difficult, since the selection of the individuals in the HSIR and LSIR groups was based on SI responses to an exercise training program. Our study contrasted the differences in gene expression between LSIR and HSIR groups and not the effects of exercise training as such on gene expression patterns. Nonetheless, we intentionally searched our data for results on genes that, according to the literature, are modified by regular exercise or insulin-resistant states. Interestingly, there were no remarkable effects before or after exercise training on HSIR/LSIR expression ratios for genes involved in pathways of insulin signaling (IRS-1, IRS-2, PI3K, MAPK, AMPK), glucose transport (GLUT1, GLUT4), glycogen metabolism (GS, GSK-3), glycolysis (HKII, PFK), mitochondrial function (COX4, ND4), mitochondrial biogenesis (PGC-1, NRF-1, NRF-2, TFAM), and fatty acid oxidation (CPT I, UCP3, NADH6). These findings are undoubtedly due to the uniqueness of the experimental design on which the current study is based.
Differences in SI responses are expected to involve the transcriptional reprogramming of sets of skeletal muscle genes. Differences in gene expression, particularly in clinical studies, are represented by modest changes in groups of related genes such as those associated with key metabolic pathways (13, 21, 49). Such small differences are often difficult to identify in microarray studies but can be readily evaluated using QRT-PCR methodologies.
Genes upregulated before exercise training. Given the matching criteria between the HSIR and LSIR groups, we did not expect to find dramatic differences in gene expression before the exercise training intervention. Nevertheless, we verified whether there were differences in gene expression that could predispose the HSIR groups to increase SI by regular exercise more than the LSIR group. None of the differences was greater than 2.0-fold, and the differentially expressed transcripts encode proteins involved in glycolysis, nitrogen metabolism, signal transduction, transcription regulation, muscle contraction and development, and several new sequences with unknown or unclassified functions.
Among the novel candidate genes upregulated in the HSIR before exercise training we identified were sorting nexin 10 (SNX10), inositol (myo)-1(or -4)-monophosphatase 2 (IMPA2), diacylglycerol kinase- (DGKD), and PDK4. SNX10 contains a phosphoinositide-binding domain and belongs to a family of proteins involved in intracellular trafficking. IMPA2 shares similar enzyme activity with enzymes of the inositol phosphate second-messenger signaling pathway. DGKD is a cytoplasmic enzyme that phosphorylates diacylglycerol to produce phosphatidic acid, which, in turn, acts as second messenger in signaling cascades; therefore, DGKD is thought to play an important role in cellular signal transduction. Whether these genes play a role in the regular exercise-induced SI response will require additional studies.
We further validated the increased PDK4 expression in the HSIR group before exercise training. Skeletal muscle PDK4 expression increases in situations where carbohydrate availability and insulin levels are decreased and free fatty acids are increased, such as in fasting, high-fat/low-carbohydrate diets, and acute exercise (37, 40, 41). In skeletal muscle, PDK4 is induced during metabolic states in which there is a perceived deficit in whole body glucose availability (i.e., insulin resistance). A shift in substrate utilization in exercising muscle is driven by the increased delivery and oxidation of fatty acids, eliciting a progressive rise in acetyl-CoA that allosterically inhibits pyruvate dehydrogenase (PDH) activity (43). The induction of PDK4 suggests that PDK4-mediated inhibition of PDH in muscle represents a mechanism for conserving carbohydrate substrates by gradually limiting the entry of glycolytic products into the mitochondria for oxidation and thereby conserving tricarboxylic acids for gluconeogenesis. This function may be particularly important in muscle fibers that rely heavily on glycolytic metabolism, such as vastus lateralis. We speculate that a persistent elevation in PDK4 expression may ensure that glucose entering the cell is preferentially used for muscle glycogen resynthesis, reflecting the high metabolic priority given to the replenishment of energy reserves in the HSIR group, even in the sedentary state. This could represent one of the biological markers of the HSIR group's predisposition to benefit more from a training program in terms of SI increment than the LSIR group. Interestingly, there was no difference in PDK4 expression levels between the HSIR and LSIR groups after exercise training. Further studies will be required to more thoroughly examine whether the expression of PDK4 or other PDK isoforms like PDK2, PDH phosphorylation, and/or activity contribute to the changes in SI observed in certain individuals with exercise training.
Genes upregulated after exercise training. More transcripts were upregulated in the HSIR group after exercise training than in the sedentary state. These upregulated transcripts relate to genes involved in energetic and signal functions, muscle development and contraction, cell growth regulation and differentiation, and sequences with unknown functions (Table 4). Some of the upregulated genes were validated by QRT-PCR methodologies, including SKI, TTN, and FHL1.
SKI is a nuclear proto-oncoprotein that can induce both oncogenic transformation and terminal muscle differentiation (OMIM no. 164780 [OMIM] ). SKI could function as either a positive or negative regulator of transcription, depending on its tissue expression and/or the promoter context of the genes that use SKI as a transcription factor. In transgenic mice, Ski overexpression induces muscle differentiation in embryo fibroblasts and causes postnatal hypertrophy of type II muscle fibers. The Ski-null mice show a marked decrease in skeletal muscle mass in addition to other abnormalities (for review see ref. 31). The human SKI gene has been associated with congenital fiber type-disproportion type myopathy (CFTD), hypotonia, and craniofacial dysmorphism (1p36 deletion syndrome) (35). Severe hyperinsulinemia and insulin resistance have been described in patients with CFTD (OMIM no. 255310 [OMIM] ). It remains to be clarified whether the increased expression of the SKI gene that we observed in the HSIR group is causally related to the improved SI in muscle.
FHL1, also known as skeletal muscle LIM-1 (SLIM1) protein, is mainly expressed in skeletal muscle; FHL proteins are new members of the LIM-only protein family. FHL1 plays an important role in muscle development (28, 34), and its expression is more abundant in oxidative fibers (30). FHL1 is also thought to function as a scaffold for protein assembly in the actin-based cytoskeleton; it is located in an integrin-dependent manner to the nucleus, focal adhesions, and stress fibers. It has been suggested that one of the FHL1 LIM domains interacts with -actinin, a component of the Z-discs, whereas interactions of the other LIM domains with actin filaments and spectrin have been demonstrated for FHL3 (8). FHL1 was one of the genes identified by Roth et al. (46) as being upregulated in response to a 4-mo strength training program. It was recently reported that FHL1 might play a role during the early stages of skeletal muscle differentiation, specifically in the
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1-integrin-mediated signaling pathway (32). In addition, FHL proteins have been implicated in one of the downstream mechanisms by which the extracellular signal-regulated kinase 2 (ERK2) signaling pathway affects the regulation of differentiated growth (42). Interestingly, FHL2 (a cardiac muscle FHL protein) binds TTN, and one of its domains may couple the metabolic enzymes creatine kinase, adenylate kinase, and PFK to the sarcomeric structures (27). The binding partners for the homologous FHL1 and FHL3 proteins have not been defined.
TTN is a giant protein, the largest known polypeptide (3 MDa) and contributes to the maintenance of sarcomere organization and myofibrillar elasticity (for review see ref. 53). TTN may also participate in myofibrillar cell signaling. Tissue-specific expression of various TTN isoforms results in differential myocyte elasticity. Mutations located in the Z-disc binding region of TTN have been identified in tibial myopathies (17). Disruption of TTN has been reported to cause impaired sarcomerogenesis and results in thin, poorly contractile muscle cells. More than half of the TTN molecule is attached to the thick filament, where it appears to control the exact assembly of myosin and other filament components. From the end of the thick filament and the Z-line, TTN forms elastic connections. The Z-line domain of TTN interacts with telethonin/T-cap, which in turn interacts with the muscle growth factor myostatin and the muscle LIM protein (16). Near the middle of the thick filament, TTN has a kinase domain, but its functions and substrate(s) are not fully understood. Both ends of the molecule have potential phosphorylation sites that may be involved in protein signaling. For example, the COOH terminus of a TTN molecule is integrated in the myosin lattice and contains a Ser/Thr kinase domain whose absence leads to sarcomeric disassembly (15). The main functions of the TTN family seem to be to interconnect myosin and actin filaments axially and provide passive elasticity. TTN also makes it possible for equal forces to be distributed by myosin in both halves of the sarcomere (53).
Because FHL1 and TTN were upregulated after exercise training in the HSIR group, it is tempting to speculate on the potential role of these genes in the improved SI. The mechanisms by which exercise training improves SI in healthy skeletal muscle involve enhanced insulin action and signaling, glucose transport, and overall metabolic capability (10). The adaptive response to exercise training requires the sensing of biomechanical signals involving the interface between the contractile cytoskeleton (myofibrils) and the sarcolemma at specialized cell-cell junctions (intercalated discs) and cell-substrate adhesion complexes (costameres). New evidence suggests that complexes associated with the TTN protein sense myocyte stretch, and TTN has recently been proposed as an ideal biomechanical sensor (33). If one of the complexes associated with TTN is FHL1, which in turn could interact with metabolic enzymes, it could explain how increased TTN and FHL1 contribute to an improved SI response with exercise training. Thus far, there is little direct evidence to support this hypothesis, and experimental data will be required to validate this concept.
In conclusion, we have identified a unique profile of genes that are differentially expressed between individuals with high SI response to exercise training vs. others carefully matched who showed absolutely no improvement. Among the genes identified with this approach we found that, before exercise training, PDK4 is overexpressed by 80% in the high SI responders to regular exercise. In those who expressed the highest SI improvement, SKI, FHL1, and TTN were overexpressed by 50470% compared with those whose SI remained stable. The proteins encoded by these novel genes could play a role through either cell-matrix interactions or enhanced signaling pathways involved in the SI response. Moreover, our studies have identified other transcripts of unknown functions that could participate in the regulation of SI response to regular exercise. The data presented here offer new candidate genes to account for human variation in the ability to improve SI in response to regular exercise.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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REFERENCES |
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