Novel reference gene RPN1 for normalization of quantitative data in lung and kidney cancer

Quantitative methods of gene expression analysis in tumors require accurate data normalization, which allows comparison of different mRNA/cDNA samples with unknown concentration. For this purpose reference genes with stable expression level (such as GAPDH, ACTB, HPRT1, TBP) are used. The choice of appropriate reference genes is still actual because well-known reference genes are not suitable for certain cancer types frequently and their unreasonable use without additional tests lead to wrong conclusions. We have developed the bioinformatic approach and selected a new potential reference gene RPN1 for lung and kidney tumors. This gene is located at the long arm of chromosome 3. Our method includes mining of the dbEST and Oncomine databases and functional analysis of genes. The RPN1 was selected from 1500 candidate housekeeping genes. Using comparative genomic hybridization with NotI-microarrays we found no methylation, deletions and/or amplifications at the RPN1-containing locus in 56 non-small cell lung and 42 clear cell renal cancer samples. Using RT-qPCR we showed low variability of RPN1 mRNA level comparable to those of reference genes GAPDH and GUSB in lung and kidney cancer. The mRNA levels of two target genes coding hyalouronidases--HYAL1 and HYAL2--were estimated and normalized relative to pair RPN1--GAPDH genes for lung cancer and RPN1--GUSB for kidney cancer. These combinations were shown to be optimal for obtaining accurate and reproducible data. All obtained results allow us to suggest RPN1 as novel reference gene for quantitative data normalization in gene expression studies for lung and kidney cancers.     

Gene expression in the cell cycle of human T-lymphocytes: II. Experimental determination by DNASER technology

Human lymphocytes gene expression before and after PHA stimulation is monitored by DNASER technology, a novel bioinstrumentation entirely constructed in our laboratories as previously reported. The validity of the DNASER measurements is confirmed by standard fluorescence microscopy equipped with CCD. The human lymphocytes gene expression here experimentally probed using commercially available DNA microarrays such as Human Starter, appears compatible both with independent bioinformatic prediction and with existing experimental data, pointing to MYC as the key gene in the G0-G1 transition induced by PHA in resting lymphocytes. It does not escape our notice that in cell biology and cancer research DNASER technology based on microarray constructed with few leader genes identified from bioinformatics represents a meaningful cost-effective route alternative to massive frequently misleading molecular genomics.     

High-throughput phenotypic characterization of Pseudomonas aeruginosa membrane transport genes

The deluge of data generated by genome sequencing has led to an increasing reliance on bioinformatic predictions, since the traditional experimental approach of characterizing gene function one at a time cannot possibly keep pace with the sequence-based discovery of novel genes. We have utilized Biolog phenotype MicroArrays to identify phenotypes of gene knockout mutants in the opportunistic pathogen and versatile soil bacterium Pseudomonas aeruginosa in a relatively high-throughput fashion. Seventy-eight P. aeruginosa mutants defective in predicted sugar and amino acid membrane transporter genes were screened and clear phenotypes were identified for 27 of these. In all cases, these phenotypes were confirmed by independent growth assays on minimal media. Using qRT-PCR, we demonstrate that the expression levels of 11 of these transporter genes were induced from 4- to 90-fold by their substrates identified via phenotype analysis. Overall, the experimental data showed the bioinformatic predictions to be largely correct in 22 out of 27 cases, and led to the identification of novel transporter genes and a potentially new histamine catabolic pathway. Thus, rapid phenotype identification assays are an invaluable tool for confirming and extending bioinformatic predictions.     

Identification of tumor suppressors and oncogenes from genomic and epigenetic features in ovarian cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.     

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.     

Dietary wheat germ oil influences gene expression in larvae and eggs of the oriental fruit fly

Changes in animal nutrition, particularly essential dietary components, alter global gene expression patterns. Our goal is to identify molecular markers that serve as early indicators of the quality of insect culture media. Markers of deficient culture media will increase the efficiency of developing optimal systems for mass rearing beneficial insects and some pest species because decisions on culture media quality can be made without waiting through one or several life cycles. The objective of our current study is to discover molecular markers of essential dietary lipid deficiency in the oriental fruit fly, Bactrocera dorsalis. We reared groups of fruit flies separately on media either devoid of or supplemented with wheat germ oil (WGO) and analyzed gene expression in third instar larvae and F(1) eggs using 2D electrophoresis. Gel densitometry revealed significant changes in expression levels of genes encoding eight proteins in larvae and 22 proteins in eggs. We identified these proteins by using mass spectrometry (MALDI TOF/TOF) and bioinformatic analyses of the protein sequences. Among these, we identified one gene encoding the receptor of activated C Kinase 1 (RACK1) that increased in expression by 6.8-fold in eggs from adults that were reared as larvae on media supplemented with WGO. RACK1 is an essential component of at least three intracellular signal transduction pathways, making it a good molecular marker candidate of lipid deficiency in fruit flies and possibly many other insect species.     

Functional hierarchical models for identifying genes with different time-course expression profiles

Time-course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. We introduce a functional hierarchical model for detecting temporally differentially expressed (TDE) genes between two experimental conditions for cross-sectional designs, where the gene expression profiles are treated as functional data and modeled by basis function expansions. A Monte Carlo EM algorithm was developed for estimating both the gene-specific parameters and the hyperparameters in the second level of modeling. We use a direct posterior probability approach to bound the rate of false discovery at a pre-specified level and evaluate the methods by simulations and application to microarray time-course gene expression data on Caenorhabditis elegans developmental processes. Simulation results suggested that the procedure performs better than the two-way ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set show clear patterns of changes between the two experimental conditions.     

PlantRGS: a web server for the identification of most suitable candidate reference genes for quantitative gene expression studies in plants

Normalization of quantitative gene expression data with a suitable reference gene is essential for accurate and reliable results. However, the availability and choice of most suitable reference gene(s) showing uniform expression across all the experimental conditions remain a drawback. We have developed a web server, PlantRGS (http://www.nipgr.res.in/PlantRGS), for the identification of most suitable candidate reference gene(s) at the whole-genome level using microarray data for quantitative gene expression studies in plants. Microarray data from more than 11 000 tissue samples for nine plant species have been included in the PlantRGS for meta-analysis. The web server provides a user-friendly graphical user interface-based analysis tool for the identification of most suitable reference genes in the selected plant species under user-defined experimental conditions. Various parameter options and output formats will help users to investigate desired number of most suitable reference genes with wide range of expression levels. Validation of results revealed that novel reference genes identified by the PlantRGS outperforms the traditionally used reference genes in terms of expression stability. We anticipate that the PlantRGS will provide a platform for the identification of most suitable reference gene(s) under given experimental conditions and facilitate quantitative gene expression studies in plants.     

Phenotype prediction using regularized regression on genetic data in the DREAM5 Systems Genetics B Challenge

A major goal of large-scale genomics projects is to enable the use of data from high-throughput experimental methods to predict complex phenotypes such as disease susceptibility. The DREAM5 Systems Genetics B Challenge solicited algorithms to predict soybean plant resistance to the pathogen Phytophthora sojae from training sets including phenotype, genotype, and gene expression data. The challenge test set was divided into three subcategories, one requiring prediction based on only genotype data, another on only gene expression data, and the third on both genotype and gene expression data. Here we present our approach, primarily using regularized regression, which received the best-performer award for subchallenge B2 (gene expression only). We found that despite the availability of 941 genotype markers and 28,395 gene expression features, optimal models determined by cross-validation experiments typically used fewer than ten predictors, underscoring the importance of strong regularization in noisy datasets with far more features than samples. We also present substantial analysis of the training and test setup of the challenge, identifying high variance in performance on the gold standard test sets.     

Dissecting the molecular mechanisms of cancer through bioinformatics-based experimental approaches

Cancer is a disease of aberrant gene expression characterized by inappropriate (temporal or quantitative) expression of positive mediators of cell proliferation in conjunction with diminished expression of negative mediators of cell growth. Alteration of the normal balance of these positive and negative mediators leads to the abnormal growth of cells and tissues that typify neoplastic disease. Development of a better understanding of the genetic and epigenetic mechanisms that induce neoplastic transformation and drive the cancer phenotype is essential for continued progress towards the design of practical molecular diagnostics and effective treatment strategies. Over the past decades, molecular techniques that facilitate the assessment of gene expression, identification of gene mutations, and characterization of chromosome abnormalities (numeric and structural) have been established and applied to cancer research. However, many of these techniques are slow and labor-intensive. More recently, high-throughput technologies have emerged that generate large volumes of data related to the genetics and epigenetics of cancer (or other disorders). These advances in molecular genetic technology required the development of sophisticated bioinformatic tools to manage the large datasets generated. The combination of high-throughput molecular assays and bioinformatic-based data mining strategies has significantly impacted our understanding of the molecular pathogenesis of cancer, classification of tumors, and now the management of cancer patients in the clinic. This article will review basic molecular techniques and bioinformatic-based experimental approaches used to dissect the molecular mechanisms of carcinogenesis.     

AftrRAD: a pipeline for accurate and efficient de novo assembly of RADseq data

An increase in studies using restriction site‐associated DNA sequencing (RADseq) methods has led to a need for both the development and assessment of novel bioinformatic tools that aid in the generation and analysis of these data. Here, we report the availability of AftrRAD, a bioinformatic pipeline that efficiently assembles and genotypes RADseq data, and outputs these data in various formats for downstream analyses. We use simulated and experimental data sets to evaluate AftrRAD's ability to perform accurate de novo assembly of loci, and we compare its performance with two other commonly used programs, stacks and pyrad. We demonstrate that AftrRAD is able to accurately assemble loci, while accounting for indel variation among alleles, in a more computationally efficient manner than currently available programs. AftrRAD run times are not strongly affected by the number of samples in the data set, making this program a useful tool when multicore systems are not available for parallel processing, or when data sets include large numbers of samples.     

Transcriptomics and proteomics. Applications to ecotoxicology

Researchers from Europe and the USA met at the Joint Research Center (JRC) of the European Commission to discuss how to integrate gene and protein expression analyses with bioinformatic tools in the field of ecotoxicology and how this new approach could be translated in improved risk assessment procedures. The measurements of gene and/or protein expression levels, upon exposure to a chemical or a stressor, can be used to develop robust molecular biomarkers that will allow the early detection of environmental stress, study long-term exposure and infer the mechanism of action. These molecular biomarkers should be linked to phenotypic end points of exposure such as adverse effects in growth and reproduction in single organisms and populations. At environmentally realistic exposure levels there could be “non-linear” dose-response curves, which should be accounted for in the experimental design and in the analyses of microarray and proteomic data. The application of gene and protein expression profiling in ecotoxicology will have a significant impact on the ecotoxicology field in the near future and international collaborations will play an important role in accelerating the application of those techniques.     

Mechanisms of colorectal and lung cancer prevention by vegetables: a genomic approach

Colorectal cancer (CRC) and lung cancer (LC) occur at high incidence, and both can be effectively prevented by dietary vegetable consumption. This makes these two types of cancer highly suitable for elucidating the underlying molecular mechanisms of cancer chemoprevention. Numerous studies have shown that vegetables exert their beneficial effects through various different mechanisms, but effects on the genome level remain mostly unclear. This review evaluates current knowledge on the mechanisms of CRC and LC prevention by vegetables, thereby focusing on the modulation of gene and protein expressions. The majority of the effects found in the colon are changes in the expression of genes and proteins involved in apoptosis, cell cycle, cell proliferation and intracellular defense, in favor of reduced CRC risk. Furthermore, vegetables and vegetable components changed the expression of many more genes and proteins involved in other pathways for which biologic meaning is less clear. The number of studies investigating gene and protein expression changes in the lungs is limited to only a few in vitro and animal studies. Data from these studies show that mostly genes involved in biotransformation, apoptosis and cell cycle regulation are affected. In both colon and lungs, genomewide analyses of gene and protein expression changes by new genomics and proteomics technologies, as well as the investigation of whole vegetables, are few in number. Further studies applying these 'omics' approaches are needed to provide more insights on affected genetic/biologic pathways and, thus, in molecular mechanisms by which different chemopreventive compounds can protect against carcinogenesis. Particularly studies with combinations of phytochemicals and whole vegetables are needed to establish gene expression changes in the colon, but especially in the lungs.     

Bayesian infinite mixture model based clustering of gene expression profiles

MOTIVATION: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model. AVAILABILITY: The MS Windows(TM) based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm CONTACT: medvedm@email.uc.edu