A methodology for global validation of microarray experiments

Background  

DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies.     

Virus-based expression systems facilitate rapid target in vivo functionality validation and high-throughput screening

Target validation is one of rate-limiting steps in the modern drug discovery. The authors developed a strategy of combining adenovirus-mediated gene transfer for efficient target functionality validation, both in vivo and in vitro, with baculovirus expression to produce sufficient quantities of protein for high-throughput screening (HTS). The incorporation of green fluorescent protein (GFP) in the adenovirus vectors accelerates recombinant adenovirus plaque purification, whereas the use of epitope and affinity tags facilitates the identification and purification of recombinant protein. In this generalized scheme, the flexible modular design of viral vectors facilitates the transition between target validation and HTS. In the example presented, functional target validation in vivo was achieved by overexpressing the target gene in cell-based models and in the mouse cortex following adenovirus-mediated gene delivery. In this context, target overexpression resulted in the accumulation of a disease-related biomarker both in vitro and in vivo. A baculovirus-based expressional system was then generated to produce enough target protein for HTS. Thus, the use of these viral expression systems represents a generalized method for rapid target functionality validation and HTS assay development, which could be applied to numerous target candidates being elucidated in gene discovery programs.     

基于SVM和平均影响值的人肿瘤信息基因提取

基于基因表达谱的肿瘤分类信息基因选取是发现肿瘤特异表达基因、探索肿瘤基因表达模式的重要手段。借助由基因表达谱获得的分类信息进行肿瘤诊断是当今生物信息学领域中的一个重要研究方向,有望成为临床医学上一种快速而有效的肿瘤分子诊断方法。鉴于肿瘤基因表达谱样本数据维数高、样本量小以及噪音大等特点,提出一种结合支持向量机应用平均影响值来寻找肿瘤信息基因的算法,其优点是能够搜索到基因数量尽可能少而分类能力尽可能强的多个信息基因子集。采用二分类肿瘤数据集验证算法的可行性和有效性,对于结肠癌样本集,只需3个基因就能获得100%的留一法交叉验证识别准确率。为避免样本集的不同划分对分类性能的影响,进一步采用全折交叉验证方法来评估各信息基因子集的分类性能,优选出更可靠的信息基因子集。与基它肿瘤分类方法相比,实验结果在信息基因数量以及分类性能方面具有明显的优势。     

A mathematical model for the validation of gene selection methods

Gene selection methods aim at determining biologically relevant subsets of genes in DNA microarray experiments. However, their assessment and validation represent a major difficulty since the subset of biologically relevant genes is usually unknown. To solve this problem a novel procedure for generating biologically plausible synthetic gene expression data is proposed. It is based on a proper mathematical model representing gene expression signatures and expression profiles through Boolean threshold functions. The results show that the proposed procedure can be successfully adopted to analyze the quality of statistical and machine learning-based gene selection algorithms.     

Gene expression studies of reference genes for quantitative real-time PCR: an overview in insects

Whenever gene expression is being examined, it is essential that a normalization process is carried out to eliminate non-biological variations. The use of reference genes, such as glyceraldehyde-3-phosphate dehydrogenase, actin, and ribosomal protein genes, is the usual method of choice for normalizing gene expression. Although reference genes are used to normalize target gene expression, a major problem is that the stability of these genes differs among tissues, developmental stages, species, and responses to abiotic factors. Therefore, the use and validation of multiple reference genes are required. This review discusses the reasons that why RT-qPCR has become the preferred method for validating results of gene expression profiles, the use of specific and non-specific dyes and the importance of use of primers and probes for qPCR as well as to discuss several statistical algorithms developed to help the validation of potential reference genes. The conflicts arising in the use of classical reference genes in gene normalization and their replacement with novel references are also discussed by citing the high stability and low stability of classical and novel reference genes under various biotic and abiotic experimental conditions by employing various methods applied for the reference genes amplification.     

Familial and sporadic idiopathic pulmonary fibrosis: making the diagnosis from peripheral blood

Background

Peripheral blood biomarkers might improve diagnostic accuracy for idiopathic pulmonary fibrosis (IPF).

Results

Gene expression profiles were obtained from 89 patients with IPF and 26 normal controls. Samples were stratified according to severity of disease based on pulmonary function. The stratified dataset was split into subsets; two-thirds of the samples were selected to comprise the training set, while one-third was reserved for the validation set. Bayesian probit regression was used on the training set to develop a gene expression model for IPF versus normal. The gene expression model was tested by using it on the validation set to perform class prediction. Unsupervised clustering failed to discriminate between samples of different severity. Therefore, samples of all severities were included in the training and validation sets, in equal proportions. A gene signature model was developed from the training set. The model was built in an iterative fashion with the number of gene features selected to minimize the misclassification error in cross validation. The final model was based on the top 108 discriminating genes in the training set. The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001. Using the optimal threshold (0.74) accurate class predictions were made for 77% of the test cases with sensitivity = 0.70, specificity = 1.00.

Conclusions

By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-902) contains supplementary material, which is available to authorized users.     

Seasonal changes in bacterial and archaeal gene expression patterns across salinity gradients in the Columbia River coastal margin

Through their metabolic activities, microbial populations mediate the impact of high gradient regions on ecological function and productivity of the highly dynamic Columbia River coastal margin (CRCM). A 2226-probe oligonucleotide DNA microarray was developed to investigate expression patterns for microbial genes involved in nitrogen and carbon metabolism in the CRCM. Initial experiments with the environmental microarrays were directed toward validation of the platform and yielded high reproducibility in multiple tests. Bioinformatic and experimental validation also indicated that >85% of the microarray probes were specific for their corresponding target genes and for a few homologs within the same microbial family. The validated probe set was used to query gene expression responses by microbial assemblages to environmental variability. Sixty-four samples from the river, estuary, plume, and adjacent ocean were collected in different seasons and analyzed to correlate the measured variability in chemical, physical and biological water parameters to differences in global gene expression profiles. The method produced robust seasonal profiles corresponding to pre-freshet spring (April) and late summer (August). Overall relative gene expression was high in both seasons and was consistent with high microbial abundance measured by total RNA, heterotrophic bacterial production, and chlorophyll a. Both seasonal patterns involved large numbers of genes that were highly expressed relative to background, yet each produced very different gene expression profiles. April patterns revealed high differential gene expression in the coastal margin samples (estuary, plume and adjacent ocean) relative to freshwater, while little differential gene expression was observed along the river-to-ocean transition in August. Microbial gene expression profiles appeared to relate, in part, to seasonal differences in nutrient availability and potential resource competition. Furthermore, our results suggest that highly-active particle-attached microbiota in the Columbia River water column may perform dissimilatory nitrate reduction (both dentrification and DNRA) within anoxic particle microniches.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Validating nutrient-related gene expression changes from microarrays using RT2 PCR-arrays

Microarray technology allows us to perform high-throughput screening of changes in gene expression. The outcome of microarray experiments largely depends on the applied analysis methods and cut-off values chosen. Results are often required to be verified using a more sensitive detection technique, such as quantitative real-time PCR (qPCR or RT-PCR). Throughout the years, this technique has become a de facto golden standard. Individual qPCRs are time-consuming, but the technology to perform high-throughput qPCR reactions has become available through PCR-arrays that allow up to 384 PCR reactions simultaneously. Our current aim was to investigate the usability of a RT² Profiler? PCR-array as validation in a nutritional intervention study, where the measured changes in gene expression were low. For some differentially expressed genes, the PCR-array confirmed the microarray prediction, though not for all. Furthermore, the PCR-array allowed picking up the expression of genes that were not measurable on the microarray platform but also vice versa. We conclude that both techniques have their own (dis)advantages and specificities, and for less pronounced changes using both technologies may be useful as complementation rather than validation.     

RNAi及其在肿瘤研究中的应用

RNA干扰(RNA interference,RNAi)是指在生物体细胞内,外源性或内源性的双链RNA(double-stranded RNA,dsRNA)引起与其同源mRNA特异性的降解,因而抑制其相应的基因表达过程.由于它能够高度特异性、高效性地抑制基因的表达,因此在研究基因功能及表达调控、信号传导通路、药物靶点的鉴定和基因药物开发等方面具有良好的应用前景.主要介绍RNAi可能的分子机制、分子生物学特性、产生方法及其在肿瘤研究中的应用.     

Evaluation of ovarian POMC mRNA through quantitative RT-PCR analysis in Rana esculenta

The evaluation of changes in the expression of specific genes requires accurate measurement of the corresponding mRNA concentration, especially when the gene is expressed at a very low level. We previously showed that the proopiomelanocortin (POMC) gene is expressed in the ovary of the frog Rana esculenta, and, to evaluate its mRNA content in frog ovary, we have now developed a sensitive quantitative RT-PCR method. This study provides evidence for the validation of this method and for the effects of captivity and hypophysectomy on POMC gene expression in the ovary of this anuran. Our data indicate that ovarian POMC gene is involved in short-term captivity stress response and seems not influenced by pituitary. These results are discussed taking into account the knowledge of the role played by opioids in stress response; moreover, a local control of POMC gene expression is also suggested.     

Adapting chromophore-assisted laser inactivation for high throughput functional proteomics.

Recent advances in genomics and proteomics have generated a change in emphasis from hypothesis-based to discovery-based investigations. Genomic and proteomic studies based on differential expression microarrays or comparative proteomics often provide many potential candidates for functionally important roles in normal and diseased cells. High throughput technologies to address protein and gene function in situ are still necessary to exploit these emerging advances in gene and protein discovery in order to validate these identified targets. The pharmaceutical industry is particularly interested in target validation, and has identified it as the critical early step in drug discovery. An especially powerful approach to target validation is a direct protein knockdown strategy called chromophore-assisted laser inactivation (CALI) which is a means of testing the role of specific proteins in particular cellular processes. Recent developments in CALI allow for its high throughput application to address many proteins in tandem. Thus, CALI may have applications for high throughput hypothesis testing, target validation or proteome-wide screening.     

Evaluation of methods for oligonucleotide array data via quantitative real-time PCR

Background  

There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay.     

New miRNA labeling method for bead-based quantification

Background  

microRNAs (miRNAs) are small single-stranded non-coding RNAs that act as crucial regulators of gene expression. Different methods have been developed for miRNA expression profiling in order to better understand gene regulation in normal and pathological conditions. miRNAs expression values obtained from large scale methodologies such as microarrays still need a validation step with alternative technologies.