The environmental contribution to gene expression profiles

Microarray analysis provides a bridge between the molecular genetic analysis of model organisms in laboratory settings and studies of physiology, development, and adaptation in the wild. By sampling species across a range of environments, it is possible to gain a broad picture of the genomic response to environmental perturbation. Incorporating estimates of genetic relationships into study designs will facilitate genomic analysis of environmental plasticity by aiding the identification of major regulatory loci in natural populations.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

ESPD: a pattern detection model underlying gene expression profiles

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Interaction of rearing environment and reproductive tactic on gene expression profiles in Atlantic salmon

Organisms that share the same genotype can develop into divergent phenotypes, depending on environmental conditions. In Atlantic salmon, young males of the same age can be found either as sneakers or immature males that are future anadromous fish. Just as the organism-level phenotype varies between divergent male developmental trajectories, brain gene expression is expected to vary as well. We hypothesized that rearing environment can also have an important effect on gene expression in the brain and possibly interact with the reproductive tactic adopted. We tested this hypothesis by comparing brain gene expression profiles of the two male tactics in fish from the same population that were reared in either a natural stream or under laboratory conditions. We found that expression of certain genes was affected by rearing environment only, while others varied between male reproductive tactics independent of rearing environment. Finally, more than half of all genes that showed variable expression varied between the two male tactics only in one environment. Thus, in these fish, very different molecular pathways can give rise to similar macro-phenotypes depending on rearing environment. This result gives important insights into the molecular underpinnings of developmental plasticity in relationship to the environment.     

PCP: a program for supervised classification of gene expression profiles

PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net     

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.     

Genome-wide gene expression profiles in response to plastid division perturbations

Plastids are vital organelles involved in important metabolic functions that directly affect plant growth and development. Plastids divide by binary fission involving the coordination of numerous protein components. A tight control of the plastid division process ensures that: there is a full plastid complement during and after cell division, specialized cell types have optimal plastid numbers; the division rate is modulated in response to stress, metabolic fluxes and developmental status. However, how this control is exerted by the host nucleus is unclear. Here, we report a genome-wide microarray analysis of three accumulation and replication of chloroplasts (arc) mutants that show a spectrum of altered plastid division characteristics. To ensure a comprehensive data set, we selected arc3, arc5 and arc11 because they harbour mutations in protein components of both the stromal and cytosolic division machinery, are of different evolutionary origin and display different phenotypic severities in terms of chloroplast number, size and volume. We show that a surprisingly low number of genes are affected by altered plastid division status, but that the affected genes encode proteins important for a variety of fundamental plant processes.     

Anterior boundaries of Hox gene expression in mesoderm-derived structures correlate with the linear gene order along the chromosome

The developmental expression patterns of four genes, Hox 1.1, Hox 1.2, Hox 1.3 and Hox 3.1, were examined by in situ hybridization to serial embryonic sections. The three genes of the Hox 1 cluster, used in this study, map to adjacent positions along chromosome 6, whereas the Hox 3.1 gene maps to the Hox 3 cluster on chromosome 15. The anterior expression limits in segmented mesoderm varied among the four genes examined. Interestingly, a linear correlation exists between the position of the gene along the chromosome and the extent of anterior expression. Genes that are expressed more posterior are also more restricted in their expression in other mesoderm-derived tissues. The order of expression anterior to posterior was determined as: Hox 1.3, Hox 1.2, Hox 1.1 and Hox 3.1. Similarly, genes of the Drosophila Antennapedia and Bithorax complex specifying segment identity also exhibit anterior expression boundaries that correlate with gene position. The data suggest that Hox genes may specify positional information along the anterior-posterior axis during the formation of the body plan.     

Phylogenetic profiles: a graphical method for detecting genetic recombinations in homologous sequences

Phylogenetic profiles constitute a novel way of graphically displaying the coherence of the sequence relationships over the entire length of a set of aligned homologous sequences. Using a sliding-window technique, this method determines the pairwise distances of all sequences in the windows and evaluates, for each sequence, the degree to which the patterns of distances in these regions agree. This method is suited for exploring data consistency as well as detecting recombinant sequences. A computer program implementing the algorithm has been developed, and examples with simulated and natural sequences are given to demonstrate the sensitivity and accuracy of the method for identifying recombinant sequences and their recombination junctions as well as detecting hot spots of recombinational activity.      

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters

MOTIVATION: Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. RESULTS: We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.     

Rhythms of gene expression in a fluctuating intertidal environment

The physiological strategies that enable organisms to thrive in habitats where environmental factors vary dramatically on a daily basis are poorly understood. One of the most variable and unpredictable habitats on earth is the marine rocky intertidal zone located at the boundary between the terrestrial and marine environments. Mussels dominate rocky intertidal habitats throughout the world and, being sessile, endure wide variations in temperature, salinity, oxygen, and food availability due to diurnal, tidal, and climatic cycles. Analysis of gene-expression changes in the California ribbed mussel (Mytilus californianus) at different phases in the tidal cycle reveals that intertidal mussels exist in at least four distinct physiological states, corresponding to a metabolism and respiration phase, a cell-division phase, and two stress-response signatures linked to moderate and severe heat-stress events. The metabolism and cell-division phases appear to be functionally linked and are anticorrelated in time. The magnitudes and timings of these states varied by vertical position on the shore and appear to be driven by microhabitat conditions. The results provide new insights into the strategies that allow life to flourish in fluctuating environments and demonstrate the importance of time course data collected from field animals in situ in understanding organism-environment interactions.     

PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.     

Combining gene expression profiles and protein-protein interaction data to infer gene functions

The ever-increasing flow of gene expression profiles and protein-protein interactions has catalyzed many computational approaches for inference of gene functions. Despite all the efforts, there is still room for improvement, for the information enriched in each biological data source has not been exploited to its fullness. A composite method is proposed for classifying unannotated genes based on expression data and protein-protein interaction (PPI) data, which extracts information from both data sources in novel ways. With the noise nature of expression data taken into consideration, importance is attached to the consensus expression patterns of gene classes instead of the actual expression profiles of individual genes, thus characterizing the composite method with enhanced robustness against microarray data variation. With regard to the PPI network, the traditional clear-cut binary attitude towards inter- and intra-functional interactions is abandoned, whereas a more objective perspective into the PPI network structure is formed through incorporating the varied function-function interaction probabilities into the algorithm. The composite method was implemented in two numerical experiments, where its improvement over single-data-source based methods was observed and the superiority of the novel data handling operations was discussed.     

Large-scale analysis of gene expression profiles

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.