Breaking up prolonged sitting has been beneficially associated with cardiometabolic risk markers in both observational and intervention studies. We aimed to define the acute transcriptional events induced in skeletal muscle by breaks in sedentary time. Overweight/obese adults participated in a randomized three-period, three-treatment crossover trial in an acute setting. The three 5-h interventions were performed in the postprandial state after a standardized test drink and included seated position with no activity and seated with 2-min bouts of light- or moderate-intensity treadmill walking every 20 min. Vastus lateralis biopsies were obtained in eight participants after each treatment, and gene expression was examined using microarrays validated with real-time quantitative PCR. There were 75 differentially expressed genes between the three conditions. Pathway analysis indicated the main biological functions affected were related to small-molecule biochemistry, cellular development, growth and proliferation, and carbohydrate metabolism. Interestingly, differentially expressed genes were also linked to cardiovascular disease. For example, relative to prolonged sitting, activity bouts increased expression of nicotamide N -methyltransferase, which modulates anti-inflammatory and anti-oxidative pathways and triglyceride metabolism. Activity bouts also altered expression of 10 genes involved in carbohydrate metabolism, including increased expression of dynein light chain, which may regulate translocation of the GLUT-4 glucose transporter. In addition, breaking up sedentary time reversed the effects of chronic inactivity on expression of some specific genes. This study provides insight into the muscle regulatory systems and molecular processes underlying the physiological benefits induced by interrupting prolonged sitting.

prolonged sitting is adversely linked to cardiometabolic risk biomarkers and premature mortality, independent of purposeful physical activity level (10, 15). Recent epidemiological (15) and intervention studies (11) have indicated that frequent interruptions of prolonged sitting through brief activity bouts may attenuate the adverse effects of prolonged sitting (11, 15). These studies have implicated both anti-inflammatory (high-sensitivity C-reactive protein) and metabolic (postprandial glucose) mechanisms in the beneficial effects of breaking up sedentary time (11, 15).

The central role of skeletal muscle in postural support and movement results in high energy demand, making this tissue the major site of glucose and free fatty acid consumption for ATP production in the body. In the setting of overnutrition, accumulation of excess lipid and glucose within skeletal muscle promotes an inflammatory response and insulin resistance, which reduces further substrate accumulation (16). The well-known benefits of regular purposeful exercise in bouts of 30 min or more are underpinned by restoration of metabolic balance, in part through elevated energy consumption and adaptation in other muscle molecular pathways (23, 26). Although current guidelines for physical activity state that activity in bouts from as little as 10 min are recommended, the effects of breaking up sedentary time with short low- to moderate-intensity activity breaks represent a very different activity paradigm. While it appears that breaking up sedentary time delivers benefits independent of purposeful exercise, the skeletal muscle signaling pathways involved have not been investigated.

Complex regulatory systems at multiple levels control skeletal muscle function. At the gene-expression level, microarrays provide a useful high-throughput approach to assess variations of transcriptional regulation in skeletal muscle under different experimental conditions. The current investigation was an ancillary study conducted on a subset of participants from a larger study that has been previously described (11). That study showed that interrupting sitting time with intermittent brief bouts of either light- or moderate-intensity walking lowered acute postprandial glucose and insulin levels (11). The aim of the present study was to define the acute transcriptional events induced in skeletal muscle by breaks in sedentary time using microarrays. Our data analysis focused on identifying significantly regulated biological processes and molecular pathways. We specifically hypothesized that interrupting sitting time by brief intermittent activity bouts of light or moderate intensity would change expression of genes involved in energy substrate metabolism.

METHODS

Study overview.

This randomized, three-period, three-treatment crossover trial was approved by the Alfred Hospital Human Ethics committee and was carried out in accordance with the Declaration of Helsinki. Participants provided signed, written, informed consent. The study is registered as a clinical trial with the Australian New Zealand Clinical Trials Registry (ACTRN12609000656235).

A description of the participant characteristics and the screening and testing procedures for the main study have been previously described in detail (11). Eight (7 men/1 woman) obese/overweight adults from the 19 participants in the main study were involved in the current investigation. The inclusion criteria for participation were 1) age between 45 and 65 yr, 2) overweight or obese (body mass index between 25 and 45 kg/m2), 3) no diabetes, and 4) not taking glucose-lowering and/or lipid-lowering medication. Participants attended the laboratory on three separate occasions to complete each of the trial conditions in a randomized order (11).

For uninterrupted sitting, participants remained seated throughout the experimental period and were instructed to minimize excessive movement, only rising from the chair to void. For sitting plus light-intensity activity breaks, participants rose from the seated position every 20 min throughout the experimental period (to achieve 3 breaks/h) and completed a 2-min bout of light-intensity walking on a motorized treadmill with a level surface at 3.2 km/h. They then returned to the seated position. This procedure was undertaken on 14 occasions, providing a total of 28 min of light-intensity activity. The sitting plus moderate-intensity activity breaks was identical procedure to the sitting plus light-intensity activity breaks condition, but participants completed 2-min bouts of moderate-intensity walking on the treadmill between 5.8 and 6.4 km/h every 20 min, providing a total of 28 min of moderate-intensity activity.

Participants were asked to refrain from any exercise, alcohol, and caffeine in the 48 h before each of the three trial conditions. During this time, physical activity was tightly monitored via an Actigraph GT1M accelerometer (Actigraph, Pensacola, FL), worn around the hip during waking hours. People who regularly engaged in moderate-intensity exercise ≥150 min/wk for at least 3 mo were excluded from the study. Since an acute bout of physical activity may enhance insulin sensitivity for up to 72 h, we employed a minimum wash-out period of 6 days between each condition.

Participants reported to the laboratory between 0700 and 0800, having fasted overnight. A cannula was inserted into an antecubital vein for hourly blood sampling. For all of the three experimental conditions, and following the initial blood collection (time point: −2 h), participants remained seated for 2 h to achieve a steady state before the consumption of a standardized test drink (time point: 0 h). The 200-ml test drink consisted of 75 g carbohydrate (100% corn maltodextrin powder; Natural Health) and 50 g fat (Calogen; Nutricia). The specific nutritional components were as follows: energy 3,195 kJ; fat 50.0 g; saturated fat 5 g; monounsaturated fat 30.4 g; polyunsaturated fat 14.3 g; carbohydrate 75 g; total sugars 12.8 g; protein nil; fiber <1 g; sodium 46.9 mg; and water 90 g. Blood was sampled at baseline before drink consumption and hourly postdrink consumption, and the incremental area under the glucose-time, insulin-time, and insulin/glucose-time curves was calculated.

Muscle biopsy.

Muscle biopsies were obtained from the vastus lateralis using standard aseptic technique and local anesthesia ∼20–30 min after final blood collection, which was ∼40–50 min after the last activity bout, and 5 h after the drink ingestion. In brief, a ∼7-mm skin incision was made, and the fascia opened. A side-cutting muscle biopsy needle was passed through the incision to obtain ∼100 mg of muscle tissue under suction. All biopsies were snap frozen in liquid nitrogen and subsequently stored at −80°C until further analysis.

RNA purification and microarray processing.

Total RNA was extracted from frozen muscle (∼30 mg) with a motorized rotor-stator homogenizer (3 pulses of 15 s) and TRIzol reagent (InVitrogen, Carlsbad, CA) using the manufacturer's recommended protocol. RNA quality was determined by the MultiNA system (Shimadzu Biotech, Sydney, NSW, Australia). Biotin-labeled cRNA was produced by means of a linear amplification kit (AMIL1791; Ambion, Austin, TX) using 250 ng of quality-checked total RNA as input. Hybridization to the Illumina Human HT-12 v4 BeadChip was for 16 h at 58°C on a BeadChip Hyb Wheel using 1,500-ng biotin-labeled cRNA as input. The chip was then washed, blocked, and stained with Cy3-streptavidin (Amersham Bioscience, Piscataway, NJ) according to the manufacturer's protocols (Illumina, San Diego, CA). The iScan Control Software V1.6.20.7 (Illumina), together with the Illumina iSCAN platform (Illumina), was used for RNA quantification. Annotation information for the individual chip formats is publicly available from Illumina (http://www.illumina.com). The Illumina Human HT-12 v4 BeadChip targets more than 47,000 probes derived from the National Center for Biotechnology Information Reference Sequence RefSeq Release 38 (November 7, 2009) and other sources.

Processing and analysis of the microarray data were performed with the Illumina Beadstudio 3.1.3.0 software. Raw data were background subtracted and normalized using the “Quantile” algorithm. Quantile normalization assumes that all samples have similar distribution of transcript abundance and makes the distribution, median, and means of probe intensities the same for every sample. Data files were deposited into the National Center for Biotechnology Information Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39576, accession no. GSE39576). Further analysis of the effects of activity breaks vs. prolonged sitting was performed in GeneSpring GX7.3 expression analysis software (Agilent Technologies, Mulgrave, VIC, Australia). To identify genes with the most marked changes in expression level, we first applied an absolute fold change threshold of ≥1.5. Then the resulting gene list was analyzed with a repeated-measures ANOVA to identify genes that were differentially expressed between the three groups after correction for multiple testing [false discovery rate (FDR) < 0.05 by the Benjamini and Hochberg method (24)]. Gene expression was considered significantly different between groups when the corrected P value was < 0.05.

Pathway analysis.

Biological interpretation of the data was performed on differentially expressed genes, meeting the criteria described above through the use of Ingenuity Pathway Analysis (IPA; Ingenuity Systems, www.ingenuity.com). IPA is proprietary online software that aids researchers in modeling and analyzing the biological significance of microarray data sets. IPA uses the Ingenuity Knowledge Base to identify statistically significant biological functions and pathways. The Knowledge base consists of millions of relationships (between genes, proteins, small molecules, and complexes) manually extracted by PhD-level scientists from over 200,000 peer-reviewed articles. The significance of the association between genes in the data set and the biological function was determined by using Fisher's exact test to calculate a P value determining the probability that the association was explained by chance alone. Associated networks were then algorithmically generated based on their connectivity. Genes included in the IPA analysis varied significantly between the three activity conditions at a corrected P value of <0.05.

Reverse transcription and quantitative real-time PCR.

Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was used to validate a subset of genes expressed differentially based on the microarray results. This subset included genes with high signal intensity, or demonstrating a dose response between the three experimental conditions in array analysis, or involved in the principal molecular and cellular functions described. Primers were designed using Primer Express software from Applied Biosystems (Carlsbad, CA). Reverse transcription was performed with 1 μg of total RNA and the iScript select cDNA synthesis kit (Bio-Rad, Hercules, CA), according to the manufacturer's protocol. Transcripts levels were analyzed on the Applied Biosystems 7500 Real-Time PCR System with SYBR Green I detection. The reactions were performed in duplicate for each sample using an iTaq fast SYBR Green supermix with ROX (Bio-Rad), with 300 nmol/l of each reverse and forward primer and 50 ng of cDNA in 20 μl total volume. The thermal cycling parameters were as follows: initial denaturation at 95°C for 2 min, followed by 40 cycles at 95°C for 3 s and 60°C for 30 s. β-Actin was used as an internal control to calibrate the expression levels of target genes; the relative changes in gene expression were calculated using the 2ΔΔCt method (20).

Statistics.

As previously reported, generalized estimating equations (GEE models) with exchangeable working correlation to account for dependency in the data (repeated measures) were used (22) to evaluate the differential effects of the trial conditions on glucose homeostasis. All models were adjusted for potentially important covariates explaining residual outcome variance (age, sex, and weight), baseline predrink outcome values, and period effects. Postestimation contrasts were used to examine the differential effects of pairs of conditions. GEE models were used to examine between-trial condition differences in pretrial plasma glucose and serum insulin. A probability level of <0.05 was considered significant. All statistical analyses were performed using Stata 10.1 for Windows (StataCorp LP, College Station, TX).

The global gene array expression data were analyzed using GeneSpring software. To determine differential expression between the three activity conditions, an ANOVA for repeated measures was applied after correction for multiple testing [FDR < 0.05 by the Benjamini and Hochberg method (24)].

Other statistical analyses were performed with SPSS. The qRT-PCR data were compared using an ANOVA for repeated measures, with a Student-Newman-Keuls post-hoc test to compare individual means. Relationships between gene expression assessed by microarray and qRT-PCR were assessed using the Pearson correlation coefficient.

Data are reported as means ± SE, unless otherwise indicated.

RESULTS

The eight participants recruited for the present study had a mean age of 55 ± 6 yr (mean ± SD) and body mass index of 30.9 ± 2.9 kg/m2. In the main study (n = 19), interrupting prolonged sitting with both light-intensity and moderate-intensity activity reduced the postprandial glucose and insulin areas under the curve (iAUC) relative to uninterrupted sitting. In the subset of eight participants in the present study, relative to uninterrupted sitting, there was a trend toward a decrease in the glucose iAUC (change to uninterrupted sitting: −22 and −20% with light- or moderate-intensity breaks, respectively, P = 0.1, Fig. 1A). The incremental iAUC was reduced in this same subset by 24.8% (P = 0.004) after sitting interrupted with light-intensity activity and 23.4% (P = 0.015) after interruptions with moderate-intensity breaks relative to uninterrupted sitting (Fig. 1B). The insulin-to-glucose ratio incremental area under the curve was 25.1% (P = 0.001) lower after sitting interrupted with light-intensity activity and 21.9% (P = 0.014) lower after sitting interrupted with moderate-intensity activity compared with uninterrupted sitting (Fig. 1C).

Fig. 1.Effect of the three trial conditions on postprandial plasma glucose and insulin levels in the 8 participants included in the substudy. Positive incremental (5-h postprandial) glucose (A), insulin (B), and insulin-to-glucose ratio (C) area under the curves (AUC) are shown. Values are means ± SE (adjusted for age, sex, body weight, period effects, and predrink levels). *P < 0.05, **P < 0.005, significantly different from uninterrupted sitting condition. ns, Nonsignificant.

The microarray analysis identified 7,995 transcripts expressed in the skeletal muscle. Of these, 326 satisfied the fold-change criteria described in methods in at least one out of three comparisons between the experimental-condition pairs (light-intensity breaks vs. sedentary; moderate-intensity breaks vs. sedentary; moderate- vs. light-intensity breaks), and 75 genes were significantly differentially expressed when assessed by ANOVA with a FDR P value < 0.05 (Table 1). This list of 75 differentially expressed genes was imported into the IPA software: 71 corresponded to an entity in the Ingenuity Knowledge Base, the reference set, and met the eligibility criteria for inclusion in IPA analysis.

Table 1. Genes differentially expressed between the three experimental conditions, by relationships with networks, as identified by Ingenuity pathway analysis Symbol P Value Light vs. Sed Mod vs. Sed Mod vs. Light Definition Cellular development, tissue development, cardiovascular system development and function ATXN7L2 0.017 0.98 0.60 0.61 Ataxin 7-like 2 CREB5 0.023 1.02 1.94 1.90 cAMP responsive element binding protein 5, transcript variant 1 LYVE1 0.023 0.86 2.62 3.05 Lymphatic vessel endothelial hyaluronan receptor 1 CCL13 0.024 0.69 1.76 2.54 Chemokine (C-C motif) ligand 13 DAB2 0.025 1.07 1.76 1.65 Disabled homolog 2, mitogen-responsive phosphoprotein TGFBR2 0.026 0.88 1.33 1.50 Transforming growth factor, β-receptor II (70/80 kDa), transcript variant 1 HIST1H4C 0.035 1.25 1.50 1.21 Histone cluster 1, H4c ARPC5 0.038 0.86 1.33 1.56 Actin-related protein 2/3 complex, subunit 5, 16 kDa CAV2 0.038 0.99 0.65 0.66 Caveolin 2, transcript variant 1 EMP1 0.038 0.93 1.63 1.76 Epithelial membrane protein 1 PDK4 0.038 1.38 2.64 1.91 Pyruvate dehydrogenase kinase, isozyme 4 CSF1R 0.043 0.91 1.70 1.86 Colony stimulating factor 1 receptor, formerly McDonough feline sarcoma viral (v-fms) oncogene homolog THBS4 0.043 1.04 1.89 1.81 Thrombospondin 4 FSTL1 0.044 0.94 2.57 2.72 Follistatin-like 1 TNFRSF12A 0.045 0.81 2.85 3.52 Tumor necrosis factor receptor superfamily, member 12A DIO2 0.046 1.13 1.99 1.76 Deiodinase, iodothyronine, type II, transcript variant 3 COL6A3 0.047 0.86 1.63 1.91 Collagen, type VI, α3, transcript variant 1 COL6A3 0.047 0.95 1.58 1.68 Collagen, type VI, α3, transcript variant 3 LAMB1 0.047 1.03 1.51 1.47 Laminin, β1 P4HA2 0.047 0.90 1.44 1.60 Prolyl 4-hydroxylase, α-polypeptide II, transcript variant 2 PPAP2A 0.047 1.10 1.73 1.56 Phosphatidic acid phosphatase type 2A, transcript variant 1 Cell-to-cell signaling and interaction, organ morphology, developmental disorder ARID5B 0.017 1.34 1.89 1.41 AT rich interactive domain 5B (MRF1-like) AHCTF1 0.017 0.90 0.63 0.71 AT hook containing transcription factor 1 OTUD1 0.023 1.17 4.22 3.61 OTU domain containing 1 AAMP 0.023 0.86 0.65 0.76 Angio-associated, migratory cell protein TARBP1 0.024 0.96 0.66 0.68 TAR (HIV-1) RNA binding protein 1 TMEM70 0.024 0.71 0.40 0.57 Transmembrane protein 70, transcript variant 1 MYADM 0.024 1.09 1.77 1.61 Myeloid-associated differentiation marker, transcript variant 4 SMAP2 0.028 0.87 1.33 1.54 Small ArfGAP2 ANKRD37 0.038 1.34 1.70 1.27 Ankyrin repeat domain 37 COLQ 0.038 1.40 1.98 1.41 Collagen-like tail subunit (single strand of homotrimer) of asymmetric acetylcholinesterase, transcript variant III RHOBTB1 0.038 0.84 0.64 0.76 Rho-related BTB domain containing 1, transcript variant 2 HSD17B11 0.044 0.73 1.09 1.50 Hydroxysteroid (17-β) dehydrogenase 11 RAB23 0.045 1.19 1.67 1.40 RAB23, member RAS oncogene family, transcript variant 1 VGLL2 0.047 1.09 1.54 1.42 Vestigial like 2, transcript variant 2 WBP5 0.049 1.05 1.52 1.45 WW domain binding protein 5, transcript variant 2 Cell-to-cell signaling and interaction, cellular development, embryonic development RNASE1 0.017 1.13 1.82 1.61 Ribonuclease, RNase A family 1, transcript variant 3 SF3A3 0.018 0.95 0.54 0.57 Splicing factor 3a, subunit 3, 60 kDa RNASE1 0.019 0.98 1.79 1.82 Ribonuclease, RNase A family 1, transcript variant 1 MS4A6A 0.023 1.03 2.18 2.12 Membrane-spanning 4-domains, subfamily A, member 6A, transcript variant 3 FLRT3 0.024 1.22 1.83 1.50 Fibronectin leucine rich transmembrane protein 3, transcript variant 2 MYF6 0.024 1.31 1.69 1.29 Myogenic factor 6 (herculin) TAGLN2 0.024 1.14 1.58 1.38 Transgelin 2 SRGN 0.044 0.77 1.25 1.63 Serglycin CLIC1 0.046 0.88 1.44 1.65 Chloride intracellular channel 1 GOLPH4 0.047 1.06 1.90 1.79 Golgi phosphoprotein 4 FOLR2 0.047 0.94 2.00 2.13 Folate receptor 2 MS4A6A 0.048 0.94 2.14 2.26 Membrane-spanning 4-domains, subfamily A, member 6A, transcript variant 2 Metabolic disease, cardiovascular disease, reproductive system development and function COLEC12 0.024 0.97 1.66 1.71 Collectin subfamily member 12 PIK3C2B 0.024 1.06 0.69 0.66 Phosphoinositide-3-kinase, class 2, β-polypeptide DNAJA4 0.026 1.11 1.56 1.40 DnaJ (Hsp40) homolog, subfamily A, member 4 DNCL1 0.028 1.04 1.79 1.73 Dynein, cytoplasmic, light polypeptide 1 PIM3 0.038 0.89 0.60 0.67 Pim-3 oncogene LGMN 0.038 1.26 1.84 1.46 Legumain, transcript variant 2 MAMDC2 0.038 1.01 1.66 1.64 MAM domain containing 2 C13orf33 0.040 1.10 2.30 2.09 Chromosome 13 open reading frame 33 C1QB 0.043 1.01 2.19 2.17 Complement component 1, q subcomponent, B chain DYNLL1 0.047 1.18 1.85 1.57 Dynein, light chain, LC8-type 1, transcript variant 1 Lipid metabolism, small molecule biochemistry, endocrine system development and function NNMT 0.017 1.73 3.15 1.82 Nicotinamide N-methyltransferase USP6 0.038 1.19 0.79 0.67 Ubiquitin specific peptidase 6 (Tre-2 oncogene) DNAJB4 0.044 0.95 1.47 1.55 DnaJ (Hsp40) homolog, subfamily B, member 4 VSTM2A 0.044 1.06 0.63 0.59 V-set and transmembrane domain containing 2A Unclassified IDs GALNTL1 0.043 1.09 2.03 1.86 UDP-N-acetyl-α- d -galactosamine:polypeptide N-acetylgalactosaminyltransferase-like 1 0.017 0.74 0.58 0.79 mRNA; cDNA DKFZp779K2051 C10orf92 0.017 1.07 0.71 0.66 Chromosome 10 open reading frame 92 C4orf18 0.024 1.28 1.95 1.52 Chromosome 4 open reading frame 18, transcript variant 2 CDR2L 0.024 1.36 1.98 1.46 Cerebellar degeneration-related protein 2-like ZNF385B 0.026 1.18 1.75 1.49 Zinc finger protein 385B, transcript variant 1 FLJ22140 0.042 1.48 1.61 1.09 cDNA: FLJ22140 fis, clone HEP20977 LOH3CR2A 0.043 0.89 0.59 0.67 Loss of heterozygosity, 3, chromosomal region 2, gene A LOC339240 0.047 2.13 0.42 0.20 Keratin pseudogene, noncoding RNA Unmapped IDs LOC100134584 0.023 0.84 0.53 0.63 Hypothetical protein LOC100134584 LOC401076 0.023 0.77 1.24 1.61 misc_RNA (LOC401076) HSPC047 0.023 0.92 0.53 0.58 HSPC047 protein FLJ32310 0.023 0.96 1.89 1.97 Hypothetical protein FLJ32310

The modulated gene list was classified by biological functions, revealing that the main molecular and cellular functions affected were significantly related to small-molecule biochemistry, cellular development, growth and proliferation, and carbohydrate metabolism. It also showed modifications in tissue morphology and development (Table 2). Interestingly, differentially expressed genes were also linked to cardiovascular disease (Table 2).

Table 2. Main biological functions identified by Ingenuity pathway analysis Fisher's P Value No. of Genes Diseases and disorders Organismal injury and abnormalities 2.33E-04-4.31E-02 5 Cardiovascular disease 2.26E-03-3.33E-02 7 Cancer 3.38E-03-4.95E-02 25 Connective tissue disorders 3.38E-03-3.67E-02 4 Developmental disorder 3.38E-03-4.63E-02 9 Molecular and cellular functions Small molecule biochemistry 1.11E-04-4.49E-02 16 Cellular development 3.89E-04-4.95E-02 18 Cellular growth and proliferation 3.89E-04-4.63E-02 21 Amino acid metabolism 3.38E-04-3.33E-02 6 Carbohydrate metabolism 3.38E-04-3.56E-02 10 Physiological system development and function Endocrine system development and function 1.11E-04-4.45E-02 6 Cardiovascular system development and function 1.62E-04-4.95E-02 9 Tissue development 1.62E-04-4.95E-02 15 Embryonic development 3.10E-04-4.95E-02 16 Tissue morphology 3.10E-04-4.82E-02 17

Among individual IPA networks associated with our data set of genes differentially expressed between the experimental conditions, the ones with the highest number of involved genes were cellular development, tissue development, cardiovascular system development, and function (score 48, molecules 21), while the next two networks were related to cell-to-cell signaling and cellular development (scores 32 and 18, molecules 15 and 10), and, interestingly, two other networks were associated with metabolic disease and lipid metabolism. The associated networks are indicated in Table 1.

To validate array data, the same 24 muscle samples were analyzed by RT-quantitative PCR for the expression of 10 genes (see methods) found to be differentially expressed between the experimental conditions on the microarray analysis (indicated in bold in Table 1). Correlation analysis indicated a significant concordance between fold changes in gene expression tested by microarray and RT-quantitative PCR (r = 0.67, P < 0.0001; Fig. 2).

Fig. 2.Correlation between gene expression measured by microarray and RT-quantitative PCR (qPCR) for 10 differentially expressed genes. Data for each gene are presented for all three activity conditions. Pearson's correlation test: R = 0.67, P < 0.0001.

To illustrate different profiles of gene regulation, Fig. 3 shows four of the validated genes. Nicotamide N-methyltransferase (NNMT) and dynein light chain (DYNLL1) expression were increased 1.5- to 2-fold from sitting interrupted with moderate-intensity breaks, while sitting interrupted with light-intensity activity returned an intermediate level of expression (Fig. 3, A and B). Legumain expression also increased twofold, with sitting interrupted with moderate intensity; however, sitting interrupted with light-intensity activity induced no change in expression compared with uninterrupted sitting (Fig. 3C). The ataxin 7-like 2 gene showed a graded reduction in expression from uninterrupted sitting through to moderate-intensity breaks (# for trend P = 0.035) (Fig. 3D).

Fig. 3.Validation of differentially expressed genes identified in microarray analysis: nicotamide N-methyltransferase (NNMT; A), dynein light chain 1 (DYNLL1; B), legumain (LGMN; C), and ataxin-7-like protein 2 (ATXN7L2; D). mRNA levels (RT-qPCR) were normalized for β-actin expression and expressed as fold change from the uninterrupted sitting condition. Values are means ± SE. *P < 0.05, **P < 0.01 (one-way ANOVA for repeated measures, Student Newman and Keuls post hoc test). Lines represent trends, #P < 0.05.

DISCUSSION

The major novel finding of this study is that breaking up postprandial sedentary time with short activity bouts is associated with changes in the expression of skeletal muscle genes involved in cellular development, growth and proliferation, and lipid and carbohydrate metabolism. Microarray technology and high-throughput software tools have allowed us to examine not only the global patterns of gene expression associated with light- and moderate-intensity activity breaks in prolonged sedentary time, but also the potential biological significance of these changes.

Of the 75 differentially expressed genes, 18 were related to cellular development, 21 with cellular growth and proliferation, 16 with small-molecule biochemistry, and 10 with carbohydrate metabolism. Among the genes involved in cellular development, some have a role in the formation and differentiation of skeletal muscle cells, as well as migration of muscle cells. Such effects are consistent with adaptation expected in response to the intensity of muscle contraction during the short activity breaks. Indeed, we observed similarities between genes regulated by breaks and by continuous acute exercise bouts of 30–90 min. For example, genes involved in muscle growth (TNFRSF12A, ARID5B) or metabolism [pyruvate dehydrogenase kinase 4 (PDK4)], upregulated by moderate-intensity activity breaks in sedentary time in the present study, also increase 3 h after a 60-min bout of eccentric exercise (21). PDK4, which inhibits the pyruvate dehydrogenase complex and increases glucose metabolism, has also been shown to be increased by both continuous and interval exercise (28).

Breaks in sedentary time alter expression of metabolic regulatory networks.

While this study took a broad discovery approach to identifying differential gene expression associated with breaking up sedentary time, a primary objective was to identify genes that might contribute to the previously reported (11) changes in postprandial glucose metabolism seen with light- and moderate-intensity breaks in sedentary time in this sample. Ten genes involved in carbohydrate metabolism were identified as being differentially expressed between the experimental conditions, including four involved in carbohydrate uptake into cells: CCL13, showing a slight decrease with light-intensity activity compared with uninterrupted sitting but an increased expression with moderate-intensity activity breaks; PDK4, progressively increasing with the intensity of the activity breaks; and C13orf33 and CSF1R increased only with moderate-intensity activity breaks compared with uninterrupted sitting. PIK3C2B, involved in the production and accumulation of phosphatidylinositol-3-phosphate; LYVE1, involved in the catabolism of glycosaminoglycan; as well as PPAP2A, responsible for the degradation of lysophosphatidic acid, were also differentially expressed following moderate-intensity breaks vs. uninterrupted sitting.

Although not classified under the carbohydrate metabolism grouping in the biological function analysis, several other genes regulated by breaking up sitting time could contribute to the observed changes in glucose metabolism. Among them, COLEC12 encodes a member of the C-lectin family, proteins that possess collagen-like sequences and carbohydrate recognition domains. This protein is a scavenger receptor, a cell surface glycoprotein that can bind to several carbohydrates facilitating their recognition and removal. In addition, this receptor can mediate the recognition, internalization, and degradation of oxidized low-density lipoprotein. NNMT is a cytosolic enzyme that catalyzes the N-methylation of nicotinamide, producing 1-methylnicotinamide (MNA) (1, 2). Experimental studies have shown that MNA, long considered biologically inactive, has both anti-inflammatory and anti-thrombotic properties (involving a cyclooxygenase 2/PGI 2 pathway), scavenges oxygen radicals, enhances cell proliferation and migration, as well as lowers plasma triglyceride levels (5–8, 19, 30). Upregulation of NNMT with activity bouts even of light intensity could also potentially contribute to the changes in glucose metabolism, as anti-diabetic actions of NNMT have been shown in rats with streptozotocin-induced diabetes with MNA to lower fasting plasma glucose concentration, lipid peroxidation, and protein carbonylation, and to restore aortic endothelium-dependent nitric oxide-mediated vasodilatation (29). Moreover, DYNLL1 (cytoplasmic DYNLL1), which increased expression in association with the intensity of activity breaks, may have a role in glucose transport. Cytoplasmic dynein 1 acts as one of several noncatalytic accessory components of the cytoplasmic dynein 1 complex that are thought to be involved in linking dynein to cargos and to adapter proteins that regulate dynein function (18). The protein acts as a motor for the intracellular motility of vesicles and organelles along microtubules and thus may play a role in facilitating translocation of the GLUT-4 glucose transporter to the cell surface for cellular glucose internalization (13). Binding of DYNLL1 also inhibits TNF-α-induced NF-κB activation by interacting with IκB-α, thereby preventing its phosphorylation by IκB-α kinase, its nuclear translocation, and its regulatory role in inflammation and apoptosis (9). Of note, inhibition of NF-κB signaling could also contribute to the beneficial effects of breaking up sedentary time on metabolism. Indeed, a growing literature shows that NF-κB inflammatory pathways contribute to the pathology of metabolic disorders (4); recent clinical trials have found significant amelioration of insulin resistance and glucose homeostasis in human type 2 diabetes patients treated with salicylates, which have been reported to inhibit NF-κB activation (12, 14).

Breaking up sedentary time may counteract expression of a specific gene shown to be induced by prolonged inactivity.

A final key issue is whether breaks in sedentary time can counteract the gene expression patterns associated with sedentary behavior. Whereas bed-rest studies report a reduction in muscle oxidative phosphorylation gene expression and mitochondrial function (25), these processes did not appeared regulated by breaks in the present study. However, 48 h of immobilization have been shown to increase USP6 expression, which has a role in protein degradation and is of relevance to muscle atrophy (27). USP6 is a de-ubiquitinating enzyme that cleaves ubiquitin from ubiquitin-tagged proteins so that ubiquitin molecules can be recycled and reused. The increase in USP6 expression associated with immobilization thus mobilizes ubiquitin to tag muscle proteins for degradation (3). In our microarray, moderate-intensity activity breaks decreased USP6 expression, which would theoretically decrease activity of the ATP ubiquitin-dependent proteolitic pathway. Reduction in protein breakdown could thereby counteract the muscle atrophy associated with extreme states of sedentary behavior.

Limitations.

The present study assessed only the acute effects of light- and moderate-activity breaks in sedentary time over a single day and cannot, therefore, provide insight into the chronic effects of breaking up sedentary time on muscle gene expression. The highly controlled study design, including within-subject comparison across three interventions administered in random order, is a strength. The number of participants, although relatively small, was nevertheless sufficient to detect significant changes in muscle gene expression. A larger cohort may undercover a greater number of regulated genes. The absolute fold change criteria of ≥1.5 for selection of significant effects may result in failure to detect important regulated genes, but was applied to ensure that only robust differences were reported. False positives were also minimized by use of repeated-measures ANOVA to allow comparison of all three interventions and correction for multiple testing with the FDR method by Benjamini and Hochberg (24). Future studies are warranted to examine the skeletal muscle transcriptional networks regulated by breaking up sedentary time over longer periods of weeks or months.

The brevity of the interruptions to sitting (2 min) indicates that such breaks would not count toward the minimum amount of aerobic activity necessary for substantial health benefits within current physical activity guidelines, since at least 10-min episodes of activity are stipulated. However, consistent with studies of the effects of continuous exercise on glucose metabolism (17), the sum of total activity time over a 5-h period was 28 min. A logical next step to build on these findings would be a study comparing a single continuous exercise bout to the breaking up of prolonged sitting.

Conclusion.

Whereas the effects of chronic physical inactivity as well as the response to continuous exercise bouts have been well studied, our study is the first to characterize the acute transcriptional events induced by breaking up sitting time with short activity bouts. Our study highlights some of the regulated mechanisms (metabolic pathways, muscular system development) underlying the response to breaking up sedentary time. We have identified candidate genes that are likely to contribute to the improvements in glucose homeostasis and cardiometabolic risk markers associated with the breaking up of sedentary time. In addition, breaking up sedentary time reversed the effects of chronic inactivity on expression of some specific genes. This study provides insight into the muscle regulatory systems and molecular processes underlying the physiological benefits induced by interrupting prolonged sitting. Change in expression of a number of health-related genes in skeletal muscle after such brief and regular interruptions (2 min of activity for every 20 min of sitting) to prolonged sitting time may have important implications for settings where continuous ambulatory activity may not be feasible or practical, such as workplaces.

GRANTS

This work was supported by a National Health and Medical Research Council (NHMRC)/Institut National de la Santé et de la Recherche Médicale (INSERM) Exchange Fellowship (NHMRC no. 1440122); a NHMRC Project Grant (NHMRC no. 540107); two NHMRC Program Grants (NHMRC nos. 569940 and 1000900); a National Health and Medical Senior Principal Research Fellowship to N. Owen (NHMRC no. 1003960); a Victorian Public Health Research Fellowship and an Australian Research Council Future Research Fellowship to D. W. Dunstan (FT100100918); a NHMRC Principal Research Fellowship to B. A. Kingwell (NHMRC no. 526604); and was supported in part by the Victorian Government's Operational Infrastructure Support Program. The funders of this study had no role in the trial.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the author(s).

AUTHOR CONTRIBUTIONS

Author contributions: C.L. and J.B.J. performed experiments; C.L. and J.B.J. analyzed data; C.L. and J.B.J. interpreted results of experiments; C.L. prepared figures; C.L. and B.A.K. drafted manuscript; C.L., J.B.J., A.L.C., D.A.B., N.O., D.W.D., and B.A.K. approved final version of manuscript; A.L.C., D.A.B., N.O., and D.W.D. edited and revised manuscript; N.O., D.W.D., and B.A.K. conception and design of research.