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Fix the root cause — the focus on metabolic healthMetabolic health enhancers -
Hi-C libraries were generated from skeletal muscle myotubes followed by hybridization-based capture of 21, human promoters, using a collection of 37, biotinylated RNA baits approximately two baits per promoter previously designed and tested by others By sequencing the captured ligation fragments and testing for a difference in mapped Hi-C interactions by palmitate or TNFα treatment, we did not detect any significant changes Supplementary Fig.
This agrees with another study showing that TNFα-responsive enhancers are already in contact with their target promoters before transient activation or repression of enhancer activity by TNFα treatment in human fibroblasts Next, we pooled all Promoter Capture Hi-C conditions in order to obtain a general chromatin conformation capture of myotubes.
This identified 36, significant promoter-enhancer interactions Fig. Genomic distances of identified promoter-enhancer interactions ranged up to 6. b Histogram showing the distance between interacting promoter-fragments and enhancer-fragments.
The median distance is c Histogram showing the number of enhancer interactions pr. The median number of interactions is 4.
X -axis is the RNA-seq logFC, y -axis is the fraction of genes with this logFC or less. Differences between empirical cumulative distribution functions were tested using a Kolmogorov-Smirnov test KS-test.
To validate if our Promoter Capture Hi-C data identified functional enhancer-promoter interactions, i. Taken together, we have generated an enhancer-promoter connectivity map of skeletal muscle myotubes and demonstrated a general capture of promoter-enhancer pairs with concurrent changes in activity by palmitate or TNFα treatment.
Given that the vast majority of disease-associated variants are predicted to be located in regulatory regions 9 , 10 , 11 , our data represent an opportunity to identify target genes of GWAS SNPs in skeletal muscle cells by combining our enhancer mapping with information on chromatin conformation and gene transcription.
After overlapping the variants with enhancer regions regulated by either palmitate or TNFα treatment, we identified 58 palmitate-regulated enhancers and TNFα-regulated enhancers each harboring one or more GWAS SNPs Fig. Next, we selected enhancers that were both captured by our Promoter Capture Hi-C analysis and linked to genes differentially expressed after palmitate or TNFα treatment.
When only considering enhancer-gene pairs where enhancer activity and gene expression were regulated in the same direction i. The predicted target genes included several known players in metabolism such as IRS1 , IGFBP3 , PPARG , SOCS2, and LEPR , providing a link between disease-associated SNPs and the ability of skeletal muscle to adapt to metabolic and inflammatory stress.
To further narrow down the list of potential gene targets, we investigated the association between genotype of the enhancer-overlapping GWAS SNPs and the basal expression of each of their target genes in skeletal muscle biopsies of individuals by expression quantitative trait locus eQTL analysis.
This approach identified 13 significant skeletal muscle eGenes CEP68 , GAB2 , LAMB1 , MACF1 , EIF6 , PABPC4 , BTBD1 , FILIP1L , TCEA3 , NRP1 , ZHX3 , TBX15 , and TNFAIP8 for 61 GWAS-SNPs, located within 20 distinct enhancer regions Fig.
Thus, by overlapping our genomic datasets, we have identified numerous putative target genes of metabolic GWAS SNPs, which may play a functional role under lipid toxicity or in response to proinflammatory stimuli.
Moreover, for 13 genes, we demonstrate a significant association between GWAS SNP genotype and basal gene expression levels in human skeletal muscle. a Overview of the number of original and LD linked T2D, IR, BMI, or WHR GWAS SNPs. b Overlapping of and 17, palmitate-regulated or TNFα-regulated enhancers with selected GWAS SNPs, and integrating Promoter Capture Hi-C and gene expression data identifies 11 and palmitate-regulated or TNFα-regulated enhancers encompassing GWAS SNPs and connected to a predicted target gene.
See also Supplementary Data 6 and the Methods section for a detailed description of the analysis. In order to understand the role of the identified putative GWAS-SNP target genes in whole body metabolism in vivo, we analyzed the association between 48 metabolic traits in the BXD murine genetic reference population fed a control diet CD or high fat diet HFD 39 , 40 , 41 Supplementary Data 7 , and expression levels of the 13 identified eGenes in skeletal muscle Supplementary Data 8 , adipose tissue Supplementary Data 9 and liver Supplementary Data Strikingly, expression of 12 out of the 13 genes Cep68 , Gab2 , Lamb1 , Macf1 , Eif6 , Btbd1 , Filip1l , Tcea3 , Nrp1 , Zhx3 , Tbx15 , and Tnfaip8 showed associations with metabolic measures, such as blood glucose levels during glucose tolerance tests GTTs , plasma lipid levels, body composition, and exercise performance, in at least one of the tested tissues Table 1.
For some target genes, metabolic measurements were specifically associated with expression in skeletal muscle. For example, expression of Tbx15 Fig. Interestingly, the expression of Cep68 , which we find linked to SNPs associated with T2D, was correlated with blood glucose levels during GTTs in HFD-fed mice in both muscle and liver Fig.
More specifically, Cep68 expression was negatively correlated with blood glucose levels during an intraperitoneal GTT in skeletal muscle of both male Fig. Moreover, Cep68 association with body fat mass and lean mass percentages in adipose tissue Fig.
Collectively, these data demonstrate that the expression of identified putative GWAS SNP targets correlates with metabolic measures in mice, and suggest a role for these genes in the regulation of energy metabolism in vivo.
a Heatmap representation of rho-values from correlations between 48 metabolic measurements in CD or HFD fed mice and Tbx15 expression in skeletal muscle, adipose or liver tissue. Statistics was performed using Spearmans rank correlation analysis.
For some candidate genes identified as regulated by non-coding GWAS SNPs, including EIF6 , the gene was not located at close vicinity of the differentially activated enhancer region, but connected through long-range chromatin interactions. The SNPs that we found linked to EIF6 are located within the UQCC1 locus and associate with WHR Fig.
The enhancer regions overlapped several highly linked WHR-associated SNPs Fig. From our chromatin interaction data, we found all enhancers to interact with the promoter of EIF6 Fig. Out of these genes, MMP24 , EIF6 and GDF5 remained candidates to be under the regulation of the enhancers, since the expression of these genes was concurrently decreased by TNFα treatment Fig.
Importantly, the UQCC1 promoter was not found linked to the enhancer nor did UQCC1 change expression by TNFα. While GDF5 expression was below detection limit in skeletal muscle and could not be analyzed for eQTLs, we found associations of several LD-linked WHR associated SNPs, including rs, with the expression of EIF6 Supplementary Data 6 and Fig.
In the case of rs, the major allele associates with an increased WHR, which establishes a link between lower EIF6 expression and an unhealthy body fat distribution. Consistently, we found that Eif6 expression in muscle from BXD mice positively associates with running distance Fig.
To further validate our findings, we used siRNAs siEif6 1 and siEif6 2 to knock down Eif6 expression in skeletal muscle cells Fig. We assessed mitochondrial respiration by measuring oxygen consumption rate OCR at basal state or during FCCP-induced uncoupling Fig.
Moreover, after differentiating C2C12 cells into myotubes, we found that Eif6 knockdown Supplementary Fig. a Regional visualization of WHR GWAS data 7 at the region around UQCC1 with highlight of rs and linked SNPs.
Position of all enhancers green , palmitate or TNFα regulated enhancers red are indicated below. b Quantification of H3K27ac counts pr. c , Quantification of MMP24, EIF6 and GDF5 RNA-seq counts pr. million from control, palmitate-treated or TNFα-treated cells.
d , e eQTL analysis in skeletal muscle between rs and EIF6 d or MMP24 e expression. Data are presented as box plots where the horizontal line represent the median, vertical middle bars represent the first and third quartiles, and black dots denote outliers beyond 1. f — h Skeletal muscle expression of Eif6 is positively correlated with running distance f , VO2 basal g and VO2 max h in BXD mice strains.
Statistics were performed using Spearmans rank correlation analysis. i , Eif6 mRNA levels in siScr or siEif6 2 transfected C2C12 myoblasts.
Expression data was normalized to housekeeping Gapdh expression levels. OCR was measured under basal conditions and after injection of oligomycin, FCCP, and antimycin A combined with rotenone at indicated time points.
k — l OCR area under the curve AUC values k or mean OCR for the time points during FCCP-induced maximal respiration l for siScr or siEif6 2 transfected C2C12 myoblasts.
Thus, long-distance interactions networks suggest that EIF6 is regulated by genetic variants associated with body fat distribution. Accordingly, we identified correlations between lower skeletal muscle Eif6 expression and reduced exercise performance, and further provide evidence for a role of EIF6 in the regulation of mitochondrial function in skeletal muscle.
Here, we mapped the transcriptome and enhancerome of human skeletal muscle cells subjected to lipid-induced toxicity or a proinflammatory cytokine.
We demonstrate a profound transcriptional reprogramming with thousands of promoter and enhancer regions showing altered activity. Integrating these data with GWAS of T2D, IR, BMI and WHR measures as well as genome-wide chromatin interaction studies, allowed us to detect concurrent changes in the activity of enhancers encompassing GWAS SNPs and transcription from a connected promoter, thereby establishing links between numerous non-coding disease-associated SNPs and gene targets.
Using the murine BXD genetic reference population we provide further insight into the role of the identified target genes in the regulation of metabolic phenotypes like body composition, glucose response and exercise performance in vivo. In particular, we provide evidence that one of our identified targets, Eif6 , controls mitochondrial respiration in skeletal muscle cells.
Our cell-system using chronic exposure with palmitate or TNFα in human primary muscle cells allowed investigation into the distinct mechanisms by which the metabolic function of the skeletal muscle cell is impaired.
Palmitate induces insulin resistance at the level of AKT phosphorylation 42 , impairs mitochondrial function 43 , lowers expression of the master regulator of mitochondrial function peroxisome proliferator-activated receptor-gamma coactivator PGC -1 α 44 , and induces ER stress Interestingly, incubation of skeletal muscle cells with palmitate induces TNFα secretion by the muscle cell, suggesting that while saturated fatty acids and TNFα appear to activate distinct intracellular pathways, these pathways may share common nodes Saturated free fatty acid and TNFα treatment both alter upstream insulin signaling, but TNFα treatment does not alter insulin-stimulated glucose uptake in muscle cells whereas palmitate does 42 , In vivo however, TNFα infusion is associated with both lower activation of the upstream insulin-signal pathway and impaired glucose transport Even though TNFα exposure is not associated with lower fatty acid oxidation in muscle ex vivo 49 , we identified EIF6 as a gene regulated by TNFα exposure and show EIF6 plays a role in fatty-acid oxidation.
The discrepancy between the effects of palmitate and TNFα on primary skeletal muscle cell cultures compared to in vivo may be due to specific tissue-culture conditions, different extracellular milieus or the influence of systemic factors.
While the activity of enhancers and promoters were markedly changed after palmitate or TNFα exposure, promoter-enhancer interactions did not appear to be affected. These findings are consistent with a previous study showing that enhancers-promoter interactions are unchanged in fibroblasts treated with TNFα We cannot rule out, however, that palmitate or TNFα exposure could remodel chromatin in myotubes, as low sequencing depth or low power may have limited our capacity to detect subtle changes.
From previous studies it seems clear that dynamic remodeling of promoter-enhancer interactions occurs during cellular differentiation, particularity at cell type-specific enhancers 23 , 50 , 51 , 52 , Interestingly, the discrepancy between activation of cell type-specific enhancers and enhancers induced by treatments such as TNFα seems to correlate with H3K4me1 levels.
Indeed, treatment-induced enhancers appear to exhibit largely unchanged levels of H3K4me1, despite a quick induction of H3K27ac, whereas cell type-specific enhancers display highly variable H3K4me1 levels This is consistent with our data, where palmitate- and TNFα-induce large changes in H3K27ac levels at enhancers but only minor changes in H3K4me1.
Still, certain chromatin interactions were recently described to be variable in a circadian fashion 54 , suggesting that promoter-enhancer interactions can indeed be dynamic even within a defined cell type. Our mapping of the chromatin interactome of human myotubes identified 36, specific enhancer-promoter interactions.
Integrating these data with RNA transcription and enhancer activity analyses allowed us to specifically capture enhancer-promoter interactions where 1 the enhancer overlaps one or more SNPs associated with T2D, IR, BMI or WHR and 2 the enhancer activity and gene expression were regulated in the same direction by either palmitate or TNFα exposure.
Our analysis retrieved more than predicted GWAS target genes, which included several known players in metabolism such as IRS1 , IGFBP3 , PPARG , SOCS2 , and LEPR. However, our eQTL analysis did not detect an association between genotype and gene expression for most of these genes.
We therefore speculate that GWAS SNPs may be functionally linked with gene expression in situations of cellular stress encountered in metabolic disease such as increased plasma levels of fatty acids or proinflammatory cytokines.
For the genes identified as significant eGenes in our eQTL analysis, we analyzed the association between their expression levels in skeletal muscle, adipose, or liver tissue and measures of 48 metabolic traits in the BXD murine genetic reference population. We found that 12 out of 13 genes Cep68 , Gab2 , Lamb1 , Macf1 , Eif6 , Btbd1 , Filip1l , Tcea3 , Nrp1 , Zhx3 , Tbx15 , and Tnfaip8 exhibited marked associations with metabolic phenotypes in one or more of the tested tissues.
For some targets, including Tbx15 , the associations appeared specific for skeletal muscle expression and were not detected in either adipose or liver tissue, suggesting a muscle-specific role of Tbx This is consistent with the earlier finding that Tbx15 regulates muscle metabolism in mice and Tbx15 knockout animals are resistant to diet induced obesity and impaired glucose tolerance For other targets, such as Cep68 , we identified associations in all of the tested tissues revealing the metabolic role of these genes in multiple organs.
Linking gene expression with metabolic phenotypes represents a valuable tool to gain insight into gene function, although it does not infer on causality. Circulating leptin levels, for instance, are positively associated with fat mass 56 , but loss-of-function mutations of LEP are associated with obesity In our study, we observed a similar phenomenon where the CEP68 T2D risk variants are associated with increased CEP68 expression, but Cep68 expression is negatively associated with blood glucose levels during GTTs in mice.
While further investigations are warranted to establish causal relationships and the mechanism by which CEP68 may regulate whole body metabolism, we speculate that dysregulated expression of CEP68 is involved in the pathogenesis of T2D. For some genes that we identified as potential targets of metabolic GWAS SNPs, the SNP-enhancer locus was not located in close proximity to the predicted target gene, but engaged in long-range DNA looping formations.
For example, we identified interactions between the promoter of the translation initiation factor EIF6 and several enhancers located within the UQCC1 gene, each spanning SNPs associated with WHR in humans. We found both enhancers and EIF6 expression were downregulated by TNFα and we detected significant eQTLs for EIF6 expression with SNPs of all loci.
In the BXD mice, Eif6 muscle expression was associated with increased running distance, as well as with basal and maximal VO 2 uptake after training. These findings are consistent with a study linking EIF6 to the regulation of energy metabolism during endurance training in humans and showing reduced exercise performance in Eif6 haploinsufficient mice Moreover, hypermethylation of the EIF6 promoter is linked to childhood obesity In support of this, we demonstrate that Eif6 knockdown in murine muscle cells causes lower mitochondrial respiration and reduced levels of the mitochondrial oxidative complex II.
The identified link between EIF6 and modulation of WHR are consistent with data demonstrating that genetic variants within mitochondrial genes are associated with metabolic measures including WHR Notably, we did not detect a physical link between the UQCC1 intronic enhancers and the UQCC1 promoter, nor did UQCC1 change expression by TNFα.
A recent study has shown that human UQCC1 coding variants are associated with WHR Interestingly, eQTL analysis indicates that these variants associate not only with the expression levels of UQCC1 , but also EIF6 61 , suggesting that several genes within this locus could contribute to the modulation of WHR in humans.
In conclusion, our study identified skeletal muscle enhancer elements that are dysregulated in the context of lipid-toxicity or under exposure of the proinflammatory cytokine TNFα. We identify hundreds of dysregulated enhancers which overlap with genetic loci previously implicated in metabolic disease and, using chromatin conformation assay, we predict the corresponding gene targets.
We identify genes with known roles in metabolism, as well as targets that have not previously been linked to human metabolic disease, and demonstrate their association with metabolic phenotypes in mice. Given the influence of lifestyle and genetic factors in the development of obesity and T2D, and the prominent contribution of skeletal muscle in energy metabolism in humans, our investigations constitute a resource for identifying genes participating in the progression of metabolic disorders.
Cells were differentiated for 5—7 days. For palmitate and TNFα treatment, the differentiated myotubes were added 0. Cells were differentiated for 5 days. Immunoblotting was performed according to standard protocols using total-AKT CSTS, dilution , Phospho-Ser AKT CSTS, dilution , or OXPHOS cocktail Abcam , dilution as primary antibodies and goat anti-rabbit Bio-Rad , , dilution or goat anti-mouse IgG Bio-Rad , , dilution horseradish peroxidase conjugate secondary antibodies.
Total protein on the membrane was quantified using Bio-Rad stain-free gels. All constructs were verified by Sanger sequencing. Firefly luciferase counts were normalized to Renilla luciferase counts.
Real-time measurements of OCR were performed using a Seahorse XFe96 Extracellular Flux Analyzer Agilent Technologies. The measured OCR values were normalized to protein levels by lysing the cells and performing BCA protein assay Pierce BCA Protein Assay Kit from Thermo Scientific.
Radioactivity was determined by liquid scintillation counting after the addition of Ultima Gold LSC. Values were normalized to protein levels performing BCA protein assay Pierce BCA Protein Assay Kit from Thermo Scientific. mRNA primer sequences are listed in Supplementary Data One microgram of total RNA was depleted of rRNA and subsequently used to generate libraries using the TruSeq standard total RNA with Ribo-Zero Gold kit Illumina.
An overview of all RNA-seq experiments are given in Supplementary Data For bioinformatic analysis of RNA-seq data, reads were aligned to the hg38 GENCODE Comprehensive gene annotations 62 version 27 using STAR v2.
Read summation onto genes was performed by featureCounts v1. Differential expression testing was performed with edgeR v3. Differential expression was found by testing e.
GO enrichments were found using the camera function 66 , which takes both inter-gene correlations and the distribution of log fold changes in the data-set into consideration and is part of the edgeR package.
Only gene ontologies containing between 10 and genes were investigated. Initial visualization of samples was performed by multi-dimensional scaling MDS plots, which are similar to PCA plots but use average log fold changes of the most divergent interactions.
The pelleted nuclei were lysed in 1. The following antibodies were used for ChIP: H3K27ac Ab , H3K4me1 Ab , H3K4me3 CSTS , H3 Ab An overview of all ChIP-seq experiments are given in Supplementary Data ChIP-qPCR validations were performed by ChIP followed by real-time PCR using Brilliant III Ultra-fast SYBR Green QPCR Master Mix AH Diagnostic and a C Thermal cycler Bio-Rad.
All reactions were analyzed in quadruplicates. ChIP-qPCR primer sequences are listed in Supplementary Data For bioinformatic analysis of ChIP-seq data, sequenced reads were aligned using the sub-read aligner v1. Peaks were called using MACS2 v2. H3K4me1 peaks were called as broad peaks, while H3K27ac peaks were called as narrow peaks.
The quality of individual samples was assessed by testing whether fragment lengths could be estimated and whether more than , peaks could be called with a P -value cutoff of 0. These individual peak lists were only used to identify samples where the IP-step had failed and were not used in the downstream analysis.
All samples passed these two tests. The consensus peak list used in the analysis was generated following the ENCODE IDR pipeline. For each histone modification a consensus peak set was generated as follows.
All samples were pooled and the pooled reads were shuffled and split in two pseudo replicates. Initial peak lists were called as above on each of these three samples pool and two pseudo replicates , with a P -value cutoff of 0.
Finally, a consensus peak list was generated using the irreproducible discovery rate IDR software v2. The IDR is analogous to an FDR, and has been shown to be a better measure of reproducibility in peak-calling experiments A lenient IDR threshold of 0.
For each sample, reads were summarized into consensus peaks using featureCounts v1. Differentially bound peaks were detected in edgeR v3. Peaks were considered overlapping if they overlapped by any amount.
Promoter Capture Hi-C was performed using similar protocols as described in 22 , The pellet was resuspended in 1.
Triton X was added final concentration of 1. Enzymes were deactivated by adding SDS final concentration of 1. Ligation was performed using 50 units T4 DNA ligase Invitrogen per 5 million cells starting material in a total volume of 8.
DNA concentration was measured using a Qubit Fluorometer and Qubit dsDNA HS Assay Kit Life technologies. For addition of dATP to the Hi-C libraries, DNA was incubated with Klenow exo- and 0. DNA fragments were size-selected by a double-sided SPRI bead purification SPRI beads solution volume to sample volume to 0.
Biotin-marked ligation products were isolated using MyOne Streptavidin C1 Dynabeads LifeTechnologies. The bead-bound library DNA was amplified with 12—14 PCR amplification cycles according to the SureSelect XT library prep kit ILM Agilent Technologies protocol before promoter capture.
Promoter capture was performed by using 37, biotin-labeled RNA baits each nucleotides covering 21, human promoters approximately two baits per promoter, targeting each end of a HindIII fragment For bioinformatic analysis of Promoter Capture Hi-C data, di-tags reads were filtered and mapped against the main chromosomes of the hg38 reference genome by the HiCUP pipeline v0.
The HiCUP pipeline also removes PCR duplicate reads and filters out re-ligations and other experimental artefacts. These criteria and cut-offs were as described in the diffHic package manual.
The set of interactions interrogated for differential interactions is the one used in downstream analysis and reported in the Supplementary tables. To visualize Promoter Capture Hi-C data as heatmaps, rotated plaid plots were generated by the rotPlaid function supplied by the diffHic package on the merged dataset.
Each chromosome was split in bins, and colored by the amount of reads in the interaction. Any interaction with more than 20 reads was colored a solid red.
GWAS studies for T2D 6 , BMI 8 , and WHR 7 have identified , and distinct association signals, respectively. For IR we collected distinctive GWAS signals covering from studies of fasting insulin FI with and without adjustment for BMI 34 , 36 , 37 , HOMA-IR 33 , the modified Stumvoll Insulin Sensitivity Index ISI 38 , and 53 genomic variants associated with both higher FI levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels 35 , leading to a total of 82 distinct association signals with IR.
The variant positions were converted into genome build38 before overlapping them with palmitate and TNFα responsive enhancer regions. Regional plots were generated using standalone LocusZoom v1. The ADIGEN study participants 79 , 80 were selected from the Danish draft boards records. The study was approved by the Ethics Committee from the Capital Region of Denmark and informed consent was obtained from all participants in accordance with the Declaration of Helsinki II.
In total individuals volunteered to participate. From a subset of these Danish white men, 71 juvenile obese and 74 age-matched control individuals, skeletal muscle biopsies were taken under lidocaine local anesthesia from their right thigh using a thin Bergström needle and snap frozen in liquid nitrogen.
The participants were healthy by self-report and under 65 years of age at the time of ADIGEN examination. Gene expression analysis was performed by extracting total RNA using miRNeasy kit Qiagen.
The yield was optically measured and a randomly selected subset of the RNA samples were examined using an Experion electrophoresis station BioRad for integrity RIN value , which was good in all cases.
Gene expression of ~47, transcripts was measured by the HumanExpression HT Chip Illumina, USA. cRNA was synthesized from total RNA using the Nano Labeling Kit from Illumina Epicentre , and the cRNA concentration was measured by Qubit fluorescent dye Invitrogen, Germany before loading the arrays.
Hybridization was performed as recommended by Illumina and the Illumina HiScan was used to obtain the raw probe intensity level data. For failed expression arrays cRNA was resynthesized and rerun. The raw probe intensity values were exported from GenomeStudio without background correction and imported into R where the lumi package 81 was used for pre-processing.
The array pre-processing included; quantile normalization, log2 transformation and probe filtering to remove probes with a detected P -value above 0. The participants were genotyped using the Illumina CoreExome Chip v1. Genotypes were called using the Genotyping module version 1.
egt cluster file. The genotype data were subjected to standard quality control and then phased with EAGLE2 82 and imputed with the Genomes Project Phase III panel using Minimac3 We selected 29 or SNPs located within 11 or enhancer regions, which changed activity by palmitate or TNFα treatment, respectively see text for further description on how SNPs were selected.
Matrix eQTL 84 was used to assess the association between TNFα and 39 palmitate gene-SNP pairs selected based on our Promoter Capture Hi-C data in a total of individuals with both expression and SNP data available R version 3.
To account for complex non-genetic factors, we used probabilistic estimation of expression residuals PEER Specifically, eQTL analysis was performed on inverse normal-transformed expression residuals adjusted for age, BMI-group obese or control and 15 PEER factors which is the number of factors recommended by the GTEX consortium 86 for studies with less than individuals.
The models were also run without the adjustment for BMI. Significant e-genes were identified after hierarchical multiple testing correction of the p-values from TNFα and palmitate eQTL tests using the Bonferroni-BH procedure recommended by Huang et al. We selected and extracted the mean values of 48 metabolic phenotypes Supplementary Data 7 that were measured across 42 and 37 BXD cohorts fed on CD and HFD, respectively 39 , 40 , 41 , Spearmans rank correlation analysis was performed to determine significant associations between phenotypes and gene expression.
The p-values from the 48 correlations from each diet and tissue were adjusted using false discovery rate correction FDR Further information on research design is available in the Nature Research Reporting Summary linked to this article.
All novel sequencing data have been deposited in the NCBI Gene Expression Omnibus GEO and are accessible through GEO SuperSeries accession number GSE RNA-seq data from GSE have been used to generate Fig. ChIP-seq data from GSE have been used to generate Fig. Promoter Capture Hi-C data from GSE have been used to generate Fig.
The source data underlying Figs. Zheng, Y. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Article PubMed Google Scholar. Zurlo, F. Skeletal muscle metabolism is a major determinant of resting energy expenditure.
Article CAS PubMed PubMed Central Google Scholar. DeFronzo, R. Skeletal muscle insulin resistance is the primary defect in type 2 diabetes. Diabetes Care 32 Suppl 2 , S—S Huang, T. Gene-environment interactions and obesity: recent developments and future directions.
BMC Med. Genomics 8 Suppl 1 , S2 Article PubMed PubMed Central CAS Google Scholar. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.
Pulit, S. Meta-analysis of genome-wide association studies for body fat distribution in , individuals of European ancestry. Article PubMed Central CAS Google Scholar. Yengo, L. Meta-analysis of genome-wide association studies for height and body mass index in approximately individuals of European ancestry.
Maurano, M. Systematic localization of common disease-associated variation in regulatory DNA. Metabolism, in simple terms, is the vital process by which the body converts the nutrition we consume from our diet foods and beverages into energy to fuel our bodies every day.
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This famous catechin is a phytonutrient known for its potent antioxidant properties. Ready to learn the best ways to boost your metabolism and burn fat?
Here are some of the top metabolism boosters to try. These are signs of your metabolism becoming more sluggish — in other words a decrease in your basal metabolic rate.
On the other hand, keep your body properly fueled and it will perform much better in all areas of life for many years to come. This is a long-term solution instead of a quick fix. If you live in a calorie deficit because your exercise level is too high and your food intake is too low, your metabolism gets the message that it must slow down all functioning to conserve energy.
Which metabolism booster is good for weight loss then? I recommend you stop counting calories and instead focus on nutrient density. Eating enough every day, especially when you consume calories from a variety of unprocessed whole foods, is critical for metabolic health. It also supports cognitive, hormonal, sexual and digestive health.
People who are well-fed and avoid yo-yo dieting often experience better digestion, positive moods and more motivation, stronger desire to be active, better mental health, stronger sex drive, and more stable blood sugar levels.
Eating enough also usually means you have more motivation to be active, gain strength and muscle mass quicker , and feel less fatigued. This is why sleep deprivation can contribute to trouble with weight loss. According to the one meta-analysis, sleep restriction decreases insulin sensitivity and causes changes in brain activity in response to food stimuli, meaning food especially unhealthy types becomes more rewarding.
Make it a priority to get seven to nine hours of sleep every night in order to keep hormone levels in check, including cortisol.
High cortisol levels associated with a lack of sleep are tied to poor mental functioning, weight gain and becoming more resistant to insulin that controls blood glucose levels. Another way to maintain hormonal balance is to rest enough between exercise days. Overtraining repeatedly causes fatigue, muscle loss and a lower basal metabolic rate, not the opposite as you might think.
Exercise impacts your hormonal status, and intense workouts without rest elevate cortisol levels. Exercise of any kind is important for keeping metabolic function working into older age.
Therefore, a decline in metabolism seems to be related most to age-associated reductions in exercise volume and calorie consumption than aging itself.
What is a good metabolism booster when it comes to exercise? High-intensity interval training HIIT , a form of exercise that features intervals that vary between all-out effort and short periods of rest, is known to especially jump-start metabolic functioning better than steady-state workouts can.
One of the best things about HIIT workouts is that they require less time than traditional cardio workouts, yet they have more profound benefits. This phenomenon is due to the way the body uses higher levels of oxygen to recover following intense physical activity.
HIIT burns more fat over the duration of the day, builds more muscle and improves metabolic function compared to steadier exercises. It can also generally improve cardiometabolic functions and even hormonal balance, such as in women with PCOS. Strength training — whether lifting weights or using your own body weight — can support your resting metabolic rate because it builds lean muscle mass , which naturally uses more calories than body fat does.
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Metabolic Mteabolic contains targeted groups of Metabolic health enhancers, minerals, botanicals, Metabolc other natural nutrients in combination with pure, lyophilized New Cage-Free Eggs tissue ehhancers to ejhancers a healthy metabolism. This product features:. We do not ship internationally at this time. Pick Up Orders: pick-up orders placed before are generally ready same day. Texas Orders: local Austin orders are generally delivered next business day - without any additional fees. Most major Texas cities are also eligible for next business day delivery call to learn if your city is eligible for next day delivery.
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