Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Sequential analysis of global gene expression profiles in immature and in vitro matured bovine oocytes: potential molecular markers of oocyte maturation

Background: Without intensive selection, the majority of bovine oocytes submitted to in vitro embryo production (IVP) fail to develop to the blastocyst stage. This is attributed partly to their maturation status and competences. Using the Affymetrix GeneChip Bovine Genome Array, global mRNA expression analysis of immature (GV) and in vitro matured (IVM) bovine oocytes was carried out to characterize the transcriptome of bovine oocytes and then use a variety of approaches to determine whether the observed transcriptional changes during IVM was real or an artifact of the techniques used during analysis. Results: 8489 transcripts were detected across the two oocyte groups, of which similar to 25.0% (2117 transcripts) were differentially expressed (p < 0.001); corresponding to 589 over-expressed and 1528 under-expressed transcripts in the IVM oocytes compared to their immature counterparts. Over expression of transcripts by IVM oocytes is particularly interesting, therefore, a variety of approaches were employed to determine whether the observed transcriptional changes during IVM were real or an artifact of the techniques used during analysis, including the analysis of transcript abundance in oocytes in vitro matured in the presence of a-amanitin. Subsets of the differentially expressed genes were also validated by quantitative real-time PCR (qPCR) and the gene expression data was classified according to gene ontology and pathway enrichment. Numerous cell cycle linked (CDC2, CDK5, CDK8, HSPA2, MAPK14, TXNL4B), molecular transport (STX5, STX17, SEC22A, SEC22B), and differentiation (NACA) related genes were found to be among the several over-expressed transcripts in GV oocytes compared to the matured counterparts, while ANXA1, PLAU, STC1and LUM were among the over-expressed genes after oocyte maturation. Conclusion: Using sequential experiments, we have shown and confirmed transcriptional changes during oocyte maturation. This dataset provides a unique reference resource for studies concerned with the molecular mechanisms controlling oocyte meiotic maturation in cattle, addresses the existing conflicting issue of transcription during meiotic maturation and contributes to the global goal of improving assisted reproductive technology.

Comprehensive gene expression profiling in lungs of mice infected with Mycobacterium tuberculosis following DNAhsp65 immunotherapy

Background The continued increase in tuberculosis (TB) rates and the appearance of extremely resistant Mycobacterium tuberculosis strains (XDR-TB) worldwide are some of the great problems of public health. In this context, DNA immunotherapy has been proposed as an effective alternative that could circumvent the limitations of conventional drugs. Nonetheless, the molecular events underlying these therapeutic effects are poorly understood. Methods We characterized the transcriptional signature of lungs from mice infected with M. tuberculosis and treated with heat shock protein 65 as a genetic vaccine (DNAhsp65) combining microarray and real-time polymerase chain reaction analysis. The gene expression data were correlated with the histopathological analysis of lungs. Results The differential modulation of a high number of genes allowed us to distinguish DNAhsp65-treated from nontreated animals (saline and vector-injected mice). Functional analysis of this group of genes suggests that DNAhsp65 therapy could not only boost the T helper (Th)1 immune response, but also could inhibit Th2 cytokines and regulate the intensity of inflammation through fine tuning of gene expression of various genes, including those of interleukin-17, lymphotoxin A, tumour necrosis factor-cl, interleukin-6, transforming growth factor-beta, inducible nitric oxide synthase and Foxp3. In addition, a large number of genes and expressed sequence tags previously unrelated to DNA-therapy were identified. All these findings were well correlated with the histopathological lesions presented in the lungs. Conclusions The effects of DNA therapy are reflected in gene expression modulation; therefore, the genes identified as differentially expressed could be considered as transcriptional biomarkers of DNAhsp65 immunotherapy against TB. The data have important implications for achieving a better understanding of gene-based therapies. Copyright (C) 2008 John Wiley & Sons, Ltd.

Hypothyroidism decreases proinsulin gene expression and the attachment of its mRNA and eEF1A protein to the actin cytoskeleton of INS-1E cells

The actions of thyroid hormone (TH) on pancreatic beta cells have not been thoroughly explored, with current knowledge being limited to the modulation of insulin secretion in response to glucose, and beta cell viability by regulation of pro-mitotic and pro-apoptotic factors. Therefore, the effects of TH on proinsulin gene expression are not known. This led us to measure: a) proinsulin mRNA expression, b) proinsulin transcripts and eEF1A protein binding to the actin cytoskeleton, c) actin cytoskeleton arrangement, and d) proinsulin mRNA poly(A) tail length modulation in INS-1E cells cultured in different media containing: i) normal fetal bovine serum - FBS (control); ii) normal FBS plus 1 µM or 10 nM T3, for 12 h, and iii) FBS depleted of TH for 24 h (Tx). A decrease in proinsulin mRNA content and attachment to the cytoskeleton were observed in hypothyroid (Tx) beta cells. The amount of eEF1A protein anchored to the cytoskeleton was also reduced in hypothyroidism, and it is worth mentioning that eEF1A is essential to attach transcripts to the cytoskeleton, which might modulate their stability and rate of translation. Proinsulin poly(A) tail length and cytoskeleton arrangement remained unchanged in hypothyroidism. T3 treatment of control cells for 12 h did not induce any changes in the parameters studied. The data indicate that TH is important for proinsulin mRNA expression and translation, since its total amount and attachment to the cytoskeleton are decreased in hypothyroid beta cells, providing evidence that effects of TH on carbohydrate metabolism also include the control of proinsulin gene expression.

Xylella fastidiosa gene expression analysis by DNA microarrays

Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Gene expression profile of the plant pathogen Fusarium graminearum under the antagonistic effect of Pantoea agglomerans

The pathogenic fungus Fusarium graminearum is an ongoing threat to agriculture, causing losses in grain yield and quality in diverse crops. Substantial progress has been made in the identification of genes involved in the suppression of phytopathogens by antagonistic microorganisms; however, limited information regarding responses of plant pathogens to these biocontrol agents is available. Gene expression analysis was used to identify differentially expressed transcripts of the fungal plant pathogen F. graminearum under antagonistic effect of the bacterium Pantoea agglomerans. A macroarray was constructed, using 1014 transcripts from an F. graminearum cDNA library. Probes consisted of the cDNA of F. graminearum grown in the presence and in the absence of P. agglomerans. Twenty-nine genes were either up (19) or down (10) regulated during interaction with the antagonist bacterium. Genes encoding proteins associated with fungal defense and/or virulence or with nutritional and oxidative stress responses were induced. The repressed genes coded for a zinc finger protein associated with cell division, proteins containing cellular signaling domains, respiratory chain proteins, and chaperone-type proteins. These data give molecular and biochemical evidence of response of F. graminearum to an antagonist and could help develop effective biocontrol procedures for pathogenic plant fungi.

Validation of reference genes for gene expression studies in the honey bee, Apis mellifera, by quantitative real-time RT-PCR

For obtaining accurate and reliable gene expression results it is essential that quantitative real-time RT-PCR (qRT-PCR) data are normalized with appropriate reference genes. The current exponential increase in postgenomic studies on the honey bee, Apis mellifera, makes the standardization of qRT-PCR results an important task for ongoing community efforts. For this aim we selected four candidate reference genes (actin, ribosomal protein 49, elongation factor 1-alpha, tbp-association factor) and used three software-based approaches (geNorm, BestKeeper and NormFinder) to evaluate the suitability of these genes as endogenous controls. Their expression was examined during honey bee development, in different tissues, and after juvenile hormone exposure. Furthermore, the importance of choosing an appropriate reference gene was investigated for two developmentally regulated target genes. The results led us to consider all four candidate genes as suitable genes for normalization in A. mellifera. However, each condition evaluated in this study revealed a specific set of genes as the most appropriated ones.

Global gene expression under nitrogen starvation in Xylella fastidiosa: contribution of the sigma(54) regulon

Background: Xylella fastidiosa, a Gram-negative fastidious bacterium, grows in the xylem of several plants causing diseases such as citrus variegated chlorosis. As the xylem sap contains low concentrations of amino acids and other compounds, X. fastidiosa needs to cope with nitrogen limitation in its natural habitat. Results: In this work, we performed a whole-genome microarray analysis of the X. fastidiosa nitrogen starvation response. A time course experiment (2, 8 and 12 hours) of cultures grown in defined medium under nitrogen starvation revealed many differentially expressed genes, such as those related to transport, nitrogen assimilation, amino acid biosynthesis, transcriptional regulation, and many genes encoding hypothetical proteins. In addition, a decrease in the expression levels of many genes involved in carbon metabolism and energy generation pathways was also observed. Comparison of gene expression profiles between the wild type strain and the rpoN null mutant allowed the identification of genes directly or indirectly induced by nitrogen starvation in a sigma(54)-dependent manner. A more complete picture of the sigma(54) regulon was achieved by combining the transcriptome data with an in silico search for potential sigma(54)-dependent promoters, using a position weight matrix approach. One of these sigma(54)-predicted binding sites, located upstream of the glnA gene (encoding glutamine synthetase), was validated by primer extension assays, confirming that this gene has a sigma(54)-dependent promoter. Conclusions: Together, these results show that nitrogen starvation causes intense changes in the X. fastidiosa transcriptome and some of these differentially expressed genes belong to the sigma(54) regulon.

A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling

Background: High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis. Results: This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system. Conclusion: These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/.S3T source code and datasets can also be downloaded from the aforementioned website.

Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation

Positional information in developing embryos is specified by spatial gradients of transcriptional regulators. One of the classic systems for studying this is the activation of the hunchback (hb) gene in early fruit fly (Drosophila) segmentation by the maternally-derived gradient of the Bicoid (Bcd) protein. Gene regulation is subject to intrinsic noise which can produce variable expression. This variability must be constrained in the highly reproducible and coordinated events of development. We identify means by which noise is controlled during gene expression by characterizing the dependence of hb mRNA and protein output noise on hb promoter structure and transcriptional dynamics. We use a stochastic model of the hb promoter in which the number and strength of Bcd and Hb (self-regulatory) binding sites can be varied. Model parameters are fit to data from WT embryos, the self-regulation mutant hb(14F), and lacZ reporter constructs using different portions of the hb promoter. We have corroborated model noise predictions experimentally. The results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, rather than on Bcd fluctuations. The constructs and mutant, which lack self-regulation, indicate that the multiple Bcd binding sites in the hb promoter (and their strengths) also play a role in buffering noise. The model is robust to the variation in Bcd binding site number across a number of fly species. This study identifies particular ways in which promoter structure and regulatory dynamics reduce hb output noise. Insofar as many of these are common features of genes (e. g. multiple regulatory sites, cooperativity, self-feedback), the current results contribute to the general understanding of the reproducibility and determinacy of spatial patterning in early development.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Differential gene expression between the biotrophic-like and saprotrophic mycelia of the witches` broom pathogen Moniliophthora perniciosa

Moniliophthora perniciosa is a hemibiotrophic fungus that causes witches` broom disease (WBD) in cacao. Marked dimorphism characterizes this fungus, showing a monokaryotic or biotrophic phase that causes disease symptoms and a later dikaryotic or saprotrophic phase. A combined strategy of DNA microarray, expressed sequence tag, and real-time reverse-transcriptase polymerase chain reaction analyses was employed to analyze differences between these two fungal stages in vitro. In all, 1,131 putative genes were hybridized with cDNA from different phases, resulting in 189 differentially expressed genes, and 4,595 reads were clusterized, producing 1,534 unigenes. The analysis of these genes, which represent approximately 21% of the total genes, indicates that the biotrophic-like phase undergoes carbon and nitrogen catabollite repression that correlates to the expression of phytopathogenicity genes. Moreover, downregulation of mitochondrial oxidative phosphorylation and the presence of a putative ngr1 of Saccharomyces cerevisiae could help explain its lower growth rate. In contrast, the saprotrophic mycelium expresses genes related to the metabolism of hexoses, ammonia, and oxidative phosphorylation, which could explain its faster growth. Antifungal toxins were upregulated and could prevent the colonization by competing fungi. This work significantly contributes to our understanding of the molecular mechanisms of WBD and, to our knowledge, is the first to analyze differential gene expression of the different phases of a hemibiotrophic fungus.