Detection of chromosomal regions showing differential gene expression in human skeletal muscle and in alveolar rhabdomyosarcoma

BACKGROUND: Rhabdomyosarcoma is a relatively common tumour of the soft tissue, probably due to regulatory disruption of growth and differentiation of skeletal muscle stem cells. Identification of genes differentially expressed in normal skeletal muscle and in rhabdomyosarcoma may help in understanding mechanisms of tumour development, in discovering diagnostic and prognostic markers and in identifying novel targets for drug therapy. RESULTS: A Perl-code web client was developed to automatically obtain genome map positions of large sets of genes. The software, based on automatic search on Human Genome Browser by sequence alignment, only requires availability of a single transcribed sequence for each gene. In this way, we obtained tissue-specific chromosomal maps of genes expressed in rhabdomyosarcoma or skeletal muscle. Subsequently, Perl software was developed to calculate gene density along chromosomes, by using a sliding window. Thirty-three chromosomal regions harbouring genes mostly expressed in rhabdomyosarcoma were identified. Similarly, 48 chromosomal regions were detected including genes possibly related to function of differentiated skeletal muscle, but silenced in rhabdomyosarcoma. CONCLUSION: In this study we developed a method and the associated software for the comparative analysis of genomic expression in tissues and we identified chromosomal segments showing differential gene expression in human skeletal muscle and in alveolar rhabdomyosarcoma, appearing as candidate regions for harbouring genes involved in origin of alveolar rhabdomyosarcoma representing possible targets for drug treatment and/or development of tumor markers

Dys-regulated Gene Expression Networks by Meta-Analysis of Microarray Data on Oral Squamous Cell Carcinoma

Background: Oral squamous cell carcinoma (OSCC) is the sixth most common type of carcinoma worldwide. Development of OSCC is a multi-step process involving genes related to cell cycle, growth control, apoptosis, DNA damage response and other cellular regulators. The pathogenic pathways involved in this tumor are mostly unknown and therefore a better characterization of OSCC gene expression profile would represent a considerable advance. The availability of publicly available gene expression datasets has opened up new challenges especially for the integration of data generated by different research groups and different array platforms with the purpose of obtaining new insights on the biological process investigated.

Results: In this work we performed a meta-analysis on four microarray and four datasets of gene expression data on OSCC in order to evaluate the degree of agreement of the biological results obtained by these different studies and to identify common regulatory pathways that could be responsible of tumor growth. Sixteen dys-regulated pathways implicated in OSCC were mined out from the four published datasets, and most importantly three pathways were first reported. Those regulatory pathways and biological processes which are significantly enriched have been investigated by means of literatures and meanwhile, four genes of the maximally altered pathways, ECM-receptor interaction, were validated and identified by qRT-PCR as a possible candidate of aggressiveness of OSCC.

Conclusion: we have developed a robust method for analyzing pathways altered in OSCC using three expression array data sets. This study sets a stage for the further discovery of the basic mechanisms that may underlie a diseased state and would help in identifying critical nodes in the pathway that can be targeted for diagnosis and therapeutic intervention. In addition, those who are interested in our approach can obtain the software package (MATLAB platform) by email freely

SVD-based Anatomy of Gene Expressions for Correlation Analysis in Arabidopsis thaliana

Gene co-expression analysis has been widely used in recent years for predicting unknown gene function and its regulatory mechanisms. The predictive accuracy depends on the quality and the diversity of data set used. In this report, we applied singular value decomposition (SVD) to array experiments in public databases to find that co-expression linkage could be estimated by a much smaller number of array data. Correlations of co-expressed gene were assessed using two regulatory mechanisms (feedback loop of the fundamental circadian clock and a global transcription factor Myb28), as well as metabolic pathways in the AraCyc database. Our conclusion is that a smaller number of informative arrays across tissues can suffice to reproduce comparable results with a state-of-the-art co-expression software tool. In our SVD analysis on Arabidopsis data set, array experiments that contributed most as the principal components included stamen development, germinating seed and stress responses on leaf

TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription

Motivation: Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional and synergistic interactions that emerge when collectively analysing genes subject to different regulatory mechanisms. This limitation reduces overall predictive power and thus the reliability of downstream biological inference. Results: We introduce an analytical modelling framework (TREEOME: tree of models of expression) that integrates epigenetic and transcriptomic data by separating genes into putative regulatory classes. Current predictive modelling approaches have found both DNA methylation and histone modification epigenetic data to provide little or no improvement in accuracy of prediction of transcript abundance despite, for example, distinct anti-correlation between mRNA levels and promoter-localised DNA methylation. To improve on this, in TREEOME we evaluate four possible methods of formulating gene-level DNA methylation metrics, which provide a foundation for identifying gene-level methylation events and subsequent differential analysis, whereas most previous techniques operate at the level of individual CpG dinucleotides. We demonstrate TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript abundance (RNA-seq) for H1-hESC and GM12878 cell lines. Availability: TREEOME is implemented using open-source software and made available as a pre-configured bootable reference environment. All scripts and data presented in this study are available online at http://sourceforge.net/projects/budden2015treeome/.Comment: 14 pages, 6 figure

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Selection of optimized candidate reference genes for qRT-PCR normalization in rice (Oryza sativa L.) during Magnaporthe oryzae infection and drought.

Drought and rice blast disease caused by Magnaporthe oryzae are two of the most serious threats to global rice production. To explore the mechanisms underlying gene expression induced in rice by stresses, studies involving transcriptome analyses have been conducted over the past few years. Thus, it is crucial to have a reliable set of reference genes to normalize the expression levels of rice genes affected by different stresses. To identify potential reference genes for studies of the differential expression of target genes in rice under M. oryzae infection and drought conditions, the present study evaluated five housekeeping genes for the normalization of gene expression. The stability of the expression of these genes was assessed using the analytical software packages geNorm and NormFinder. For all samples analyzed, the stability rank was UBQ5 > GAPDH > eIF-4α > β-TUB > 18S rRNA. The data showed that the UBQ5, GAPDH, and eIF-4α genes are appropriate, high-performing reference genes and will be highly useful in future expression studies of fungal infections and drought in rice

Genes to Monitor Ecophysiological Parameters for Plants Under Abiotic Stress Conditions

In plants, different mechanisms are activated by gene expression in response to abiotic stresses. The great challenge is to study the physiological patterns and the genes involved in each pathway to evaluate the mechanisms used by plant the overcame de stressfully conditions.  To mitigate the negative impact of adverse environmental conditions, the particle films technique has become a potential crop management tool for plants to overcome the adverse environmental conditions. The mean goal of this work is to identify genes expressed and involved in different ecophysiological pathways responding to abiotic stresses. GeneMANIA and Cytoscape software were fundamental in the establishment of gene networks. In addition, a graphical representation encompassed other closely linked genes with similar functions. From the perspective of particle film technology, the genes involved in each ecophysiological parameter may, in the future, elucidate new plant response mechanisms, and therefore, a more adaptive molecular response to adverse environmental conditions

MADIBA: A web server toolkit for biological interpretation of Plasmodium and plant gene clusters

<p>Abstract</p> <p>Background</p> <p>Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. Data mining is thus essential for deducing significant biological information such as the identification of new biological mechanisms or putative drug targets. While many algorithms and software have been developed for analysing gene expression, the extraction of relevant information from experimental data is still a substantial challenge, requiring significant time and skill.</p> <p>Description</p> <p>MADIBA (MicroArray Data Interface for Biological Annotation) facilitates the assignment of biological meaning to gene expression clusters by automating the post-processing stage. A relational database has been designed to store the data from gene to pathway for <it>Plasmodium</it>, rice and <it>Arabidopsis</it>. Tools within the web interface allow rapid analyses for the identification of the Gene Ontology terms relevant to each cluster; visualising the metabolic pathways where the genes are implicated, their genomic localisations, putative common transcriptional regulatory elements in the upstream sequences, and an analysis specific to the organism being studied.</p> <p>Conclusion</p> <p>MADIBA is an integrated, online tool that will assist researchers in interpreting their results and understand the meaning of the co-expression of a cluster of genes. Functionality of MADIBA was validated by analysing a number of gene clusters from several published experiments – expression profiling of the <it>Plasmodium </it>life cycle, and salt stress treatments of <it>Arabidopsis </it>and rice. In most of the cases, the same conclusions found by the authors were quickly and easily obtained after analysing the gene clusters with MADIBA. </p

Vitamins D3 and D2 have marked but different global effects on gene expression in a rat oligodendrocyte precursor cell line

Background: Vitamin D deficiency increases the risk of developing multiple sclerosis (MS) but it is unclear whether vitamin D supplementation improves the clinical course of MS, and there is uncertainty about the dose and form of vitamin D (D2 or D3) to be used. The mechanisms underlying the effects of vitamin D in MS are not clear. Vitamin D3 increases the rate of differentiation of primary oligodendrocyte precursor cells (OPCs), suggesting that it might help remyelination in addition to modulating the immune response. Here we analyzed the transcriptome of differentiating rat CG4 OPCs treated with vitamin D2 or with vitamin D3 at 24 h and 72 h following onset of differentiation. Methods: Gene expression in differentiating CG4 cells in response to vitamin D2 or D3 was quantified using Agilent DNA microarrays (n=4 replicates), and the transcriptome data were processed and analysed using the R software environment. Differential expression between the experimental conditions was determined using LIMMA, applying the Benjamini and Hochberg multiple testing correction to p-values, and significant genes were grouped into co-expression clusters by hierarchical clustering. The functional significance of gene groups was explored by pathway enrichment analysis using the clusterProfiler package. Results: Differentiation alone changed the expression of about 10% of the genes at 72 h compared to 24 h. Vitamin D2 and D3 exerted different effects on gene expression, with D3 influencing 1,272 genes and D2 574 at 24 h. The expression of the vast majority of these genes was either not changed in differentiating cells not exposed to vitamin D or followed the same trajectory as the latter. D3-repressed genes were enriched for Gene Ontology (GO) categories including transcription factors and the Notch pathway, while D3-induced genes were enriched for the Ras pathway. Conclusions: This study shows that vitamin D3, compared with D2, changes the expression of a larger number of genes in OLs. Identification of genes affected by D3 in OLs should help to identify mechanisms mediating its action in MS

Computational analysis of small RNAs and the RNA degradome with application to plant water stress

Water shortage is one of the most important environmental stress factors that affects plants, limiting crop yield in large areas worldwide. Plants can survive water stress by regulating gene expression at several levels. One of the recently discovered regulatory mechanisms involves small RNAs (sRNAs), which can regulate gene expression by targeting messenger RNAs (mRNAs) and directing endonucleolytic cleavage resulting in mRNA degradation. A snapshot of an mRNA degradation profile (degradome) can be captured through a new high-throughput technique called Parallel Analysis of RNA Ends (PARE) by using next generation sequencing technologies. In this thesis we describe a new user friendly degradome analysis software tool called PAREsnip that we have used for the rapid genome-wide discovery of sRNA/target interactions evidenced through the degradome. In addition to PAREsnip and based upon PAREsnip’s rapid capability, we also present a new software tool for the construction, analysis and visualisation of sRNA regulatory interaction networks. The two new tools were used to analyse PARE datasets obtained fromMedicago truncatula and Arabidopsis thaliana. In particular, we have used PAREsnip for the high-throughput analysis of PARE data obtained from Medicago when subjected to dehydration and found several sRNA/mRNA interactions that are potentially responsive to water stress. We also present how we used our new network visualisation and analysis tool with PARE datasets obtained from Arabidopsis and discovered several novel sRNA regulatory interaction networks. In building tools and using them for this kind of analysis, we gain a better understanding of the processes and mechanisms involved in sRNA mediated gene regulation and how plants respond to water stress which could lead to new strategies in improving stress tolerance