Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

MOTIVATION: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types. RESULTS: We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.     

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.     

Microarray: gateway to unravel the mystery of abiotic stresses in plants

Environmental factors, such as drought, salinity, extreme temperature, ozone poisoning, metal toxicity etc., significantly affect crops. To study these factors and to design a possible remedy, biological experimental data concerning these crops requires the quantification of gene expression and comparative analyses at high throughput level. Development of microarrays is the platform to study the differential expression profiling of the targeted genes. This technology can be applied to gene expression studies, ranging from individual genes to whole genome level. It is now possible to perform the quantification of the differential expression of genes on a glass slide in a single experiment. This review documents recently published reports on the use of microarrays for the identification of genes in different plant species playing their role in different cellular networks under abiotic stresses. The regulation pattern of differentially-expressed genes, individually or in group form, may help us to study different pathways and functions at the cellular and molecular level. These studies can provide us with a lot of useful information to unravel the mystery of abiotic stresses in important crop plants.     

Gene expression profiling of coronary artery disease and its relation with different severities

Global gene expression profiling is a powerful tool enabling the understanding of pathophysiology and subsequent management of diseases. This study aims to explore functionally annotated differentially expressed genes (DEGs); their biological processes for coronary artery disease (CAD) and its different severities of atherosclerotic lesions. This study also aims to identify the change in expression patterns of DEGs in atherosclerotic lesions of single-vessel disease (SVD) and triple-vessel disease (TVD). The weight of different severities of lesion was estimated using a modified Gensini score. The gene expression profiling was performed using the Affymetrix microarray platform. The functional annotation for CAD was performed using DAVID v6.8. The biological network gene ontology tool (BiNGO) and ClueGO were used to explore the biological processes of functionally annotated genes of CAD. The changes in gene expression from SVD to TVD were determined by evaluating the fold change. Functionally annotated genes were found in an unique set and could be distinguishing two distinct severities of CAD. The biological processes such as cellular migration, locomotion, cell adhesion, cytokine production, positive regulation of cell death etc. enriched the functionally annotated genes in SVD, whereas, wound healing, negative regulation of cell death, blood coagulation, angiogenesis and fibrinolysis were enriched significantly in TVD patients. The genes THBS1 and CAPN10 were functionally annotated for CAD in both SVD and TVD. The 61 DEGs were identified, those have changes their expression with different severities of atherosclerotic lesions, in which 13 genes had more than two-fold change in expression between SVD and TVD. The consistent findings were obtained on validation of microarray gene expression of selected 10 genes in a separate cohort using real-time PCR. This study identified putative candidate genes and their biological processes predisposing toward and affecting the severity of CAD.     

Gene expression analysis in LLC-PK1 renal tubular cells by atrial natriuretic peptide (ANP): correlation of homologous human genes with renal response

We used human DNA microarray to explore the differential gene expression profiling of atrial natriuretic peptide (ANP)-stimulated renal tubular epithelial kidney cells (LLC-PK1) in order to understand the biological effect of ANP on renal kidney cell's response. Gene expression profiling revealed 807 differentially expressed genes, consisting of 483 up-regulated and 324 down-regulated genes. The bioinformatics tool was used to gain a better understanding of differentially expressed genes in porcine genome homologous with human genome and to search the gene ontology and category classification, such as cellular component, molecular function and biological process. Four up-regulated genes of ATP1B1, H3F3A, ITGB1 and RHO that were typically validated by real-time quantitative PCR (RT-qPCR) analysis serve important roles in the alleviation of renal hypertrophy as well as other related effects. Therefore, the human array can be used for gene expression analysis in pig kidney cells and we believe that our findings of differentially expressed genes served as genetic markers and biological functions can lead to a better understanding of ANP action on the renal protective system and may be used for further therapeutic application.     

Toxicogenomics in the assessment of immunotoxicity

Microarray analysis is used for simultaneous measurement of expression of thousands of genes in a given sample and as such extends and deepens our understanding of biological processes. Application of the technique in toxicology is referred to as toxicogenomics. The examples of assessment of immunotoxicity by gene expression profiling presented and discussed here, show that microarray analysis is able to detect known and novel effects of a wide range of immunomodulating agents. Besides the elucidation of mechanisms of action, toxicogenomics is also applied to predict consequences of exposing biological systems to toxic agents. Successful attempts to classify compounds using signature gene expression profiles have been reported. These did, however, not specifically focus on immunotoxicity. Databases containing expression profiles can facilitate the applications of toxicogenomics. Platforms and methodologies for gene expression profiling may vary, however, hampering data compiling across different laboratories. Therefore, attention is paid to standardization of the generation, reporting, and management of microarray data. Obtained gene expression profiles should be anchored to pathological and functional endpoints for correct interpretation of results. These issues are also important when using toxicogenomics in risk assessment. The application of toxicogenomics in evaluation of immunotoxicity is thus not yet without challenges. It already contributes to the understanding of immunotoxic processes and the development of in vitro screening assays, though, and is therefore expected to be of value for mechanistic insight into immunotoxicity and hazard identification of existing and novel compounds.     

Gene expression profiling of the rewarding effect caused by methamphetamine in the mesolimbic dopamine system

Methamphetamine, a commonly used addictive drug, is a powerful addictive stimulant that dramatically affects the CNS. Repeated METH administration leads to a rewarding effect in a state of addiction that includes sensitization, dependence, and other phenomena. It is well known that susceptibility to the development of addiction is influenced by sources of reinforcement, variable neuroadaptive mechanisms, and neurochemical changes that together lead to altered homeostasis of the brain reward system. These behavioral abnormalities reflect neuroadaptive changes in signal transduction function and cellular gene expression produced by repeated drug exposure. To provide a better understanding of addiction and the mechanism of the rewarding effect, it is important to identify related genes. In the present study, we performed gene expression profiling using microarray analysis in a reward effect animal model. We also investigated gene expression in four important regions of the brain, the nucleus accumbens, striatum, hippocampus, and cingulated cortex, and analyzed the data by two clustering methods. Genes related to signaling pathways including G-protein-coupled receptor-related pathways predominated among the identified genes. The genes identified in our study may contribute to the development of a gene modeling network for methamphetamine addiction.     

绝经对血液单核细胞基因表达谱的影响:基因芯片初步研究

绝经是女性一生中很重要的生理现象之一,它能增加一系列复杂免疫、神经退化、新陈代谢和心血管方面的疾病。血液单核细胞能分化成各种各样的细胞,这些细胞在组织形态发生和免疫应答方面起着很重要的作用。本研究中采用了包含大约14,500个基因探针的Affymetrix Human U133A基因芯片来研究健康的绝经前和绝经后女性外周血液单核细胞中的基因表达谱。样本之间的对比分析表明有20个基因上调,20个基因下调。其中的28个基因根据它们的生物过程如细胞繁殖、免疫应答、细胞代谢等等被分成了6个主要的GO类别;剩下的12个基因其生物学功能还没有被鉴定。研究结果支持了我们的假设:血液单核细胞的功能状态确实受到绝经的影响,而且由此带来的改变可能是由全基因组范围的基因表达谱而决定的。本研究中鉴定的一些差异表达基因有可能作为以后研究与绝经相关的系统免疫、神经退化和心血管疾病的候选基因研究。此工作是这个研究方向的第一次尝试,为将来的进一步研究奠定了基础。     

Analysis of host responses to microbial infection using gene expression profiling

Gene expression profiling offers new opportunities for understanding host-cell responses to microbial pathogens and their products. Current strategies involve either first identifying mRNAs that differ in their expression status under different experimental conditions and later defining the identity of the respective genes (for example, differential display or serial analysis of gene expression), or alternatively assessing changes in the expression of already defined genes (for example, cDNA or oligonucleotide microarrays). Early studies indicate the power of gene expression profiling for providing new insights into groups of genes whose expression is altered during the course of host-microbe interactions, and for the discovery of cellular genes that were not previously recognized to be regulated by infection.     

Molecular profiling of antipsychotic drug function

Despite great progress in antipsychotic drug research, the molecular mechanisms by which these drugs work have remained elusive. High-throughput gene profiling methods have advanced this field by allowing the simultaneous investigation of hundreds to thousands of genes. However, different methodologies, choice of brain region, and drugs studied have made comparisons across different studies difficult. Because of the complexity of gene expression changes caused by drugs, teasing out the most relevant expression differences is a challenging task. One approach is to focus on gene expression changes that converge on the same systems that were previously deemed important to the pathology of psychiatric disorders. From the microarray studies performed on human postmortem brain samples from schizophrenics, the systems most implicated to be dysfunctional are synaptic machinery, oligodendrocyte/myelin function, and mitochondrial/ubiquitin metabolism. Drugs may act directly or indirectly to compensate for underlying pathological deficits in schizophrenia or via other mechanisms that converge on these pathways. Side effects, consisting of motor and metabolic dysfunction (which occur with typical and atypical drugs, respectively), also may be mediated by gene expression changes that have been reported in these studies. This article surveys both the convergent antipsychotic mechanisms and the genes that may be responsible for other effects elicited by antipsychotic drugs.     

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.