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

REEF: searching REgionally Enriched Features in genomes

BACKGROUND: In Eukaryotic genomes, different features including genes are not uniformly distributed. The integration of annotation information and genomic position of functional DNA elements in the Eukaryotic genomes opened the way to test novel hypotheses of higher order genome organization and regulation of expression. RESULTS: REEF is a new tool, aimed at identifying genomic regions enriched in specific features, such as a class or group of genes homogeneous for expression and/or functional characteristics. The method for the calculation of local feature enrichment uses test statistic based on the Hypergeometric Distribution applied genome-wide by using a sliding window approach and adopting the False Discovery Rate for controlling multiplicity. REEF software, source code and documentation are freely available at . CONCLUSION: REEF can aid to shed light on the role of organization of specific genomic regions in the determination of their functional role

A multistep bioinformatic approach detects putative regulatory elements in gene promoters

BACKGROUND: Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs. RESULTS: Novel software (COOP) was developed for extraction of sequence motifs, based on clustering of exact or approximate patterns according to the frequency of their overlapping occurrences. Genomic sequences of 1 Kb upstream of 91 genes differentially expressed and/or encoding proteins with relevant function in adult human retina were analyzed. Methodology and results were tested by analysing 1,000 groups of putatively unrelated sequences, randomly selected among 17,156 human gene promoters. When applied to a sample of human promoters, the method identified 279 putative motifs frequently occurring in retina promoters sequences. Most of them are localized in the proximal portion of promoters, less variable in central region than in lateral regions and similar to known regulatory sequences. COOP software and reference manual are freely available upon request to the Authors. CONCLUSION: The approach described in this paper seems effective for identifying a tractable number of sequence motifs with putative regulatory role

A new locus for arrhythmogenic right ventricular cardiomyopathy (ARVD2) maps to chromosome 1q42-q43

Autosomal dominant arrhythmogenic right ventricular cardiomyopathy (ARVD, MIM 107970) is one of the major causes of juvenile sudden death. We have previously assigned the disease locus to chromosome 14q23-q24. Here we report on a novel variant of ARVD, which is transmitted associated to 1q42-q43 and is characterized by a concealed form, showing effort-induced polymorphic tachycardias. Since both loci ARVD1 and ARVD2 map in proximity of a-actinin genes, the possible implication of these myofibrillar proteins in the pathogenesis of ARVD is discussed. Two additional ARVD families, tested with markers of chromosomes 1q42-q43 and 14q23-q24, failed to show linkage, providing evidence of further genetic heterogeneit

Genomic expression during human myelopoiesis

<p>Abstract</p> <p>Background</p> <p>Human myelopoiesis is an exciting biological model for cellular differentiation since it represents a plastic process where multipotent stem cells gradually limit their differentiation potential, generating different precursor cells which finally evolve into distinct terminally differentiated cells. This study aimed at investigating the genomic expression during myeloid differentiation through a computational approach that integrates gene expression profiles with functional information and genome organization.</p> <p>Results</p> <p>Gene expression data from 24 experiments for 8 different cell types of the human myelopoietic lineage were used to generate an integrated myelopoiesis dataset of 9,425 genes, each reliably associated to a unique genomic position and chromosomal coordinate. Lists of genes constitutively expressed or silent during myelopoiesis and of genes differentially expressed in commitment phase of myelopoiesis were first identified using a classical data analysis procedure. Then, the genomic distribution of myelopoiesis genes was investigated integrating transcriptional and functional characteristics of genes. This approach allowed identifying specific chromosomal regions significantly highly or weakly expressed, and clusters of differentially expressed genes and of transcripts related to specific functional modules.</p> <p>Conclusion</p> <p>The analysis of genomic expression during human myelopoiesis using an integrative computational approach allowed discovering important relationships between genomic position, biological function and expression patterns and highlighting chromatin domains, including genes with coordinated expression and lineage-specific functions.</p

Novel definition files for human GeneChips based on GeneAnnot

<p>Abstract</p> <p>Background</p> <p>Improvements in genome sequence annotation revealed discrepancies in the original probeset/gene assignment in Affymetrix microarray and the existence of differences between annotations and effective alignments of probes and transcription products. In the current generation of Affymetrix human GeneChips, most probesets include probes matching transcripts from more than one gene and probes which do not match any transcribed sequence.</p> <p>Results</p> <p>We developed a novel set of custom Chip Definition Files (CDF) and the corresponding Bioconductor libraries for Affymetrix human GeneChips, based on the information contained in the GeneAnnot database. GeneAnnot-based CDFs are composed of unique custom-probesets, including only probes matching a single gene.</p> <p>Conclusion</p> <p>GeneAnnot-based custom CDFs solve the problem of a reliable reconstruction of expression levels and eliminate the existence of more than one probeset per gene, which often leads to discordant expression signals for the same transcript when gene differential expression is the focus of the analysis. GeneAnnot CDFs are freely distributed and fully compliant with Affymetrix standards and all available software for gene expression analysis. The CDF libraries are available from <url>http://www.xlab.unimo.it/GA_CDF</url>, along with supplementary information (CDF libraries, installation guidelines and R code, CDF statistics, and analysis results).</p

Red Blood Cell Distribution Width Improves Reclassification of Patients Admitted to the Emergency Department with Acute Decompensated Heart Failure

Background: The usual history of chronic heart failure (HF) is characterized by frequent episodes of acute decompensation (ADHF), needing urgent management in the emergency department (ED). Since the diagnostic accuracy of routine laboratory tests remains quite limited for predicting short-term mortality in ADHF, this retrospective study investigated the potential significance of combining red blood cell distribution width (RDW) with other conventional tests for prognosticating ADHF upon ED admission. Methods: We conducted a retrospective study including visits for episodes of ADHF recorded in the ED of the Uni versity Hospital of Verona throughout a 4-year period. Demo - graphic and clinical features were recorded upon patient presentation. All patients were subjected to standard Chest X-ray, electrocardiogram (ECG) and laboratory testing in - cluding creatinine, blood urea nitrogen, B-type natriuretic peptide (BNP), complete blood cell count (CBC), sodium, chloride, potassium and RDW. The 30-day overall mortality after ED presentation was defined as primary endpoint. Results: The values of sodium, creatinine, BNP and RDW were higher in patients who died than in those who survived, whilst hypochloremia was more frequent in patients who died than in those who survived. The multivariate model, incorporating these parameters, displayed a modest efficiency for predicting 30-day mortality after ED admission (AUC, 0.701; 95% CI, 0.662-0.738; p=0.001). Notably, the inclusion of RDW in the model significantly enhanced prediction efficiency, with an AUC of 0.723 (95% CI, 0.693-0.763; p<0.001). These results were confirmed with net reclassification improvement (NRI) analysis, showing that combination of RDW with conventional laboratory tests resulted in a much better prediction performance (net reclassification index, 0.222; p=0.001). Conclusions: The results of our study show that prognostic assessment of ADHF patients in the ED can be significantly improved by combining RDW with other conventional laboratory tests

Missense mutations in Desmocollin-2 N-terminus, associated with arrhythmogenic right ventricular cardiomyopathy, affect intracellular localization of desmocollin-2 in vitro

<p>Abstract</p> <p>Background</p> <p>Mutations in genes encoding desmosomal proteins have been reported to cause arrhythmogenic right ventricular cardiomyopathy (ARVC), an autosomal dominant disease characterised by progressive myocardial atrophy with fibro-fatty replacement.</p> <p>We screened 54 ARVC probands for mutations in desmocollin-2 (<it>DSC2</it>), the only desmocollin isoform expressed in cardiac tissue.</p> <p>Methods</p> <p>Mutation screening was performed by denaturing high-performance liquid chromatography and direct sequencing.</p> <p>To evaluate the pathogenic potentials of the <it>DSC2 </it>mutations detected in patients affected with ARVC, full-length wild-type and mutated cDNAs were cloned in eukaryotic expression vectors to obtain a fusion protein with green fluorescence protein (GFP); constructs were transfected in neonatal rat cardiomyocytes and in HL-1 cells.</p> <p>Results</p> <p>We identified two heterozygous mutations (c.304G>A (p.E102K) and c.1034T>C (p.I345T)) in two probands and in four family members. The two mutations p.E102K and p.I345T map to the N-terminal region, relevant to adhesive interactions.</p> <p>In vitro functional studies demonstrated that, unlike wild-type DSC2, the two N-terminal mutants are predominantly localised in the cytoplasm.</p> <p>Conclusion</p> <p>The two missense mutations in the N-terminal domain affect the normal localisation of DSC2, thus suggesting the potential pathogenic effect of the reported mutations. Identification of additional DSC2 mutations associated with ARVC may result in increased diagnostic accuracy with implications for genetic counseling.</p

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

<p>Abstract</p> <p>Background</p> <p>Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.</p> <p>Results</p> <p>TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.</p> <p>Conclusions</p> <p>TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at <url>http://apollo11.isto.unibo.it/software/</url>, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.</p