Genomic basis of a social polymorphism in a halictid bee

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Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation.</p> <p>Results</p> <p>We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at <url>http://gepat.sourceforge.net</url>.</p> <p>Conclusion</p> <p>GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at <url>http://gepat.bioapps.biozentrum.uni-wuerzburg.de</url>.</p

Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma

Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

<p>Abstract</p> <p>Background</p> <p>Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.</p> <p>Methods</p> <p>We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.</p> <p>Results</p> <p>On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.</p> <p>Conclusion</p> <p>The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.</p

Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma

Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing

The genomic and transcriptional landscape of primary central nervous system lymphoma

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations

Genom Expression Pathway Analysis Tool - Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

Die Messung der Genexpression ist für viele Bereiche der Biologie und Medizin wichtig geworden und unterstützt Studien über Behandlung, Krankheiten und Entwicklungsstadien. Microarrays können verwendet werden, um die Expression von tausenden mRNA-Molekülen gleichzeitig zu messen und ermöglichen so einen Einblick und einen Vergleich der verschiedenen zellulären Bedingungen. Die Daten, die durch Microarray-Experimente gewonnen werden, sind hochdimensional und verrauscht, eine Interpretation der Daten ist deswegen nicht einfach. Obwohl Programme für die statistische Auswertung von Microarraydaten existieren, fehlt vielen eine Integration der Analyseergebnisse mit einer automatischen Interpretationsmöglichkeit. In dieser Arbeit wurde GEPAT, Genome Expression Pathway Analysis Tool, entwickelt, das eine Analyse der Genexpression unter dem Gesichtspunkten der Genomik, Proteomik und Metabolik ermöglicht. GEPAT integriert statistische Methoden zum Datenimport und -analyse mit biologischer Interpretation für Genmengen oder einzelne Gene, die auf dem Microarray gemessen werden. Verschiedene Typen von Oligonukleotid- und cDNAMicroarrays können importiert werden, unterschiedliche Normalisierungsmethoden können auf diese Daten angewandt werden, anschließend wird eine Datenannotation durchgeführt. Nach dem Import können mit GEPAT verschiedene statische Datenanalysemethoden wie hierarchisches, k-means und PCA-Clustern, ein auf einem linearen Modell basierender t-Test, oder ein Vergleich chromosomaler Profile durchgeführt werden. Die Ergebnisse der Analysen können auf Häufungen biologischer Begriffe und Vorkommen in Stoffwechselwegen oder Interaktionsnetzwerken untersucht werden. Verschiedene biologische Datenbanken wurden integriert, um zu jeder Gensonde auf dem Array Informationen zur Verfügung stellen zu können. GEPAT bietet keinen linearen Arbeitsablauf, sondern erlaubt die Benutzung von beliebigen Teilmengen von Genen oder biologischen Proben als Startpunkt einer neuen Analyse oder Interpretation. Dabei verlässt es sich auf bewährte Datenanalyse-Pakete, bietet einen modularen Ansatz zur einfachen Erweiterung und kann auf einem verteilten Computernetzwerk installiert werden, um eine große Zahl an Benutzern zu unterstützen. Es ist unter der LGPL Open-Source Lizenz frei verfügbar und kann unter http://gepat.sourceforge.net heruntergeladen werden.The measurement of gene expression data is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and noisy, and interpretation of the results can get tricky. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. In this work GEPAT, Genome Expression Pathway Analysis Tool, was developed, offering an analysis of gene expression data under genomic, proteomic and metabolic context. GEPAT integrates statistical methods for data import and data analysis together with an biological interpretation for subset of genes or single genes measured on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-Test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various informations for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as start for a new data analysis or interpretation. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net

En-bloc resection of a giant retroperitoneal lipoma: a case report and review of the literature

Background Retroperitoneal lipomas are an extremely rare condition with only 17 cases described in the literature since 1980. They can reach enormous size and cause significant abdominal symptoms. The most important differential diagnosis is the well-differentiated liposarcoma, which preoperatively often may not definitely be ruled out. Case presentation We present the case of a 73 year-old Caucasian patient with a giant retroperitoneal lipoma of 9 kg measuring 55 cm in diameter. The patient presented with abdominal pain and swelling that had been slowly progressive for the last 15 years. On computerized tomography an immense retroperitoneal tumor was revealed. Intraoperatively, the tumor did not show any signs of infiltrative growth, therefore sole tumor extirpation was performed. Conclusion Retroperitoneal lipomas are not clearly distinguishable from well-differentiated liposarcomas on imaging and even biopsies may be misleading. Moreover, abdominal symptoms, i.e. pain, obstipation and dysphagia may occur due to mechanical displacement. Therefore, surgical exploration with complete oncological resection is the therapy of choice if malignity cannot be ruled out