Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3

Comparison of global DNA methylation analysis by whole genome bisulfite sequencing and the Infinium Mouse Methylation BeadChip using fresh and fresh-frozen mouse epidermis

Until recently, studying the murine methylome was restricted to sequencing-based methods. In this study we compared the global DNA methylation levels of hairless mouse epidermis using the recently released Infinium Mouse Methylation BeadChip from Illumina and whole genome bisulphite sequencing (WGBS). We also studied the effect of sample storage conditions by using fresh and fresh-frozen epidermis. The DNA methylation levels of 123,851 CpG sites covered by both the BeadChip and WGBS were compared. DNA methylation levels obtained with WGBS and the BeadChip were strongly correlated (Pearson correlation r = 0.984). We applied a threshold of 15 reads for the WGBS methylation analysis. Even at a threshold of 10 reads, we observed no substantial difference in DNA methylation levels compared with that obtained with the BeadChip. The DNA methylation levels from the fresh and the fresh-frozen samples were strongly correlated when analysed with both the BeadChip (r = 0.999) and WGBS (r = 0.994). We conclude that the two methods of analysis generally work equally well for studies of DNA methylation of mouse epidermis and find that fresh and fresh-frozen epidermis can generally be used equally well. The choice of method will depend on the specific study's aims and the available resources in the laboratory.

Genetically regulated gene expression and proteins revealed discordant effects

Background

Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers.

Methods and results

We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD.

Conclusion

While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers

Background

For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.

Methods and results

From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants’ medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.

Conclusions

Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.

EPIphany—A Platform for Analysis and Visualization of Peptide Immunoarray Data

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates. High-resolution analysis of these populations typically requires advanced techniques such as B cell receptor repertoire sequencing, mass spectrometry of isolated immunoglobulins, or phage display libraries that are dependent upon equipment and expertise which are prohibitive for many labs. High-density peptide microarrays representing diverse populations of putative linear epitopes (immunoarrays) are an effective alternative for high-throughput examination of antibody reactivity and diversity. While a promising technology, widespread adoption of immunoarrays has been limited by the need for, and relative absence of, user-friendly tools for consideration and visualization of the emerging data. To address this limitation, we developed EPIphany, a software platform with a simple web-based user interface, aimed at biological users, that provides access to important analysis parameters, data normalization options, and a variety of unique data visualization options. This platform provides researchers the greatest opportunity to extract biologically meaningful information from the immunoarray data, thereby facilitating the discovery and development of novel immuno-therapeutics.

Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located near NEDD4L and SLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (Rg ranging from 0.11 to 0.76, P-values <0.002). A negative genetic correlation of childhood BMI with age at menarche was observed. Our results suggest that the biological processes underlying childhood BMI largely, but not completely, overlap with those underlying adult BMI. The well-known observational associations of BMI in childhood with cardio-metabolic diseases in adulthood may reflect partial genetic overlap, but in light of previous evidence, it is also likely that they are explained through phenotypic continuity of BMI from childhood into adulthood.

How to do quantile normalization correctly for gene expression data analyses

Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split (“Class-specific”). Via simulations with both real and simulated batch effects, we demonstrate that the “Class-specific” strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the “Class-specific” strategy.

Signaling networks in inflammatory pathways and risk for suicidal behavior

BACKGROUND

Suicide is a leading cause of death in the young adult population, with few biological markers identified thus far to be associated with suicidality. Cytokines (including IL-6 and TNFα) may contribute to increased risk for depression and suicidality. Few studies have examined the associations of cytokine mRNA expression with depression and suicidal ideation and behavior. This study examines these associations and whether cytokine signaling networks differentiate suicide attempters (SA), suicide ideators (SI), and healthy controls (HC).

METHODS

Cytokine pathway marker (CPM; e.g. cytokines and proteins in cytokine signaling pathways) mRNA gene expression in whole blood was examined in suicide attempters (n = 38), suicide ideators (n = 38), and healthy controls (n = 36). Between-group differences in CPM gene expression were examined. We also examined association of the mRNA of these genes with the severity of depression and suicidal ideation. Novel Gaussian Graphical Model (GGM) techniques were utilized to examine between-network partial correlation differences in cytokine signaling networks relevant to IL-6 and TNFα signaling pathways.

RESULTS

The severity of depression symptoms was positively associated with TNFα mRNA levels and negatively associated with IL-10 mRNA levels, but CPM expression was not associated with suicidal ideation severity. There were no between-group differences in CPM markers among healthy controls, SI and SA groups after correcting for multiple comparisons. In network analyses, we found suggestive results of between-group network differences between SI and control groups in gene pairs with IL-6R and STAT3 as common nodes.

DISCUSSION

In a cohort of suicide attempters and ideators, TNFα and IL-10 mRNA levels appear to be associated with depressive symptomology, consistent with elevation of pro-inflammatory cytokine production and reduction of anti-inflammatory cytokine production. Additionally, cytokine signaling networks may differentiate suicide ideators from healthy controls based on between-network differences, with differences possibly related to relationships of IL6R or STAT3 with other components of cytokine signaling networks.

Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer

CONTEXT

Adipose tissue inflammation and dysregulated energy homeostasis are key mechanisms linking obesity and cancer. Distinct adipose tissue depots strongly differ in their metabolic profiles; however, comprehensive studies of depot-specific perturbations among patients with cancer are lacking.

OBJECTIVE

We compared transcriptome profiles of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) from patients with colorectal cancer and assessed the associations of different anthropometric measures with depot-specific gene expression.

DESIGN

Whole transcriptomes of VAT and SAT were measured in 233 patients from the ColoCare Study, and visceral and subcutaneous fat area were quantified via CT.

RESULTS

VAT compared with SAT showed elevated gene expression of cytokines, cell adhesion molecules, and key regulators of metabolic homeostasis. Increased fat area was associated with downregulated lipid and small molecule metabolism and upregulated inflammatory pathways in both compartments. Comparing these patterns between depots proved specific and more pronounced gene expression alterations in SAT and identified unique associations of integrins and lipid metabolism-related enzymes. VAT gene expression patterns that were associated with visceral fat area poorly overlapped with patterns associated with self-reported body mass index (BMI). However, subcutaneous fat area and BMI showed similar associations with SAT gene expression.

CONCLUSIONS

This large-scale human study demonstrates pronounced disparities between distinct adipose tissue depots and reveals that BMI poorly correlates with fat mass-associated changes in VAT. Taken together, these results provide crucial evidence for the necessity to differentiate between distinct adipose tissue depots for a correct characterization of gene expression profiles that may affect metabolic health of patients with colorectal cancer.

Shift Work Disrupts Circadian Regulation of the Transcriptome in Hospital Nurses

Circadian misalignment between sleep and behavioral/feeding rhythms is thought to lead to various health impairments in shift workers. Therefore, we investigated how shift work leads to genome-wide circadian dysregulation in hospital nurses. Female nurses from the University of Alabama at Birmingham (UAB) Hospital working night shift ( n = 9; 29.6 ± 11.4 y) and day shift ( n = 8; 34.9 ± 9.4 y) participated in a 9-day study measuring locomotor activity and core body temperature (CBT) continuously. Additionally, cortisol and melatonin were assayed and peripheral blood mononuclear cells (PBMCs) were harvested for RNA extraction every 3 h on a day off from work. We saw phase desynchrony of core body temperature, peak cortisol, and dim light melatonin onset in individual night-shift subjects compared with day-shift subjects. This variability was evident even though day- and night-shift nurses had similar sleep timing and scheduled meal times on days off. Surprisingly, the phase and rhythmicity of the expression of the clock gene, PER1, in PBMCs were similar for day-shift and night-shift subjects. Genome-wide microarray analysis of PBMCs from a subset of nurses revealed distinct gene expression patterns between night-shift and day-shift subjects. Enrichment analysis showed that day-shift subjects expressed pathways involved in generic transcription and regulation of signal transduction, whereas night-shift subjects expressed pathways such as RNA polymerase I promoter opening, the matrisome, and endocytosis. In addition, there was large variability in the number of rhythmic transcripts among subjects, regardless of shift type. Interestingly, the amplitude of the CBT rhythm appeared to be more consistent with the number of cycling transcripts for each of the 6 subjects than was melatonin rhythm. In summary, we show that shift-work patterns affect circadian alignment and gene expression in PBMCs.

Exploratory Analysis of Single-Gene Predictive Biomarkers in HERA DASL Cohort Reveals That C8A mRNA Expression Is Prognostic of Outcome and Predictive of Benefit of Trastuzumab

Purpose

The Herceptin Adjuvant study is an international multicenter randomized trial that compared 1 or 2 years of trastuzumab given every 3 weeks with observation in women with human epidermal growth factor 2-positive (HER2+) breast cancer after chemotherapy. Identification of biomarkers predictive of a benefit from trastuzumab will minimize overtreatment and lower health care costs.

Methods

To identify possible single-gene biomarkers, an exploratory analysis of 3,669 gene probes not expected to be expressed in normal breast tissue was conducted. Disease-free survival (DFS) was used as the end point in a Cox regression model, with the interaction term between C8A mRNA and treatment as a categorical variable split on the cohort mean.

Results

A significant interaction between C8A mRNA and treatment was detected (P < .001), indicating a predictive response to trastuzumab treatment. For the C8A-low subgroup (mRNA expression lower than the cohort mean), no significant treatment benefit was observed (P = .73). In the C8A-high subgroup, patients receiving trastuzumab experienced a lower hazard of a DFS event by approximately 75% compared with those in the observation arm (hazard ratio [HR], 0.25; P < .001). A significant prognostic effect of C8A mRNA also was seen (P < .001) in the observation arm, where the C8A-high group hazard of a DFS event was three times the respective hazard of the C8A-low group (HR, 3.27; P < .001). C8A mRNA is highly prognostic in the Hungarian Academy of Science HER2+ gastric cancer cohort (HR, 1.72; P < .001).

Conclusion

C8A as a single-gene biomarker prognostic of DFS and predictive of a benefit from trastuzumab has the potential to improve the standard of care in HER2+ breast cancer if validated by additional studies. Understanding the advantage of overexpression of C8A related to the innate immune response can give insight into the mechanisms that drive cancer.

Early isolated V-lesion may not truly represent rejection of the kidney allograft

Intimal arteritis is known to be a negative prognostic factor for kidney allograft survival. Isolated v-lesion (IV) is defined as intimal arteritis with minimal tubulointerstitial inflammation (TI). Although the Banff classification assesses IV as T cell-mediated rejection (TCMR), clinical, and prognostic significance of early IV (early IV, eIV) with negative C4d and donor-specific antibodies (DSA) remains unclear. To help resolve if such eIV truly represents acute rejection, a molecular study was performed. The transcriptome of eIV (n=6), T cell-mediated vascular rejection with rich TI (T cell-mediated vascular rejection, TCMRV, n=4) and non-rejection histologic findings (n=8) was compared using microarrays. A total of 310 genes were identified to be deregulated in TCMRV compared with eIV. Gene enrichment analysis categorized deregulated genes to be associated primarily with T-cells associated biological processes involved in an innate and adaptive immune and inflammatory response. Comparison of deregulated gene lists between the study groups and controls showed only a 1.7% gene overlap. Unsupervised hierarchical cluster analysis revealed clear distinction of eIV from TCMRV and showed similarity with a control group. Up-regulation of immune response genes in TCMRV was validated using RT-qPCR in a different set of eIV (n=12) and TCMRV (n=8) samples. The transcriptome of early IV (< 1 month) with negative C4d and DSA is associated with a weak immune signature compared with TCMRV and shows similarity with normal findings. Such eIV may feature non-rejection origin and reflect an injury distinct from an alloimmune response. The present study supports use of molecular methods when interpreting kidney allograft biopsy findings.

Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?

The ultra-high risk (UHR) state was originally conceived to identify individuals at imminent risk of developing psychosis. Although recent studies have suggested that most individuals designated UHR do not, they constitute a distinctive group, exhibiting cognitive and functional impairments alongside multiple psychiatric morbidities. UHR characterization using molecular markers may improve understanding, provide novel insight into pathophysiology, and perhaps improve psychosis prediction reliability. Whole-blood gene expressions from 56 UHR subjects and 28 healthy controls are checked for existence of a consistent gene expression profile (signature) underlying UHR, across a variety of normalization and heterogeneity-removal techniques, including simple log-conversion, quantile normalization, gene fuzzy scoring (GFS), and surrogate variable analysis. During functional analysis, consistent and reproducible identification of important genes depends largely on how data are normalized. Normalization techniques that address sample heterogeneity are superior. The best performer, the unsupervised GFS, produced a strong and concise 12-gene signature, enriched for psychosis-associated genes. Importantly, when applied on random subsets of data, classifiers built with GFS are "meaningful" in the sense that the classifier models built using genes selected after other forms of normalization do not outperform random ones, but GFS-derived classifiers do. Data normalization can present highly disparate interpretations on biological data. Comparative analysis has shown that GFS is efficient at preserving signals while eliminating noise. Using this, we demonstrate confidently that the UHR designation is well correlated with a distinct blood-based gene signature.

DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

Background

Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.

Results

This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.

Conclusions

The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1912-5) contains supplementary material, which is available to authorized users.

Mapping eQTL by leveraging multiple tissues and DNA methylation

Background

DNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.

Results

Our method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeobox member gene (LHX9), which may have a role in the neural development.

Conclusions

Our score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model’s flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1856-9) contains supplementary material, which is available to authorized users.

Distinct carbon sources affect structural and functional maturation of cardiomyocytes derived from human pluripotent stem cells

The immature phenotype of human pluripotent stem cell derived cardiomyocytes (hPSC-CMs) constrains their potential in cell therapy and drug testing. In this study, we report that shifting hPSC-CMs from glucose-containing to galactose- and fatty acid-containing medium promotes their fast maturation into adult-like CMs with higher oxidative metabolism, transcriptional signatures closer to those of adult ventricular tissue, higher myofibril density and alignment, improved calcium handling, enhanced contractility, and more physiological action potential kinetics. Integrated "-Omics" analyses showed that addition of galactose to culture medium improves total oxidative capacity of the cells and ameliorates fatty acid oxidation avoiding the lipotoxicity that results from cell exposure to high fatty acid levels. This study provides an important link between substrate utilization and functional maturation of hPSC-CMs facilitating the application of this promising cell type in clinical and preclinical applications.

Genome-wide expression and response to exposure-based psychological therapy for anxiety disorders

Exposure-based psychological treatments for anxiety have high efficacy. However, a substantial proportion of patients do not respond to therapy. Research examining the potential biological underpinnings of therapy response is still in its infancy, and most studies have focussed on candidate genes. To our knowledge, this study represents the first investigation of genome-wide expression profiles with respect to treatment outcome. Participants (n=102) with panic disorder or specific phobia received exposure-based cognitive behavioural therapy. Treatment outcome was defined as percentage reduction from baseline in clinician-rated severity of their primary anxiety diagnosis at post treatment and 6 month follow-up. Gene expression was determined from whole blood samples at three time points using the Illumina HT-12v4 BeadChip microarray. Linear regression models tested the association between treatment outcome and changes in gene expression from pre-treatment to post treatment, and pre-treatment to follow-up. Network analysis was conducted using weighted gene co-expression network analysis, and change in the detected modules from pre-treatment to post treatment and follow-up was tested for association with treatment outcome. No changes in gene expression were significantly associated with treatment outcomes when correcting for multiple testing (q<0.05), although a small number of genes showed a suggestive association with treatment outcome (q<0.5, n=20). Network analysis showed no association between treatment outcome and change in gene expression for any module. We report suggestive evidence for the role of a small number of genes in treatment outcome. Although preliminary, these findings contribute to a growing body of research suggesting that response to psychological therapies may be associated with changes at a biological level.

Improving cross-study prediction through addon batch effect adjustment or addon normalization

Motivation

To date most medical tests derived by applying classification methods to high-dimensional molecular data are hardly used in clinical practice. This is partly because the prediction error resulting when applying them to external data is usually much higher than internal error as evaluated through within-study validation procedures. We suggest the use of addon normalization and addon batch effect removal techniques in this context to reduce systematic differences between external data and the original dataset with the aim to improve prediction performance.

Results

We evaluate the impact of addon normalization and seven batch effect removal methods on cross-study prediction performance for several common classifiers using a large collection of microarray gene expression datasets, showing that some of these techniques reduce prediction error.

Availability and Implementation

All investigated addon methods are implemented in our R package bapred.

Contact

hornung@ibe.med.uni-muenchen.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Separate and combined effects of genetic variants and pre-treatment whole blood gene expression on response to exposure-based cognitive behavioural therapy for anxiety disorders

OBJECTIVES

Exposure-based cognitive behavioural therapy (eCBT) is an effective treatment for anxiety disorders. Response varies between individuals. Gene expression integrates genetic and environmental influences. We analysed the effect of gene expression and genetic markers separately and together on treatment response.

METHODS

Adult participants (n ≤ 181) diagnosed with panic disorder or a specific phobia underwent eCBT as part of standard care. Percentage decrease in the Clinical Global Impression severity rating was assessed across treatment, and between baseline and a 6-month follow-up. Associations with treatment response were assessed using expression data from 3,233 probes, and expression profiles clustered in a data- and literature-driven manner. A total of 3,343,497 genetic variants were used to predict treatment response alone and combined in polygenic risk scores. Genotype and expression data were combined in expression quantitative trait loci (eQTL) analyses.

RESULTS

Expression levels were not associated with either treatment phenotype in any analysis. A total of 1,492 eQTLs were identified with q < 0.05, but interactions between genetic variants and treatment response did not affect expression levels significantly. Genetic variants did not significantly predict treatment response alone or in polygenic risk scores.

CONCLUSIONS

We assessed gene expression alone and alongside genetic variants. No associations with treatment outcome were identified. Future studies require larger sample sizes to discover associations.

Blunted HPA axis activity prior to suicide attempt and increased inflammation in attempters

BACKGROUND

Hypothalamic-Pituitary-Adrenal (HPA) axis dysregulation is associated with increased risk for suicidal behavior. However, it is not clear whether such dysregulation exists prior to or is a consequence of attempt. Studies also show an activation of inflammatory responses in suicidal behavior but often combine attempters with those with ideation.

METHODS

The sample consisted of psychiatric inpatients, aged 15-30 years, admitted for suicide attempt (SA, n=38), inpatients admitted for suicidal ideation with no prior history of attempts (SI, n=40), and healthy controls (n=37). We compared SA, SI, and controls on hair cortisol concentrations (HCC), which provides retrospective levels of cortisol and thus prior to the attempt in SA. We also compared them on the expression of genes in the HPA axis and inflammatory pathways previously implicated in suicidal behavior (GR or NR3C1, SKA2, FKBP5, IL-1β, TNF-α); plasma C-Reactive Protein (CRP); and cellular measures of glucocorticoid receptor (GR) sensitivity and stimulated production of IL-6.

RESULTS

We found lower HCC [β=-0.55, 95% CI (-0.96, -0.13), p=0.01, ES=-0.54] in first-time SA compared to SI and controls. In addition, SA showed lower GR or NR3C1 (α isoform) mRNA [β=-5.11, 95% CI (-10.9, 0.73), p=0.09, ES=-0.46], higher CRP [β=0.94, 95% CI (-0.004, 1.9), p=0.05, ES=0.60], and higher TNF-α mRNA [β=26.4, 95% CI (7.7, 45.2), p=0.006, ES=0.73].

CONCLUSIONS

This is the first study to differentiate youth who attempt suicide from those with suicidal ideation on HCC and to show that low HCC precedes suicide attempt. Suicide attempters also showed a distinct biological profile on several markers in both the HPA axis and inflammatory pathways. Future longitudinal studies are needed to examine the ability of these biomarkers to predict suicidal behavior.

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

BACKGROUND

Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis.

METHODS

All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals).

RESULTS

The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation.

CONCLUSIONS

Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.

Alteration of Multiple Leukocyte Gene Expression Networks is Linked with Magnetic Resonance Markers of Prognosis After Acute ST-Elevation Myocardial Infarction

Prognostic relevant pathways of leukocyte involvement in human myocardial ischemic-reperfusion injury are largely unknown. We enrolled 136 patients with ST-elevation myocardial infarction (STEMI) after primary angioplasty within 12 h after onset of symptoms. Following reperfusion, whole blood was collected within a median time interval of 20 h (interquartile range: 15-25 h) for genome-wide gene expression analysis. Subsequent CMR scans were performed using a standard protocol to determine infarct size (IS), area at risk (AAR), myocardial salvage index (MSI) and the extent of late microvascular obstruction (lateMO). We found 398 genes associated with lateMO and two genes with IS. Neither AAR, nor MSI showed significant correlations with gene expression. Genes correlating with lateMO were strongly related to several canonical pathways, including positive regulation of T-cell activation (p = 3.44 × 10^-5), and regulation of inflammatory response (p = 1.86 × 10^-3). Network analysis of multiple gene expression alterations associated with larger lateMO identified the following functional consequences: facilitated utilisation and decreased concentration of free fatty acid, repressed cell differentiation, enhanced phagocyte movement, increased cell death, vascular disease and compensatory vasculogenesis. In conclusion, the extent of lateMO after acute, reperfused STEMI correlated with altered activation of multiple genes related to fatty acid utilisation, lymphocyte differentiation, phagocyte mobilisation, cell survival, and vascular dysfunction.

Blood Transcriptional Profiling Reveals Immunological Signatures of Distinct States of Infection of Humans with Leishmania infantum

Visceral leishmaniasis (VL) can be lethal if untreated; however, the majority of human infections with the etiological agents are asymptomatic. Using Illumina Bead Chip microarray technology, we investigated the patterns of gene expression in blood of active VL patients, asymptomatic infected individuals, patients under remission of VL and controls. Computational analyses based on differential gene expression, gene set enrichment, weighted gene co-expression networks and cell deconvolution generated data demonstrating discriminative transcriptional signatures. VL patients exhibited transcriptional profiles associated with pathways and gene modules reflecting activation of T lymphocytes via MHC class I and type I interferon signaling, as well as an overall down regulation of pathways and gene modules related to myeloid cells, mainly due to differences in the relative proportions of monocytes and neutrophils. Patients under remission of VL presented heterogeneous transcriptional profiles associated with activation of T lymphocytes via MHC class I, type I interferon signaling and cell cycle and, importantly, transcriptional activity correlated with activation of Notch signaling pathway and gene modules that reflected increased proportions of B cells after treatment of disease. Asymptomatic and uninfected individuals presented similar gene expression profiles, nevertheless, asymptomatic individuals exhibited particularities which suggest an efficient regulation of lymphocyte activation and a strong association with a type I interferon response. Of note, we validated a set of target genes by RT-qPCR and demonstrate the robustness of expression data acquired by microarray analysis. In conclusion, this study profiles the immune response during distinct states of infection of humans with Leishmania infantum with a novel strategy that indicates the molecular pathways that contribute to the progression of the disease, while also providing insights into transcriptional activity that can drive protective mechanisms.

Infections of humans with the protozoan parasites L. donvani and L. infantum can lead to the development of the disease visceral leishmaniasis, but also to an asymptomatic status. However, the mechanisms that result in these clinical outcomes after infection are poorly understood. In this study, we applied a data-driven approach to obtain insights into the immunological processes linked to the progression of the disease or to protective mechanisms. For this purpose, we evaluated the patterns of expression for genes that code proteins from the entire human genome in the peripheral blood from patients with visceral leishmaniasis, from individuals who remained asymptomatic after infections with L. infantum, from patients who were recovering from disease after treatment and from uninfected individuals. By employing computational analysis to evaluate the blood transcriptional activity of each group, we identified transcriptional signatures that correlate with previous findings obtained through different analytical methods. Moreover, our analyses uncovered hitherto unidentified molecular pathways and gene networks associated with the transcriptional profiles of individuals recovering from disease or that did not develop symptoms after infection. This suggests that activation of protective responses can be useful targets for the development of new therapies for visceral leishmaniasis.

Comparison of pre-processing methods for multiplex bead-based immunoassays

BACKGROUND

High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data.

RESULTS

We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay.

CONCLUSIONS

The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Whole Blood Gene Expression Differentiates between Atrial Fibrillation and Sinus Rhythm after Cardioversion

Background

Treatment to restore sinus rhythm among patients with atrial fibrillation (AF) has limited long-term success rates. Gene expression profiling may provide new insights into AF pathophysiology.

Objective

To identify biomarkers and improve our understanding of AF pathophysiology by comparing whole blood gene expression before and after electrical cardioversion (ECV).

Methods

In 46 patients with persistent AF that underwent ECV, whole blood samples were collected 1–2 hours before and 4 to 6 weeks after successful cardioversion. The paired samples were sent for microarray and plasma biomarker comparison.

Results

Of 13,942 genes tested, expression of SLC25A20 and PDK4 had the strongest associations with AF. Post-cardioversion, SLC25A20 and PDK4 expression decreased by 0.8 (CI 0.7–0.8, p = 2.0x10^-6) and 0.7 (CI 0.6–0.8, p = 3.0x10^-5) fold respectively. Median N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations decreased from 127.7 pg/mL to 44.9 pg/mL (p = 2.3x10^-13) after cardioversion. AF discrimination models combining NT-proBNP and gene expression (NT-proBNP + SLC25A20 area under the curve = 0.88, NT-proBNP + PDK4 AUC = 0.86) had greater discriminative capacity as compared with NT-proBNP alone (AUC = 0.82). Moreover, a model including NT-proBNP, SLC25A20 and PDK4 significantly improved AF discrimination as compared with other models (AUC = 0.87, Net Reclassification Index >0.56, p<5.8x10^-3). We validated the association between SLC25A20 and PDK4 with AF in an independent sample of 17 patients.

Conclusion

This study demonstrates that SLC25A20, PDK4, and NT-proBNP have incremental utility as biomarkers discriminating AF from sinus rhythm. Elevated SLC25A20 and PDK4 expression during AF indicates an important role for energy metabolism in AF.

Integration of flow studies for robust selection of mechanoresponsive genes

Blood flow is an essential contributor to plaque growth, composition and initiation. It is sensed by endothelial cells, which react to blood flow by expressing > 1000 genes. The sheer number of genes implies that one needs genomic techniques to unravel their response in disease. Individual genomic studies have been performed but lack sufficient power to identify subtle changes in gene expression. In this study, we investigated whether a systematic meta-analysis of available microarray studies can improve their consistency. We identified 17 studies using microarrays, of which six were performed in vivo and 11 in vitro. The in vivo studies were disregarded due to the lack of the shear profile. Of the in vitro studies, a cross-platform integration of human studies (HUVECs in flow cells) showed high concordance (> 90 %). The human data set identified > 1600 genes to be shear responsive, more than any other study and in this gene set all known mechanosensitive genes and pathways were present. A detailed network analysis indicated a power distribution (e. g. the presence of hubs), without a hierarchical organisation. The average cluster coefficient was high and further analysis indicated an aggregation of 3 and 4 element motifs, indicating a high prevalence of feedback and feed forward loops, similar to prokaryotic cells. In conclusion, this initial study presented a novel method to integrate human-based mechanosensitive studies to increase its power. The robust network was large, contained all known mechanosensitive pathways and its structure revealed hubs, and a large aggregate of feedback and feed forward loops.

Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis

BACKGROUND AND PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Tumor Specific Recruitment and Reprogramming of Mesenchymal Stem Cells in Tumorigenesis

Non-neoplastic stromal cells harvested from patient tumors were identified as tumor-derived mesenchymal stem cells (MSCs) by their multipotential capacity to differentiate into adipocytes, osteoblasts, and chondrocytes and by the expression of MSC specific cell surface markers. These procedures yielded also epithelial cancer cells and their counterpart MSC from gastric carcinoma (GSC1) and lung carcinoma (LC2). While the LC2 cancer cell growth is independent of their LC-MSC, the GSC1 cancer cell growth is critically dependent on the presence of their counterpart GSC-MSC or their conditioned medium (CM). The fact that none of the various other tumor-derived MSCs was able to restore the specific effect of GSC-MSC on GSC1 cancer cell growth suggests specificity of tumor-derived MSC, which are specifically recruited and "educated"/reprogrammed by the cancer cells to support tumor growth. Using cytokine array analysis, we were able to demonstrate that GSC1 cell growth is mediated through hepatocyte growth factor (HGF)/c-MET signaling pathway which is activated exclusively by HGF secreted from GSC-MSC. An innovative approach demonstrates GSC1-mediated specific tropism of "naïve" MSC from the adjacent tissue in a tumor specific manner to support tumor progression. The results suggest that specific tumor tropic "naïve" MSC are reprogrammed in a tumor-specific manner to support gastric tumor progression. Understanding the mechanisms involved in the interactions of the tumor cancer cells and tumor-derived MSC will constitute the basis for developing multimodal anticancer therapeutic strategies that will also take into account the specific tumor tropism properties of MSC and their reprogramming.

The Inflammatory Transcription Factors NFκB, STAT1 and STAT3 Drive Age-Associated Transcriptional Changes in the Human Kidney

Human kidney function declines with age, accompanied by stereotyped changes in gene expression and histopathology, but the mechanisms underlying these changes are largely unknown. To identify potential regulators of kidney aging, we compared age-associated transcriptional changes in the human kidney with genome-wide maps of transcription factor occupancy from ChIP-seq datasets in human cells. The strongest candidates were the inflammation-associated transcription factors NFκB, STAT1 and STAT3, the activities of which increase with age in epithelial compartments of the renal cortex. Stimulation of renal tubular epithelial cells with the inflammatory cytokines IL-6 (a STAT3 activator), IFNγ (a STAT1 activator), or TNFα (an NFκB activator) recapitulated age-associated gene expression changes. We show that common DNA variants in RELA and NFKB1, the two genes encoding subunits of the NFκB transcription factor, associate with kidney function and chronic kidney disease in gene association studies, providing the first evidence that genetic variation in NFκB contributes to renal aging phenotypes. Our results suggest that NFκB, STAT1 and STAT3 underlie transcriptional changes and chronic inflammation in the aging human kidney.

The structure and function of human kidneys deteriorate steadily with age, yet little is known about the underlying causes of kidney aging. In this work, we first used a genomics approach to identify candidate regulators of gene expression changes in the aging human kidney and identified inflammation-related transcription factors NFκB, STAT1 and STAT3 as the top candidate regulators. We found that kidney aging is associated with activation of NFκB, STAT1 and STAT3 in the renal parenchyma, and that the gene expression signatures evoked by activation of these transcription factors in human renal epithelial cells mimics age-associated gene expression changes in the kidney. Furthermore, we identified specific genetic variants in the NFκB transcription factor genes RELA and NFKB1 that associate with renal function and chronic kidney disease in humans, implicating NFκB as a potential contributor to the pathogenesis of chronic kidney disease and renal dysfunction in old age. Our findings suggest that activation of the inflammatory transcription factors STAT1, STAT3 and NFκB underlie transcriptional changes and reduced renal function in the elderly.

A Synthetic Kinome Microarray Data Generator

Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods.

Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood

Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.

Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†

Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait-associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters and enrichment of co-localized functional elements. We found eQTLs for about 85% of analysed genes, and 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 Mb to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might frequently be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. Yet, we show for strongly heritable transcripts that still little trans-chromosomal heritability is explained by all identified trans-eSNPs; however, our data suggest that most cis-heritability of these transcripts seems explained. Dissection of co-localized functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene expression, pathways and disease-related processes.

Robust Expansion of Human Pluripotent Stem Cells: Integration of Bioprocess Design With Transcriptomic and Metabolomic Characterization

: Human embryonic stem cells (hESCs) have an enormous potential as a source for cell replacement therapies, tissue engineering, and in vitro toxicology applications. The lack of standardized and robust bioprocesses for hESC expansion has hindered the application of hESCs and their derivatives in clinical settings. We developed a robust and well-characterized bioprocess for hESC expansion under fully defined conditions and explored the potential of transcriptomic and metabolomic tools for a more comprehensive assessment of culture system impact on cell proliferation, metabolism, and phenotype. Two different hESC lines (feeder-dependent and feeder-free lines) were efficiently expanded on xeno-free microcarriers in stirred culture systems. Both hESC lines maintained the expression of stemness markers such as Oct-4, Nanog, SSEA-4, and TRA1-60 and the ability to spontaneously differentiate into the three germ layers. Whole-genome transcriptome profiling revealed a phenotypic convergence between both hESC lines along the expansion process in stirred-tank bioreactor cultures, providing strong evidence of the robustness of the cultivation process to homogenize cellular phenotype. Under low-oxygen tension, results showed metabolic rearrangement with upregulation of the glycolytic machinery favoring an anaerobic glycolysis Warburg-effect-like phenotype, with no evidence of hypoxic stress response, in contrast to two-dimensional culture. Overall, we report a standardized expansion bioprocess that can guarantee maximal product quality. Furthermore, the "omics" tools used provided relevant findings on the physiological and metabolic changes during hESC expansion in environmentally controlled stirred-tank bioreactors, which can contribute to improved scale-up production systems.

SIGNIFICANCE

The clinical application of human pluripotent stem cells (hPSCs) has been hindered by the lack of robust protocols able to sustain production of high cell numbers, as required for regenerative medicine. In this study, a strategy was developed for the expansion of human embryonic stem cells in well-defined culture conditions using microcarrier technology and stirred-tank bioreactors. The use of transcriptomic and metabolic tools allowed detailed characterization of the cell-based product and showed a phenotypic convergence between both hESC lines along the expansion process. This study provided valuable insights into the metabolic hallmarks of hPSC expansion and new information to guide bioprocess design and media optimization for the production of cells with higher quantity and improved quality, which are requisite for translation to the clinic.

Endothelial inflammatory transcriptional responses to an altered plasma exposome following inhalation of diesel emissions

BACKGROUND

Air pollution, especially emissions derived from traffic sources, is associated with adverse cardiovascular outcomes. However, it remains unclear how inhaled factors drive extrapulmonary pathology.

OBJECTIVES

Previously, we found that canonical inflammatory response transcripts were elevated in cultured endothelial cells treated with plasma obtained after exposure compared with pre-exposure samples or filtered air (sham) exposures. While the findings confirmed the presence of bioactive factor(s) in the plasma after diesel inhalation, we wanted to better examine the complete genomic response to investigate (1) major responsive transcripts and (2) collected response pathways and ontogeny that may help to refine this method and inform the pathogenesis.

METHODS

We assayed endothelial RNA with gene expression microarrays, examining the responses of cultured endothelial cells to plasma obtained from six healthy human subjects exposed to 100 μg/m(3) diesel exhaust or filtered air for 2 h on separate occasions. In addition to pre-exposure baseline samples, we investigated samples obtained immediately-post and 24 h-post exposure.

RESULTS

Microarray analysis of the coronary artery endothelial cells challenged with plasma identified 855 probes that changed over time following diesel exhaust exposure. Over-representation analysis identified inflammatory cytokine pathways were upregulated both at the 2 and 24 h conditions. Novel pathways related to FOXO transcription factors and secreted extracellular factors were also identified in the microarray analysis.

CONCLUSIONS

These outcomes are consistent with our recent findings that plasma contains bioactive and inflammatory factors following pollutant inhalation and provide a novel pathway to explain the well-reported extrapulmonary toxicity of ambient air pollutants.

No DNA damage response and negligible genome-wide transcriptional changes in human embryonic stem cells exposed to terahertz radiation

Terahertz (THz) radiation was proposed recently for use in various applications, including medical imaging and security scanners. However, there are concerns regarding the possible biological effects of non-ionising electromagnetic radiation in the THz range on cells. Human embryonic stem cells (hESCs) are extremely sensitive to environmental stimuli, and we therefore utilised this cell model to investigate the non-thermal effects of THz irradiation. We studied DNA damage and transcriptome responses in hESCs exposed to narrow-band THz radiation (2.3 THz) under strict temperature control. The transcription of approximately 1% of genes was subtly increased following THz irradiation. Functional annotation enrichment analysis of differentially expressed genes revealed 15 functional classes, which were mostly related to mitochondria. Terahertz irradiation did not induce the formation of γH2AX foci or structural chromosomal aberrations in hESCs. We did not observe any effect on the mitotic index or morphology of the hESCs following THz exposure.

Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies

Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios.

Seminoma and embryonal carcinoma footprints identified by analysis of integrated genome-wide epigenetic and expression profiles of germ cell cancer cell lines

Background

Originating from Primordial Germ Cells/gonocytes and developing via a precursor lesion called Carcinoma In Situ (CIS), Germ Cell Cancers (GCC) are the most common cancer in young men, subdivided in seminoma (SE) and non-seminoma (NS). During physiological germ cell formation/maturation, epigenetic processes guard homeostasis by regulating the accessibility of the DNA to facilitate transcription. Epigenetic deregulation through genetic and environmental parameters (i.e. genvironment) could disrupt embryonic germ cell development, resulting in delayed or blocked maturation. This potentially facilitates the formation of CIS and progression to invasive GCC. Therefore, determining the epigenetic and functional genomic landscape in GCC cell lines could provide insight into the pathophysiology and etiology of GCC and provide guidance for targeted functional experiments.

Results

This study aims at identifying epigenetic footprints in SE and EC cell lines in genome-wide profiles by studying the interaction between gene expression, DNA CpG methylation and histone modifications, and their function in the pathophysiology and etiology of GCC. Two well characterized GCC-derived cell lines were compared, one representative for SE (TCam-2) and the other for EC (NCCIT). Data were acquired using the Illumina HumanHT-12-v4 (gene expression) and HumanMethylation450 BeadChip (methylation) microarrays as well as ChIP-sequencing (activating histone modifications (H3K4me3, H3K27ac)). Results indicate known germ cell markers not only to be differentiating between SE and NS at the expression level, but also in the epigenetic landscape.

Conclusion

The overall similarity between TCam-2/NCCIT support an erased embryonic germ cell arrested in early gonadal development as common cell of origin although the exact developmental stage from which the tumor cells are derived might differ. Indeed, subtle difference in the (integrated) epigenetic and expression profiles indicate TCam-2 to exhibit a more germ cell-like profile, whereas NCCIT shows a more pluripotent phenotype. The results provide insight into the functional genome in GCC cell lines.

Pontin is a critical regulator for AML1-ETO-induced leukemia

The oncogenic fusion protein AML1-ETO, also known as RUNX1-RUNX1T1 is generated by the t(8;21)(q22;q22) translocation, one of the most frequent chromosomal rearrangements in acute myeloid leukemia (AML). Identifying the genes that cooperate with or are required for the oncogenic activity of this chimeric transcription factor remains a major challenge. Our previous studies showed that Drosophila provides a genuine model to study how AML1-ETO promotes leukemia. Here, using an in vivo RNA interference screen for suppressors of AML1-ETO activity, we identified pontin/RUVBL1 as a gene required for AML1-ETO-induced lethality and blood cell proliferation in Drosophila. We further show that PONTIN inhibition strongly impaired the growth of human t(8;21)(+) or AML1-ETO-expressing leukemic blood cells. Interestingly, AML1-ETO promoted the transcription of PONTIN. Moreover, transcriptome analysis in Kasumi-1 cells revealed a strong correlation between PONTIN and AML1-ETO gene signatures and demonstrated that PONTIN chiefly regulated the expression of genes implicated in cell cycle progression. Concordantly, PONTIN depletion inhibited leukemic self-renewal and caused cell cycle arrest. All together our data suggest that the upregulation of PONTIN by AML1-ETO participate in the oncogenic growth of t(8;21) cells.

Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility

BACKGROUND

Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility.

FINDINGS

To support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types.

CONCLUSIONS

The enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies.

Divergent effects of muscarinic receptor subtype gene ablation on murine colon tumorigenesis reveals association of M3R and zinc finger protein 277 expression in colon neoplasia

Background

M3 and M1 subtype muscarinic receptors are co-expressed in normal and neoplastic intestinal epithelial cells. In mice, ablating Chrm3, the gene encoding M3R, robustly attenuates intestinal tumor formation. Here we investigated the effects of Chrm1 gene ablation, alone and in combination with Chrm3 ablation.

Methods

We used wild-type, Chrm1^-/-, Chrm3^-/- and combined Chrm1^-/-/Chrm3^-/- knockout (dual knockout) mice. Animals were treated with azoxymethane, an intestine-selective carcinogen. After 20 weeks, colon tumors were counted and analyzed histologically and by immunohistochemical staining. Tumor gene expression was analyzed using microarray and results validated by RT-PCR. Key findings were extended by analyzing gene and protein expression in human colon cancers and adjacent normal colon tissue.

Results

Azoxymethane-treated Chrm3^-/- mice had fewer and smaller colon tumors than wild-type mice. Reductions in colon tumor number and size were not observed in Chrm1^-/- or dual knockout mice. To gain genetic insight into these divergent phenotypes we used an unbiased microarray approach to compare gene expression in tumors from Chrm3^-/- to those in wild-type mice. We detected altered expression of 430 genes, validated by quantitative RT-PCR for the top 14 up- and 14 down-regulated genes. Comparing expression of this 28-gene subset in tumors from wild-type, Chrm3^-/-, Chrm1^-/- and dual knockout mice revealed significantly reduced expression of Zfp277, encoding zinc finger protein 277, in tissue from M3R-deficient and dual knockout mice, and parallel changes in Zfp277 protein expression. Notably, mRNA and protein for ZNF277, the human analogue of Zfp277, were increased in human colon cancer compared to adjacent normal colon, along with parallel changes in expression of M3R.

Conclusions

Our results identify a novel candidate mouse gene, Zfp277, whose expression pattern is compatible with a role in mediating divergent effects of Chrm3 and Chrm1 gene ablation on murine intestinal neoplasia. The biological importance of this observation is strengthened by finding increased expression of ZNF277 in human colon cancer with a parallel increase in M3R expression. The role of zinc finger protein 277 in colon cancer and its relationship to M3R expression and activation are worthy of further investigation.

Niche-dependent gene expression profile of intratumoral heterogeneous ovarian cancer stem cell populations

Intratumoral heterogeneity challenges existing paradigms for anti-cancer therapy. We have previously demonstrated that the human embryonic stem cells (hESC)-derived cellular microenvironment in immunocompromised mice, enables functional distinction of heterogeneous tumor cells, including cells which do not grow into a tumor in a conventional direct tumor xenograft platform. We have identified and characterized six cancer cell subpopulations each clonally expanded from a single cell, derived from human ovarian clear cell carcinoma of a single tumor, to demonstrate striking intratumoral phenotypic heterogeneity that is dynamically dependent on the tumor growth microenvironment. These cancer cell subpopulations, characterized as cancer stem cell subpopulations, faithfully recapitulate the full spectrum of histological phenotypic heterogeneity known for human ovarian clear cell carcinoma. Each of the six subpopulations displays a different level of morphologic and tumorigenic differentiation wherein growth in the hESC-derived microenvironment favors growth of CD44+/aldehyde dehydrogenase positive pockets of self-renewing cells that sustain tumor growth through a process of tumorigenic differentiation into CD44-/aldehyde dehydrogenase negative derivatives. Strikingly, these derivative cells display microenvironment-dependent plasticity with the capacity to restore self-renewal markers and CD44 expression. In the current study, we delineate the distinct gene expression and epigenetic profiles of two such subpopulations, representing extremes of phenotypic heterogeneity in terms of niche-dependent self-renewal and tumorigenic differentiation. By combining Gene Set Enrichment, Gene Ontology and Pathway-focused array analyses with methylation status, we propose a suite of robust differences in tumor self-renewal and differentiation pathways that underlie the striking intratumoral phenotypic heterogeneity which characterize this and other solid tumor malignancies.

Alu elements in ANRIL non-coding RNA at chromosome 9p21 modulate atherogenic cell functions through trans-regulation of gene networks

The chromosome 9p21 (Chr9p21) locus of coronary artery disease has been identified in the first surge of genome-wide association and is the strongest genetic factor of atherosclerosis known today. Chr9p21 encodes the long non-coding RNA (ncRNA) antisense non-coding RNA in the INK4 locus (ANRIL). ANRIL expression is associated with the Chr9p21 genotype and correlated with atherosclerosis severity. Here, we report on the molecular mechanisms through which ANRIL regulates target-genes in trans, leading to increased cell proliferation, increased cell adhesion and decreased apoptosis, which are all essential mechanisms of atherogenesis. Importantly, trans-regulation was dependent on Alu motifs, which marked the promoters of ANRIL target genes and were mirrored in ANRIL RNA transcripts. ANRIL bound Polycomb group proteins that were highly enriched in the proximity of Alu motifs across the genome and were recruited to promoters of target genes upon ANRIL over-expression. The functional relevance of Alu motifs in ANRIL was confirmed by deletion and mutagenesis, reversing trans-regulation and atherogenic cell functions. ANRIL-regulated networks were confirmed in 2280 individuals with and without coronary artery disease and functionally validated in primary cells from patients carrying the Chr9p21 risk allele. Our study provides a molecular mechanism for pro-atherogenic effects of ANRIL at Chr9p21 and suggests a novel role for Alu elements in epigenetic gene regulation by long ncRNAs.

Chromosome 9p21 is the strongest genetic factor for coronary artery disease and encodes the long non-coding RNA (ncRNA) ANRIL. Here, we show that increased ANRIL expression mediates atherosclerosis risk through trans-regulation of gene networks leading to pro-atherogenic cellular properties, such as increased proliferation and adhesion. ANRIL may act as a scaffold, guiding effector-proteins to chromatin. These functions depend on an Alu motif present in ANRIL RNA and mirrored several thousand-fold in the genome. Alu elements are a family of primate-specific short interspersed repeat elements (SINEs) and have been linked with genetic disease. Current models propose that either exonisation of Alu elements or changes of cis-regulation of adjacent genes are the underlying disease mechanisms. Our work extends the function of Alu transposons to regulatory components of long ncRNAs with a central role in epigenetic trans-regulation. Furthermore, it implies a pivotal role for Alu elements in genetically determined vascular disease and describes a plausible molecular mechanism for a pro-atherogenic function of ANRIL at chromosome 9p21.

Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments

Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.

Effect of normalization on statistical and biological interpretation of gene expression profiles

An under-appreciated aspect of the genetic analysis of gene expression is the impact of post-probe level normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well Being study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of variants that affect transcript abundance (eSNPs). The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by interquartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it is shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies.

Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation-if not adequately minimized by effective normalization-may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes.

The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis

Background

Quantile and rank normalizations are two widely used pre-processing techniques designed to remove technological noise presented in genomic data. Subsequent statistical analysis such as gene differential expression analysis is usually based on normalized expressions. In this study, we find that these normalization procedures can have a profound impact on differential expression analysis, especially in terms of testing power.

Results

We conduct theoretical derivations to show that the testing power of differential expression analysis based on quantile or rank normalized gene expressions can never reach 100% with fixed sample size no matter how strong the gene differentiation effects are. We perform extensive simulation analyses and find the results corroborate theoretical predictions.

Conclusions

Our finding may explain why genes with well documented strong differentiation are not always detected in microarray analysis. It provides new insights in microarray experimental design and will help practitioners in selecting proper normalization procedures.

Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium

Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.

Gene expression signal intensities were similar after applying the log₂ or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33–48% of the variance), the RNA amplification batch (12–24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2–3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1–2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.

In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies

BACKGROUND

The advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).

METHODS

We evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.

RESULTS

Both biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.

CONCLUSIONS

Biomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness.

Hybrid-controlled neurofuzzy networks analysis resulting in genetic regulatory networks reconstruction

Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.