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Background: Nutrient deprivation, hypoxia, radiotherapy and chemotherapy induce endoplasmic reticulum (ER) stress, which activates the so-called unfolded protein response (UPR). Extensive and acute ER stress directs the UPR towards activation of death-triggering pathways. Cancer cells are selected to resist mild and prolonged ER stress by activating pro-survival UPR. We recently found that drug-resistant tumor cells are simultaneously resistant to ER stress-triggered cell death. It is not known if cancer cells adapted to ER stressing conditions acquire a chemoresistant phenotype. Methods: To investigate this issue, we generated human cancer cells clones with acquired resistance to ER stress from ER stress-sensitive and chemosensitive cells. Results: ER stress-resistant cells were cross-resistant to multiple chemotherapeutic drugs: such multidrug resistance (MDR) was due to the overexpression of the plasma-membrane transporter MDR related protein 1 (MRP1). Gene profiling analysis unveiled that cells with acquired resistance to ER stress and chemotherapy share higher expression of the UPR sensor protein kinase RNA-like endoplasmic reticulum kinase (PERK), which mediated the erythroid-derived 2-like 2 (Nrf2)-driven transcription of MRP1. Disrupting PERK/Nrf2 axis reversed at the same time resistance to ER stress and chemotherapy. The inducible silencing of PERK reduced tumor growth and restored chemosensitivity in resistant tumor xenografts. Conclusions: Our work demonstrates for the first time that the adaptation to ER stress in cancer cells produces a MDR phenotype. The PERK/Nrf2/MRP1 axis is responsible for the resistance to ER stress and chemotherapy, and may represent a good therapeutic target in aggressive and resistant tumors. [ABSTRACT FROM AUTHOR]

Background: In recent years long non coding RNAs (lncRNAs) have been the subject of increasing interest. Thanks to many recent functional studies, the existence of a large class of lncRNAs with potential regulatory functions is now widely accepted. Although an increasing number of lncRNAs is being characterized and shown to be involved in many biological processes, the functions of the vast majority lncRNA genes is still unknown. Therefore computational methods able to take advantage of the increasing amount of publicly available data to predict lncRNA functions could be very useful. Results: Since coding genes are much better annotated than lncRNAs, we attempted to project known functional information regarding proteins onto non coding genes using the guilt by association principle: if a gene shows an expression profile that correlates with those of a set of coding genes involved in a given function, that gene is probably involved in the same function. We computed gene coexpression for 30 human tissues and 9 vertebrates and mined the resulting networks with a methodology inspired by the rank product algorithm used to identify differentially expressed genes. Using different types of reference data we can predict putative new annotations for thousands of lncRNAs and proteins, ranging from cellular localization to relevance for disease and cancer. Conclusions: New function of coding genes and lncRNA can be profitably predicted using tissue specific coexpression, as well as expression of orthologous genes in different species. The data are available for download and through a user-friendly web interface at www.funcpred.com. [ABSTRACT FROM AUTHOR]

Background: Post-transcriptional regulation is a complex mechanism that plays a central role in defining multiple cellular identities starting from a common genome. Modifications in the length of 3'UTRs have been found to play an important role in this context, since alternative 3' UTRs could lead to differences for example in regulation by microRNAs and cellular localization of the transcripts thus altering their fate. Results: We propose a strategy to identify the genes undergoing regulation of 3' UTR length using RNA sequencing data obtained from standard libraries, thus widely applicable to data originally obtained to perform classical differential expression analyses. We decided to exploit previously annotated APA sites from public databases, in contrast with other approaches recently proposed in which the location of the APA site is inferred from the data together with the relative abundance of the isoforms. We demonstrate the reliability of our method by comparing it to the results of other microarray based or specific RNA-seq libraries methods and show that using APA sites databases results in higher sensitivity compared to de novo site prediction approach. Conclusions: We implemented the algorithm in a Bioconductor package to facilitate its broad usage in the scientific community. The ability of this approach to detect shortening from libraries with a number of reads comparable to that needed for differential expression analyses makes it useful for investigating if alternative polyadenylation is relevant in a certain biological process without requiring specific experimental assays. [ABSTRACT FROM AUTHOR]

Background: The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of 'ad hoc' gene expression data-sets. Results: By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence. Conclusions: Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation. [ABSTRACT FROM AUTHOR]

Background: The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results: We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions: Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. [ABSTRACT FROM AUTHOR]

Background: Different approaches have been developed to dissect the interplay between transcription factors (TFs) and their cis-acting sequences on DNA in order to identify TF target genes. Here we used a combination of computational and experimental approaches to identify novel direct targets of TFAP2A, a key TF for a variety of physiological and pathological cellular processes. Gene expression profiles of HeLa cells either silenced for TFAP2A by RNA interference or not were previously compared and a set of differentially expressed genes was revealed. Results: The regulatory regions of 494 TFAP2A-modulated genes were analyzed for the presence of TFAP2A binding sites, employing the canonical TFAP2A Positional Weight Matrix (PWM) reported in Jaspar http://jaspar.genereg.net/. 264 genes containing at least 2 high score TFAP2A binding sites were identified, showing a central role in "Cellular Movement" and "Cellular Development". In an attempt to identify TFs that could cooperate with TFAP2A, a statistically significant enrichment for SP1 binding sites was found for TFAP2A-activated but not repressed genes. The direct binding of TFAP2A or SP1 to a random subset of TFAP2A-modulated genes was demonstrated by Chromatin ImmunoPrecipitation (ChIP) assay and the TFAP2A-driven regulation of DCBLD2/ESDN/CLCP1 gene studied in details. Conclusions: We proved that our computational approaches applied to microarray selected genes are valid tools to identify functional TF binding sites in gene regulatory regions as confirmed by experimental validations. In addition, we demonstrated a fine-tuned regulation of DCBLD2/ESDN transcription by TFAP2A. [ABSTRACT FROM AUTHOR]

Background: Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance. Results: To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set. Conclusions: The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcritpion factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network. [ABSTRACT FROM AUTHOR]

Background: When generating transformed plants, a first step in their characterization is to obtain, for each new line, an estimate of how many copies of the transgene have been integrated in the plant genome because this can deeply influence the level of transgene expression and the ease of stabilizing expression in following generations. This task is normally achieved by Southern analysis, a procedure that requires relatively large amounts of plant material and is both costly and labour-intensive. Moreover, in the presence of rearranged copies the estimates are not correct. New approaches to the problem could be of great help for plant biotechnologists. Results: By using a quantitative real-time PCR method that requires limited preliminary optimisation steps, we achieved statistically significant estimates of 1, 2 and 3 copies of a transgene in the primary transformants. Furthermore, by estimating the copy number of both the gene of interest and the selectable marker gene, we show that rearrangements of the T-DNA are not the exception, and probably happen more often than usually recognised. Conclusions: We have developed a rapid and reliable method to estimate the number of integrated copies following genetic transformation. Unlike other similar procedures, this method is not dependent on identical amplification efficiency between the PCR systems used and does not need preliminary information on a calibrator. Its flexibility makes it appropriate in those situations where an accurate optimisation of all reaction components is impossible or impractical. Finally, the quality of the information produced is higher than what can be obtained by Southern blot analysis. [ABSTRACT FROM AUTHOR]

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

Background: 3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years., Results: We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs., Conclusion: Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.

Background: Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation., Results: We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest., Conclusions: CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.

Background: Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance., Results: To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set., Conclusions: The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcription factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network.