Cross platform microarray analysis for robust identification of differentially expressed genes

Impacted spike frequency adaptation associated with reduction of KCNQ2/3 exacerbates seizure activity in temporal lobe epilepsy

Numerous epilepsy-related genes have been identified in recent decades by unbiased genome-wide screens. However, the available druggable targets for temporal lobe epilepsy (TLE) remain limited. Furthermore, a substantial pool of candidate genes potentially applicable to TLE therapy awaits further validation. In this study, we reveal the significant role of KCNQ2 and KCNQ3, two M-type potassium channel genes, in the onset of seizures in TLE. Our investigation began with a quantitative analysis of two publicly available TLE patient databases to establish a correlation between seizure onset and the downregulated expression of KCNQ2/3. We then replicated these pathological changes in a pilocarpine seizure mouse model and observed a decrease in spike frequency adaptation due to the affected M-currents in dentate gyrus granule neurons. In addition, we performed a small-scale simulation of the dentate gyrus network and confirmed that the impaired spike frequency adaptation of granule cells facilitated epileptiform activity throughout the network. This, in turn, resulted in prolonged seizure duration and reduced interictal intervals. Our findings shed light on an underlying mechanism contributing to ictogenesis in the TLE hippocampus and suggest a promising target for the development of antiepileptic drugs.

Gene Expression Measurement Module (GEMM) for space application: Design and validation

In order to facilitate studies on the impact of the space environment on biological systems, we have developed a prototype of GEMM (Gene Expression Measurement Module) - an automated, miniaturized, integrated fluidic system for in-situ measurements of gene expression in microbial samples. The GEMM instrument is capable of (1) lysing bacterial cell walls, (2) extracting and purifying RNA released from cells, (3) hybridizing the RNA to probes attached to a microarray and (4) providing electrochemical readout, all in a microfluidics cartridge. To function on small, uncrewed spacecraft, the conventional, laboratory protocols for both sample preparation and hybridization required significant modifications. Biological validation of the instrument was carried out on Synechococcus elongatus, a photosynthetic cyanobacterium known for its metabolic diversity and resilience to adverse conditions. It was demonstrated that GEMM yielded reliable, reproducible gene expression profiles. GEMM is the only high throughput instrument that can be deployed in near future on space platforms other than the ISS to advance biological research in space. It can also prove useful for numerous terrestrial applications in the field.

A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-small Cell Lung Cancer

PURPOSE

This study aims to develop a multi-gene assay predictive of the clinical benefits of chemotherapy in non-small cell lung cancer (NSCLC) patients, and substantiate their protein expression as potential therapeutic targets.

PATIENTS AND METHODS

The mRNA expression of 160 genes identified from microarray was analyzed in qRT-PCR assays of independent 337 snap-frozen NSCLC tumors to develop a predictive signature. A clinical trial JBR.10 was included in the validation. Hazard ratio was used to select genes, and decision-trees were used to construct the predictive model. Protein expression was quantified with AQUA in 500 FFPE NSCLC samples.

RESULTS

A 7-gene signature was identified from training cohort (n = 83) with accurate patient stratification (P = 0.0043) and was validated in independent patient cohorts (n = 248, P < 0.0001) in Kaplan-Meier analyses. In the predicted benefit group, there was a significantly better disease-specific survival in patients receiving adjuvant chemotherapy in both training (P = 0.035) and validation (P = 0.0049) sets. In the predicted non-benefit group, there was no survival benefit in patients receiving chemotherapy in either set. The protein expression of ZNF71 quantified with AQUA scores produced robust patient stratification in separate training (P = 0.021) and validation (P = 0.047) NSCLC cohorts. The protein expression of CD27 quantified with ELISA had a strong correlation with its mRNA expression in NSCLC tumors (Spearman coefficient = 0.494, P < 0.0088). Multiple signature genes had concordant DNA copy number variation, mRNA and protein expression in NSCLC progression.

CONCLUSIONS

This study presents a predictive multi-gene assay and prognostic protein biomarkers clinically applicable for improving NSCLC treatment, with important implications in lung cancer chemotherapy and immunotherapy.

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

Gene Expression Analysis in Rat Pancreas Observed with Whole-Transcript Exon Array after Ventromedial Hypothalamic Lesions

BACKGROUND

It was reported previously that using Affymetrix Rat Genome 230 2.0, one of a class of standard 3' based arrays, ventromedial hypothalamic (VMH) lesions affected the expressions of cell proliferation-related genes, neuron-related genes, and metabolism-related genes. The released Affymetrix Rat Gene 1.0 ST array has 2 major differences compared with standard 3' based arrays, including Rat Genome 230 2.0: it interrogates the entire mRNA transcript and uses DNA targets.

PURPOSE

This study is aimed at assessing the impact of these differences on the array performance.

METHODS

The study used Rat Gene 1.0 ST array, one of a class of whole-transcript rat exon arrays to examine the cellular mechanisms of gene regulation in the rat pancreas after VMH lesions.

RESULTS

Although the results showed that VMH lesions regulated genes involved in enzymes, metabolism, transport, binding differentiation, migration, morphology, apoptosis, neuron and immunity, the probes identified by these 2 arrays were remarkably different.

CONCLUSION

This study also confirmed that VMH lesions may affect the expression of many functional genes in rat pancreas.

Mining potential biomarkers associated with space flight in Caenorhabditis elegans experienced Shenzhou-8 mission with multiple feature selection techniques

To identify the potential biomarkers associated with space flight, a combined algorithm, which integrates the feature selection techniques, was used to deal with the microarray datasets of Caenorhabditis elegans obtained in the Shenzhou-8 mission. Compared with the ground control treatment, a total of 86 differentially expressed (DE) genes in responses to space synthetic environment or space radiation environment were identified by two filter methods. And then the top 30 ranking genes were selected by the random forest algorithm. Gene Ontology annotation and functional enrichment analyses showed that these genes were mainly associated with metabolism process. Furthermore, clustering analysis showed that 17 genes among these are positive, including 9 for space synthetic environment and 8 for space radiation environment only. These genes could be used as the biomarkers to reflect the space environment stresses. In addition, we also found that microgravity is the main stress factor to change the expression patterns of biomarkers for the short-duration spaceflight.

Gene bionetworks involved in the epigenetic transgenerational inheritance of altered mate preference: environmental epigenetics and evolutionary biology

BACKGROUND

Mate preference behavior is an essential first step in sexual selection and is a critical determinant in evolutionary biology. Previously an environmental compound (the fungicide vinclozolin) was found to promote the epigenetic transgenerational inheritance of an altered sperm epigenome and modified mate preference characteristics for three generations after exposure of a gestating female.

RESULTS

The current study investigated gene networks involved in various regions of the brain that correlated with the altered mate preference behavior in the male and female. Statistically significant correlations of gene clusters and modules were identified to associate with specific mate preference behaviors. This novel systems biology approach identified gene networks (bionetworks) involved in sex-specific mate preference behavior. Observations demonstrate the ability of environmental factors to promote the epigenetic transgenerational inheritance of this altered evolutionary biology determinant.

CONCLUSIONS

Combined observations elucidate the potential molecular control of mate preference behavior and suggests environmental epigenetics can have a role in evolutionary biology.

Discrete signal transduction pathway utilization by a neuropeptide (PACAP) and a cytokine (TNF-alpha) first messenger in chromaffin cells, inferred from coupled transcriptome-promoter analysis of regulated gene cohorts

Cultured bovine adrenal chromaffin cells (BCCs) are employed to study first messenger-specific signaling by cytokines and neurotransmitters occurring in the adrenal medulla following immune-related stress responses. Here, we show that the cytokine TNF-alpha, and the neuropeptide transmitter PACAP, acting through the TNFR2 and PAC1 receptors, activate distinct signaling pathways, with correspondingly distinct transcriptomic signatures in chromaffin cells. We have carried out a comprehensive integrated transcriptome analysis of TNF-alpha and PACAP gene regulation in BCCs using two microarray platforms to maximize transcript identification. Microarray data were validated using qRT-PCR. More than 90% of the transcripts up-regulated either by TNF-alpha or PACAP were specific to a single first messenger. The final list of transcripts induced by each first messenger was subjected to multiple algorithms to identify promoter/enhancer response elements for trans-acting factors whose activation could account for gene expression by either TNF-alpha or PACAP. Distinct groups of transcription factors potentially controlling the expression of TNF-alpha or PACAP-responsive genes were found: most of the genes up-regulated by TNF-alpha contained transcription factor binding sites for members of the Rel transcription factor family, suggesting TNF-alpha-TNFR2 signaling occurs mainly through the NF-KB signaling pathway. Surprisingly, EGR1 was predicted to be the primary transcription factor controlling PACAP-modulated genes, suggesting PACAP signaling to the nucleus occurs predominantly through ERK, rather than CREB activation. Comparison of TNFR2-dependent versus TNFR1-dependent gene induction, and EGR1-mediated transcriptional activation, may provide a pharmacological avenue to the unique pathways activated by the first messengers TNF-alpha and PACAP in neuronal and endocrine cells.

Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

Toxicogenomic approaches in developmental toxicology testing

The emergence of toxicogenomic applications provides new tools to characterize, classify, and potentially predict teratogens. However, due to the vast number of experimental and statistical procedural steps, toxicogenomic studies are challenging. Here, we guide researchers through the basic framework of conducting toxicogenomic investigations in the field of developmental toxicology, providing examples of biological and technical factors that may influence response and interpretation. Furthermore, we review current, diverse applications of toxicogenomic-based approaches in teratology testing, including exposure-response characterization (dose and duration), chemical classification studies, and cross-model comparisons study designs. This review is intended to guide scientists through the challenging and complex structure of conducting toxicogenomic analyses, while considering the many applications of using toxicogenomics in study designs and the future of these types of "omics" approaches in developmental toxicology.

Where statistics and molecular microarray experiments biology meet

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microarray experiments from their construction and the biological principles the experiments rely on, to data acquisition and analysis. The role of epidemiological approaches and sample size considerations are also discussed.

Reliable and sample saving gene expression analysis approach for diagnostic tool development

This work answers the question of whether it is necessary to hybridize individual instead of pooled RNA samples on microarrays for screening gene targets suitable as diagnostic tools for radiation exposure scenarios, while at the same time meeting comparable microarray quality criteria. For developing new clinical diagnostic tools, a two-stage study design was employed in five projects. At first, pooled and not individual RNA samples were hybridized on microarrays for screening purposes. Potential gene candidates were selected based on their fold-change only. This was followed by a validation/quantification step using individual RNA samples and quantitative RT-PCR. Quality criteria from the screening approach with pooled RNA samples were compared with published data from the MicroArray Quality Control (MAQC) consortium that hybridized each reference RNA sample separately and established quality criteria for microarrays. When comparing both approaches, only insignificant differences for quality criteria such as false positives, sensitivity, specificity, and overall agreement were found. However, material, costs, and time were drastically reduced when hybridizing pooled RNA and gene targets applicable for clinical diagnostic purposes could be successfully selected. In search of new diagnostic tools for radiation exposure scenarios, the two stage study design using either pooled or individual RNA samples on microarrays shows comparable quality criteria, but the RNA pooling approach saves unique material, costs, and efforts and successfully selects gene targets that can be used for the desired diagnostic purposes.

Transcriptomic analysis comparing tumor-associated neutrophils with granulocytic myeloid-derived suppressor cells and normal neutrophils

The role of myeloid cells in supporting cancer growth is well established. Most work has focused on myeloid-derived suppressor cells (MDSC) that accumulate in tumor-bearing animals, but tumor-associated neutrophils (TAN) are also known to be capable of augmenting tumor growth. However, little is known about their evolution, phenotype, and relationship to naïve neutrophils (NN) and to the granulocytic fraction of MDSC (G-MDSC).

In the current study, a transcriptomics approach was used in mice to compare these cell types. Our data show that the three populations of neutrophils are significantly different in their mRNA profiles with NN and G-MDSC being more closely related to each other than to TAN. Structural genes and genes related to cell-cytotoxicity (i.e. respiratory burst) were significantly down-regulated in TAN. In contrast, many immune-related genes and pathways, including genes related to the antigen presenting complex (e.g. all six MHC-II complex genes), and cytokines (e.g. TNF-α, IL-1-α/β), were up-regulated in G-MDSC, and further up-regulated in TAN. Thirteen of the 25 chemokines tested were markedly up-regulated in TAN compared to NN, including striking up-regulation of chemoattractants for T/B-cells, neutrophils and macrophages.

This study characterizes different populations of neutrophils related to cancer, pointing out the major differences between TAN and the other neutrophil populations.

A review of toxicogenomic approaches in developmental toxicology

Over the past decade, the use of gene expression profiling (i.e., toxicogenomics or transcriptomics) has been established as the vanguard "omics" technology to investigate exposure-induced molecular changes that underlie the development of disease. As this technology quickly advances, researchers are striving to keep pace in grasping the complexity of toxicogenomic response while at the same time determine its applicability for the field of developmental toxicology. Initial studies suggest toxicogenomics to be a promising tool for multiple types of study designs, including exposure-response investigations (dose and duration), chemical classification, and model comparisons. In this review, we examine the use of toxicogenomics in developmental toxicology, discussing biological and technical factors that influence response and interpretation. Additionally, we provide a framework to guide toxicogenomic investigations in the field of developmental toxicology.

Transcriptomic response of murine liver to severe injury and hemorrhagic shock: a dual-platform microarray analysis

Trauma-hemorrhagic shock (HS/T) is a complex process that elicits numerous molecular pathways. We hypothesized that a dual-platform microarray analysis of the liver, an organ that integrates immunology and metabolism, would reveal key pathways engaged following HS/T. C57BL/6 mice were divided into five groups (n = 4/group), anesthetized, and surgically treated to simulate a time course and trauma severity model: 1) nonmanipulated animals, 2) minor trauma, 3) 1.5 h of hemorrhagic shock and severe trauma (HS/T), 4) 1.5 h HS/T followed by 1 h resuscitation (HS/T+1.0R), 5) 1.5 h HS/T followed by 4.5 h resuscitation (HS/T+4.5R). Liver RNA was hybridized to CodeLink and Affymetrix mouse whole genome microarray chips. Common genes with a cross-platform correlation >0.6 (2,353 genes in total) were clustered using k-means clustering, and clusters were analyzed using Ingenuity Pathways Analysis. Genes involved in the stress response and immunoregulation were upregulated early and remained upregulated throughout the course of the experiment. Genes involved in cell death and inflammatory pathways were upregulated in a linear fashion with elapsed time and in severe injury compared with minor trauma. Three of the six clusters contained genes involved in metabolic function; these were downregulated with elapsed time. Transcripts involved in amino acid metabolism as well as signaling pathways associated with glucocorticoid receptors, IL-6, IL-10, and the acute phase response were elevated in a severity-dependent manner. This is the first study to examine the postinjury response using dual-platform microarray analysis, revealing responses that may enable novel therapies or diagnostics.

Gene expression profile during chondrogenesis in human bone marrow derived mesenchymal stem cells using a cDNA microarray

Mesenchymal stem cells (MSCs) have the capacity to proliferate and differentiate into multiple connective tissue lineages, which include cartilage, bone, and fat. Cartilage differentiation and chondrocyte maturation are required for normal skeletal development, but the intracellular pathways regulating this process remain largely unclear. This study was designed to identify novel genes that might help clarify the molecular mechanisms of chondrogenesis. Chondrogenesis was induced by culturing human bone marrow (BM) derived MSCs in micromass pellets in the presence of defined medium for 3, 7, 14 or 21 days. Several genes regulated during chondrogenesis were then identified by reverse transcriptase-polymerase chain reaction (RT-PCR). Using an ABI microarray system, we determined the differential gene expression profiles of differentiated chondrocytes and BM-MSCs. Normalization of this data resulted in the identification of 1,486 differentially expressed genes. To verify gene expression profiles determined by microarray analysis, the expression levels of 10 genes with high fold changes were confirmed by RT-PCR. Gene expression patterns of 9 genes (Hrad6B, annexinA2, BMP-7, contactin-1, peroxiredoxin-1, heat shock transcription factor-2, synaptotagmin IV, serotonin receptor-7, Axl) in RT-PCR were similar to the microarray gene expression patterns. These findings provide novel information concerning genes involved in the chondrogenesis of human BM-MSCs.

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

Background

Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results

By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions

We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Distinctions in gastric cancer gene expression signatures derived from laser capture microdissection versus histologic macrodissection

Background

Gastric cancer samples obtained by histologic macrodissection contain a relatively high stromal content that may significantly influence gene expression profiles. Differences between the gene expression signature derived from macrodissected gastric cancer samples and the signature obtained from isolated gastric cancer epithelial cells from the same biopsies using laser-capture microdissection (LCM) were evaluated for their potential experimental biases.

Methods

RNA was isolated from frozen tissue samples of gastric cancer biopsies from 20 patients using both histologic macrodissection and LCM techniques. RNA from LCM was subject to an additional round of T7 RNA amplification. Expression profiling was performed using Affymetrix HG-U133A arrays. Genes identified in the expression signatures from each tissue processing method were compared to the set of genes contained within chromosomal regions found to harbor copy number aberrations in the tumor samples by array CGH and to proteins previously identified as being overexpressed in gastric cancer.

Results

Genes shown to have increased copy number in gastric cancer were also found to be overexpressed in samples obtained by macrodissection (LS P value < 10^-5), but not in array data generated using microdissection. A set of 58 previously identified genes overexpressed in gastric cancer was also enriched in the gene signature identified by macrodissection (LS P < 10^-5), but not in the signature identified by microdissection (LS P = 0.013). In contrast, 66 genes previously reported to be underexpressed in gastric cancer were enriched in the gene signature identified by microdissection (LS P < 10^-5), but not in the signature identified by macrodissection (LS P = 0.89).

Conclusions

The tumor sampling technique biases the microarray results. LCM may be a more sensitive collection and processing method for the identification of potential tumor suppressor gene candidates in gastric cancer using expression profiling.

Early viral and peripheral blood mononuclear cell responses to pegylated interferon and ribavirin treatment: the first 24 h

OBJECTIVES

This study explored gene expression differences in predicting response to pegylated interferon (IFN-PEG) and ribavirin (RBV) in hepatitis C infection. Current treatment for hepatitis C virus (HCV) with IFN-PEG alpha-2a/b and RBV is an expensive regimen with frequent significant side-effects where less than 60% of patients ultimately achieve a sustained virological response. Responders and nonresponders may not be identified for up to 6 months post-treatment. This dichotomy may be because of differences in the molecular genetic response.

METHODS

Peripheral blood mononuclear cell samples were obtained from a cohort of 31 infected individuals within the first 24 h of treatment and the extracted RNA was hybridized to genome expression microarrays. Hepatitis C viral kinetics was also examined in these patients. The ability of differentially regulated genes to predict response to therapy was assessed with treatment outcome.

RESULTS

Distinct patterns of gene expression distinguished responders from nonresponders to HCV treatment. The ultimate response to treatment with IFN-PEG and RBV was observed within the first 24 h of treatment by a greater drop in viral load (mean HCV RNA decline of 1.92+/-1.26 log10 IU/ml) in responders compared with nonresponders (P<0.007). Induced genes achieved maximal response within 12 h of therapy which coincided with a rapid decline in HCV RNA between 12 and 24 h. This study revealed that peripheral blood mononuclear cell metallothionein 2A, CCRL2, tumour necrosis factor-alpha-induced protein 6 (TNFAIP6) and IFN-induced protein with tetratricopeptide repeats 2 expression predicted viral treatment response to therapy verified by quantitative real time polymerase chain reaction.

CONCLUSION

This study has identified a noninvasive gene microarray pattern and a set of verified genes to be predictive of hepatitis C patient response to IFN-PEG and RBV treatment within the first 24 h. The potential of this noninvasive diagnostic approach and identified genes as biomarkers of response to treatment warrants further investigation.

Module-based prediction approach for robust inter-study predictions in microarray data

MOTIVATION

Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering.

RESULTS

K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP.

AVAILABILITY

http://www.biostat.pitt.edu/bioinfo/

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Temporal and functional dynamics of the transcriptome during nerve growth factor-induced differentiation

The rat pheochromocytoma cell line (PC12) is an extensively used model to study neuronal differentiation. The initial signaling cascades triggered by nerve growth factor (NGF) stimulation have been subject to thorough investigation and are well characterized. However, knowledge of temporal transcriptomal regulation during NGF-induced differentiation of PC12 cells remains far from complete. We performed a microarray study that characterized temporal and functional changes of the transcriptome during 4 subsequent days of differentiation of Neuroscreen-1 PC12 cells. By analyzing the transcription profiles of 1595 NGF-regulated genes, we show a large diversity of transcriptional regulation in time. Also, we quantitatively identified 26 out of 243 predefined biological process and 30 out of 255 predefined molecular function classes that are specifically regulated by NGF. Combining the temporal and functional transcriptomal data revealed that NGF selectively exerts a temporally coordinated regulation of genes implicated in protein biosynthesis, intracellular signaling, cell structure, chromatin packaging and remodeling, intracellular protein traffic, mRNA transcription, and cell cycle. We will discuss how NGF-induced changes may modulate the transcriptional response to NGF itself during differentiation.

Comparison study of microarray meta-analysis methods

BACKGROUND

Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.

RESULTS

We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable.

CONCLUSIONS

Existing meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors

Background

Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence.

Methods

We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets.

Results

The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer.

Conclusions

These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

WebArrayDB: cross-platform microarray data analysis and public data repository

MOTIVATION

Cross-platform microarray analysis is an increasingly important research tool, but researchers still lack open source tools for storing, integrating and analyzing large amounts of microarray data obtained from different array platforms.

RESULTS

An open source integrated microarray database and analysis suite, WebArrayDB (http://www.webarraydb.org), has been developed that features convenient uploading of data for storage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion, and allows data to be mined with a large variety of R-based tools, including data analysis across multiple platforms. Different methods for probe alignment, normalization and statistical analysis are included to account for systematic bias. Student's t-test, moderated t-tests, non-parametric tests and analysis of variance or covariance (ANOVA/ANCOVA) are among the choices of algorithms for differential analysis of data. Users also have the flexibility to define new factors and create new analysis models to fit complex experimental designs. All data can be queried or browsed through a web browser. The computations can be performed in parallel on symmetric multiprocessing (SMP) systems or Linux clusters.

AVAILABILITY

The software package is available for the use on a public web server (http://www.webarraydb.org) or can be downloaded. are available at Bioinformatics online.

Platform dependence of inference on gene-wise and gene-set involvement in human lung development

BACKGROUND

With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age.

RESULTS

We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis.

CONCLUSION

We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

Expression of SNURF-SNRPN upstream transcripts and epigenetic regulatory genes during human spermatogenesis

The imprinted domain in human 15q11-q13 is controlled by a bipartite imprinting centre (IC), which overlaps the 5' part of the paternally expressed SNURF-SNRPN gene. We have recently described two novel genes upstream of SNURF-SNRPN (PWRN1 and PWRN2), which are biallelically expressed in the testis. We have now found that PWRN1 represents an alternative 5' part of SNURF-SNRPN, and that its expression in the brain is imprinted. To determine when the locus is activated during spermatogenesis and which factors are involved in this process, we have mined gene-expression data of testicular biopsies from men with different types of spermatogenic failure. Whereas PWRN1-SNURF-SNRPN and PWRN2 are expressed in post-meiotic germ cells only, a hitherto undetected SNURF-SNRPN upstream transcript is expressed already at meiosis. Several epigenetic factors (eg, MBD1 and MBD2 isoforms, MBD3L1, SUVH39H2, BRDT, and EZH2) are upregulated at specific stages of spermatogenesis, suggesting that they play an important role in the epigenetic reprogramming during spermatogenesis.

Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction

MOTIVATION

Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored.

RESULTS

In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.

Mapping complex disease traits with global gene expression

Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues.

Sequential microarray to identify timing of molecular responses to Haemonchus contortus infection in sheep

Anthelmintics are currently the most common method of worm control. The emergence of worms with multiple-drug resistance and issues of residues in the food chain make alternative parasite control measures a priority. To develop improved and sustainable methods for controlling Haemonchus contortus such as genetic selection of resistant sheep, a better understanding of the host-parasite relationship is required. A trial was undertaken using sheep surgically implanted with abomasal fistulas to enable sequential biopsy of the abomasal mucosa during trickle infection with two strains of H. contortus. These were ivermectin-resistant CAVR and ivermectin-sensitive McMaster. From a gross parasitology perspective, this approach enabled the effect of developing immunity to be observed on both the establishment and maturation of two CAVR doses within and between groups. Since the only difference in parasite treatment between the groups was the staggering of the two CAVR doses, microarray results from biopsies taken on the same day in different groups were combined and compared between different biopsy dates to observe differential gene transcription over time. Differential gene transcription was detected by comparing transcription in our array data between different biopsy dates using a low P value screen (P<0.01) and by compiling a list of 82 immunoparasitology-related genes and examining transcription in this list with a higher P value screen (P<0.05). Our microarray data were validated in silico by comparison with intelectin 2, trefoil factor 3, calcium activated chloride channel and mucin 5 from other gene transcription studies and with phenotypic data such as the response by gammadelta T cells and immunoglobulins to H. contortus. The first four genes are involved in non-specific responses to infection and mucosal healing. These were upregulated at the early time points and intelectin 2 remained prominent throughout the trial. As the trial progressed, immunoglobulin genes became strongly upregulated. These included IgCgamma IgG2a heavy chain constant region, IGHE immunoglobulin heavy constant epsilon and IGHM immunoglobulin heavy constant mu.

Confirmation of gene expression-based prediction of survival in non-small cell lung cancer

PURPOSE

It is a critical challenge to determine the risk of recurrence in early stage non-small cell lung cancer (NSCLC) patients. Accurate gene expression signatures are needed to classify patients into high- and low-risk groups to improve the selection of patients for adjuvant therapy.

EXPERIMENTAL DESIGN

Multiple published microarray data sets were used to evaluate our previously identified lung cancer prognostic gene signature. Expression of the signature genes was further validated with real-time reverse transcription-PCR and Western blot assays of snap-frozen lung cancer tumor tissues.

RESULTS

Our previously identified 35-gene signature stratified 264 patients with NSCLC into high- and low-risk groups with distinct overall survival rates (P < 0.05, Kaplan-Meier analysis, log-rank tests). The 35-gene signature further stratified patients with clinical stage 1A diseases into poor prognostic and good prognostic subgroups (P = 0.0007, Kaplan-Meier analysis, log-rank tests). This signature is independent of other prognostic factors for NSCLC, including age, sex, tumor differentiation, tumor grade, and tumor stage. The expression of the signature genes was validated with real-time reverse transcription-PCR analysis of lung cancer tumor specimens. Protein expression of two signature genes, TAL2 and ILF3, was confirmed in lung adenocarcinoma tumors by using Western blot analysis. These two biomarkers showed correlated mRNA and protein overexpression in lung cancer development and progression.

CONCLUSIONS

The results indicate that the identified 35-gene signature is an accurate predictor of survival in NSCLC. It provides independent prognostic information in addition to traditional clinicopathologic criteria.

Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression

PURPOSE

One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed.

EXPERIMENTAL DESIGN

Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59).

RESULTS

After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data.

CONCLUSIONS

We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus

The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

Immunological profile in a family with nephrogenic diabetes insipidus with a novel 11 kb deletion in AVPR2 and ARHGAP4 genes

Background

Congenital nephrogenic diabetes insipidus (NDI) is characterised by an inability to concentrate urine despite normal or elevated plasma levels of the antidiuretic hormone arginine vasopressin. We report a Japanese extended family with NDI caused by an 11.2-kb deletion that includes the entire AVPR2 locus and approximately half of the Rho GTPase-activating protein 4 (ARHGAP4) locus. ARHGAP4 belongs to the RhoGAP family, Rho GTPases are critical regulators of many cellular activities, such as motility and proliferation which enhances intrinsic GTPase activity.

ARHGAP4 is expressed at high levels in hematopoietic cells, and it has been reported that an NDI patient lacking AVPR2 and all of ARHGAP4 showed immunodeficiency characterised by a marked reduction in the number of circulating CD3+ cells and almost complete absence of CD8+ cells.

Methods

PCR and sequencing were performed to identify the deleted region in the Japanese NDI patients. Immunological profiles of the NDI patients were analysed by flow cytometry. We also investigated the gene expression profiles of peripheral blood mononuclear cells (PBMC) from NDI patients and healthy controls in microarray technique.

Results

We evaluated subjects (one child and two adults) with 11.2-kb deletion that includes the entire AVPR2 locus and approximately half of the ARHGAP4. Hematologic tests showed a reduction of CD4+ cells in one adult patient, a reduction in CD8+ cells in the paediatric patient, and a slight reduction in the serum IgG levels in the adult patients, but none of them showed susceptibility to infection. Gene expression profiling of PBMC lacking ARHGAP4 revealed that expression of RhoGAP family genes was not influenced greatly by the lack of ARHGAP4.

Conclusion

These results suggest that loss of ARHGAP4 expression is not compensated for by other family members. ARHGAP4 may play some role in lymphocyte differentiation but partial loss of ARHGAP4 does not result in clinical immunodeficiency.

Functional meta-analysis of double connectivity in gene coexpression networks in mammals

In functional genomics, the high-throughput methods such as microarrays 1) allow analysis of the relationships between genes considering them as elements of a network and 2) lead to biological interpretations thanks to Gene Ontology. But up to now it has not been possible to find relationships between the functions and the connectivity of the genes in coexpression networks. To achieve this aim, we have defined a double connectivity for each gene by the numbers of its significant negative and positive correlations with the other genes within a given biological condition, or group. Here, based on the analysis of 1,260 DNA microarrays, we show that this double connectivity clearly separates two types of genes, those with a predominantly strong negative connectivity, hub- genes, and those with a predominantly strong positive connectivity, hub+ genes. Interestingly, the hub+ genes concerned transcription factors more often than by chance and, similarly, for the hub- genes concerning miRNA predicted targets. Furthermore, a meta-analysis of GO annotations carried out on 67 groups in humans and rats shows that these two types of genes correspond to a functional biological duality. The hub- genes were mainly involved in basic functions common to all eukaryote cells, whereas the hub+ genes were mainly involved in specialized functions related to cell differentiation and communication. The separation and the biological role of these hub- and hub+ genes provide a powerful new tool for a better understanding of the control and regulation of the key genes involved in cellular differentiation and physiopathological conditions.

Genomic analyses of Haemonchus contortus infection in sheep: abomasal fistulation and two Haemonchus strains do not substantially confound host gene expression in microarrays

To determine whether fistulation and differing strains of Haemonchus contortus complicate genome analysis of the host response to infection, two pilot experiments examined parasite development and gene expression in the abomasal mucosa of parasitised sheep. No significant differentially-expressed genes were detected in a comparison between ivermectin-susceptible McMaster and ivermectin-resistant CAVR strains of H. contortus. This demonstrated that the sheep response was not significantly altered by the ivermectin-resistance status of the parasite. However, sheep infected with McMaster strain had a significantly lower proportion of larvae and a higher mean FEC at post-mortem than sheep infected with CAVR, suggesting that McMaster larvae advance to patency faster than CAVR larvae. Abomasal fistulation resulted in significant upregulation of three genes and significant downregulation of two genes. Fistulated sheep had significantly lower FEC than the other groups but the proportion of larvae at post-mortem was not significantly different to other groups infected with the same strain (CAVR). Hence fistulation does not alter establishment of the CAVR isolate, but may slow its progression to patency. The observation that different H. contortus strains and abomasal fistulation induced minimal changes in mucosal gene expression validated the design of a subsequent experiment (manuscript in preparation) where sequential biopsies taken during infection were analysed by microarray to describe the molecular responses which inhibit larval establishment.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

Background

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results

We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion

The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.