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How To Test and Improve Your Body CompositionBody composition enhancers -
A combination of elements can support burning visceral fat including frequent intense training and using a low carbohydrate diet to keep insulin levels in check.
A Ground Breaking 8 week study published in the British Journal of Medicine during demonstrated that daily intake of Lactoferrin produced a significant decrease in visceral fat and total body fat. The group using Lactoferrin lost an average of 1. The GCX10 Physique and Performance Enhancer proprietary ingredient blend includes more Lactoferrin in 4 capsules than what was used in the study to improve client belly fat burning.
Michael L DC. Michael now that we have a firm base in place we can use more aggressive training and nutrition tactics to get you Leaner and Meaner by summer. After reviewing your training records I can see that following week 3 you did not increase loads as frequently which indicates the training stimuli became stale to a degree.
The short rest periods are necessary to keep the training sessions under an hour which helps to maximize the anabolic hormonal response from training and prevent over training. Inadequate rest periods and the high volume of reps will increase muscle blood flow dramatically. Double Rates of Muscle Growth and Strength Levels with GCX We highly recommend the New GCX10 Physique and performance Enhancer 30 minutes before the training sessions to potentially double your rate of muscle growth and strength while combating fatigue during highly intense training phases.
Serrano used multiple published studies and his own extensive patient research to assemble the exclusive blend of Lactoferrin, ATP and Glycine to change the Iron Game once again with GCX GCX10 contains an even higher amount of ATP used in a published research study which demonstrated doubling strength increases of the 1 rep max of the squat and dead lift over 12 weeks vs.
the control group. The participants using ATP also gained nearly twice the amount of lean muscle tissue over the 12 week study while showing fewer signs of fatigued during higher volume training phases. You will start with the heaviest load possible that you can execute properly for 8 reps and do as many sets as needed in Giant set fashion to hit 50 quality reps of each exercise.
This means do a set of A1, rest 20 seconds, Set of A2, rest 20 seconds, Set of A3, rest 20 seconds, Set of A4, rest 20 seconds and repeat the sequence as many times needed.
Challenging loads are crucial to stimulate a wide cross section of muscle fibers with the greatest potential for growth. On average it will take clients 8 sets to hit 50 reps and training with a partner for a reliable spot is helpful. During the later sets you may only be able to earn 4 or 5 reps per set which is to be expected.
Your performance should be better during week 6 than week 3 if you keep your head and your heart in the game. Advanced clients may change the order of the exercises during week 6 to keep stimuli fresh.
Bullet proof your chest and back while earning your dream physique A1. I am pretty lean already, but need to burn stubborn fat covering up my abs, love handles and thighs within 60 days before a 2 week vacation and photo shoot. What do I need to do to drop this body fat fast and keep it off for good?
You need to recalibrate your fat cellular network to send powerful fat burning signals 24×7 and optimize the hormonal environment which in combination governs fat loss.
We all have billions of fat cells which define the size of our fat storage capacity and some of us through bad food choice consumption and genetic traits have a much larger fat storage capacity than others. A review of your nutrition journal revealed a lot of problems and to say the least your cell network strength is very weak right now, allowing for great improvements.
A customized radical nutrition and training plan for 60 days does the job every time. Eliminating hunger and cravings for bad food choices through proper program design and refinements are keys to long term fat loss success. Effectively burning stubborn body fat and keeping it off for good goes far beyond the overly simple calories in vs.
calories expended equation. Dietary fat loading times per week sends fat burning enzymes through the roof while preventing metabolic plateaus and elevating fat burning hormone levels. Consuming grass fed beef at every meal for example provides a huge mental and physical energy boost as well.
A clear strategy changing every weeks must be put into place to force the body into burning more stored fat as fuel while optimizing fat burning hormones. When is the last time you made strategic improvements to your nutrition plan? Gene ontology GO analysis of the differentially expressed genes Supplementary Data 2 demonstrated strong upregulation of genes involved in lipid metabolism, as well as regulation of inflammatory responses Fig.
GO analysis of TNFα upregulated genes returned several terms related to immune signaling Fig. Interestingly, both treatments seemed to significantly upregulate genes involved in inflammation Supplementary Data 2 and Fig.
Thus, our transcriptomic analyses of human muscle myotubes reveal thousands of target genes of which many are related to metabolic dysfunction. a MDS plot of RNA-seq data from control ctrl , palmitate palm or TNFα treated human skeletal myotubes. Leading log fold-change logFC is the mean logFC between the most divergent genes between each pair of samples.
b , c Volcano plot representation of genes regulated by palmitate b or TNFα c. d , e Top 10 GO terms upregulated d or downregulated e by palmitate. f , g Top 10 GO terms upregulated f or downregulated g by TNFα. The P -value is calculated using the CAMERA method.
All terms have an FDR of less than 0. h Examples of palmitate and TNFα upregulated genes related to acute inflammatory response. Relative CPM indicates RNA-seq counts per million relative to TNFα treatment. i Examples of palmitate and TNFα downregulated genes related to muscle filament sliding.
Relative CPM indicates RNA-seq counts per million relative to control. Through chromatin immunoprecipitation followed by sequencing ChIP-seq , we mapped the distribution of the enhancer-associated histone H3 modifications, H3K4me1 and H3K27ac, in the muscle myotubes treated with TNFα or palmitate.
Genome-wide, we identified , and 80, significant peaks of H3K4me1 or H3K27ac, respectively Fig. These were mostly located in non-coding DNA, such as introns and intergenic regions, as well as in promoters Supplementary Fig.
In order to identify enhancers, we subtracted active promoter regions defined by the promoter-associated H3K4me3 mark. We found that most These findings support the notion that enhancers can be primed marked by only H3K4me1 or active marked by both H3K4me1 and H3K27ac 16 , MDS plots of non-promoter associated H3K27ac and H3K4me1 ChIP-seq data demonstrated a clear treatment-based separation of samples for H3K27ac Fig.
Therefore, to identify enhancers that were differentially activated after palmitate or TNFα treatment, we searched for peaks within the 62, identified active enhancers covered by both H3K4me1 and H3K27ac that showed significant changes in H3K27ac levels.
The changes in H3K27ac were validated independently by ChIP-qPCR Supplementary Fig. None of the validated enhancer regions showed enrichment of the promoter-associated H3K4me3 mark Supplementary Fig.
Consistent with increased enhancer activity, expression of PDK4 , ANGPTL4 , CCL11, and CCXL8 were markedly upregulated after palmitate or TNFα treatment Fig. When transfected into muscle cells, luciferase activity was markedly increased in response to palmitate or TNFα treatment Supplementary Fig.
Collectively, our results identify thousands of dynamic enhancer activities in human skeletal muscle cells after treatment with palmitate or TNFα. a Overlay of H3K4me1, H3K27ac and promoter-associated H3K4me3 ChIP-seq data from human skeletal myotubes.
b, c MDS plot of non-promoter associated H3K27ac b and H3K4me1 c ChIP-seq data from control ctrl , palmitate palm or TNFα treated cells. Leading log fold-change logFC is the mean logFC between the most divergent H3K27ac b or H3K4me1 c ChIP-seq peaks between each pair of samples.
The ChIP-seq and enhancer analyses are described in detail in the Methods section. f and h , UCSC genome browser hg38 H3K27ac and RNA-seq tracks from control ctrl , palmitate palm or TNFα treated cells around PDK4 and ANGPTL4 g or CCL11 and CXCL8 i.
g and i Quantification of H3K27ac counts pr. million CPM at the selected enhancer regions in the individual replicate samples. j , k Quantification of mRNA counts pr. million CPM of the indicated genes in the individual replicate samples. Besides interacting with nearby promoters, enhancers can also engage in long-range interactions, which makes enhancer-target prediction challenging.
To overcome this, we performed genome-wide mapping of enhancer-promoter interactions in skeletal muscle cells by the use of high-resolution Promoter Capture Hi-C 22 , First, we tested if treatment of myotubes with palmitate or TNFα was associated with a dynamic reorganization of promoter-enhancer interactions.
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. Taking creatine is also not without its side effects. For example, creatine can cause weight gain, muscle cramping, and stomach cramping. Additionally, the liver and kidneys must filter creatine. Taking an excessive amount can put a strain on these important organs, which could potentially damage them.
Individual results may vary for all of these supplements. Talk with your doctor or athletic trainer about the safety and effectiveness of individual supplements. The NCAA and the Olympics commission have banned some substances because they offer an unfair advantage or can cause harm to the athlete.
Anabolic and other steroids are illegal in sporting events and according to the law. The side effects are numerous and potentially fatal.
Examples include androstenedione, stanozolol, axiron, and fortesta. DHEA is possibly the most abundant steroid in humans. Using synthetic versions to increase steroid production is potentially dangerous.
Diuretics are medications that cause a person to urinate more frequently. Diuretics can cause a variety of harmful side effects, such as cramping, dizziness, blood pressure drops, and electrolyte imbalances. Blood doping is the process of boosting red blood cells to help carry more oxygen to the muscles and lungs.
It can be done through a blood transfusion or through use of drugs like erythropoietin. Athletes use the medication to make more red blood cells in their bodies. Endurance athletes may especially try to use erythropoietin, believing they they can perform longer with more oxygen.
Ephedrine is a central nervous stimulant. Ephedrine produces similar effects to adrenaline, but it too can be dangerous. It can cause serious cardiovascular effects, including stroke, and a whole host of other problems. Both athletic organizations and the FDA have banned it.
HGH is a drug developed to help treat growth disorders in children. It stimulates cell reproduction and regeneration. Athletes looking to gain an edge may misuse this drug to achieve it.
Potential complications include enlarged organs and chronic disease. They can cause more damage than stripping an athlete of a title. Training, dedication, hydrating fluids, and proper diet are safer options and better than any ergogenic aids for boosting performance. Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available.
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Background: Compoition semaglutide is the first glucagon-like peptide-1 receptor compositjon GLP-1RA Body composition enhancers for oral enhancerz it offers Insulin sensitivity and homeostasis model assessment promising opportunity enhanecrs facilitate Insulin sensitivity and homeostasis model assessment early approach to Type 2 Diabetes T2D. The study aimed to evaluate, Cardiovascular exercises at home a composigion setting, the effects of oral semaglutide on the body composition of patients with T2D after 26 weeks of therapy. Methods: Thirty-two patients with T2D were evaluated at baseline T0 and after three T3 and six T6 months of therapy with oral semaglutide. At each time point, body composition was assessed using a phase sensitive bioimpedance analyzer. Clinical, anthropometric and laboratory parameters, and the main biometric surrogates of liver steatosis and fibrosis, were also analyzed and compared. Results: A significant and early reduction in anthropometric and glucometabolic parameters, alanine aminotransferase, Fatty Liver Index, and Fat Mass was observed.
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