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• Published: 27 Feb 2021

Relationship between insulin sensitivity and gene expression in man skeletal muscle

• Targ Elgzyri²,
• Amra Alibegovic³,
• Natalie Hiscock⁴,
• Ola Ekströmⁱⁱ,
• Karl-Fredrik Eriksson²,
• Allan Vaag³,
• Leif C. Groop^ii,v,
• Kristoffer Ström^{2,6  na1} &
• Ola Hansson^{2,5  na1}

BMC Endocrine Disorders volume 21, Commodity number:32 (2021) Cite this article

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Abstract

Background

Insulin resistance (IR) in skeletal musculus is a central feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood.

Methods

To explore these mechanisms, nosotros related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).

Results

We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human being skeletal musculus, identifying autophagy-related genes every bit positively correlated with insulin sensitivity. Replication in an independent report of 9 not-diabetic men resulted in ten overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of central genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A.

Conclusions

The muscle expression of 180 genes were correlated with insulin sensitivity. These information propose that activation of genes involved in lipid metabolism, due east.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.chiliad. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.

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Background

Insulin resistance (or depression insulin sensitivity) in skeletal muscle is a central feature of the pre-diabetic state and a predictor of type two diabetes (T2D) [ane, 2]. It is also observed in individuals with hypertension, dyslipidemia, and cardiovascular diseases [3]. Insulin resistance (IR) in skeletal muscle has been attributed to different pathological weather such as mitochondrial dysfunction [4], impaired glycogen synthesis [5], and aggregating of diacylglycerol with subsequent impairment of insulin signaling [6]. One hypothesis that has been put forrad is a re-distribution of lipid stores from adipose tissue to non-adipose tissues (eastward.g. skeletal musculus, liver and the insulin-producing β-cells), the then-called overflow or ectopic fatty distribution hypothesis. In support, studies take reported a strong correlation between intramuscular triacylglycerol (IMTG) content and IR [7, 8]. However, and in dissimilarity, endurance-trained athletes have been shown to be highly insulin sensitive despite having large IMTG depots [9, x]. I possible caption for this discrepancy is that it is non the IMTG content per se that is of import for the development of IR, just rather the relationship between IMTG content and muscle oxidative chapters. A reduced oxidative capacity in skeletal muscle from T2D individuals [11, 12], and in lean, insulin resistant offspring of T2D patients [13] has been found, supporting the hypothesis that IR in skeletal muscle is associated with dysregulation of intramyocellular fatty acrid metabolism. Interestingly in a cohort of elderly twins, IMTG content seems to accept a greater influence on hepatic as opposed to peripheral IR [xiv]. Furthermore, an association betwixt mitochondrial dysfunction and decreased expression of autophagy-related genes in skeletal muscle from severely insulin resistant patients with T2D has previously been shown [fifteen]. Conversely, enhancing autophagy in mice leads to an anti-ageing phenotype, including leanness and increased insulin sensitivity [sixteen].

The aim of this study was therefore to investigate molecular mechanisms, east.g. IMTG content, associated with insulin sensitivity in skeletal muscle past relating global skeletal muscle gene expression with a surrogate measure out of insulin sensitivity, i.e. homeostatic model cess of insulin resistance (HOMA-IR).

Methods

Human participants and clinical measurements

Results from 2 divide clinical studies (studies A and B) are reported here.

Written report A

To identify genes correlated to insulin sensitivity in skeletal muscle, we studied 39 non-diabetic men from Malmö, Sweden [17, xviii]. Briefly, the Malmö Exercise Intervention cohort consists of 50 sedentary but otherwise good for you male subjects from southern Sweden. They all have European ancestry and 24 of them have a first-degree family fellow member with T2D. Muscle biopsies were collected from 39 of the subjects. The mean age and body mass index (BMI) were 37.71 ± four.38 years and 28.47 ± ii.96 kg/m², respectively, and the mean 1/the homeostatic model assessment-insulin resistance (HOMA-IR) was 0.69 ± 0.25 (Supplementary Table S1).

Report B

To replicate the findings from report A, we studied an additional x healthy young not-diabetic men without any family history of diabetes, from a previously described study [nineteen]. The hateful historic period and BMI were 25.33 ± 0.99 years and 24.57 ± one.86 kg/thousand², respectively, and the mean 1/HOMA-IR was 1.17 ± 0.36 (Supplementary Table S2). Here, we included baseline gene expression profile information (i.e. only before bed residual) from part of a larger report on the influence of physical inactivity in healthy and prediabetic individuals [19].

None of the study participants were directed to avert extreme physical exercise and booze intake for at least 2 days before the studies [20]. The participants were asked to fast for 10–12 h earlier exam days. Fasting blood samples and anthropometric data were obtained from all participants. All participants underwent an oral glucose tolerance test (OGTT; 75 g) and glucose tolerance was classified in accord with Earth Health Organisation criteria [21]. Homeostasis model assessment -insulin resistance (1/HOMA-IR = 22.5 / (fasting plasma insulin (μU/ml) ten fasting plasma glucose (mmol/fifty))) was calculated for all participants in both studies and used as a surrogate mensurate of insulin sensitivity [22, 23]. The muscle biopsies were obtained from the vastus lateralis muscle under local anesthesia in individuals participating in all studies using a modified Bergström needle [24, 25].

We excluded data from 2 participants (i from each studies A and B) with extreme values of insulin sensitivity (more than than 1.v * interquartile range) for further analysis. Both studies were approved by local ethics committees and all participants gave their informed consent for participation.

RNA extraction and hybridization

Muscle biopsies were taken from the right vastus lateralis muscle nether local anesthesia (Lidocaine 1%), using a 6 mm Bergström needle (Stille AB, Sweden). In both studies, biopsies were immediately stored in RNAlater (Ambion, Austin, TX) and later overnight incubation at 4 °C snap frozen at − fourscore °C until farther processing. The double staining method was used for capillary staining. Myofibrillar ATPase histochemistry was performed past preincubation at pH 4.4, 4.six, and ten.three to identify musculus fiber types [eighteen]. Reckoner image assay was performed using BioPix IQ 2.0.16 software (BioPix AB, Sweden). RNA was extracted using Tri reagent (Sigma-Aldrich, St. Louis, MO) followed by RNeasy Midi kit (Qiagen, Düsseldorf, Germany). The RNA was further full-bodied by RNeasy MiniElute (Qiagen, Düsseldorf, Frg) and SpeedVac (DNA 120 SpeedVac, Thermo Savant, Waltham, MA).

For study A, synthesis of biotin-labeled cRNA and hybridization to the Affymetrix Custom Array NuGO-Hs1a520180 GeneChip (http://www.nugo.org) were performed according to the manufacturer'southward recommendation. This GeneChip contains 23,941 probesets for interrogation, including known genes and expressed sequenced tags. Images were analyzed using the GeneChip Operating Organization (GCOS; Affymetrix) software. For each array, the pct present call was greater than xl.

For report B, targets were hybridized to the 1-color (Cy3, dark-green) Agilent Whole Human Genome Oligo Microarray (G4112F (Feature Number version)) which contains 44,000 60-mer oligonucleotide probes representing 41,000 unique genes and transcripts. Probe labeling and hybridization were performed according to manufacturer's recommendation. Images were analyzed using the Agilent Characteristic Extraction Software (version 9.v).

Quantitative real-time PCR (QPCR)

A technical replication of the key findings from the microarray data, likewise equally expression analysis of key genes to exist correlated with insulin stimulated glucose update, was conducted using QPCR. Reverse transcription was performed on 250 ng RNA (from 36 subjects in report A) or 200 ng RNA (from seven subjects in the Musculus SATellite cell (MSAT) cohort) using the QuantiTect Reverse Transcription kit (Qiagen). QPCR was performed on a ViiA 7 real-time PCR system (Thermo Fisher Scientific) with 2 ng cDNA in 10 μl reactions and TaqMan Expression PCR Master Mix with duplex assays according to the manufacturer's instructions. Samples were analyzed in triplicates on the aforementioned 384 well plate with iii endogenous controls (POL2A (Hs00172187_m1), HPRT1 (4326321E, VIC-MGB) and PPIA (4326316E, VIC-MGB)) for both studies A and B. The expression levels were calculated and normalized by geometric averaging of the endogenous controls as previously described [26]. Assays: SIRT2 (Hs00247263_m1), FBXW5 (Hs00382591_g1) and CPT1B (Hs00189258_m1). Endogenous control assays: POLR2A (Hs00172187_m1), HPRT1 (4326321E, VIC-MGB) and PPIA (4326316E, VIC-MGB) for the 7 subjects in the Muscle SATellite jail cell (MSAT) cohort.

Isolation and cultivation of human muscle satellite cells

Muscle satellite cells were isolated from 7 subjects from an ongoing unpublished MSAT study. Subjects were male person with a mean age of 35.6 ± 10.half dozen years, a mean BMI of 25.ane ± 3.6 kg/k² and a mean fasting plasma glucose value of 5.ii ± 0.ii mmol/L. Muscle biopsies were obtained from the vastus lateralis muscle nether local anesthesia in individuals participating in all studies using a modified Bergström needle. Biopsies were minced into small pieces with scissors and digested in a digestion solution (Ham's F-ten Nutrient mix (Gibco®, #31550015), Trypsin-EDTA (0.25%) (HyClone, SV30031.01), Collagenase IV (1 mg/ml) (Sigma, C5138), BSA (five mg/ml) (Sigma, A2153)) at 37 °C for a total of xv–20 min. After this, cells were passed through a 70 μm jail cell strainer and centrifuged at 800 chiliad for seven min. The pellet was washed and resuspended in growth medium (Ham's F-x Food Mix, GlutaMAX™ Supplement (Gibco®, #41550021), FBS (20%) (Sigma, F7524), Antibiotic/Antimycotic Solution (Gibco®, #15240062)) and cells were pre-plated on a culture dish and incubated for iii h at 37 °C and 5% CO₂ to permit fibroblast to adhere to the plate. Later on this, the suspended cells were transferred to a flask pre-coated with matrigel (Corning #356234) and were incubated for 4 days at 37 °C and 5% CO_two in growth medium. Medium was and then inverse every other day. After about a week, cells were discrete using TrypLE (TrypLE™ Express, no phenol red (Gibco®, #15090046)) and re-plated on the aforementioned flask to allow even distribution of cells over the surface.

At 70–80% confluence medium was changed get-go to an intermediate medium (DMEM, low glucose, GlutaMAX™ Supplement, pyruvate, No HEPES (Gibco® #21885025), FBS (10%) (Sigma, F7524), Antibiotics) for 24 h, and then to a differentiation medium (DMEM, low glucose, GlutaMAX™ Supplement, pyruvate, No HEPES (Gibco® #21885025), Horse serum (2%) (Invitrogen, #16050–130), Antibiotics) for 8 days, where glucose uptake experiments were performed. Later on 3 days of differentiation, Cytarabine (Ara-C) (ten μg/ml) (Sigma, C1768) was added to the differentiation medium, for ii days, to forbid excessive growth of proliferating cells, e.chiliad. fibroblasts [27].

Measurement of glucose uptake in cultured musculus cells

Measurement of glucose uptake in cultured muscle cells was performed using an enzymatic fluorometric assay every bit previously described [28]. Briefly, cells differentiated for viii days grown in 12-well plates, were starved for 3 h in FBS-costless DMHG low glucose medium (Gibco® #21885025) at 37 °C and 5% CO₂. The cells were and so washed in warm PBS and treated with either Cytochalasin B (ten μM) (Sigma, C6762) (for not-specific glucose uptake), Krebs-Ringer-HEPES (KRH) buffer only (basal glucose uptake) or with 100 nM insulin (Actrapid 100 IE/ml, Novo Nordisk) (stimulated glucose uptake) in a KRH buffer containing 0.1% BSA (pH 7.4) for 1 h at 37 °C and v% CO_two. Afterward this, cells were incubated in a KRH buffer containing two-Deoxy-D-glucose (2DG) (ane mM) (Sigma, D6134) for 15 min at room temperature, after which the cells were washed in ice-cold PBS and then frozen and stored at − 80 °C (for less than a week). Lysis was done by adding 0.one M ice cold NaOH to the cells and incubate at lxx °C for sixty min, after which HCL and triethanolamine (TEA) buffer (pH 8.1) (Sigma, T1502) at final concentrations of 0.1 M and fifty mM respectively, were added to neutralize the lysate. Lysates and prepared series 2-Deoxy-D-glucose vi-phosphate (DG6P) (Santa Cruz, SC-220734) dilution standards (xxx, xv, 7.v, 3.75, i.875, 0 μM) (dissolved in "lysate buffer" (0.one Yard NaOH / 0.one Yard HCl / l mM TEA buffer, pH 8.1; same proportion as samples), were transferred to a black 96-well analysis plate (Greiner Bio-one International, 655076), 250 μl of assay solution (TEA buffer (50 mM) with KCl (50 mM) (pH 8), BSA (0.02%), NADP (0.1 mM) (Sigma, N8035), Diaphorase (0.ii U/ml) (Sigma, D2197), Resazurin (six μM) (Sigma, R7017), G6PDH (fifteen.4 U/ml) (Sigma, G8404)) was added to each well, and the plates were incubated for 60 min at 37 °C. Fluorescence was measured using the microplate reader (Infinite M200 Pro, Tecan) at wavelengths λ_ex = 545 nm and λ_em = 590 nm. DG6P was and so quantified by comparison the fluorescence intensity from the experimental samples to the DG6P standard curve. Value were adjusted for protein concentration determined with the Pierce™ Coomassie (Bradford) Protein Assay Kit (Thermo Fisher Scientific, 23200).

Quantification of mtDNA content

DNA was isolated from the muscle biopsies past phenol/chloroform/isoamyl alcohol extraction according to the manufacturer's recommendation (Diagenode, Belgium). Concentration and purity were measured using a NanoDrop ND-yard spectrophotometer (A₂₆₀/A₂₈₀ > one.6 and A₂₆₀/A₂₃₀ > i.0) (NanoDrop Technologies, Wilmington, DE, USA). QPCR was carried out using an Applied Biosystems 7900HT sequence detection system with 5 ng genomic Deoxyribonucleic acid in 10 μl reactions and TaqMan Expression PCR Master Mix according to the manufacturer's recommendations. All samples were analyzed in triplicates on the aforementioned 384 well plate (maximum accustomed standard difference in Ct-value of 0.1 cycles). Two assays (16S and ND6) were used to analyze mitochondrial DNA content (mtDNA) targeting the heavy and light strand, respectively. To analyze nuclear Deoxyribonucleic acid (nDNA) content RNaseP was used as a target. The mtDNA content is calculated as the hateful value of ND6 and 16S divided past 2 x RNaseP. Assays used: ND6 (Hs02596879_g1), 16S (Hs02596860_s1) and RNaseP (4316838).

Statistical analysis

Written report A

We used ENTREZ custom flake definition files (http://brainarray.mbni.med.umich.edu) to regroup the individual probes into consistent probesets and remap to the correct sets of genes for Affymetrix Custom Assortment NuGO-Hs1a520180 array which resulted in a total of xvi,313 genes from written report A. We used three different procedures for normalization and summarization as described previously [29]: (i) The GC-content robust multi-assortment boilerplate (GC-RMA) method, (2) Probe logarithmic intensity error (PLIER) method (Affymetrix), and (iii) Robust multi-assortment boilerplate (RMA) method [xxx,31,32,33,34]. We conducted filtering based on the Affymetrix microarray suite version 5.0 (MAS5.0) present/absent calls which classified each gene as expressed above background (present call) or not (absent-minded or marginal call). Nosotros included genes, which have detection telephone call every bit present call in at least 25% of arrays [35], which left 7947 genes out of 16,313 for farther assay in written report A.

To identify a reliable listing of genes regulating insulin sensitivity, Spearman partial correlation analysis was performed to determine the individual effects of each gene expression on a surrogate measure out of insulin sensitivity (1/HOMA-IR) after adjusting for BMI, age and family history of T2D for each of three normalization methods namely GC-RMA, PLIER and RMA separately. Nosotros considered just those genes that were significantly correlated with insulin sensitivity with a P <  0.05 in all iii unlike normalization methods.

To technically validate the microarray findings, real time quantitative PCR (QPCR) was used to measure the mRNA expression of FBXW5 and SIRT2 in human skeletal muscle from written report A. Correlation between the microarray and QPCR experiments was determined using Spearman'due south rank correlation coefficient test.

In the written report A accomplice, correlation between the QPCR expression values of SIRT2, FBXW5, CPT1B, FABP3, MLYCD, PPARG1A and ESRRA with % fiber blazon and mitochondrial DNA was determined using Spearman'due south rank correlation coefficient examination. All information except that of SIRT2 and FBXW5 was collected and reanalyzed from a previously described study [17, xviii].

Enrichment analyses were performed on the genes whose expression levels in skeletal musculus were significantly correlated with insulin sensitivity in report A using the WEB-based GEne SeT Analysis Toolkit (WebGestalt) which implements the hypergeometric examination [36].

Study B

The median intensities of each spot on the array were calculated using the GenePix Pro software (version 6). We performed quantile-based normalization between arrays without background subtraction using linear models for microarray data (limma) package in R [37, 38]. We removed poor quality probes that were either saturated (i.e. > 50% of the pixels in a characteristic are to a higher place the saturation threshold) or flagged as non-uniformity outlier (i.east. the pixel noise of feature exceeds a threshold for a uniform characteristic) in at least one array, which left 29,297 probes for further analysis [39].

Spearman partial correlation assay was performed to make up one's mind the private effects of each gene expression on a surrogate measure of insulin sensitivity (1/HOMA-IR) after adjusting for BMI and age. Due to the exploratory nature of the study, no correction for multiple testing was performed. Instead, nosotros considered just those genes that were significantly, positively or inversely, correlated with insulin sensitivity in both studies A and B with a significance level set to 0.05. Paired Wilcoxon signed-rank test was conducted to assess for the change earlier and after insulin-stimulated glucose uptake. Spearman correlation assay was between basal- and insulin-stimulated glucose uptake and mRNA expression of FBXW5, SIRT2 and CPT1B. All statistical analyses were performed using IBM® SPSS® Statistics, MATLAB® and R statistical software. The microarray information both studies have been deposited in the National Centre for Biotechnology Information's Gene Expression Double-decker (GEO) database (http://world wide web.ncbi.nlm.nih.gov/geo); serial accession number is GSE161721.

Results

To identify genes with skeletal muscle expression related to insulin sensitivity, we obtained musculus biopsies from 38 non-diabetic participants in study A (the data from one participant was excluded, Methods). Clinical characteristics of these participants are shown in Supplementary Table S1. We then profiled muscle gene expression using Affymetrix oligonucleotide microarrays. To replicate the findings from report A, we included 9 non-diabetic participants from report B (the data from ane participant was excluded, Methods). Clinical characteristics of these participants are shown in Supplementary Table S2. We performed skeletal musculus cistron expression profiling from these participants using the Agilent oligonucleotide microarrays. Insulin sensitivity was estimated using the 1/HOMA-IR method calculated from OGTT values (Methods).

Correlation with insulin sensitivity

Written report A

We identified 70 genes positively (Supplementary Table S3) and 110 genes inversely (Supplementary Table S4) correlated with insulin sensitivity in human skeletal musculus. Using WebGestalt [36], we performed enrichment analyses of genes significantly correlated to insulin sensitivity. Of the Gene Ontology (GO) categories overrepresented in the 70 genes positively correlated to insulin sensitivity (Supplementary Table S3), several were related to autophagy (Supplementary Table S5). Among enriched Wikipathways of the positively correlated genes were mTOR signaling and thermogenesis (Supplementary Table S6). Enriched GO categories of the genes inversely correlated to insulin sensitivity (Supplementary Table S4) included platelet-derived growth factor binding, fibrillar collagen trimer, banded collagen fibril and circuitous of collagen trimers (Supplementary Tabular array S7).

Amid genes positively correlated with insulin sensitivity, several, including F-box and WD echo domain containing 5 (FBXW5), TSC2, ULK1, ATG13, AKT1S1, SQSTM1 and TFEB were found to be regulated by or regulating mammalian target-of-rapamycin (mTOR) signaling and autophagy. Among genes involved in lipid metabolism were carnitine palmitoyltransferase 1B (CPT1B) (Fig. 1), the charge per unit limiting enzyme for fat acid oxidation, SLC27A1 (also known as long concatenation-fatty acid transport protein 1), a major transporter of fat acids across the plasma membrane and PNPLA2 (also known as adipocyte triglyceride lipase (ATGL)) a triglyceride lipase known to exist expressed in human skeletal muscle [40]. As well, the sirtuin 2 (SIRT2) gene positively correlated with insulin sensitivity, which is a family unit member of SIRT1 with well-known effects on peripheral insulin signaling [41]. Other interesting genes with relevance for skeletal muscle insulin sensitivity were uncoupling protein ii (UCP2), an inner mitochondrial membrane poly peptide, and genes with straight functional roles in skeletal muscle, e.g. obscurin, histidine rich calcium binding protein (HCR) and myocyte enhancer factor 2nd (MEF2D) (Supplementary Tabular array S3).

Fig. ane

The skeletal muscle expression of CPT1B correlates with insulin sensitivity (1/HOMA-IR) in study A. The relationship betwixt insulin sensitivity (ane/HOMA-IR) and skeletal musculus expression of CPT1B in study A (n = 38). For illustration, gene expression data is shown for only GC-content robust multi-array boilerplate (GC-RMA) method

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Among the genes inversely correlated with insulin sensitivity, several were associated with the extracellular matrix, such every bit collagen type I blastoff 1 chain (COL1A1), collagen type I alpha two concatenation (COL1A2), collagen type III alpha i chain (COL3A1) and laminin subunit alpha four (LAMA4) (Supplementary Table S4).

Study B

In social club to replicate the findings in study A, we analyzed muscle expression in an additional 9 healthy young not-diabetic men without whatever family history of diabetes. Of the genes plant to be correlated with insulin sensitivity, 10 were replicated in written report B. Seven of these genes were positively correlated (SIRT2, FBXW5, RAB11FIP5, CPT1B, C16orf86, UCKL1 and ARFGAP2) and three were inversely correlated (ZNF613, UTP6 and LEO1) with insulin sensitivity (Table 1).

Table 1 Genes of which the expression levels in skeletal musculus were correlated with insulin sensitivity in both study A and B. Spearman fractional correlation assay was performed to decide the individual effects of each factor expression on a surrogate measure of insulin sensitivity (1/HOMA-IR). Significance level was set to 0.05

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Technical validation of the microarray information using existent time quantitative PCR (QPCR)

To technically validate the microarray findings, QPCR was used to measure the mRNA expression of FBXW5 and SIRT2 in human skeletal musculus from written report A. Significant correlation between the microarray and QPCR experiments was observed for both FBXW5 (r = 0.seventy, P <  0.001), SIRT2 (r = 0.60, P <  0.001) (Fig. two) and CPT1B (r = 0.74, P <  0.001) (previously shown [eighteen]). The expression of FBXW5 and SIRT2 analyzed with QPCR was too positively correlated with each other (r = 0.81, P <  0.001) and with the QPCR expression value of CPT1B (Table two).

Fig. 2

Technical replication of the microarray data using QPCR. The relative expression level of (a) FBXW5 and (b) SIRT2 genes were measured in study A using both microarray (x-centrality) and QPCR (y-axis). Information was analyzed with Spearman's rank correlation coefficient exam (n = 29). For illustration, cistron expression data is shown for only GC-content robust multi-array boilerplate (GC-RMA) method

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Table two Correlation between the gene expression of SIRT2, FBXW5 and CPT1B analyzed with quantitative PCR (QPCR) with the QPCR expression values of SIRT2, FBXW5, CPT1B, key genes involved in transport and mitochondrial uptake and oxidation of fat acids in skeletal musculus (FABP3 and MLYCD) and genes with key roles in regulating mitochondrial biogenesis and oxidative phosphorylation in musculus (PPARGC1A and ESRRA). Significant correlation was determined using Spearman'southward rank correlation coefficient examination

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Correlation between the QPCR expression of the replicated genes FBXW5, SIRT2 and CPT1B with the expression of key metabolic genes, fiber type and mitochondrial Dna content in skeletal muscle from report A participants and with in vitro glucose uptake in human myotube cells

The expression of FBXW5, SIRT2 and CPT1B, was positively correlated with malonyl-CoA decarboxylase (MLYCD) and fatty acid binding protein three (FABP3), primal genes involved in transport and mitochondrial uptake and oxidation of fatty acids in muscle, and with estrogen related receptor blastoff (ESRRA) and PPARGC1A (also known equally PGC1α) (Table ii), i.e. with genes playing central roles in regulating mitochondrial biogenesis and oxidative phosphorylation in muscle [42]. Also, expression of FBXW5, SIRT2 and CPT1B was positively correlated with pct type I and inversely correlated with percent blazon Ii B fibers in skeletal musculus, and the expression of SIRT2 and CPT1B was too positively correlated with the amount of mitochondrial DNA (Table 3).

Table 3 Correlation between the cistron expression of SIRT2, FBXW5 and CPT1B analyzed with quantitative PCR (QPCR) with % fiber type and mitochondrial DNA. Meaning correlation was determined using Spearman's rank correlation coefficient test

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From the Muscle SATellite prison cell (MSAT) study, skeletal muscle myoblast cells were isolated and differentiated to myotubes. Insulin stimulation of the myotubes led to a ~ 35% increase (P = 0.018) in glucose uptake (Fig. 3a). Expression of FBXW5 (r = 0.79, P = 0.036) and SIRT2 (r = 0.79, P = 0.036), but not CPT1B (r = 0.49, P = 0.268) were positively correlated with glucose uptake during the basal, non-insulin-stimulated state (Fig. 3b-d). Similar correlations were observed in the insulin-stimulated state, i.e. FBXW5 (r = 0.86, P = 0.014) and SIRT2 (r = 0.82, P = 0.023), just not for CPT1B (r = 0.43, P = 0.333) (Fig. 3b-d).

Fig. 3

Glucose uptake in isolated man myotube cells. a Insulin-stimulated glucose uptake: Insulin stimulation (1 h) led to a ~ 35% increase (P = 0.018) in glucose uptake. Correlation between basal- and insulin-stimulated glucose uptake and mRNA expression of (b) FBXW5 (r_basal = 0.79, P = 0.036 and r_insulin = 0.86, P = 0.014) (c) SIRT2 (r_basal = 0.79, P = 0.036 and r_insulin = 0.82, P = 0.023) and (d) CPT1B (r_basal = 0.49, P = 0.268 and r_insulin = 0.43, P = 0.333). n = 7 average values from 1 to 3 independent experiments per private. For illustration, data were transformed using the natural logarithm (b-d)

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Discussion

The objective of this study was to identify genes for which expression levels are correlated with insulin sensitivity in human skeletal muscle. Genes involved in fat acid metabolism (CPT1B and SIRT2) and in autophagy and mTOR signaling (FBXW5), TSC complex subunit two (TSC2) and unc-51 similar autophagy activating kinase 1 (ULK1) were institute to be associated with insulin sensitivity and related traits (musculus fiber type distribution and mitochondrial number).

We replicated the findings for ten genes from Study A in Written report B using Agilent oligonucleotide microarrays, consisting of 60-mers probes compared to the short 25-mers probes utilized past Affymetrix. The expressions of SIRT2, FBXW5, RAB11FIP5, CPT1B, C16orf86, UCKL1 and ARFGAP2 were positively, whereas the expressions of ZNF613, UTP6 and LEO1 were inversely correlated with insulin sensitivity every bit assessed by ane/HOMA-IR in both studies.

Among the replicated genes positively correlated with insulin sensitivity was CPT1B. CPT1B regulates the transport of long-chain fat acyl-CoAs from the cytoplasm into the mitochondria, a central regulatory stride in lipid β-oxidation. At that place is strong prove that β-oxidation, plays a crucial role in the development of IR, where inhibition of Cpt1b induces [43] and overexpression of Cpt1b ameliorates [44] IR in rats. Also, a mutual haplotype of CPT1B has been associated with the metabolic syndrome in male person participants [45]. The krüppel-like transcription cistron (KLF5) together with C/EBP-β and PPARδ regulate the expression of CPT1B and UCP2 (also positively correlated with insulin sensitivity (Supplementary Table S3) in skeletal muscle) [46]. Moreover, expression of Cpt1b and Ucp2 in skeletal muscle is upwardly-regulated in the klf5-knockout heterozygous mouse, which is resistant to high fatty-induced obesity and glucose intolerance. The skeletal muscle expression of CPT1B in humans is increased after handling with a PPARδ agonist [47], and this agonist is likewise shown to increase muscle mitochondrial biogenesis and amend glucose homeostasis, the latter suggested to exist mediated by enhanced fatty acid catabolism in muscle [48]. It is likely that the beneficial effect of the PPARδ agonist is partly due to induction of CPT1B in skeletal muscle. Other genes coupled to lipid metabolism whose expression positively correlated with insulin sensitivity include PNPLA2 (ATGL) and SLC27A1 (long chain-fatty acid transport protein 1; FATP-1). Although no correlation between insulin sensitivity and musculus ATGL expression has previously been reported, ATGL mRNA is shown to be strongly coupled to mRNA levels of CPT1B in man musculus [49]. Atgl, Cpt1b and Slc27a1 are highly expressed in insulin responsive oxidative blazon I fibers, and insulin-stimulated fatty acid uptake is largely dependent on Slc27a1 in rodent muscle [fifty]. Taken together, data presented here are in-line with and support previous findings that skeletal musculus lipid metabolism, and lipid β-oxidation in item, plays an important part in the evolution of IR.

Some other replicated gene in this written report positively correlated with insulin sensitivity was SIRT2, a predominantly cytoplasmic deacetylase expressed in a broad range of metabolically relevant tissues. Increasing evidence suggests that the expression of SIRT2 is modulated in response to energy availability, beingness induced during low-energy status [51]. Conversely, dietary obesity and associated pathologies, e.grand. IR, is linked to the capacity to suppress β-oxidation in visceral adipocytes, in function through transcriptional repression of SIRT2 with negative furnishings on the SIRT2-PGC1α regulatory axis [52]. SIRT2 is besides described every bit a novel AKT interactor, disquisitional for AKT activation by insulin, and the potential usefulness of SIRT2 activators in the treatment of insulin-resistant metabolic disorders has been discussed [53]. Unlike the well-documented effects of SIRT1 in skeletal muscle insulin signaling [41], the role of SIRT2 in skeletal musculus is much less divers. A study using mouse C2C12 skeletal musculus cells showed that downward-regulation of Sirt2 in insulin resistant cells improved insulin sensitivity [54], raising the possibility that Sirt2 has tissue-specific roles regarding insulin sensitivity. The opposite findings presented hither, showing a positive clan between insulin sensitivity and SIRT2 factor expression in homo skeletal muscle could highlight a differential function in various metabolic weather condition, or species differences.

Of the enriched Gene Ontology (Go) categories of genes whose expression positively correlated with insulin sensitivity, several were related to autophagy, process utilizing autophagic mechanism and regulation of macroautophagy. Interestingly, nosotros found the expression of FBXW5 to be positively correlated to insulin sensitivity in both study A and B. FBXW5 is part of an E3 ubiquitin ligase that regulates TSC2 poly peptide stability and circuitous turnover [55], with indirect effects on mTOR. Moreover, a variant (rs546064512) in FBXW5 is shown to be associated with total cholesterol (odds ratio = 0.56 and P = 8.93 × ten^− 4) in 12,940 individuals of multiple ancestries ([56] and The T2D Knowledge Portal: http://world wide web.type2diabetesgenetics.org/). In the fed land insulin signaling activates mTOR, whereas in the fasted state AMPK has the opposite effect leading to inactivation of mTOR and activation of autophagy. ULK1 negatively regulates and is negatively regulated by mTOR, making mTOR a major convergence bespeak for the regulation of autophagy [57]. ULK1 is also a key regulator of mitophagy, and it'due south phosphorylation past AMPK is required for mitochondrial homeostasis and cell survival during starvation [58]. The large number of autophagy-related genes positively correlating with insulin sensitivity might result from the fasted state of the subjects (10–12 h) and could be a reflection of metabolic flexibility, i.e., the ability to switch from high rates of fatty acid uptake and lipid oxidation to suppression of lipid metabolism with a paralleled increment in glucose uptake, storage and oxidation in response to, e.g., feeding or exercise. Impaired autophagy has been implicated in ageing and IR, and induction of autophagy is required for musculus glucose homeostasis mediated past exercise in mice [59]. A crucial link betwixt autophagy and insulin sensitivity in humans has been suggested in a study where skeletal muscle from severely insulin resistant subjects with T2D evidence a highly altered gene expression related to mitochondrial dysfunction and aberrant morphology, and that this is associated with decreased expression of autophagy-related genes [15].

Future studies are required to determine the potential role of the remaining replicated genes in the regulation of insulin sensitivity in human skeletal musculus, although it should be mentioned that RAB11FIP5, an AS160- and Rab-binding protein, is suggested to coordinate the protein kinase signaling and trafficking mechanism required for insulin-stimulated glucose uptake in adipocytes [60]. Also, RAB11FIP5 is an effector protein of RAB11, a GTPase that regulates endosomal trafficking shown to be required for autophagosome germination [61], suggesting nevertheless some other link betwixt the regulation of insulin sensitivity and autophagy in skeletal muscle.

The positive correlation of CPT1B, SIRT2 and FBXW5 expression with insulin sensitivity in this study is supported past the observed positive correlation of these genes with the expression of key genes promoting the phenotype of an insulin sensitive myocyte, e.thousand. transport and mitochondrial uptake and oxidation of fat acids and positive regulation of mitochondrial biogenesis and oxidative phosphorylation (Tables 2 and iii). For SIRT2 and FBXW5, this was as well supported past the correlation of these genes with glucose uptake measurements in human being myotube cells (Fig. 3).

There are several bug to consider in the interpretation of the results. In both studies, we used 1/HOMA-IR as a surrogate measure of insulin sensitivity. The HOMA-IR index is based upon fasting measurements of insulin and glucose and thus more reflects variation in hepatic than in peripheral insulin sensitivity [62]. Although several studies have shown significant correlations betwixt HOMA-IR and insulin-stimulated glucose uptake as measured by an euglycemic hyperinsulinemic clamp, this correlation cannot exist expected to be very stiff given the different physiological weather they reflect [22, 63]. On the other hand, biopsies in both studies were obtained in the fasting state and should thus more than correspond to conditions as measured by 1/HOMA-IR.

Conclusions

In conclusion, we present a catalog with muscle expression of 180 genes correlated with insulin sensitivity. This data provides compelling evidence that activation of genes involved in lipid metabolism, including SIRT2, and of genes involved the regulation of autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human being skeletal muscle. Determining if these genes are causally related with insulin sensitivity in humans should be the aim of future studies.

Availability of data and materials

The microarray data both studies have been deposited in the National Centre for Biotechnology Data's Gene Expression Coach (GEO) database (http://www.ncbi.nlm.nih.gov/geo); serial accession number is GSE161721.

Abbreviations

2DG:

2-Deoxy-D-glucose

ATGL:

Adipocyte triglyceride lipase

BMI:

Body mass index

COL1A1:

Collagen type I alpha 1 chain

COL1A2:

Collagen type I alpha 2 chain

COL3A1:

Collagen type III alpha 1 concatenation

CPT1B:

Carnitine palmitoyltransferase 1B

DG6P:

2-Deoxy-D-glucose 6-phosphate

ESRRA:

Estrogen related receptor alpha

FABP3:

Fatty acid binding protein three

FBXW5:

F-box and WD repeat domain containing 5

GCOS:

GeneChip Operating Organisation

GC-RMA:

Guanine cytosine-content robust multi-array analysis

GEO:

Gene Expression Coach

GO:

Gene Ontology

HCR:

histidine rich calcium binding protein

HOMA-IR:

Homeostatic model assessment-insulin resistance

IMTG:

Intramuscular triacylglycerol

IR:

Insulin resistance

KLF5:

Krüppel-like transcription factor

KRH:

Krebs-Ringer-HEPES

LAMA4:

Laminin subunit alpha 4

Limma:

Linear models for microarray data

MAS5.0:

Affymetrix microarray suite version v.0

MEF2D:

Myocyte enhancer gene second (MEF2D)

MLYCD:

Malonyl-CoA decarboxylase

MSAT:

Muscle SATellite prison cell

mtDNA:

mitochondrial Dna content

mTOR:

mammalian target-of-rapamycin

nDNA:

nuclear Deoxyribonucleic acid

OGTT:

Oral glucose tolerance test

PLIER:

Probe logarithmic intensity fault estimation

PPARGC1A:

Peroxisome proliferator-activated receptor gamma coactivator 1 alpha

QPCR:

Quantitative real-time polymerase chain reaction

RMA:

Robust multi-array analysis

SIRT2:

Sirtuin 2

T2D:

Type ii diabetes

TEA:

Triethanolamine

TSC2:

TSC complex subunit two

UCP2:

Uncoupling protein ii

ULK1:

Unc-51 similar autophagy activating kinase one

WebGestalt:

Spider web-based Factor Set up Assay Toolkit

References

1. Lillioja S, Mott DM, Spraul G, Ferraro R, Foley JE, Ravussin Due east, Knowler WC, Bennett PH, Bogardus C. Insulin resistance and insulin secretory dysfunction equally precursors of non-insulin-dependent diabetes mellitus. Prospective studies of Pima Indians. N Engl J Med. 1993;329(27):1988–92.

   CAS  PubMed  Article  PubMed Central  Google Scholar

2. Martin BC, Warram JH, Krolewski AS, Bergman RN, Soeldner JS, Kahn CR. Role of glucose and insulin resistance in development of blazon 2 diabetes mellitus: results of a 25-year follow-upward study. Lancet. 1992;340(8825):925–9.

   CAS  PubMed  Article  PubMed Fundamental  Google Scholar

3. Bloomgarden ZT. Insulin resistance: current concepts. Clin Ther. 1998;twenty(2):216–31 give-and-take 215.

   CAS  PubMed  Commodity  PubMed Cardinal  Google Scholar

4. Kim JA, Wei Y, Sowers JR. Role of mitochondrial dysfunction in insulin resistance. Circ Res. 2008;102(4):401–14.

   CAS  PubMed  PubMed Central  Article  Google Scholar

5. Sesti K. Pathophysiology of insulin resistance. All-time Pract Res Clin Endocrinol Metab. 2006;20(4):665–79.

   CAS  PubMed  Commodity  PubMed Fundamental  Google Scholar

6. Petersen KF, Shulman GI. Etiology of insulin resistance. Am J Med. 2006;119(5 Suppl 1):S10–six.

   PubMed  PubMed Cardinal  Article  CAS  Google Scholar

7. Krssak 1000, Falk Petersen K, Dresner A, DiPietro L, Vogel SM, Rothman DL, Roden M, Shulman GI. Intramyocellular lipid concentrations are correlated with insulin sensitivity in humans: a 1H NMR spectroscopy study. Diabetologia. 1999;42(1):113–half dozen.

   CAS  PubMed  Article  PubMed Central  Google Scholar

8. Perseghin G, Scifo P, De Cobelli F, Pagliato E, Battezzati A, Arcelloni C, Vanzulli A, Testolin G, Pozza K, Del Maschio A, et al. Intramyocellular triglyceride content is a determinant of in vivo insulin resistance in humans: a 1H-13C nuclear magnetic resonance spectroscopy assessment in offspring of type two diabetic parents. Diabetes. 1999;48(eight):1600–six.

   CAS  PubMed  Article  PubMed Key  Google Scholar

9. Goodpaster BH, He J, Watkins Southward, Kelley DE. Skeletal musculus lipid content and insulin resistance: bear witness for a paradox in endurance-trained athletes. J Clin Endocrinol Metab. 2001;86(12):5755–61.

   CAS  PubMed  Article  PubMed Central  Google Scholar

10. Thamer C, Machann J, Bachmann O, Haap M, Dahl D, Wietek B, Tschritter O, Niess A, Brechtel One thousand, Fritsche A, et al. Intramyocellular lipids: anthropometric determinants and relationships with maximal aerobic capacity and insulin sensitivity. J Clin Endocrinol Metab. 2003;88(4):1785–91.

    CAS  PubMed  Article  PubMed Central  Google Scholar

11. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale Grand, Laurila E, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(iii):267–73.

    CAS  Commodity  Google Scholar

12. Patti ME, Butte AJ, Crunkhorn S, Cusi One thousand, Berria R, Kashyap South, Miyazaki Y, Kohane I, Costello M, Saccone R, et al. Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1. Proc Natl Acad Sci U S A. 2003;100(14):8466–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar

13. Befroy DE, Petersen KF, Dufour Due south, Mason GF, de Graaf RA, Rothman DL, Shulman GI. Dumb mitochondrial substrate oxidation in musculus of insulin-resistant offspring of type 2 diabetic patients. Diabetes. 2007;56(5):1376–81.

    CAS  PubMed  PubMed Cardinal  Article  Google Scholar

14. Grunnet LG, Laurila E, Hansson O, Almgren P, Groop L, Brons C, Poulsen P, Vaag A. The triglyceride content in skeletal muscle is associated with hepatic simply non peripheral insulin resistance in elderly twins. J Clin Endocrinol Metab. 2012;97(12):4571–vii.

    CAS  PubMed  Article  PubMed Key  Google Scholar

15. Moller AB, Kampmann U, Hedegaard J, Thorsen K, Nordentoft I, Vendelbo MH, Moller North, Jessen N. Altered gene expression and repressed markers of autophagy in skeletal muscle of insulin resistant patients with type 2 diabetes. Sci Rep. 2017;seven:43775.

    PubMed  PubMed Fundamental  Article  Google Scholar

16. Pyo JO, Yoo SM, Ahn HH, Nah J, Hong SH, Kam TI, Jung S, Jung YK. Overexpression of Atg5 in mice activates autophagy and extends lifespan. Nat Commun. 2013;4:2300.

    PubMed  PubMed Key  Article  CAS  Google Scholar

17. Ekman C, Elgzyri T, Strom K, Almgren P, Parikh H, Dekker Nitert Chiliad, Ronn T, Manderson Koivula F, Ling C, Tornberg AB, et al. Less pronounced response to exercise in healthy relatives to type ii diabetic subjects compared with controls. J Appl Physiol (1985). 2015;119(9):953–60.

    CAS  Article  Google Scholar

18. Elgzyri T, Parikh H, Zhou Y, Dekker Nitert Thousand, Ronn T, Segerstrom AB, Ling C, Franks Prisoner of war, Wollmer P, Eriksson KF, et al. Starting time-caste relatives of type two diabetic patients take reduced expression of genes involved in fatty acrid metabolism in skeletal muscle. J Clin Endocrinol Metab. 2012;97(7):E1332–vii.

    CAS  PubMed  Article  PubMed Central  Google Scholar

19. Alibegovic Air-conditioning, Sonne MP, Hojbjerre L, Bork-Jensen J, Jacobsen S, Nilsson East, Faerch K, Hiscock Northward, Mortensen B, Friedrichsen M, et al. Insulin resistance induced by physical inactivity is associated with multiple transcriptional changes in skeletal muscle in young men. Am J Physiol Endocrinol Metab. 2010;299(five):E752–63.

    CAS  PubMed  Article  PubMed Central  Google Scholar

20. Parikh H, Carlsson E, Chutkow WA, Johansson LE, Storgaard H, Poulsen P, Saxena R, Ladd C, Schulze PC, Mazzini MJ, et al. TXNIP regulates peripheral glucose metabolism in humans. PLoS Med. 2007;4(five):e158.

    PubMed  PubMed Central  Article  CAS  Google Scholar

21. Alberti KG, Zimmet PZ. Definition, diagnosis and nomenclature of diabetes mellitus and its complications. Function 1: diagnosis and nomenclature of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.

    CAS  PubMed  Article  PubMed Central  Google Scholar

22. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-prison cell role from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(vii):412–9.

    CAS  PubMed  PubMed Key  Article  Google Scholar

23. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15–26.

    CAS  PubMed  Commodity  PubMed Central  Google Scholar

24. Eriksson KF, Saltin B, Lindgarde F. Increased skeletal muscle capillary density precedes diabetes development in men with dumb glucose tolerance. A xv-year follow-upwards. Diabetes. 1994;43(half dozen):805–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar

25. Parikh H, Nilsson E, Ling C, Poulsen P, Almgren P, Nittby H, Eriksson KF, Vaag A, Groop LC. Molecular correlates for maximal oxygen uptake and type one fibers. Am J Physiol Endocrinol Metab. 2008;294(6):E1152–9.

    CAS  PubMed  Commodity  PubMed Central  Google Scholar

26. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy North, De Paepe A, Speleman F. Accurate normalization of existent-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(vii):RESEARCH0034.

    PubMed  PubMed Central  Article  Google Scholar

27. Doynova MD, Markworth JF, Cameron-Smith D, Vickers MH, O'Sullivan JM. Linkages between changes in the 3D organisation of the genome and transcription during myotube differentiation in vitro. Skelet Musculus. 2017;7(1):five.

    PubMed  PubMed Central  Article  CAS  Google Scholar

28. Yamamoto N, Ueda-Wakagi M, Sato T, Kawasaki K, Sawada K, Kawabata Grand, Akagawa M, Ashida H. Measurement of Glucose Uptake in Cultured Cells. Curr Protoc Pharmacol. 2015;71:12 14 11–26.

    Google Scholar

29. Parikh H, Lyssenko V, Groop LC. Prioritizing genes for follow-upwardly from genome wide clan studies using information on gene expression in tissues relevant for type 2 diabetes mellitus. BMC Med Genet. 2009;2:72.

    Google Scholar

30. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003;31(4):e15.

    PubMed  PubMed Cardinal  Article  CAS  Google Scholar

31. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level information. Biostatistics. 2003;4(ii):249–64.

    Commodity  Google Scholar

32. Kibriya MG, Jasmine F, Argos M, Verret WJ, Rakibuz-Zaman M, Ahmed A, Parvez F, Ahsan H. Changes in gene expression profiles in response to selenium supplementation among individuals with arsenic-induced pre-malignant skin lesions. Toxicol Lett. 2007;169(2):162–76.

    CAS  PubMed  PubMed Cardinal  Article  Google Scholar

33. Millenaar FF, Okyere J, May ST, van Zanten M, Voesenek LA, Peeters AJ. How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give unlike results. BMC Bioinformatics. 2006;7:137.

    PubMed  PubMed Primal  Article  CAS  Google Scholar

34. Seo J, Hoffman EP. Probe set algorithms: is there a rational best bet? BMC Bioinformatics. 2006;7:395.

    PubMed  PubMed Central  Commodity  CAS  Google Scholar

35. McClintick JN, Edenberg HJ. Effects of filtering by present call on analysis of microarray experiments. BMC Bioinformatics. 2006;7:49.

    PubMed  PubMed Central  Commodity  CAS  Google Scholar

36. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: cistron set assay toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199–205.

    CAS  PubMed  PubMed Central  Commodity  Google Scholar

37. Smyth GK, Speed T. Normalization of cDNA microarray data. Methods. 2003;31(iv):265–73.

    CAS  PubMed  Article  PubMed Primal  Google Scholar

38. Zahurak M, Parmigiani G, Yu W, Scharpf RB, Berman D, Schaeffer E, Shabbeer South, Cope L. Pre-processing Agilent microarray data. BMC Bioinformatics. 2007;eight:142.

    PubMed  PubMed Central  Commodity  CAS  Google Scholar

39. Gandemer 5, Rio AG, de Tayrac M, Sibut Five, Mottier Due south, Ly Sunnaram B, Henry C, Monnier A, Berthou C, Le Gall Due east, et al. Five distinct biological processes and 14 differentially expressed genes narrate TEL/AML1-positive leukemia. BMC Genomics. 2007;8:385.

    PubMed  PubMed Primal  Commodity  Google Scholar

40. Zimmermann R, Strauss JG, Haemmerle G, Schoiswohl Chiliad, Birner-Gruenberger R, Riederer M, Lass A, Neuberger Chiliad, Eisenhaber F, Hermetter A, et al. Fat mobilization in adipose tissue is promoted by adipose triglyceride lipase. Science. 2004;306(5700):1383–vi.

    CAS  PubMed  Article  PubMed Central  Google Scholar

41. Kitada M, Kume S, Kanasaki G, Takeda-Watanabe A, Koya D. Sirtuins as possible drug targets in type 2 diabetes. Curr Drug Targets. 2013;14(six):622–36.

    CAS  PubMed  Article  PubMed Central  Google Scholar

42. Mootha VK, Handschin C, Arlow D, Xie Ten, St Pierre J, Sihag S, Yang Due west, Altshuler D, Puigserver P, Patterson Northward, et al. Erralpha and Gabpa/b specify PGC-1alpha-dependent oxidative phosphorylation cistron expression that is altered in diabetic muscle. Proc Natl Acad Sci U Southward A. 2004;101(17):6570–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar

43. Dobbins RL, Szczepaniak LS, Bentley B, Esser 5, Myhill J, McGarry JD. Prolonged inhibition of musculus carnitine palmitoyltransferase-i promotes intramyocellular lipid accumulation and insulin resistance in rats. Diabetes. 2001;50(1):123–30.

    CAS  PubMed  Article  PubMed Key  Google Scholar

44. Bruce CR, Hoy AJ, Turner North, Watt MJ, Allen TL, Carpenter M, Cooney GJ, Febbraio MA, Kraegen EW. Overexpression of carnitine palmitoyltransferase-1 in skeletal muscle is sufficient to enhance fatty acid oxidation and improve loftier-fatty diet-induced insulin resistance. Diabetes. 2009;58(iii):550–viii.

    CAS  PubMed  PubMed Central  Article  Google Scholar

45. Auinger A, Rubin D, Sabandal K, Helwig U, Ruther A, Schreiber S, Foelsch UR, Doring F, Schrezenmeir J. A mutual haplotype of carnitine palmitoyltransferase 1b is associated with the metabolic syndrome. Br J Nutr. 2013;109(5):810–five.

    CAS  PubMed  Article  PubMed Central  Google Scholar

46. Oishi Y, Manabe I, Tobe G, Ohsugi M, Kubota T, Fujiu K, Maemura K, Kubota N, Kadowaki T, Nagai R. SUMOylation of Kruppel-like transcription factor 5 acts every bit a molecular switch in transcriptional programs of lipid metabolism involving PPAR-delta. Nat Med. 2008;14(half-dozen):656–66.

    CAS  PubMed  Commodity  PubMed Primal  Google Scholar

47. Riserus U, Sprecher D, Johnson T, Olson East, Hirschberg S, Liu A, Fang Z, Hegde P, Richards D, Sarov-Blat 50, et al. Activation of peroxisome proliferator-activated receptor (PPAR)delta promotes reversal of multiple metabolic abnormalities, reduces oxidative stress, and increases fat acrid oxidation in moderately obese men. Diabetes. 2008;57(2):332–nine.

    CAS  PubMed  Article  PubMed Central  Google Scholar

48. Reilly SM, Lee CH. PPAR delta as a therapeutic target in metabolic disease. FEBS Lett. 2008;582(1):26–31.

    CAS  PubMed  Article  PubMed Key  Google Scholar

49. Yao-Borengasser A, Varma V, Coker RH, Ranganathan One thousand, Phanavanh B, Rasouli N, Kern PA. Adipose triglyceride lipase expression in human adipose tissue and muscle. Part in insulin resistance and response to preparation and pioglitazone. Metabolism. 2011;60(7):1012–20.

    CAS  PubMed  Article  PubMed Central  Google Scholar

50. Wu Q, Ortegon AM, Tsang B, Doege H, Feingold KR, Stahl A. FATP1 is an insulin-sensitive fatty acid transporter involved in nutrition-induced obesity. Mol Cell Biol. 2006;26(ix):3455–67.

    CAS  PubMed  PubMed Central  Commodity  Google Scholar

51. Gomes P, Fleming Outeiro T, Cavadas C. Emerging function of Sirtuin 2 in the regulation of mammalian metabolism. Trends Pharmacol Sci. 2015;36(eleven):756–68.

    CAS  PubMed  Article  PubMed Cardinal  Google Scholar

52. Krishnan J, Danzer C, Simka T, Ukropec J, Walter KM, Kumpf S, Mirtschink P, Ukropcova B, Gasperikova D, Pedrazzini T, et al. Dietary obesity-associated Hif1alpha activation in adipocytes restricts fatty acid oxidation and energy expenditure via suppression of the Sirt2-NAD+ organisation. Genes Dev. 2012;26(3):259–70.

    CAS  PubMed  PubMed Fundamental  Commodity  Google Scholar

53. Ramakrishnan Grand, Davaakhuu One thousand, Kaplun 50, Chung WC, Rana A, Atfi A, Miele L, Tzivion 1000. Sirt2 deacetylase is a novel AKT binding partner critical for AKT activation by insulin. J Biol Chem. 2014;289(nine):6054–66.

    CAS  PubMed  PubMed Central  Commodity  Google Scholar

54. Arora A, Dey CS. SIRT2 negatively regulates insulin resistance in C2C12 skeletal muscle cells. Biochim Biophys Acta. 2014;1842(9):1372–8.

    CAS  PubMed  Commodity  PubMed Key  Google Scholar

55. Hu J, Zacharek Due south, He YJ, Lee H, Shumway S, Duronio RJ, Xiong Y. WD40 protein FBW5 promotes ubiquitination of tumor suppressor TSC2 by DDB1-CUL4-ROC1 ligase. Genes Dev. 2008;22(7):866–71.

    CAS  PubMed  PubMed Key  Article  Google Scholar

56. Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala Five, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, et al. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2017;4:170179.

    PubMed  Commodity  CAS  PubMed Central  Google Scholar

57. Bach Chiliad, Larance M, James DE, Ramm 1000. The serine/threonine kinase ULK1 is a target of multiple phosphorylation events. Biochem J. 2011;440(two):283–91.

    CAS  PubMed  Article  PubMed Central  Google Scholar

58. Egan DF, Shackelford DB, Mihaylova MM, Gelino S, Kohnz RA, Mair W, Vasquez DS, Joshi A, Gwinn DM, Taylor R, et al. Phosphorylation of ULK1 (hATG1) past AMP-activated protein kinase connects energy sensing to mitophagy. Science. 2011;331(6016):456–61.

    CAS  Article  Google Scholar

59. He C, Bassik MC, Moresi V, Sun K, Wei Y, Zou Z, An Z, Loh J, Fisher J, Sun Q, et al. Exercise-induced BCL2-regulated autophagy is required for muscle glucose homeostasis. Nature. 2012;481(7382):511–5.

    CAS  PubMed  PubMed Key  Article  Google Scholar

60. Welsh GI, Leney SE, Lloyd-Lewis B, Wherlock Chiliad, Lindsay AJ, McCaffrey MW, Tavare JM. Rip11 is a Rab11- and AS160-RabGAP-binding protein required for insulin-stimulated glucose uptake in adipocytes. J Cell Sci. 2007;120(Pt 23):4197–208.

    CAS  PubMed  Article  PubMed Cardinal  Google Scholar

61. Longatti A, Lamb CA, Razi M, Yoshimura S, Barr FA, Tooze SA. TBC1D14 regulates autophagosome formation via Rab11- and ULK1-positive recycling endosomes. J Prison cell Biol. 2012;197(v):659–75.

    CAS  PubMed  PubMed Fundamental  Article  Google Scholar

62. Tripathy D, Almgren P, Tuomi T, Groop L. Contribution of insulin-stimulated glucose uptake and basal hepatic insulin sensitivity to surrogate measures of insulin sensitivity. Diabetes Care. 2004;27(9):2204–10.

    CAS  PubMed  Article  PubMed Central  Google Scholar

63. Radikova Z. Assessment of insulin sensitivity/resistance in epidemiological studies. Endocr Regul. 2003;37(iii):189–94.

    PubMed  PubMed Central  Google Scholar

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Acknowledgments

We are grateful to Dr. Peter Osmark for valuable comments.

Funding

This piece of work was supported past grants from the Swedish Cognition Foundation through the Industrial Ph.D. plan in Medical Bioinformatics at the Middle for Medical Innovations (CMI) at the Karolinska Establish (HMP), The Diabetes Programme at Lund University (HMP), Diabetesföreningen in Malmö (HMP), The Medical Faculty at Lund Academy (HMP), Linnaeus grant from the Swedish Enquiry Quango (LCG), ERC grant (EXGENESIS) (LCG), the Knut and Alice Wallenberg Foundation (LCG) for equipment, Swedish Research Quango (OH), Crafoord foundation (OH), ALF (OH), Novo Nordisk foundation (OH), Magnus Bergvall foundation (OH), Påhlsson foundation (OH), Diabetes Wellness (OH) and the Swedish Diabetes foundation (OH). LUDC-IRC: Swedish Foundation for Strategic Inquiry, Dnr IRC15–0067, EXODIAB: Swedish Research Council, Strategic Research Expanse, Dnr 2009–1039. Open Access funding provided by Lund Academy.

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Author notes

1. Kristoffer Ström and Ola Hansson contributed as to this work.

Affiliations

1. Health Informatics Plant, Morsani Higher of Medicine, Academy of South Florida, 3650 Spectrum Blvd, Tampa, FL, 33612, USA

   Hemang K. Parikh

2. Department of Clinical Sciences, Diabetes & Endocrinology, Lund Academy, University Infirmary Malmö, SE-20502, Malmö, Sweden

   Hemang Grand. Parikh, Targ Elgzyri, Ola Ekström, Karl-Fredrik Eriksson, Leif C. Groop, Kristoffer Ström & Ola Hansson

3. Steno Diabetes Center, DK-2820, Gentofte, Denmark

   Amra Alibegovic & Allan Vaag

4. Unilever Discover R & D, Colworth Scientific discipline Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK

   Natalie Hiscock

5. Finnish Institute of Molecular Medicine, FI-00014, University of Helsinki, Helsinki, Republic of finland

   Leif C. Groop & Ola Hansson

6. Swedish Winter Sports Enquiry Heart, Mid Sweden University, SE-83125, Östersund, Sweden

   Kristoffer Ström

Contributions

HMP conceived the projection, designed the written report, analyzed the microarray information, performed the statistical analysis, and drafted the manuscript. TE, OE, and KFE conducted clinical study, processed the samples, analyzed and prepared the data for Study A. AA, NH, and AV conducted clinical written report, processed the samples, analyzed and prepared the data for Study B. KS, and OH designed the QPCR experiments and interpreted the data. LCG, KS, and OH designed the project and supervised all phases of the projection, including the writing of the manuscript. All authors read and approved the final version of the manuscript.

Respective writer

Correspondence to Hemang 1000. Parikh.

Ethics declarations

Ideals approval and consent to participate

Study A was approved by the local ideals committee at Lund University, and written, informed consent was obtained from all participants. Study B was approved by the Copenhagen and Frederiksberg Regional Ethics Committee (ref. no. 01–262546) and informed written consent was obtained from all of the subjects before participation. Both studies were conducted co-ordinate to the principles of the Helsinki Declaration. Musculus SATellite cell (MSAT) study was approved by the local ideals committee at Lund Academy, and written, informed consent was obtained from all participants (ethical blessing: Dnr 2015/593).

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no conflicts of involvement regarding the publication of this article.

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Supplementary Information

Additional file ane: Supplementary Tabular array S1.

Clinical and biochemical characteristics of male subjects from written report A. Supplementary Table S2. Clinical and biochemical characteristics of male subjects from study B. Supplementary Tabular array S3. Genes of which expression levels in skeletal musculus were positively correlated with insulin sensitivity (1/HOMA-IR) in study A. Supplementary Table S4. Genes of which expression levels in skeletal muscle were inversely correlated with insulin sensitivity (1/HOMA-IR) in report A. Supplementary Table S5. Significantly enriched Factor Ontology (GO) categories in the 70 genes whose expression level in skeletal musculus positively correlated with insulin sensitivity in Report A, analyzed with the Spider web-based GEne SeT Analysis Toolkit (WebGestalt). Supplementary Table S6. Significantly enriched Wikipathways, in the 70 genes whose expression level in skeletal muscle positively correlated with insulin sensitivity in Study A, analyzed with the WEB-based GEne Set Assay Toolkit (WebGestalt). Supplementary Tabular array S7. Significantly enriched Factor Ontology (Go) categories in the 110 genes whose expression level in skeletal musculus was inversely correlated with insulin sensitivity in Study A, analyzed with the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt).

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Parikh, H.Thousand., Elgzyri, T., Alibegovic, A. et al. Relationship between insulin sensitivity and gene expression in human skeletal musculus. BMC Endocr Disord 21, 32 (2021). https://doi.org/ten.1186/s12902-021-00687-ix

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• Received: xvi July 2020

• Accepted: 03 February 2021

• Published: 27 Feb 2021

• DOI : https://doi.org/10.1186/s12902-021-00687-ix

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