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Background: Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment., Methods: Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques., Results: The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK., Conclusions: Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring., (Copyright © 2013 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes.Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets.Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression.Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD.Trial Registration: The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960. [ABSTRACT FROM AUTHOR]

Background: Identifying non-invasive and reliable blood-derived biomarkers for early detection of acute cellular rejection in heart transplant recipients is of great importance in clinical practice. MicroRNAs are small molecules found to be stable in serum and their expression patterns reflect both physiological and underlying pathological conditions in human. Methods: We compared a group of heart transplant recipients with histologically-verified acute cellular rejection (ACR, n = 26) with a control group of heart transplant recipients without allograft rejection (NR, n = 37) by assessing the levels of a select set of microRNAs in serum specimens. Results: The levels of seven microRNAs, miR-142-3p, miR-101-3p, miR-424-5p, miR-27a-3p, miR-144-3p, miR-339-3p and miR-326 were significantly higher in ACR group compared to the control group and could discriminate between patients with and without allograft rejection. MiR-142-3p and miR-101-3p had the best diagnostic test performance among the microRNAs tested. Serum levels of miR-142-3p and miR-101-3p were independent of calcineurin inhibitor levels, as measured by tacrolimus and cyclosporin; kidney function, as measured by creatinine level, and general inflammation state, as measured by CRP level. Conclusion: This study demonstrated two microRNAs, miR-142-3p and miR-101-3p, that could be relevant as non-invasive diagnostic tools for identifying heart transplant patients with acute cellular rejection. [ABSTRACT FROM AUTHOR]

Background: Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. Results: We present 'Enumerateblood', a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data. Conclusions: 'Enumerateblood' significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website. [ABSTRACT FROM AUTHOR]

Background: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. Results: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. Conclusions: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues. [ABSTRACT FROM AUTHOR]

Introduction: Smoking is the number one modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD). Clinical, epidemiological and increasingly "omics" studies assess or adjust for current smoking status using only self-report, which may be inaccurate. Objective measures such as exhaled carbon monoxide (eCO) may also be problematic owing to limitations in the measurements and the relatively short half life of the molecule. In this study, we determined the impact of different case definitions of current cigarette smoking on gene expression in peripheral blood of patients with COPD.Methods: Peripheral blood gene expression from 573 former- and current-smokers with COPD in the ECLIPSE study was used to find genes whose expression was associated with smoking status. Current smoking was defined using self-report, eCO concentrations, or both. Linear regression was used to determine the association of current smoking status with gene expression adjusting for age, sex and propensity score. Pathway enrichment analyses were performed on genes with P < .001.Result: Using self-report or eCO, only two genes were differentially expressed between current and ex-smokers, with no enrichment in biological processes. When current smoking was defined using both eCO and self-report, four genes were differentially expressed (LRRN3, PID1, FUCA1, GPR15) with enrichment in 40 biological pathways related to metabolic processes, response to hypoxia and hormonal stimulus. Additionally, the combined definition provided better distributions of test statistics for differential gene expression.Conclusion: A combined phenotype of eCO and self report allows for better discovery of genes and pathways related to current smoking.Implications: Studies relying only on self report of smoking status to assess or adjust for the impact of smoking may not fully capture its effect and will lead to residual confounding of results. [ABSTRACT FROM AUTHOR]

Background: COPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown. Objective: Identify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington’s disease, Parkinson’s disease and acute myeloid leukemia gene signatures. Conclusion: The blood gene signature of statins’ use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology. [ABSTRACT FROM AUTHOR]

Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay. [ABSTRACT FROM AUTHOR]

Background: The molecular profile of circulating blood can reflect physiological and pathological events occurring in other tissues and organs of the body and delivers a comprehensive view of the status of the immune system. Blood has been useful in studying the pathobiology of many diseases. It is accessible and easily collected making it ideally suited to the development of diagnostic biomarker tests. The blood transcriptome has a high complement of globin RNA that could potentially saturate next-generation sequencing platforms, masking lower abundance transcripts. Methods to deplete globin mRNA are available, but their effect has not been comprehensively studied in peripheral whole blood RNA-Seq data. In this study we aimed to assess technical variability associated with globin depletion in addition to assessing general technical variability in RNA-Seq from whole blood derived samples. Results: We compared technical and biological replicates having undergone globin depletion or not and found that the experimental globin depletion protocol employed removed approximately 80% of globin transcripts, improved the correlation of technical replicates, allowed for reliable detection of thousands of additional transcripts and generally increased transcript abundance measures. Differential expression analysis revealed thousands of genes significantly up-regulated as a result of globin depletion. In addition, globin depletion resulted in the down-regulation of genes involved in both iron and zinc metal ion bonding. Conclusions: Globin depletion appears to meaningfully improve the quality of peripheral whole blood RNA-Seq data, and may improve our ability to detect true biological variation. Some concerns remain, however. Key amongst them the significant reduction in RNA yields following globin depletion. More generally, our investigation of technical and biological variation with and without globin depletion finds that high-throughput sequencing by RNA-Seq is highly reproducible within a large dynamic range of detection and provides an accurate estimation of RNA concentration in peripheral whole blood. High-throughput sequencing is thus a promising technology for whole blood transcriptomics and biomarker discovery. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression. Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD. Trial registration The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960 Electronic supplementary material The online version of this article (doi:10.1186/s12931-017-0558-1) contains supplementary material, which is available to authorized users.

Background: Non-small cell lung cancer (NSCLC) presents as a progressive disease spanning precancerous, preinvasive, locally invasive, and metastatic lesions. Identification of biological pathways reflective of these progressive stages, and aberrantly expressed genes associated with these pathways, would conceivably enhance therapeutic approaches to this devastating disease. Methodology/Principal Findings: Through the construction and analysis of SAGE libraries, we have determined transcriptome profiles for preinvasive carcinoma-in-situ (CIS) and invasive squamous cell carcinoma (SCC) of the lung, and compared these with expression profiles generated from both bronchial epithelium, and precancerous metaplastic and dysplastic lesions using Ingenuity Pathway Analysis. Expression of genes associated with epidermal development, and loss of expression of genes associated with mucociliary biology, are predominant features of CIS, largely shared with precancerous lesions. Additionally, expression of genes associated with xenobiotic metabolism/detoxification is a notable feature of CIS, and is largely maintained in invasive cancer. Genes related to tissue fibrosis and acute phase immune response are characteristic of the invasive SCC phenotype. Moreover, the data presented here suggests that tissue remodeling/fibrosis is initiated at the early stages of CIS. Additionally, this study indicates that alteration in copy-number status represents a plausible mechanism for differential gene expression in CIS and invasive SCC. Conclusions/Significance: This study is the first report of large-scale expression profiling of CIS of the lung. Unbiased expression profiling of these preinvasive and invasive lesions provides a platform for further investigations into the molecular genetic events relevant to early stages of squamous NSCLC development. Additionally, up-regulated genes detected at extreme differences between CIS and invasive cancer may have potential to serve as biomarkers for early detection. [ABSTRACT FROM AUTHOR]

Background: An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer) may not be suitable in another (e.g. breast cancer). Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate. Methods: Serial analysis of gene expression (SAGE) profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR), and their impact on differential expression analysis of microarray data was evaluated. Results: We show that (i) conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii) reference genes identified for lung cancer do not perform well for other cancer types (breast and brain), (iii) reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv) normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung cancer exhibit higher statistical significance using a dataset normalized with our reference genes relative to normalization without using our reference genes. Conclusions: Our analyses found NDUFA1, RPL19, RAB5C, and RPS18 to occupy the top ranking positions among 15 suitable reference genes optimal for normalization of lung tissue expression data. Significantly, the approach used in this study can be applied to data generated using new generation sequencing platforms for the identification of reference genes optimal within diverse contexts. [ABSTRACT FROM AUTHOR]

Background: Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms. Results: To address this issue, we have developed an integrative genomic analysis platform MDSeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations. Conclusion: MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the most common cause of cancer-related deaths. Tobacco smoke exposure is the strongest aetiological factor associated with lung cancer. In this study, using serial analysis of gene expression (SAGE), we comprehensively examined the effect of active smoking by comparing the transcriptomes of clinical specimens obtained from current, former and never smokers, and identified genes showing both reversible and irreversible expression changes upon smoking cessation. Results: Twenty-four SAGE profiles of the bronchial epithelium of eight current, twelve former and four never smokers were generated and analyzed. In total, 3,111,471 SAGE tags representing over 110 thousand potentially unique transcripts were generated, comprising the largest human SAGE study to date. We identified 1,733 constitutively expressed genes in current, former and never smoker transcriptomes. We have also identified both reversible and irreversible gene expression changes upon cessation of smoking; reversible changes were frequently associated with either xenobiotic metabolism, nucleotide metabolism or mucus secretion. Increased expression of TFF3, CABYR, and ENTPD8 were found to be reversible upon smoking cessation. Expression of GSK3B, which regulates COX2 expression, was irreversibly decreased. MUC5AC expression was only partially reversed. Validation of select genes was performed using quantitative RT-PCR on a secondary cohort of nine current smokers, seven former smokers and six never smokers. Conclusion: Expression levels of some of the genes related to tobacco smoking return to levels similar to never smokers upon cessation of smoking, while expression of others appears to be permanently altered despite prolonged smoking cessation. These irreversible changes may account for the persistent lung cancer risk despite smoking cessation. [ABSTRACT FROM AUTHOR]

Serial Analysis of Gene Expression (SAGE) has proven to be an important alternative to microarray techniques for global profiling of mRNA populations. We have developed preprocessing methodologies to address problems in analyzing SAGE data due to noise caused by sequencing error, normalization methodologies to account for libraries sampled at different depths, and missing tag imputation methodologies to aid in the analysis of poorly sampled SAGE libraries. We have also used subspace selection using the Wilcoxon rank sum test to exclude tags that have similar expression levels regardless of source. Using these methodologies we have clustered, using the OPTICS algorithm, 88 SAGE libraries derived from cancerous and normal tissues as well as cell line material. Our results produced eight dense clusters representing ovarian cancer cell line, brain cancer cell line, brain cancer bulk tissue, prostate tissue, pancreatic cancer, breast cancer cell line, normal brain, and normal breast bulk tissue. The ovarian cancer and brain cancer cell lines clustered closely together, leading to a further investigation on possible associations between these two cancer types. We also investigated the utility of gene expression data in the classification between normal and cancerous tissues. Our results indicate that brain and breast cancer libraries have strong identities allowing robust discrimination from their normal counterparts. However, the SAGE expression data provide poor predictive accuracy in discriminating between prostate and ovarian cancers and their respective normal tissues. [ABSTRACT FROM AUTHOR]

Background: Significant progress has been made in cardiac transplantation over the past 30 years; however, the means for detection of acute cardiac allograft rejection remains in need of improvement. At present, the endomyocardial biopsy, an invasive and inconvenient procedure for patients, is required for the surveillance and diagnosis of acute cardiac allograft rejection. In the Biomarkers in Transplantation initiative, we investigated gene expression profiles in peripheral blood of cardiac transplant subjects as potential biomarkers for diagnosis of allograft rejection. Methods: Whole blood samples were obtained from 28 cardiac transplant subjects who consented to the study. Serial samples were collected from pre-transplant through 3 years post-transplant according to the standard protocol. Temporally correspondent biopsies were also collected, reviewed in a blinded manner, and graded according to current ISHLT guidelines. Blood samples were analyzed using Affymetrix microarrays. Genomic profiles were compared in subjects with acute rejection (AR; ISHLT Grade ≥2R) and no rejection (NR; Grade 0R). Biomarker panel genes were identified using linear discriminant analysis. Results: We found 1,295 differentially expressed probe-sets between AR and NR samples and developed a 12-gene biomarker panel that classifies our internal validation samples with 83% sensitivity and 100% specificity. Conclusions: Based on our current results, we believe whole blood genomic biomarkers hold great potential in the diagnosis of acute cardiac allograft rejection. A prospective, Canada-wide trial will be conducted shortly to further evaluate the classifier panel in diverse patients and a range of clinical programs. [Copyright &y& Elsevier]