Whole Blood Gene Expression Research Articles

Time-dependent genetic effects on gene expression implicate aging processes.

Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature.

Abstract 19: Machine Learning Approach Identifies a Pattern of Gene Expression in Peripheral Blood Which Can Accurately Detect Ischemic Stroke

Objective: The identification of stroke-associated biomarkers represents a means by which prehospital triage could be expedited to increase the probability of successful intervention. Thus, the objective of this work was to use high-throughput transcriptomics in combination with basic machine learning techniques to identify a pattern of gene expression in peripheral whole blood which could be used to identify acute ischemic stroke (AIS) in the acute care setting. Methods: A two-stage study design was used which included a discovery cohort and an independent validation cohort. In the discovery cohort, peripheral whole blood samples were obtained from 39 AIS patients upon emergency department admission, and from 24 neurologically asymptomatic controls. Microarray was used to measure the expression of over 22,000 genes and a pattern recognition technique known as genetic algorithm k-nearest neighbors (GA/kNN) identified a pattern of gene expression that optimally discriminated between AIS and controls. In an independent validation cohort, the gene expression pattern was tested for its ability to discriminate between 39 AIS patients and each of two different control groups, one consisting of 30 neurologically asymptomatic controls, and the other consisting of 15 stroke mimics, with gene expression levels being assessed by qRT-PCR. Results: In the discovery cohort, GA/kNN identified ten transcripts (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) whose coordinate pattern of expression correctly identified 98.4% of subjects (97.4% sensitive, 100% specific). In the validation cohort, the same 10 transcripts correctly identified 95.6% of subjects when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 96.3% of subjects when comparing AIS patients to stroke mimics (97.4% specific, 93.3% sensitive). Conclusion: These results demonstrate that a highly accurate RNA-based companion diagnostic for AIS is plausible using a relatively small number of markers. The pattern of gene expression identified in this study shows strong diagnostic potential, and warrants further evaluation to determine true clinical efficacy.

HLA gene expression is altered in whole blood and placenta from women who later developed preeclampsia.

Preeclampsia is a multisystem disease that significantly contributes to maternal and fetal morbidity and mortality. In this study, we used a non-biased microarray approach to identify dysregulated genes in maternal whole blood samples which may be associated with the development of preeclampsia. Whole blood samples were obtained at 28 wk of gestation from 5 women who later developed preeclampsia (cases) and 10 matched women with normotensive pregnancies (controls). Placenta samples were obtained from an independent cohort of 19 women with preeclampsia matched with 19 women with normotensive pregnancies. We studied gene expression profiles using Illumina microarray in blood and validated changes in gene expression in whole blood and placenta tissue by qPCR. We found a transcriptional profile differentiating cases from controls; 336 genes were significantly dysregulated in blood from women who developed preeclampsia. Functional annotation of microarray results indicated that most of the genes found to be dysregulated were involved in inflammatory pathways. While general trends were preserved, only HLA-A was validated in whole blood samples from cases using qPCR (2.30- ± 0.9-fold change) whereas in placental tissue HLA-DRB1 expression was found to be significantly increased in samples from women with preeclampsia (5.88- ± 2.24-fold change). We have identified that HLA-A is upregulated in the circulation of women who went on to develop preeclampsia. In placenta of women with preeclampsia we identified that HLA-DRB1 is upregulated. Our data provide further evidence for involvement of the HLA gene family in the pathogenesis of preeclampsia.

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The correlation-based feature selection (CFS, one of the machine learning methods) algorithm was applied in this study, and 103 significantly differentially expressed genes between patients and controls, called “feature genes,” were selected; then, a model for SCZ diagnosis was built. The samples were subdivided into 10 groups, and cross-validation showed that the model we constructed achieved nearly 100% classification accuracy. Mathematical evaluation of the datasets before and after data processing proved the effectiveness of our algorithm. Feature genes were enriched in Parkinson's disease, oxidative phosphorylation, and TGF-beta signaling pathways, which were previously reported to be associated with SCZ. These results suggest that the analysis of gene expression in whole blood by our model could be a useful tool for diagnosing SCZ.

T26 - Brief Assessment Of Major Depression For Genetic Studies: Validation Of Cidi-Sf Screening With Scid Interviews

Background Studies of common genetic variants in complex psychiatric disorders such as major depression require samples that are too large (tens or hundreds of thousands) than can typically be collected with phenotyping methods involving direct interviews by trained clinicians. Many studies are no identifying cases and controls with self-report questionnaires, electronic medical records or registry data. Few data are available to determine the accuracy of these methods compared to direct interviews. Here, we present data on 1,263 individuals for whom online and direct interview data were available. Methods The Depression Genes and Networks study (DGN) recruited cases with recurrent MDD and controls with no lifetime MDD for a study of whole-blood gene expression. A research company emailed invitations to 14,463 survey panel members; 9,569 completed an online screen including the CIDI-Short Form depression and substance dependence modules, of whom 1,263 eventually gave blood samples and completed telephone SCID-IV interviews: 669 prospective cases (CIDI-SF recurrent MDD without current substance dependence) and 594 prospective controls (no 2-week period of depression or anhedonia with >2 MDD criteria; no current substance dependence). For this analysis we also identified narrow screening criteria for controls (no 2-week period with both depression and anhedonia; or one of these but not most of the day, nearly every day; no lifetime antidepressant [AD] use [SCID]). Polygenic score predictions were then examined in the GenRED-I GWAS cohort (which used non-overlapping controls screened with CIDI-SF) using broad vs. narrow controls. Results Among prospective cases, SCID diagnosis was MDD recurrent (N=547, 81.2%; some were excluded from DGN for other reasons) or uncomplicated single episode (39, 5.8%) totaling 87% with diagnoses that would be included in most genetic studies of MDD; 18 (2.7%) had major depressive episodes with complications that would typically be excluded (bereavement, medical or substance-related factors), and 65 (9.7%) had such exclusions as bipolar disorders (28, 4.2%), no MDD (30, 4.5%), or unreliable histories (7, 1%). Among prospective controls, 108 (18.2%) were excluded by SCID, primarily for MDD (57, 9.6%), sub-threshold depression (25, 4.2%), or a bipolar disorder (6, 1%). Narrow screening criteria would have excluded 76 (70%) of the 108 ineligible controls (57 by CIDI-SF clinical screening, 19 for self-reported AD use) and 44 (9.1%) of the SCID-eligible controls, thus predicting 93.2% validation by SCID. PRS prediction was improved in the GenRED cohort using only narrow controls. Discussion Online CIDI-SF screening for MDD-R has a high rate of validation by SCID interviews: 81.8% for MDD-R, and 87.6% if uncomplicated single-episode MDD is considered part of the genetic spectrum. Only 4.5% had no major depressive episode by SCID, but 4.2% received bipolar diagnoses. Although self-report screening for mania is only modestly accurate, we recommend using it to increase the proportion of positive cases. The false-positive rate in controls could be largely controlled by using a very strict threshold for self-reported history of depression (basically, not endorsing depression screening items) and by excluding for any self-reported antidepressant use. This excludes a small proportion of true controls, but an excess of prospective controls is typically available

Peripheral blood gene expression profiles linked to monoamine metabolite levels in cerebrospinal fluid.

The blood–brain barrier separates circulating blood from the central nervous system (CNS). The scope of this barrier is not fully understood which limits our ability to relate biological measurements from peripheral to central phenotypes. For example, it is unknown to what extent gene expression levels in peripheral blood are reflective of CNS metabolism. In this study, we examine links between central monoamine metabolite levels and whole-blood gene expression to better understand the connection between peripheral systems and the CNS. To that end, we correlated the prime monoamine metabolites in cerebrospinal fluid (CSF) with whole-genome gene expression microarray data from blood (N=240 human subjects). We additionally applied gene-enrichment analysis and weighted gene co-expression network analyses (WGCNA) to identify modules of co-expressed genes in blood that may be involved with monoamine metabolite levels in CSF. Transcript levels of two genes were significantly associated with CSF serotonin metabolite levels after Bonferroni correction for multiple testing: THAP7 (P=2.8 × 10−8, β=0.08) and DDX6 (P=2.9 × 10−7, β=0.07). Differentially expressed genes were significantly enriched for genes expressed in the brain tissue (P=6.0 × 10−52). WGCNA revealed significant correlations between serotonin metabolism and hub genes with known functions in serotonin metabolism, for example, HTR2A and COMT. We conclude that gene expression levels in whole blood are associated with monoamine metabolite levels in the human CSF. Our results, including the strong enrichment of brain-expressed genes, illustrate that gene expression profiles in peripheral blood can be relevant for quantitative metabolic phenotypes in the CNS.

A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking.

Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.

T cell abundance in blood predicts acute organ toxicity in chemoradiotherapy for head and neck cancer.

Treatment of head and neck squamous cell carcinoma (HNSCC) by chemoradiotherapy (CRT) often results in high-grade acute organ toxicity (HGAOT). As these adverse effects impair the patients' quality of life and the feasibility of the planned therapy, we sought to analyze immunological parameters in tumor material and blood samples obtained from 48 HNSCC patients in order to assess the potential to predict the individual acute organ toxicity. T cells in the tumor stroma were enriched in patients developing HGAOT whereas levels of soluble factors in the plasma and gene expression in whole blood did not coincide with the occurrence of acute organ toxicity. In contrast, the frequency and absolute numbers of selected leukocyte subpopulations measured in samples of peripheral blood mononuclear cells (PBMCs) directly before the beginning of CRT were significantly different in patients with HGAOT as compared to those without. When we validated several potential markers including the abundance of T cells in a small prospective study with 16 HNSCC patients, we were able to correctly predict acute organ toxicity in up to 81% of the patients. We conclude that analysis of PBMCs by fluorescence-activated cell sorting (FACS) might be a convenient strategy to identify patients at risk of developing HGAOT caused by CRT, which might allow to adapt the treatment regimen and possibly improve disease outcome.

Postprandial alterations in whole-blood DNA methylation are mediated by changes in white blood cell composition

Background: DNA methylation is an essential nuclear process associated with genomic functions such as transcription factor binding and the regulation of gene expression. DNA methylation patterns can also serve as potential biomarkers for disease progression and response to therapy. However, the full dynamics of DNA methylation across daily physiologic events have not been fully elucidated.Objective: We sought to study how ingesting a standardized meal acutely affects peripheral blood DNA methylation.Design: We performed an observational study in healthy men (n = 26) on DNA methylation and gene expression in whole blood before and 160 min after the ingestion of a standardized meal. Cytosine-phosphate-guanine (CpG) methylation was assayed on the HumanMethylation450k microarray, and gene expression was measured with the Human Gene 2.1 ST Array.Results: Differential methylation after food intake was detected in 13% of the analyzed probes (63,207 CpG probes) at a 5% false discovery rate (FDR). This effect was driven by changes in leukocyte fractions as estimated from comparisons against methylation datasets generated from sorted leukocytes. When methylation values were adjusted for estimated leukocyte fractions, 541 probes were observed to be altered in the postprandial state (5% FDR).Conclusions: Apparent alterations in DNA methylation 160 min after meal ingestion mainly reflect changes in the estimated leukocyte population in whole blood. These results have major methodologic implications for genome-wide methylation studies because they highlight the strong underlying effects of changes in leukocyte fractions on CpG methylation patterns as well as the potential importance of meal-standardized sampling procedures for future investigations when alterations in white blood cell fractions are unavailable. This trial was registered at clinicaltrials.gov as LSF008786.

Identification of baseline gene expression signatures predicting therapeutic responses to three biologic agents in rheumatoid arthritis: a retrospective observational study.

BackgroundAccording to EULAR recommendations, biologic DMARDs (bDMARDs) such as tumor necrosis factor inhibitor, tocilizumab (TCZ), and abatacept (ABT) are in parallel when prescribing to rheumatoid arthritis (RA) patients who have shown insufficient response to conventional synthetic DMARDs. However, most prediction studies of therapeutic response to bDMARDs using gene expression profiles were focused on a single bDMARD, and consideration of the results from the perspective of RA pathophysiology was insufficient. The aim of this study was to identify the specific molecular biological features predicting the therapeutic outcomes of three bDMARDs (infliximab [IFX], TCZ, and ABT) by studying blood gene expression signatures of patients before biologic treatment in a unified test platform.MethodsRA patients who responded inadequately to methotrexate and were later commenced on any one of IFX (n = 140), TCZ (n = 38), or ABT (n = 31) as their first biologic between May 2007 and November 2011 were enrolled. Whole-blood gene expression data were obtained before biologic administration. Patients were categorized into remission (REM) and nonremission (NON-REM) groups according to CDAI at 6 months of biologic therapy. We employed Gene Set Enrichment Analysis (GSEA) to identify functional gene sets differentially expressed between these two groups for each biologic. Then, we compiled “signature scores” for these gene sets, and the prediction performances were assessed.ResultsGSEA showed that inflammasome genes were significantly upregulated with IFX in the NON-REM group compared with the REM group. With TCZ in the REM group, B-cell-specifically expressed genes were upregulated. RNA elongation, apoptosis-related, and NK-cell-specifically expressed genes were upregulated with ABT in the NON-REM group. Logistic regression analyses showed that “signature scores” of inflammasomes, B-cell-specifically expressed, and NK-cell-specifically expressed genes were significant, independently predictive factors for treatment outcome with IFX, TCZ, and ABT, respectively. The AUCs of ROC curves of these signature scores were 0.637, 0.796, and 0.768 for IFX, TCZ, and ABT, respectively.ConclusionsWe have identified original gene expression predictive signatures uniquely underlying the therapeutic effects of IFX, TCZ, and ABT. This is, to our knowledge, the first attempt to predict therapeutic effects of three drugs concomitantly using a unified gene expression test platform.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-016-1052-8) contains supplementary material, which is available to authorized users.

Gene-expression analysis of clozapine treatment in whole blood of patients with psychosis.

Clozapine is an atypical antipsychotic primarily prescribed for treatment-resistant schizophrenia. We tested the specific effect of clozapine versus other drug treatments on whole-blood gene expression in a sample of patients with psychosis from the UK. A total of 186 baseline whole-blood samples from individuals receiving treatment for established psychosis were analysed for gene expression on Illumina HumanHT-12.v4 BeadChips. After standard quality-control procedures, 152 samples remained, including 55 from individuals receiving clozapine. In a within-case study design, weighted gene correlation network analysis was used to identify modules of coexpressed genes. The influence of mood stabilizers, lithium carbonate/lithium citrate and sodium valproate was studied to identify their possible roles as confounders. Individuals receiving clozapine as their only antipsychotic (clozapine monotherapy) had a nominal association with one gene-expression module, whereas no significant change in gene expression was found for other drugs. Overall, this study does not provide evidence that clozapine treatment induces medium to large different gene-expression patterns in human whole blood versus other antipsychotic treatments. This does not rule out the possibility of smaller effects as observed for other common antipsychotic treatments.

Whole Blood RNA as a Source of Transcript-Based Nutrition- and Metabolic Health-Related Biomarkers.

Blood cells are receiving an increasing attention as an easily accessible source of transcript-based biomarkers. We studied the feasibility of using mouse whole blood RNA in this context. Several paradigms were studied: (i) metabolism-related transcripts known to be affected in rat tissues and peripheral blood mononuclear cells (PBMC) by fasting and upon the development of high fat diet (HFD)-induced overweight were assessed in whole blood RNA of fasted rats and mice and of HFD-fed mice; (ii) retinoic acid (RA)-responsive genes in tissues were assessed in whole blood RNA of control and RA-treated mice; (iii) lipid metabolism-related transcripts previously identified in PBMC as potential biomarkers of metabolic health in a rat model were assessed in whole blood in an independent model, namely retinoblastoma haploinsufficient (Rb+/-) mice. Blood was collected and stored in RNAlater® at -80°C until analysis of selected transcripts by real-time RT-PCR. Comparable changes with fasting were detected in the expression of lipid metabolism-related genes when RNA from either PBMC or whole blood of rats or mice was used. HFD-induced excess body weight and fat mass associated with expected changes in the expression of metabolism-related genes in whole blood of mice. Changes in gene expression in whole blood of RA-treated mice reproduced known transcriptional actions of RA in hepatocytes and adipocytes. Reduced expression of Fasn, Lrp1, Rxrb and Sorl1 could be validated as early biomarkers of metabolic health in young Rb+/- mice using whole blood RNA. Altogether, these results support the use of whole blood RNA in studies aimed at identifying blood transcript-based biomarkers of nutritional/metabolic status or metabolic health. Results also support reduced expression of Fasn, Lrp1, Rxrb and Sorl1 in blood cells at young age as potential biomarkers of metabolic robustness.

Quantitative Real-Time Polymerase Chain Reaction Measurement of HLA-DRA Gene Expression in Whole Blood Is Highly Reproducible and Shows Changes That Reflect Dynamic Shifts in Monocyte Surface HLA-DR Expression during the Course of Sepsis.

IntroductionA decrease in the expression of monocyte surface protein HLA-DR (mHLA-DR), measured by flow cytometry (FCM), has been suggested as a marker of immunosuppression and negative outcome in severe sepsis. However, FCM is not always available due to sample preparation that limits its use to laboratory operational hours. In this prospective study we evaluated dynamic changes in mHLA-DR expression during sepsis in relation to changes in HLA-DRA gene expression and Class II transactivator (CIITA), measured by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR).AimsThe aims of this study were: 1. to validate the robustness of qRT-PCR measurement of HLA-DRA- and CIITA–mRNA expression, in terms of reproducibility; and 2. to see if changes in expression of these genes reflect changes in mHLA-DR expression during the course of severe and non-severe bacteraemic sepsis.Methods and FindingsBlood samples were collected from 60 patients with bacteraemic sepsis on up to five occasions during Days 1–28 after hospital admission. We found the reproducibility of the qRT-PCR method to be high by demonstrating low threshold variations (<0.11 standard deviation (SD)) of the qRT-PCR system, low intra-assay variation of Ct-values within triplicates (≤0.15 SD) and low inter-assay variations (12%) of the calculated target gene ratios. Our results also revealed dynamic HLA-DRA expression patterns during the course of sepsis that reflected those of mHLA-DR measured by FCM. Furthermore, HLA-DRA and mHLA-DR recovery slopes in patients with non-severe sepsis differed from those in patients with severe sepsis, shown by mixed model for repeated measurements (p<0.05). However, during the first seven days of sepsis, PCR-measurements showed a higher magnitude of difference between the two sepsis groups. Mean differences (95% CI) between severe sepsis (n = 20) and non-severe sepsis (n = 40) were; on day 1–2, HLA-DRA 0.40 (0.28–0.59) p<0.001, CIITA 0.48 (0.32–0.72) p = 0.005, mHLA-DR 0.63 (0.45–1.00) p = 0.04, day 7 HLA-DRA 0.59 (0.46–0.77) p<0.001, CIITA 0.56 (0.41–0.76) p<0.001, mHLA-DR 0.81 (0.66–1.00) p = 0.28.ConclusionWe conclude that qRT-PCR measurement of HLA-DRA expression is robust, and that this method appears to be preferable to FCM in identifying patients with severe sepsis that may benefit from immunostimulation.

Systemic Inflammatory Response Syndrome After Major Abdominal Surgery Predicted by Early Upregulation of TLR4 and TLR5.

To study innate immune pathways in patients undergoing hepatopancreaticobiliary surgery to understand mechanisms leading to enhanced inflammatory responses and identifying biomarkers of adverse clinical consequences. Patients undergoing major abdominal surgery are at risk of life-threatening systemic inflammatory response syndrome (SIRS) and sepsis. Early identification of at-risk patients would allow tailored postoperative care and improve survival. Two separate cohorts of patients undergoing major hepatopancreaticobiliary surgery were studied (combined n = 69). Bloods were taken preoperatively, on day 1 and day 2 postoperatively. Peripheral blood mononuclear cells and serum were separated and immune phenotype and function assessed ex vivo. Early innate immune dysfunction was evident in 12 patients who subsequently developed SIRS (postoperative day 6) compared with 27 who did not, when no clinical evidence of SIRS was apparent (preoperatively or days 1 and 2). Serum interleukin (IL)-6 concentration and monocyte Toll-like receptor (TLR)/NF-κB/IL-6 functional pathways were significantly upregulated and overactive in patients who developed SIRS (P < 0.0001). Interferon α-mediated STAT1 phosphorylation was higher preoperatively in patients who developed SIRS. Increased TLR4 and TLR5 gene expression in whole blood was demonstrated in a separate validation cohort of 30 patients undergoing similar surgery. Expression of TLR4/5 on monocytes, particularly intermediate CD14CD16 monocytes, on day 1 or 2 predicted SIRS with accuracy 0.89 to 1.0 (areas under receiver operator curves). These data demonstrate the mechanism for IL-6 overproduction in patients who develop postoperative SIRS and identify markers that predict patients at risk of SIRS 5 days before the onset of clinical signs.

Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis.

A limitation when making early decisions on stroke management is the lack of rapid diagnostic and prognostic testing. Our study sought to identify peripheral blood RNA biomarkers associated with stroke. The secondary aims were to assess the discriminative capacity of RNA biomarkers for primary stroke type and stroke prognosis at 1-month. Whole-blood gene expression profiling was conducted on the discovery cohort: 129 first-time stroke cases that had blood sampling within 5 days of symptom onset and 170 control participants with no history of stroke. Through multiple regression analysis, we determined that expression of the gene MCEMP1 had the strongest association with stroke of 11 181 genes tested. MCEMP1 increased by 2.4-fold in stroke when compared with controls (95% confidence interval, 2.0-2.8; P=8.2×10(-22)). In addition, expression was elevated in intracerebral hemorrhage when compared with ischemic stroke cases (P=3.9×10(-4)). MCEMP1 was also highest soon after symptom onset and had no association with stroke risk factors. Furthermore, MCEMP1 expression independently improved discrimination of 1-month outcome. Indeed, discrimination models for disability and mortality that included MCEMP1 expression, baseline modified Rankin Scale score, and primary stroke type improved discrimination when compared with a model without MCEMP1 (disability Net Reclassification Index, 0.76; P=3.0×10(-6) and mortality Net Reclassification Index, 1.3; P=1.1×10(-9)). Significant associations with MCEMP1 were confirmed in an independent validation cohort of 28 stroke cases and 34 controls. This study demonstrates that peripheral blood expression of MCEMP1 may have utility for stroke diagnosis and as a prognostic biomarker of stroke outcome at 1-month.

Garlic Influences Gene Expression In Vivo and In Vitro

There is a large body of preclinical research aimed at understanding the roles of garlic and garlic-derived preparations in the promotion of human health. Most of this research has targeted the possible functions of garlic in maintaining cardiovascular health and in preventing and treating cancer. A wide range of outcome variables has been used to investigate the bioactivity of garlic, ranging from direct measures of health status such as cholesterol concentrations, blood pressure, and changes in tumor size and number, to molecular and biochemical measures such as mRNA gene expression, protein concentration, enzyme activity, and histone acetylation status. Determination of how garlic influences mRNA gene expression has proven to be a valuable approach to elucidating the mechanisms of garlic bioactivity. Preclinical studies investigating the health benefits of garlic far outnumber human studies and have made frequent use of mRNA gene expression measurement. There is an immediate need to understand mRNA gene expression in humans as well. Although safety and ethical constraints limit the types of available human tissue, peripheral whole blood is readily accessible, and measuring mRNA gene expression in whole blood may provide a unique window to understanding how garlic intake affects human health.

Abstract 17127: RVX-208 a Selective Bromodomain and Extra-terminal BET Protein Inhibitor Acts on Several Pathways to Benefit Cardiovascular Risks

The epigenetic modifying compound RVX-208 selectively inhibits the second ligand binding domain in BET proteins to increase ApoA-I/HDL production. In recent trials of CVD patients given standard of care and RVX-208 (200mg/d) there was a 55% relative risk reduction of MACE vs. placebo. This reduction is greater than expected from modest increases in ApoA-I/HDL, thus suggesting benefits of RVX-208 beyond lipid effects. Whether added benefits exist was explored by microarray survey of differential gene expression in primary human hepatocytes (PHH) and whole blood (WB). In PHH, RVX-208 downregulated genes within the complement, fibrin clotting, cholesterol biosynthesis, fatty acid biosynthesis, diabetes and acute phase response (APR) pathways. These data guided more focused analyses on the known HDL proteome encoded by 89 genes of which 30 were affected by RVX-208 and many of these were members of the APR or complement pathways. In support of the microarray data, we used RT-PCR and found RVX-208 lowered mRNA encoding complement 3, 4a/4b and 5 by &gt;50% in Huh-7 and HepG2 cells exposed to RVX-208. Consistent with the mRNA data, secretion of these complement proteins were less from the treated cells. Next the cells were exposed to cytokines in order to mimic an inflammatory state thus inducing complement expression. Treatment with RVX-208 blocked this effect by &gt;50%. Furthermore, in Huh-7 cells, RVX-208 repressed both fibrin clotting factors II (prothrombin) and XI (thromboplastin antecedent) consistent with the PHH microarray data thus suggesting an anti-thrombotic effect for RVX-208. To further extend findings in vitro from hepatoma cells, plasma from patients in our clinical trials who received RVX-208 were assayed and found to have decreased levels of both APR and complement proteins. Lastly, microarray studies of WB from healthy donors treated ex-vivo with RVX-208 uncovered differential regulation of anti- and pro-atherogenic gene sets that were activated and suppressed, respectively by RVX-208. In summary, RVX-208, a selective BET inhibitor, affects several pathways involved in CVD including; reverse cholesterol transport, atherogenesis, complement, thrombosis and vascular inflammation that cumulatively may reduce MACE in high risk CVD patients.

The transcriptional landscape of age in human peripheral blood.

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Extensive alterations of the whole-blood transcriptome are associated with body mass index: results of an mRNA profiling study involving two large population-based cohorts.

BackgroundObesity, defined as pathologically increased body mass index (BMI), is strongly related to an increased risk for numerous common cardiovascular and metabolic diseases. It is particularly associated with insulin resistance, hyperglycemia, and systemic oxidative stress and represents the most important risk factor for type 2 diabetes (T2D). However, the pathophysiological mechanisms underlying these associations are still not completely understood. Therefore, in order to identify potentially disease-relevant BMI-associated gene expression signatures, a transcriptome-wide association study (TWAS) on BMI was performed.MethodsWhole-blood mRNA levels determined by array-based transcriptional profiling were correlated with BMI in two large independent population-based cohort studies (KORA F4 and SHIP-TREND) comprising a total of 1977 individuals.ResultsExtensive alterations of the whole-blood transcriptome were associated with BMI: More than 3500 transcripts exhibited significant positive or negative BMI-correlation. Three major whole-blood gene expression signatures associated with increased BMI were identified. The three signatures suggested: i) a ratio shift from mature erythrocytes towards reticulocytes, ii) decreased expression of several genes essentially involved in the transmission and amplification of the insulin signal, and iii) reduced expression of several key genes involved in the defence against reactive oxygen species (ROS).ConclusionsWhereas the first signature confirms published results, the other two provide possible mechanistic explanations for well-known epidemiological findings under conditions of increased BMI, namely attenuated insulin signaling and increased oxidative stress. The putatively causative BMI-dependent down-regulation of the expression of numerous genes on the mRNA level represents a novel finding. BMI-associated negative transcriptional regulation of insulin signaling and oxidative stress management provide new insights into the pathogenesis of metabolic syndrome and T2D.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0141-x) contains supplementary material, which is available to authorized users.

PrimeFlow™ RNA assay as a method to elucidate the effect of protein transport inhibitors on cytokine gene expression in whole blood (TECH3P.941)

Abstract Intracellular antibody staining followed by flow cytometric analysis is commonly used to study cytokine expression in leukocytes. Detection of cytokines typically requires that cells be treated with protein transport inhibitors such as Brefeldin A and Monensin in order to accumulate sufficient levels of intracellular protein. However, it is not known whether the use of these inhibitors affects gene expression. PrimeFlow™ RNA is a new flow cytometric assay that allows the simultaneous interrogation of RNA and protein levels within single cells. We have used this assay to investigate the effect of protein transport inhibitors on the expression of human IL-2, TNF alpha and IFN gamma during the course of a 5 hour stimulation of whole blood with PMA and ionomycin. This represents first time that the effect of protein transport inhibitors on gene expression in whole blood has been examined in single cells.