Real Time Indirect Nanoplasmonic in Situ Spectroscopy of Catalyst Nanoparticle Sintering

Real Time Indirect Nanoplasmonic in Situ Spectroscopy of Catalyst Nanoparticle Sinterin

Role of peroxisome proliferator-activated receptor gamma Pro12Ala polymorphism in human adipose tissue: assessment of adipogenesis and adipocyte glucose and lipid turnover

<p>The protective mechanisms of peroxisome proliferator-activated receptor gamma (PPARγ) Pro12Ala polymorphism in type 2 diabetes (T2D) are unclear. We obtained subcutaneous adipose tissue (AT) before and 3 h after oral glucose (OGTT) in carriers and non-carriers of the Ala allele (12 Pro/Pro, 15 Pro/Ala, and 13 Ala/Ala). Adipogenesis, adipocyte glucose uptake and lipolysis as well as PPARγ target gene expression were investigated and compared between the genotype groups. During fasting and post-OGTT, neither basal nor insulin-stimulated adipocyte glucose uptake differed between genotypes. Compared to fasting, a decreased hormone-sensitive lipase gene expression in Pro/Pro (p < 0.05) was accompanied with a higher antilipolytic effect of insulin post-OGTT (p < 0.01). The adipocyte size was similar across groups. Preadipocyte differentiation rates between Pro/Pro and Ala/Ala were unchanged. In conclusion, no major differences in AT differentiation, glucose uptake, lipolysis or expression of PPARγ target genes were observed between different PPARγ Pro12Ala genotypes. Albeit small, our study may suggest that other pathways in AT or effects exerted in other tissues might contribute to the Pro12Ala-mediated protection against T2D.</p

Genotype-based recall to study metabolic effects of genetic variation: a pilot study of <i>PPARG</i> Pro12Ala carriers

<p><b>Aim:</b> To assess practical implications of genotype-based recall (GBR) studies, an increasingly popular approach for in-depth characterization of genotype–phenotype relationships.</p> <p><b>Methods:</b> We genotyped 2500 participants from the Swedish EpiHealth cohort and considered loss-of-function and missense variants in genes with relation to cardiometabolic traits as the basis for our GBR study. Therefore, we focused on carriers and non-carriers of the <i>PPARG</i> Pro12Ala (rs1801282) variant, as it is a relatively common variant with a minor allele frequency (MAF) of 0.14. It has also been shown to affect ligand binding and transcription, and carriage of the minor allele (Ala12) is associated with a reduced risk of type 2 diabetes. We re-invited 39 Pro12Pro, 34 Pro12Ala, and 30 Ala12Ala carriers and performed detailed anthropometric and serological assessments.</p> <p><b>Results:</b> The participation rates in the GBR study were 31%, 44%, and 40%, and accordingly we included 12, 15, and 13 individuals with Pro12Pro, Pro12Ala, and Ala12Ala variants, respectively. There were no differences in anthropometric or metabolic variables among the different genotype groups.</p> <p><b>Conclusions:</b> Our report highlights that from a practical perspective, GBR can be used to study genotype–phenotype relationships. This approach can prove to be a valuable tool for follow-up findings from large-scale genetic discovery studies by undertaking detailed phenotyping procedures that might not be feasible in large studies. However, our study also illustrates the need for a larger pool of genotyped or sequenced individuals to allow for selection of rare variants with larger effects that can be examined in a GBR study of the present size.</p

String-database network connections between proximal cis-gene and target plasma protein.

<p>All short String paths that connect proximal cis-genes with the target plasma protein are shown. The colour intensity of each gene shows the eQTL association-strength with the index-SNP. The nodes highlighted with bold border show paths that satisfy P<0.05 in network permutation analysis. A) the rs61598054-SNP is harboured in an intron of the <i>LACE1</i> gene, but have no paths to the target gene <i>NGF</i> and a more likely mechanism is therefore <i>FOXO3</i> -> <i>AKT1</i> -> <i>NGF</i>, which involves a rs61598054-trans-eQTL effect on <i>AKT1</i>. In permutation analysis of re-wired networks this is stronger than 95% of random networks. B) Similarly for rs693918, while located between <i>SRD5A2</i> and <i>MEMO1</i>, the path <i>XDH</i> -> <i>TLR4</i> -> <i>IL18</i> is a more likely mechanistic path, supported by eQTL effects on both <i>XDH</i> and <i>TLR4</i>. C) The rs61598054-<i>AKT1</i> trans-eQTL from panel A in 235 IFN-stimulated monocytes and the rs10947260-ATF3 trans-eQTL from panel D in 89 mammary artery samples. D) Example of ambiguous findings regarding the rs10947260 -> -> -> IL6: The SNP has a coding-proxy in <i>BTNL2</i>, literature mining evidence for the <i>AGER</i> gene, but also eQTL-weighted pathway evidence for both <i>ATF6B</i> and <i>NOTCH4</i>.</p

Systematic analysis of potential mechanisms behind trans-pQTL associations.

<p>Systematic analysis of potential mechanisms behind trans-pQTL associations.</p

Overview of pQTL associations.

<p>Overview of pQTL associations.</p

Association between pQTLs and Coronary Artery Disease (CAD) risk.

<p>Each SNP from supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006706#pgen.1006706.s003" target="_blank">S1 Table</a> was investigated in the CARDIoGRAMplusC4D data, and the P-values for the pQTL and CAD risk were extracted. An additional pooled analysis was performed in cases where one plasma protein had multiple pQTLs,. The table shows all pQTLs for which either a single-SNP or pooled CAD association had a P<0.05. P-values highlighted in italics indicate that the association was also significant after FDR correction for multiple testing.</p

Schema of instrumental variable analyses conducted in order to infer the potential causal relations between DNA methylation, gene expression, BMI, and adiposity-related disease.

<p>Schema of instrumental variable analyses conducted in order to infer the potential causal relations between DNA methylation, gene expression, BMI, and adiposity-related disease.</p

Fifty novel replicated differentially methylated CpGs associated with BMI sorted by <i>p-</i>value in the discovery cohorts.

<p>Fifty novel replicated differentially methylated CpGs associated with BMI sorted by <i>p-</i>value in the discovery cohorts.</p

Annotated genes of replicated differentially methylated CpGs identified in the BMI epigenome-wide association study.

<p>Genes are grouped by association with gene expression, association of gene expression with BMI, and Mendelian randomization analyses for causal support. Duplicate gene names within the same group are not shown. Figure does not include 18 intergenic CpGs without a gene annotation. BMI, body mass index; EWAS, epigenome-wide association study.</p