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• W2937478839 abstract "•Mice and humans show daily variance in exercise capacity•Exercise intensity and clock proteins affect daytime variance in exercise capacity•Exercise elicits distinct daytime muscle transcriptomic and metabolic signature•ZMP, an AMPK activator, is induced by exercise in a daytime-dependent manner Physical performance relies on the concerted action of myriad responses, many of which are under circadian clock control. Little is known, however, regarding the time-dependent effect on exercise performance at the molecular level. We found that both mice and humans exhibit daytime variance in exercise capacity between the early and late part of their active phase. The daytime variance in mice was dependent on exercise intensity and relied on the circadian clock proteins PER1/2. High-throughput gene expression and metabolic profiling of skeletal muscle revealed metabolic pathways that are differently activated upon exercise in a daytime-dependent manner. Remarkably, we discovered that ZMP, an endogenous AMPK activator, is induced by exercise in a time-dependent manner to regulate key steps in glycolytic and fatty acid oxidation pathways and potentially enhance exercise capacity. Overall, we propose that time of day is a major modifier of exercise capacity and associated metabolic pathways. Physical performance relies on the concerted action of myriad responses, many of which are under circadian clock control. Little is known, however, regarding the time-dependent effect on exercise performance at the molecular level. We found that both mice and humans exhibit daytime variance in exercise capacity between the early and late part of their active phase. The daytime variance in mice was dependent on exercise intensity and relied on the circadian clock proteins PER1/2. High-throughput gene expression and metabolic profiling of skeletal muscle revealed metabolic pathways that are differently activated upon exercise in a daytime-dependent manner. Remarkably, we discovered that ZMP, an endogenous AMPK activator, is induced by exercise in a time-dependent manner to regulate key steps in glycolytic and fatty acid oxidation pathways and potentially enhance exercise capacity. Overall, we propose that time of day is a major modifier of exercise capacity and associated metabolic pathways. Exercise is an effective lifestyle intervention for the prevention and mitigation of various diseases. In addition, improvement of exercise performance is of interest for elite and amateur athletes. Therefore, there is growing interest in optimizing the benefits of exercise and its performance. Weizmann Institute researchers investigated whether the time of day and circadian clock affect exercise performance and related metabolic pathways in mice and humans. They found exercise performance is better in the evening than in the morning hours; it relies on the circadian clock and produces a distinct daytime-dependent response in the muscle. These results suggest that timing exercise during the day can affect exercise capacity and be applied to improve exercise performance and potentially optimize health benefits. Physical activity is an intricate pleotropic process that relies on the coordinated function of cells, tissues, and organs. Exercise imposes a major challenge to whole-body homeostasis and thus elicits a myriad of adaptive responses at the cellular and systemic levels to redistribute resources and address the increase in muscle energy demands (Egan and Zierath, 2013Egan B. Zierath J.R. Exercise metabolism and the molecular regulation of skeletal muscle adaptation.Cell Metab. 2013; 17: 162-184Abstract Full Text Full Text PDF PubMed Scopus (1160) Google Scholar, Hawley et al., 2014Hawley J.A. Hargreaves M. Joyner M.J. Zierath J.R. Integrative biology of exercise.Cell. 2014; 159: 738-749Abstract Full Text Full Text PDF PubMed Scopus (581) Google Scholar). Nowadays, exercise is widely accepted as a major benefactor for human health and is often recommended for prevention and mitigation of morbidities such as obesity, type 2 diabetes mellitus, and cardiovascular diseases (Cartee et al., 2016Cartee G.D. Hepple R.T. Bamman M.M. Zierath J.R. Exercise promotes healthy aging of skeletal muscle.Cell Metab. 2016; 23: 1034-1047Abstract Full Text Full Text PDF PubMed Scopus (261) Google Scholar, Distefano and Goodpaster, 2018Distefano G. Goodpaster B.H. Effects of exercise and aging on skeletal muscle.Cold Spring Harb. Perspect. Med. 2018; 8https://doi.org/10.1101/cshperspect.a029785Crossref PubMed Scopus (140) Google Scholar, Fan and Evans, 2017Fan W. Evans R.M. Exercise mimetics: impact on health and performance.Cell Metab. 2017; 25: 242-247Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar). There is, therefore, a growing interest in deciphering exercise biology, namely identifying molecular events associated with physical activity that affect exercise capacity and carry health benefits (Gabriel and Zierath, 2017Gabriel B.M. Zierath J.R. The limits of exercise physiology: from performance to health.Cell Metab. 2017; 25: 1000-1011Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar). The physiology and behavior of mammals exhibit daily oscillations that are driven by an endogenous circadian clock (Bass and Lazar, 2016Bass J. Lazar M.A. Circadian time signatures of fitness and disease.Science. 2016; 354: 994-999Crossref PubMed Scopus (338) Google Scholar, Bass and Takahashi, 2010Bass J. Takahashi J.S. Circadian integration of metabolism and energetics.Science. 2010; 330: 1349-1354Crossref PubMed Scopus (1282) Google Scholar, Panda, 2016Panda S. Circadian physiology of metabolism.Science. 2016; 354: 1008-1015Crossref PubMed Scopus (526) Google Scholar). The mammalian circadian timing system consists of a central pacemaker in the brain that is entrained by daily light-dark cycles and synchronizes subsidiary oscillators in virtually every cell of the body mostly through rest-activity and feeding-fasting cycles (Dibner et al., 2010Dibner C. Schibler U. Albrecht U. The mammalian circadian timing system: organization and coordination of central and peripheral clocks.Annu. Rev. Physiol. 2010; 72: 517-549Crossref PubMed Scopus (1648) Google Scholar, Partch et al., 2014Partch C.L. Green C.B. Takahashi J.S. Molecular architecture of the mammalian circadian clock.Trends Cell Biol. 2014; 24: 90-99Abstract Full Text Full Text PDF PubMed Scopus (846) Google Scholar, Reinke and Asher, 2019Reinke H. Asher G. Crosstalk between metabolism and circadian clocks.Nat. Rev. Mol. Cell Biol. 2019; 20: 227-241Crossref PubMed Scopus (256) Google Scholar). The pervasive circadian control of metabolism raises the question of whether exercise performance exhibits daily variance. Hitherto, several human studies supported daily variance in exercise performance (Drust et al., 2005Drust B. Waterhouse J. Atkinson G. Edwards B. Reilly T. Circadian rhythms in sports performance–an update.Chronobiol. Int. 2005; 22: 21-44Crossref PubMed Scopus (396) Google Scholar, Küüsmaa et al., 2016Küüsmaa M. Schumann M. Sedliak M. Kraemer W.J. Newton R.U. Malinen J.P. Nyman K. Häkkinen A. Häkkinen K. Effects of morning versus evening combined strength and endurance training on physical performance, muscle hypertrophy, and serum hormone concentrations.Appl. Physiol. Nutr. Metab. 2016; 41: 1285-1294Crossref PubMed Scopus (44) Google Scholar, Reilly and Waterhouse, 2009Reilly T. Waterhouse J. Sports performance: is there evidence that the body clock plays a role?.Eur. J. Appl. Physiol. 2009; 106: 321-332Crossref PubMed Scopus (117) Google Scholar, Thosar et al., 2018Thosar S.S. Herzig M.X. Roberts S.A. Berman A.M. Clemons N.A. McHill A.W. Bowles N.P. Morimoto M. Butler M.P. Emens J.S. et al.Lowest perceived exertion in the late morning due to effects of the endogenous circadian system.Br. J. Sports Med. 2018; 52: 1011-1012Crossref PubMed Scopus (5) Google Scholar). Yet the circadian clock control and the associated time- and exercise-dependent molecular events are relatively unknown. Hence, we hypothesized that studying exercise biology through the prism of time is expected to shed light on molecular mechanisms that are implicated in physical activity and potentially affect exercise capacity. We characterized herein the daily variance in exercise capacity at the physiological and molecular levels. In essence, we asked the following questions: Is there a daytime difference in exercise capacity? Does it rely on core components of the circadian clock machinery? What are the underlying molecular events? To this end, mice were enrolled in exercise protocols at different times of day. Concurrently, we monitored their exercise capacity and characterized the changes in their skeletal muscle gene expression and metabolic profiles. Furthermore, we corroborated our findings with human studies. Overall, our results suggest that time of day is a central modifier of exercise capacity and related metabolic pathways. Physical performance relies on the concerted action of myriad responses, many of which are under circadian clock control. This prompted us to test whether exercise capacity differs throughout the day. Specifically, since exercise is typically performed during the active phase, we examined the difference in exercise capacity of wild-type mice between two time points within their active phase, namely 2 h and 10 h within the dark phase (i.e., zeitgeber time [ZT]14 and ZT22, thereby termed Early and Late, respectively). The food consumption and spontaneous locomotor activity of wild-type mice 2 h prior to the exercise tests did not significantly differ between the Early and Late groups (Figure S1A). We tested the treadmill performance of sedentary wild-type mice under 3 different protocols: high, moderate, and low intensities that generally correspond to 100%, 55%, and 45% of their maximal aerobic capacity, respectively (Figure 1A). Sedentary mice slightly outperformed the high-intensity exercise protocol at the early part of their active phase (Figure 1B). By contrast, in the moderate- (Figure 1C) and low-intensity (Figure 1D) protocols, the Late group performed substantially better than the Early group, running for a longer time and maintaining higher blood glucose levels (i.e., above 70 mmol/dL) for a longer duration. Hence, we concluded that exercise capacity exhibits daily variance that is dependent on exercise intensity. In view of the pervasive clock control of metabolism and physiology (Asher and Sassone-Corsi, 2015Asher G. Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock.Cell. 2015; 161: 84-92Abstract Full Text Full Text PDF PubMed Scopus (512) Google Scholar, Panda, 2016Panda S. Circadian physiology of metabolism.Science. 2016; 354: 1008-1015Crossref PubMed Scopus (526) Google Scholar, Reinke and Asher, 2019Reinke H. Asher G. Crosstalk between metabolism and circadian clocks.Nat. Rev. Mol. Cell Biol. 2019; 20: 227-241Crossref PubMed Scopus (256) Google Scholar), we examined whether the variance in exercise capacity relies on a functional circadian clock. To this end, we tested the clock mutant Per1/2−/− mice, which exhibit arrhythmic behavior in constant darkness and diminished circadian gene expression (Zheng et al., 2001Zheng B. Albrecht U. Kaasik K. Sage M. Lu W. Vaishnav S. Li Q. Sun Z.S. Eichele G. Bradley A. et al.Nonredundant roles of the mPer1 and mPer2 genes in the mammalian circadian clock.Cell. 2001; 105: 683-694Abstract Full Text Full Text PDF PubMed Scopus (716) Google Scholar). In contrast to wild-type mice, PER1/2 null mice showed no significant difference in their exercise capacity between the Early and Late groups regardless of the exercise protocol (Figures 1E–1G). Consistently, we did not observe any significant difference in their blood glucose levels upon the moderate- and low-intensity exercise protocols between the Early and Late groups (Figures 1F and 1G). Notably, the lack of daily variance in the moderate- and low-intensity exercise protocols in PER1/2 null mice stemmed from reduced performance in the late part (t test, p value 0.00017 and 0.003 for moderate- and low-intensity Late groups, respectively) rather than increased competence at the early part of the active phase in comparison to wild-type mice (t test, p > 0.05; compare Figures 1C and 1D with Figures 1F and 1G). Notably, the food consumption 2 h prior to the exercise test did not significantly differ between the two mouse strains, though we observed an overall tendency for higher food intake in the Early group. The spontaneous locomotor activity of PER1/2 null mice was elevated compared to wild-type mice in the Early group and similar in the Late group (Figure S1A). The total daily food consumption and locomotor activity of wild-type and PER1/2 null mice were comparable (Figure S1B), and the two mouse strains did not differ in their body weight (27.5 ± 1.26; 27.26 ± 0.94, t test, p = 0.88, n = 6, for wild-type and PER1/2 null mice, respectively). Taken together, our results suggest that mice exhibit daily variance in exercise capacity that is dependent on exercise intensity and relies on molecular components of the circadian clock. In view of the intricate nature of exercise biology, it is conceivable that the observed phenotype is the outcome of various effectors both at the system and tissue levels. In the current study, we focused on skeletal muscle (i.e., gastrocnemius), which is known to exhibit significant changes in nutrient demand and energy utilization upon physical activity (Baskin et al., 2015Baskin K.K. Winders B.R. Olson E.N. Muscle as a “mediator” of systemic metabolism.Cell Metab. 2015; 21: 237-248Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar, Egan and Zierath, 2013Egan B. Zierath J.R. Exercise metabolism and the molecular regulation of skeletal muscle adaptation.Cell Metab. 2013; 17: 162-184Abstract Full Text Full Text PDF PubMed Scopus (1160) Google Scholar). The daytime variance in exercise capacity can stem from differences in the starting points, namely the basal metabolic and physiological states that likely differ between the Early and Late groups, and/or be because of differences in the response to exercise between the two time points. In our pursuit for molecular components that are implicated in daily variance of exercise capacity, we considered these two scenarios and analyzed the data accordingly. Hence, to obtain gene expression signatures that capture both the time- and exercise-dependent effects, we characterized the gene expression profiles of mouse gastrocnemius muscle, comparing non-exercised mice (control) with mice performing the moderate-intensity exercise protocol (Exercise) upon which we observed a large difference in exercise capacity between the Early and Late groups. Because there was more than an hour-and-a-half difference in exercise capacity between the two cohorts (Figure 1C), to enable a standardized and well-controlled comparison between the two groups and capture changes that occur throughout the process rather than posteriori, we obtained and analyzed muscle samples from both control and exercised group 1 h from the beginning of the test (Figures 2, 3, S2, and S3; Tables S2 and S3).Figure 3Dissection of the Daytime Effect of Exercise on Skeletal Muscle Gene ExpressionShow full caption(A) Heatmap of relative expression of differentially expressed genes between non-exercised (control) and moderate-intensity exercised mice (exercise) for Early or Late group. Genes with positive and negative Z scores are depicted in yellow and blue, respectively (EdgeR exact test adjusted FDR < 0.05 and absolute log2[fold change] > 0.5, n = 3).(B) Bar graph representation of the number of genes that were significantly affected by exercise for the Early or Late group with Venn diagram below depicting the number of unique and overlapping genes between the two groups.(C) The number of genes that were significantly up- or downregulated by exercise in the Early or Late group.(D) KEGG pathway enrichment analysis for exercise-induced genes for the Early or Late group. The enrichment score (−log10 p value) is depicted in white-green-blue scale.(E) Volcano plot representation of genes that were significantly affected upon exercise in the Early or Late group. The dotted horizontal and vertical lines represent the significance threshold (Edge R exact test FDR adjusted, p < 0.05, and absolute log2[fold change] > 0.5, respectively; n = 3). Names of selected genes are indicated.See also Table S2 and Figure S3.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Heatmap of relative expression of differentially expressed genes between non-exercised (control) and moderate-intensity exercised mice (exercise) for Early or Late group. Genes with positive and negative Z scores are depicted in yellow and blue, respectively (EdgeR exact test adjusted FDR < 0.05 and absolute log2[fold change] > 0.5, n = 3). (B) Bar graph representation of the number of genes that were significantly affected by exercise for the Early or Late group with Venn diagram below depicting the number of unique and overlapping genes between the two groups. (C) The number of genes that were significantly up- or downregulated by exercise in the Early or Late group. (D) KEGG pathway enrichment analysis for exercise-induced genes for the Early or Late group. The enrichment score (−log10 p value) is depicted in white-green-blue scale. (E) Volcano plot representation of genes that were significantly affected upon exercise in the Early or Late group. The dotted horizontal and vertical lines represent the significance threshold (Edge R exact test FDR adjusted, p < 0.05, and absolute log2[fold change] > 0.5, respectively; n = 3). Names of selected genes are indicated. See also Table S2 and Figure S3. As expected, and in line with previous reports of circadian gene expression profiles from skeletal muscle of mice and humans (Andrews et al., 2010Andrews J.L. Zhang X. McCarthy J.J. McDearmon E.L. Hornberger T.A. Russell B. Campbell K.S. Arbogast S. Reid M.B. Walker J.R. et al.CLOCK and BMAL1 regulate MyoD and are necessary for maintenance of skeletal muscle phenotype and function.Proc. Natl. Acad. Sci. USA. 2010; 107: 19090-19095Crossref PubMed Scopus (241) Google Scholar, McCarthy et al., 2007McCarthy J.J. Andrews J.L. McDearmon E.L. Campbell K.S. Barber B.K. Miller B.H. Walker J.R. Hogenesch J.B. Takahashi J.S. Esser K.A. Identification of the circadian transcriptome in adult mouse skeletal muscle.Physiol. Genomics. 2007; 31: 86-95Crossref PubMed Scopus (252) Google Scholar, Perrin et al., 2018Perrin L. Loizides-Mangold U. Chanon S. Gobet C. Hulo N. Isenegger L. Weger B.D. Migliavacca E. Charpagne A. Betts J.A. et al.Transcriptomic analyses reveal rhythmic and CLOCK-driven pathways in human skeletal muscle.Elife. 2018; 7Crossref PubMed Scopus (67) Google Scholar, Zhang et al., 2014Zhang R. Lahens N.F. Ballance H.I. Hughes M.E. Hogenesch J.B. A circadian gene expression atlas in mammals: implications for biology and medicine.Proc. Natl. Acad. Sci. USA. 2014; 111: 16219-16224Crossref PubMed Scopus (1262) Google Scholar), we observed considerable changes in gene expression in gastrocnemius of sedentary mice between the early and late active phase (time effect), (Figure 2A; Table S2). Overall, 513 transcripts were differentially expressed between the Early and Late groups of non-exercised mice (Figure 2B; Tables S2B and S2C), with 275 and 238 being up- and downregulated, respectively (Figure 2C). Pathway analysis evinced that many of these genes participate in various metabolic pathways and related signaling cascades (e.g., carbohydrate metabolism and peroxisome proliferator-activated receptor [PPAR], AMP-activated protein kinase [AMPK], and hypoxia inducible factor [HIF] signaling pathways), as well as circadian rhythms (Figures 2D and S2; Table S2E). It is, therefore, conceivable that the basal differences in skeletal muscle gene expression between the Early and Late groups participate in the observed daytime variance in exercise capacity. Next, we examined the effect of exercise on skeletal muscle gene expression irrespective of the time of the day (exercise effect) (Figure 2; Tables S2C–S2E). Here again, in line with former studies, we observed changes in gene expression upon exercise (Dickinson et al., 2018Dickinson J.M. D'Lugos A.C. Naymik M.A. Siniard A.L. Wolfe A.J. Curtis D.R. Huentelman M.J. Carroll C.C. Transcriptome response of human skeletal muscle to divergent exercise stimuli.J. Appl. Physiol. 2018; 124: 1529-1540Crossref PubMed Scopus (36) Google Scholar, Pérez-Schindler et al., 2017Pérez-Schindler J. Kanhere A. Edwards L. Allwood J.W. Dunn W.B. Schenk S. Philp A. Exercise and high-fat feeding remodel transcript-metabolite interactive networks in mouse skeletal muscle.Sci. Rep. 2017; 7: 13485Crossref PubMed Scopus (13) Google Scholar). The expression levels of 628 transcripts were altered upon exercise, regardless of the time of day (Figures 2A and 2B), with 313 and 315 being up- and downregulated, respectively (Figure 2C). A subset of transcripts (i.e., 154) was altered by both time and exercise (Figures 2D and 2E; Table S2D). Notably, these common transcripts were enriched for FoxO and insulin signaling (e.g., PI3-AKT), as well as lipid metabolism based on KEGG pathway analysis (Figure 2D), and included several core clock genes (e.g., Per1, Per2, and Bmal1) and master metabolic regulators (e.g., PPARα and KLF10) (Figure 2E). Intriguingly, the unique transcripts for exercise exhibited remarkable enrichment for immune-related processes such as tumor necrosis factor (TNF) signaling and antigen processing-presenting pathways (Figure 2D; Table S2E). We concluded that the extent of daytime and exercise effects on gene expression are comparable, whereby some genes are affected by both, for example, several clock genes and master metabolic regulators, whereas others are either exercise or daytime exclusive. In an attempt to identify genes and pathways that play a role in daily variance in exercise capacity, we dissected the time-dependent effect of exercise on muscle gene expression (Figure 3). Exercise elicited a differential effect on gene expression between the Early and the Late group (503 and 285 for Early and Late group, respectively) (Figures 3A and 3B; Table S2C). In total, 160 genes overlapped between the Early and Late group and hence responded to exercise independently of the time of day (Figure 3B; Table S2F). We observed a daytime-dependent effect on gene expression with an even distribution in the Early group (247 and 256 up- and downregulated, respectively) and a higher propensity of upregulated transcripts in the Late group (178 and 107 up- and downregulated, respectively) (Figure 3C; Table S2C). KEGG pathway analysis evinced a time-dependent effect between the Early and Late groups upon exercise, whereby insulin signaling pathways and glucose metabolism were enriched specifically in the Early group (Figure 3D; Table S2G). Intriguingly, PPARα was induced exclusively in the Early group, which might suggest that this master metabolic regulator is required already at an early stage of the exercise test (i.e., within the first hour) to enable mice to cope with the physical challenge. We also observed an effect on several genes that encode for mitochondrial functions; while the oxidative-phosphorylation-related gene Coq10b levels were induced by exercise, regardless of time, both Atpaf1 and Atpif1 were repressed upon exercise exclusively in the Early group. By contrast, the mitochondrial uncoupling protein UCP3 was induced specifically in the Late group (Figure 3E; Table S2C). Thus, exercise alters muscle gene expression in a time-dependent manner, with more pronounced changes in the Early than in the Late group, including a time-specific effect on several metabolic regulators and pathways such as insulin and glucose metabolism. Since circadian clocks respond to environmental signals, and clocks in peripheral tissues are markedly influenced by metabolic cues (e.g., feeding, temperature, and oxygen) (Asher and Sassone-Corsi, 2015Asher G. Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock.Cell. 2015; 161: 84-92Abstract Full Text Full Text PDF PubMed Scopus (512) Google Scholar, Reinke and Asher, 2019Reinke H. Asher G. Crosstalk between metabolism and circadian clocks.Nat. Rev. Mol. Cell Biol. 2019; 20: 227-241Crossref PubMed Scopus (256) Google Scholar), we specifically inspected the time- and exercise-dependent effects on the expression levels of core clock genes. In agreement with our RNA sequencing (RNA-seq) data (Table S2), quantitative real-time PCR analyses of core clock transcript levels evinced that some of them respond to exercise in a time-dependent manner (Figure S3; Table S3). As expected, the basal expression levels of the majority of clock genes differed between the two time points, in particular Per1, Per2, Clock, and Bmal1, in agreement with their daily rhythmic expression in skeletal muscle (McCarthy et al., 2007McCarthy J.J. Andrews J.L. McDearmon E.L. Campbell K.S. Barber B.K. Miller B.H. Walker J.R. Hogenesch J.B. Takahashi J.S. Esser K.A. Identification of the circadian transcriptome in adult mouse skeletal muscle.Physiol. Genomics. 2007; 31: 86-95Crossref PubMed Scopus (252) Google Scholar, Perrin et al., 2018Perrin L. Loizides-Mangold U. Chanon S. Gobet C. Hulo N. Isenegger L. Weger B.D. Migliavacca E. Charpagne A. Betts J.A. et al.Transcriptomic analyses reveal rhythmic and CLOCK-driven pathways in human skeletal muscle.Elife. 2018; 7Crossref PubMed Scopus (67) Google Scholar, Zhang et al., 2014Zhang R. Lahens N.F. Ballance H.I. Hughes M.E. Hogenesch J.B. A circadian gene expression atlas in mammals: implications for biology and medicine.Proc. Natl. Acad. Sci. USA. 2014; 111: 16219-16224Crossref PubMed Scopus (1262) Google Scholar). A trend of induction by exercise was apparent for Per1, Per2, Bmal1, and Cry2. It is noteworthy that Cry2 expression responds to oxygen levels (Adamovich et al., 2017Adamovich Y. Ladeuix B. Golik M. Koeners M.P. Asher G. Rhythmic oxygen levels reset circadian clocks through HIF1alpha.Cell Metab. 2017; 25: 93-101Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar), which are altered in skeletal muscle upon exercise (Martin et al., 2009Martin D.S. Levett D.Z. Mythen M. Grocott M.P. Caudwell Xtreme Everest Research GroupChanges in skeletal muscle oxygenation during exercise measured by near-infrared spectroscopy on ascent to altitude.Crit. Care. 2009; 13: S7Crossref PubMed Scopus (18) Google Scholar). Intriguingly, exercise exclusively induced Rev-erbα in the late, but not in the early, part of the active phase. Collectively, our high-throughput gene expression analyses evinced a distinct muscle gene expression signature that is both daytime and exercise dependent. At the molecular level, it is well known that exercise induces substantial changes on skeletal muscles at multiple levels from a transcriptional response to marked changes in metabolite content, metabolic pathway activation, and energy utilization (Egan and Zierath, 2013Egan B. Zierath J.R. Exercise metabolism and the molecular regulation of skeletal muscle adaptation.Cell Metab. 2013; 17: 162-184Abstract Full Text Full Text PDF PubMed Scopus (1160) Google Scholar, Overmyer et al., 2015Overmyer K.A. Evans C.R. Qi N.R. Minogue C.E. Carson J.J. Chermside-Scabbo C.J. Koch L.G. Britton S.L. Pagliarini D.J. Coon J.J. et al.Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation.Cell Metab. 2015; 21: 468-478Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar, Starnes et al., 2017Starnes J.W. Parry T.L. O'Neal S.K. Bain J.R. Muehlbauer M.J. Honcoop A. Ilaiwy A. Christopher P.M. Patterson C. Willis M.S. Exercise-induced alterations in skeletal muscle, heart, liver, and serum metabolome identified by non-targeted metabolomics analysis.Metabolites. 2017; 7https://doi.org/10.3390/metabo7030040Crossref PubMed Scopus (27) Google Scholar). Likewise, circadian changes in metabolite composition of muscle tissue were recently reported (Dyar et al., 2018aDyar K.A. Hubert M.J. Mir A.A. Ciciliot S. Lutter D. Greulich F. Quagliarini F. Kleinert M. Fischer K. Eichmann T.O. et al.Transcriptional programming of lipid and amino acid metabolism by the skeletal muscle circadian clock.PLoS Biol. 2018; 16: e2005886Crossref PubMed Scopus (80) Google Scholar, Dyar et al., 2018bDyar K.A. Lutter D. Artati A. Ceglia N.J. Liu Y. Armenta D. Jastroch M. Schneider S. de Mateo S. Cervantes M. et al.Atlas of circadian metabolism reveals system-wide coordination and communication between clocks.Cell. 2018; 174: 1571-1585.e11Abstract Full Text Full Text PDF PubMed Scopus (175) Google Scholar). The above-described high-throughput gene expression analyses depicted the daytime- and exercise-dependent effects on the transcriptional response. To obtain a comprehensive view of the molecular events that occur in skeletal muscle upon physical activity and conceivably account for the daytime variance in exercise capacity, we complemented the transcriptomic analyses with metabolic profiling of skeletal muscle under the same experimental conditions as aforementioned for the transcriptomic analyses (Figures 4, 5, S4, and S5; Table S4).Figure 5Dissection of the Daytime Effect of Exercise on Skeletal Muscle Metabolic ProfileShow full caption(A) Heatmap of relative metabolite levels between non-exercise" @default.
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• W2937478839 title "Physiological and Molecular Dissection of Daily Variance in Exercise Capacity" @default.
• W2937478839 cites W1520440032 @default.
• W2937478839 cites W1975639185 @default.
• W2937478839 cites W1978763812 @default.
• W2937478839 cites W1980270094 @default.
• W2937478839 cites W1991137949 @default.
• W2937478839 cites W1992547263 @default.
• W2937478839 cites W1992726958 @default.
• W2937478839 cites W1995381409 @default.
• W2937478839 cites W2004849589 @default.
• W2937478839 cites W2014287442 @default.
• W2937478839 cites W2014671368 @default.
• W2937478839 cites W2021229976 @default.
• W2937478839 cites W2029334700 @default.
• W2937478839 cites W2036897871 @default.
• W2937478839 cites W2038227076 @default.
• W2937478839 cites W2039466176 @default.
• W2937478839 cites W2044874695 @default.
• W2937478839 cites W2051033597 @default.
• W2937478839 cites W2052045659 @default.
• W2937478839 cites W2053866174 @default.
• W2937478839 cites W2061075485 @default.
• W2937478839 cites W2079580613 @default.
• W2937478839 cites W2108234281 @default.
• W2937478839 cites W2114104545 @default.
• W2937478839 cites W2125668498 @default.
• W2937478839 cites W2127705355 @default.
• W2937478839 cites W2128383865 @default.
• W2937478839 cites W2133918896 @default.
• W2937478839 cites W2134526812 @default.
• W2937478839 cites W2143210222 @default.
• W2937478839 cites W2152555931 @default.
• W2937478839 cites W2157481078 @default.
• W2937478839 cites W2161491874 @default.
• W2937478839 cites W2169456326 @default.
• W2937478839 cites W2263654516 @default.
• W2937478839 cites W2400786159 @default.
• W2937478839 cites W2429359053 @default.
• W2937478839 cites W2462514513 @default.
• W2937478839 cites W2512426923 @default.
• W2937478839 cites W2534086390 @default.
• W2937478839 cites W2551085365 @default.
• W2937478839 cites W2551231535 @default.
• W2937478839 cites W2555865797 @default.
• W2937478839 cites W2607049035 @default.
• W2937478839 cites W2610469329 @default.
• W2937478839 cites W2611398367 @default.
• W2937478839 cites W2729842340 @default.
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• W2937478839 cites W2914488439 @default.
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