Associations between Serum Sex Hormone Concentrations and Whole Blood Gene Expression Profiles in the General Population

<div><p>Background</p><p>Despite observational evidence from epidemiological and clinical studies associating sex hormones with various cardiometabolic risk factors or diseases, pathophysiological explanations are sparse to date. To reveal putative functional insights, we analyzed associations between sex hormone levels and whole blood gene expression profiles.</p><p>Methods</p><p>We used data of 991 individuals from the population-based Study of Health in Pomerania (SHIP-TREND) with whole blood gene expression levels determined by array-based transcriptional profiling and serum concentrations of total testosterone (TT), sex hormone-binding globulin (SHBG), free testosterone (free T), dehydroepiandrosterone sulfate (DHEAS), androstenedione (AD), estradiol (E2), and estrone (E1) measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and immunoassay. Associations between sex hormone concentrations and gene expression profiles were analyzed using sex-specific regression models adjusted for age, body mass index, and technical covariables.</p><p>Results</p><p>In men, positive correlations were detected between AD and <i>DDIT4 </i>mRNA levels, as well as between SHBG and the mRNA levels of <i>RPIA</i>, <i>RIOK3</i>, <i>GYPB</i>, <i>BPGM</i>, and <i>RAB2B</i>. No additional significant associations were observed.</p><p>Conclusions</p><p>Besides the associations between AD and <i>DDIT4</i> expression and SHBG and the transcript levels of <i>RPIA</i>, <i>RIOK3</i>, <i>GYPB</i>, <i>BPGM</i>, and <i>RAB2B</i>, the present study did not indicate any association between sex hormone concentrations and whole blood gene expression profiles in men and women from the general population.</p></div

Baseline characteristics of the study population by sex.

<p>Baseline characteristics of the study population by sex.</p

Slamming the door on trade policy discretion? : the WTO Appellate Body’s ruling on market distortions and production costs in EU-Biodiesel (Argentina)

This paper presents a legal-economic analysis of the Appellate Body’s decision that the WTO’s Anti-Dumping Agreement (ADA) precludes countries from taking into account government-created price distortions of major inputs when calculating anti-dumping duties, made in EU-Biodiesel (Argentina). In this case, the EU made adjustments to the price of biodiesel’s principal input – soybeans – in determining the cost of production of biodiesel in Argentina. The adjustment was made based on the uncontested finding that the price of soybeans in Argentina was distorted by the existence of an export tax scheme that resulted in artificially low soybean prices. The Appellate Body found that the EU was not permitted to take tax policy-induced price distortions into account in calculating dumping margins. We analyze the economic rationale for Argentina’s export tax system, distortions in biodiesel markets in Argentina and the EU, and the remaining trade policy options for addressing distorted international prices. We also assess whether existing subsidies disciplines would be more effective in addressing this problem and conclude that they would not

Study Design.

<p>Study Design.</p

RLS-associated SNPs representing <i>cis</i>-eQTLs in peripheral blood.

<p>A total of 23 <i>cis</i>-eQTLs were found in both KORA F4 and SHIP-TREND at p_nominal<1×10⁻³. SNPs which were carried into the replication phase are printed in bold. NS = not significant.</p

Effects of SNPs within probes on signal intensities.

<p>The effects on measured log₂ transformed (L2T) gene expression levels per mismatch allele of SNPs located within probes (y-axis) are plotted against the mean L2T expression level of the samples for each probe (x-axis). Each spot represents a SNP-probe combination; associations with significant p-values after Bonferroni correction (p<2.3×10⁻⁵) are colored in red and p-values below 0.05 are colored in orange. To increase legibility the y-axis was limited from −3 to 3 excluding 176 non-significant results out of 1237 successful association results (minimum and maximum effect sizes were −174.1 and 188.7, respectively). Surprisingly, in almost 45% of the associations a positive effect per mismatch allele on expression signal intensity was observed.</p

a–d): eQTLs with simultaneous impact on expression levels of at least five genes in <i>trans</i>.

<p>a) Chromosome 12. The eQTL was located upstream of <i>lysozyme</i> (<i>LYZ</i>), a gene residing on chromosome <i>12q15</i>. It is associated with expression levels of the seven transcripts <i>cAMP responsive element binding protein 1 (CREB1), SHC SH2-domain binding protein 1 (SHCBP1), arylformamidase (AFMID), KIAA0101, ITPK1 antisense RNA 1 (ITPK1-AS1), EP300 interacting inhibitor of differentiation 2B (EID2B)</i>, and <i>CDKN2A interacting protein N-terminal like (CDKN2AIPNL)</i>. b) Chromosome 11. The eQTL was found intronic of the <i>hemoglobin beta</i> (<i>HBB</i>) gene on chromosome <i>11p15.4</i> and was associated with the regulation of 13 genes distributed across the genome in <i>trans</i>: <i>PWP1 homolog (PWP1), phosphatidylserine synthase 1 (PTDSS1), CCHC-type zinc finger, nucleic acid binding protein (CNBP), trafficking protein particle complex 11 (TRAPPC11), histone deacetylase 1 (HDAC1), WD repeat domain 59 (WDR59), G protein pathway suppressor 1 (GPS1), ArfGAP with SH3 domain, ankyrin repeat and PH domain 1 (ASAP1), aarF domain containing kinase 2 (ADCK2), deoxythymidylate kinase (thymidylate kinase) (DTYMK), WD repeat domain 37 (WDR37), spectrin repeat containing, nuclear envelope 2 (SYNE2)</i>, and <i>RAD51 paralog C (RAD51C)</i>. c) Chromosome 3. The eQTL on chromosome 3 was located intronic of the <i>rho guanin nucleotid exchange factor 3 (ARHGEF3)</i> gene at <i>3p14.3</i>. We observed a significant impact on the regulation of twelve genes, <i>integrin beta 5 (ITGB5), platelet glycoprotein IX (GP9), carboxy-terminal domain, RNA polymerase II, polypeptide A small phosphatase-like (CTDSPL), protein S alpha (PROS1), guanylate cyclase soluble subunit alpha-3 (GUCY1A3)</i>, <i>caldesmon 1 (CALD1)</i>, <i>tetraspanin 9 (TSPAN9), arachidonate 12-lipoxygenase (ALOX12), parvin beta (PARVB), N-acetyltransferase 8B (NAT8B), multimerin 1 (MMRN1)</i>, and <i>C-type lectin domain family 1, member B (CLEC1B)</i>. d) Chromosome 2. The eQTL upstream of <i>atonal homolog 8 (ATOH8)</i> residing on chromosome 2p11.2 exerts simultaneous impact on expression levels of six genes: <i>paroxysmal nonkinesigenic dyskinesia (PNKD)</i> and <i>calcium homeostasis modulator 1 (CALHM1)</i>, <i>zink finger protein 93 (ZNF93), dynein, light chain, roadblock-type 2 (DYNLRB2), growth hormone-releasing hormone receptor (GHRHR)</i>, and <i>MutL-homolog 3(MLH3)</i>.</p

<i>Eigen-R²</i> results for SHIP-TREND, KORA F4 and GHS.

*<p><i>Storage time</i>: <i>Time between blood donation and RNA isolation</i> (SHIP-TREND and KORA F4) or <i>time between RNA isolation and RNA amplification</i> (GHS).</p><p>The first six lines of the Table represent technical parameters. A dash indicates that the parameter was not available in the cohort.</p

Workflow – from blood sampling to measured mRNA intensities.

<p>From left to right: Whole blood was collected and stored in PAXgene tubes until isolation of RNA from whole blood cells in both SHIP-TREND and KORA F4. In GHS, monocytes were separated from whole blood and RNA was isolated from monocytes within 24 hours after blood sampling, subsequently storing the isolated RNA until amplification. The sample <i>storage time</i> refers to the duration the whole blood (SHIP-TREND and KORA F4) or isolated RNA (GHS) was stored before further processing, shown as mean ± standard deviation in days. The samples were processed in 96 well plates both after isolation and amplification of the RNA. The corresponding plate layouts were called <i>RNA isolation batch</i> and <i>RNA amplification batch</i>, respectively. Finally, the RNA was hybridized and the arrays were scanned, quality controlled and analyzed.</p

Triangular relationships between eQTL-SNPs, gene expression levels in <i>trans</i> and phenotypic traits.

<p>Figure 4a: Adiponectin. ₁ Measured in the fasting state. ₂ Measured 2-hours after an oral glucose load in oral glucose tolerance test. Figure 4b: Mean Platelet Volume (MPV). The association between SNP and mean platelet volume was assessed in 4,159 KORA S4 participants, those between gene expression levels and mean platelet volume in 889 participants of the KORA F4 study. Figure 4c–e: Correlation analyses combining genetic, metabolomics and transcriptomics data in 712 participants of the KORA F4 study.</p