Gene profiling for determining pluripotent genes in a time course microarray experiment

In microarray experiments, it is often of interest to identifygenes which have a prespecified gene expression profile withrespect to time. Methods available in the literature are, however,typically not stringent enough in identifying such genes, particularlywhen the profile requires equivalence of gene expression levelsat certain time points. In this paper, the authors introducea new methodology, called gene profiling, that uses simultaneousdifferential and equivalent gene expression level testing torank genes according to a prespecified gene expression profile.Gene profiling treats the vector of true gene expression levelsas a linear combination of appropriate vectors, for example,vectors that give the required criteria for the profile. Thisgene profile model is fitted to the data, and the resultingparameter estimates are summarized in a single test statisticthat is then used to rank the genes. The theoretical underpinningsof gene profiling (equivalence testing, intersection–uniontests) are discussed in this paper, and the gene profiling methodologyis applied to our motivating stem-cell experiment.     

Expression profiling and comparative sequence derived insights into lipid metabolism

Expression profiling and genomic DNA sequence comparisons are increasingly being applied to the identification and analysis of the genes that are involved in lipid metabolism. Not only has genome-wide expression profiling aided in the identification of novel genes that are involved in important processes in lipid metabolism such as sterol efflux, but also the utilization of information from these studies has added to our understanding of the regulation of pathways that participate in the process. Coupled with these gene expression studies, cross-species comparison (a technique used to search for sequences that are conserved through evolution) has proven to be a powerful tool to identify important noncoding regulatory sequences and novel genes that are relevant to lipid biology. An example of the value of this approach was the recent chance discovery of a new apolipoprotein gene (that which encodes apolipoprotein AV) that has dramatic effects on triglyceride metabolism in mice and humans.     

Genome-directed primers for selective labeling of bacterial transcripts for DNA microarray analysis

DNA microarrays have the ability to analyze the expression of thousands of the same set of genes under at least two different experimental conditions. However, DNA microarrays require substantial amounts of RNA to generate the probes, especially when bacterial RNA is used for hybridization (50 microg of bacterial total RNA contains approximately 2 microg of mRNA). We have developed a computer-based algorithm for prediction of the minimal number of primers to specifically anneal to all genes in a given genome. The algorithm predicts, for example, that 37 oligonucleotides should prime all genes in the Mycobacterium tuberculosis genome. We tested the usefulness of the genome-directed primers (GDPs) in comparison to random primers for gene expression profiling using DNA microarrays. Both types of primers were used to generate fluorescent-labeled probes and to hybridize to an array of 960 mycobacterial genes. Compared to random-primer probes, the GDP probes were more sensitive and more specific, especially when mammalian RNA samples were spiked with mycobacterial RNA. The GDPs were used for gene expression profiling of mycobacterial cultures grown to early log or stationary growth phases. This approach could be useful for accurate genome-wide expression analysis, especially for in vivo gene expression profiling, as well as directed amplification of sequenced genomes.     

Exploitation of expression profiles: examples in oncology

The analysis of biological processes has been revolutionized by the emergence of the DNA array technology. As cellular biological events are controlled by gene expression, their modulations are markers of the cellular activity. These modulations can be indicative of either a physiological process or a pathological one. Monitoring of the expression levels of thousands of genes simultaneously, the expression profiling method is based upon comparative studies where the identification of the differentially expressed genes in two samples is aimed. The two samples under study may be compared temporally or following drug treatment, they may also originate from different sources, e.g. normal versus pathological samples. In that case, gene expression profiling is conducted for diagnostics purposes or therapy monitoring, and offers an opportunity to identify new drug targets. Using different examples, we describe the potentialities of this approach in oncology.     

Validation of rat reference genes for improved quantitative gene expression analysis using low density arrays

Real-time PCR has become increasingly important in gene expression profiling research, and it is widely agreed that normalized data are required for accurate estimates of messenger RNA (mRNA) expression. With increased gene expression profiling in preclinical research and toxicogenomics, a need for reference genes in the rat has emerged, and the studies in this area have not yet been thoroughly evaluated. The purpose of our study was to evaluate a panel of rat reference genes for variation of gene expression in different tissue types. We selected 48 known target genes based on their putative invariability. The gene expression of all targets was examined in 11 types of rat tissues using TaqMan low density array (LDA) technology. The variability of each gene was assessed using a two-step statistical model. The analysis of mean expression using multiple reference genes was shown to provide accurate and reliable normalized expression data. The least five variable genes from each specific tissue were recommended for future tissue-specific studies. Finally, a subset of investigated rat reference genes showing the least variation is recommended for further evaluation using the LDA platform. Our work should considerably enhance a researcher's ability to simply and efficiently identify appropriate reference genes for given experiments.     

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.     

Discrimination of toxic impacts of various chemicals using chemical–gene expression profiling of Escherichia coli DNA microarray

The expression levels of 96 genes were characterized and differentiated using a cDNA microarray after the bacterium Escherichia coli was exposed to numerous toxic chemicals. In all, the effects of 14 different chemicals and 1 mixture were investigated using 1-h exposure data to provide information about physiological changes brought on by the stress experienced and interaction of chemical–gene expression. Hierarchical clustering analysis showed that the genes could be sub-grouped based upon their expression patterns while each also showed unique signatures to each chemical tested when examined using a principal component analysis (PCA). By constructing a chemical–gene expression profiling based on changes in the expression of the genes for each chemical, we were able to identify the chemicals effects and gene targets more systematically. Despite the fact that only a small number of genes were used for gene expression analysis, they were sufficient to discriminate between the effects of each exposure. It was found that the use of a single time point for expression analysis was insufficient for interpreting the effects a given chemical has on the bacterium. Such information cannot be obtained from conventional toxicity studies, demonstrating that chemical–gene expression profiling method based on the hierarchical clustering analysis and principal component analysis (PCA) in toxicity monitoring offers a new perspective for bio-monitoring and information on dynamic changes occurring at the sub-cellular level.     

Functional genomics approaches for the study of sporadic adrenal tumor pathogenesis: clinical implications

Although sporadic adrenal tumors are frequently encountered in the general population their pathogenesis is not well elucidated. The advent of functional genomics/bioinformatics tools enabling large scale comprehensive genome expression profiling should contribute to significant progress in this field. Some studies have already been published describing gene expression profiles of benign and malignant adrenocortical tumors and phaeochromocytomas. Several genes coding for growth factors and their receptors, enzymes involved in steroid hormone biosynthesis, genes related to the regulation of cell cycle, cell proliferation, adhesion and intracellular metabolism have been found to be up- or downregulated in various tumors. Some alterations in gene expression appear so specific for certain tumor types that their application in diagnosis, determination of prognosis and the choice of therapy can be envisaged. In this short review, the authors will present a synopsis of these recent findings that seem to open new perspectives in adrenal tumor pathogenesis, with emphasis on changes in steroidogenic enzyme expression profiles and highlighting possible clinical implications.     

Functional genomics as an emerging strategy for the investigation of central mechanisms in experimental hypertension

Centrally mediated increases in sympathetic nerve activity and attenuated arterial baroreflexes contribute to the pathogenesis of hypertension. Despite the characterization of cellular and physiological mechanisms that regulate blood pressure and alterations that contribute to hypertension, the genetic and molecular basis of this pathophysiology remains poorly understood. Strategies to identify genes that contribute to central pathophysiologic mechanisms in hypertension include integrative biochemistry and physiology as well as functional genomics. This article summarizes recent progress in applying functional genomics to elucidate the genetic basis of altered central blood pressure regulatory mechanisms in hypertension. We describe approaches others and we have undertaken to investigate gene expression profiles in hypertensive models in order to identify genes that contribute to the pathogenesis of hypertension. Finally, we provide the readers a roadmap for negotiating the route from experimental findings of gene expression profiling to translating their therapeutic potential. The combination of gene expression profiling and the phenotypic characterization of in vitro and in vivo loss or gain of function experiments for candidate genes have the potential to identify genes involved in the pathogenesis of hypertension and may present novel targets for therapy.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Technical variables in high-throughput miRNA expression profiling: much work remains to be done

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.