FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

Differential expressed genes are more likely to have variants associated with disease. A new tool, fitSNP, prioritizes candidate SNPs from association studies

Identification of new candidate genes associated with metabolic traits applying a multiomics approach in the obese mouse model BFMI861

Hintergrund: Die Berlin Fat Mouse Inzuchtlinie (BFMI) ist ein Modell für Adipositas und das metabolische Syndrom. Diese Studie zielte darauf ab, genetische Varianten zu identifizieren, die mit dem gestörten Glukosestoffwechsel assoziiert sind, indem die fettleibigen Linien BFMI861-S1 und BFMI861-S2 verwendet wurden, die genetisch eng verwandt sind, sich aber in mehreren Merkmalen unterscheiden. BFMI861-S1 ist insulinresistent und speichert ektopisches Fett in der Leber, während BFMI861-S2 insulinsensitiv ist. Methoden: Die QTL-Analyse wurde in zwei fortgeschrittenen Intercross-Linien (AIL) in der Generation durchgeführt. Eine AIL wurde aus der Kreuzung BFMI861-S1 x BFMI861-S2 und die zweite AIL aus der Kreuzung BFMI861-S1 x BFMI861-B6N erhalten. Für beide AILs wurden Phänotypen über 25 bzw. 20 Wochen gesammelt. Zur Priorisierung von positionellen Kandidatengenen wurden Gesamtgenomsequenzierung und Genexpressionsdaten der Elternlinien verwendet. Ergebnisse: Für den AIL BFMI861-S1 x BFMI861-S2 wurden überlappende QTL für das Gonadenfettgewebegewicht und die Blutglukosekonzentration auf Chromosom (Chr) 3 (95,8–100,1 Mb) und für das Gonadenfettgewebegewicht, Lebergewicht und Blut nachgewiesen Glukosekonzentration auf Chr 17 (9,5–26,1 Mb). Für die AIL BFMI861-S1 x BFMI861-B6N zeigte ein hochsignifikanter QTL auf Chromosom (Chr) 1 (157–168 Mb) einen Zusammenhang mit dem Lebergewicht. Ein QTL für das Körpergewicht nach 20 Wochen wurde auf Chr 3 (34,1 – 40 Mb) gefunden, der sich mit einem QTL für das scAT-Gewicht überlappte. In einem multiplen QTL-Mapping-Ansatz wurde ein zusätzliches QTL, das das Körpergewicht bei 16 Wochen beeinflusste, auf Chr 6 (9,5–26,1 Mb) identifiziert. Schlussfolgerungen: Die QTL-Kartierung zusammen mit einem detaillierten Priorisierungsansatz ermöglichte es uns, Kandidatengene zu identifizieren, die mit Merkmalen des metabolischen Syndroms in beiden AILs assoziiert sind.Background: The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with the impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in several traits. BFMI861-S1 is insulin resistant and stores ectopic fat in the liver, whereas BFMI861-S2 is insulin sensitive. Methods: QTL-analysis was performed in two advanced intercross lines (AIL) in generation. One AIL obtained from the cross BFMI861-S1 x BFMI861-S2 and the second AIL from the cross BFMI861-S1 x BFMI861-B6N. For both AILs phenotypes were collected over 25 and 20 weeks, respectively. For prioritization of positional candidate genes whole genome sequencing and gene expression data of the parental lines were used. Results: For the AIL BFMI861-S1 x BFMI861-S2 overlapping QTL for gonadal adipose tissue weight and blood glucose concentration were detected on chromosome (Chr) 3 (95.8-100.1 Mb), and for gonadal adipose tissue weight, liver weight, and blood glucose concentration on Chr 17 (9.5-26.1 Mb). For the AIL BFMI861-S1 x BFMI861-B6N one highly significant QTL on chromosome (Chr) 1 (157–168 Mb) showed an association with liver weight. A QTL for body weight at 20 weeks was found on Chr 3 (34.1 – 40 Mb) overlapping with a QTL for scAT weight. In a multiple QTL mapping approach, an additional QTL affecting body weight at 16 weeks was identified on Chr 6 (9.5-26.1 Mb). Conclusions: QTL mapping together with a detailed prioritization approach allowed us to identify candidate genes associated with traits of the metabolic syndrome in both AILs

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Transcriptional landscape of neuronal and cancer stem cells

Tumor mass is composed by heterogeneous cell population including a subset of “cancer stem cells” (CSC). Oncogenic signals foster CSC by transforming tissue stem cells or by reprogramming progenitor/differentiated cells towards stemness. Thus, CSC share features with cancer and stem cells (e.g. self-renewal, hierarchical developmental program leading to differentiated cells, epithelial/mesenchimal transition) and these latter are maintained by the constitutive activation of stemness-promoting signals. CSC could trigger tumor formation, drive to resistance to conventional therapeutics and underlie patients’ relapse. Indeed, stem cell signatures have been associated with poor prognosis in various. This background makes the identification of CSC molecular features mandatory to highlight the survival inner working and to design novel CSC specific therapeutic strategies. Medulloblastoma (MB) is the most common childhood malignant brain tumor and a leading cause of cancerrelated morbidity and mortality. Current multimodal therapies are effective in about 50% of patients but often cause long-term side effects, i.e. developmental, neurological, neuroendocrine and psychosocial deficits (Northcott PA Nature Rev cancer 2012). For many years, MB treated as a single tumor entity despite the divergent tumor histology, patients’ outcome and drug sensitivity, and also by the diversity of the stem cell of origin. Very recently the scenario of human MB has dramatically changed since its heterogeneous biology has been addressed by high-throughput gene expression analysis (oligonucleotide microarrays) or by the powerful genomic next-generation sequencing. These led to the identification of four tumor subgroups (WNT, SHH, Group 3 and Group 4) uncovering the existence of a highly diverse mutational spectra and gene expression. However a quantitative approach has not yet been applied to the transcriptional landscape of Medulloblastoma stem cells (MbSC) through RNA Next Generation Sequencing (RNA-Seq) technology. This is a relevant issue, since RNA-Seq is able to interrogate the genome wide global transcriptome including new transcripts, alternative spliced isoforms and non-coding RNAs. Lower rhombic lip progenitors of the dorsal brainstem are considered the trigger cells in WNT tumors; in SHH subgroup initiation cells are Prominin1+ CD15+ stem cells from the subventricular zone requiring the commitment to Math1+ granule cell progenitors [GCP] of the external granule cell layer [EGL]; while Math1+ or Math1- EGL-GCP or Prominin1+/lineage-negative stem cells sustain the MYC driven Group 3. MbSC derived from SHH tumors and postnatal normal cerebellar stem cells (NcSC) have been reported to share several features. A key signal for both of them is Hedgehog. Furthermore, both NcSC and MbSC display up-regulation of stemness genes (e.g Sox2, Nestin, Nanog, Prom1). Finally, constitutive activation of the Shh pathway by conditional deletion of Ptch1 inhibitory receptor in NcSC, promote medulloblastoma in vivo, producing a mouse model of the human SHH tumor. Acquisition of stemness features may therefore represent the first step of oncogenic conversion. Cooperation with additional oncogenic signals is however needed to enhance MbSC tumorigenicity. In order to understand the MbSCs transcriptional programs, we analyze by RNA-Seq, MbSC derived from Ptch1+/- tumors (Ptch1+/- MbSC). This choice, of a genetically determined model of MB, has allowed us to work with Ptch1+/- MbSC together with appropriate NcSC counterpart, and to analyze biological replicates doing statistical analysis. We identify a number of transcripts, annotated ones, novel isoforms, and long non-coding RNAs, characterizing MbSC and/or NcSC. Some of these genes control stemness or are cancer related and conserved in human medulloblastomas. Interestingly a subset of them, belonging to cell stress response, are of prognostic relevance being significantly related to clinical outcome. Correlation of genes expression characterizing MbSC with survival information from our human medulloblastomas database further demonstrates the significance of these findings. Our data suggest that the modulation of normal and cancer stem cell functions observed in vitro is effective in dissecting the transcriptional programs underlying the in vivo behavior of human medulloblastomas

Network-based identification of driver pathways in clonal systems

Highly ethanol-tolerant bacteria for the production of biofuels, bacterial pathogenes which are resistant to antibiotics and cancer cells are examples of phenotypes that are of importance to society and are currently being studied. In order to better understand these phenotypes and their underlying genotype-phenotype relationships it is now commonplace to investigate DNA and expression profiles using next generation sequencing (NGS) and microarray techniques. These techniques generate large amounts of omics data which result in lists of genes that have mutations or expression profiles which potentially contribute to the phenotype. These lists often include a multitude of genes and are troublesome to verify manually as performing literature studies and wet-lab experiments for a large number of genes is very time and resources consuming. Therefore, (computational) methods are required which can narrow these gene lists down by removing generally abundant false positives from these lists and can ideally provide additional information on the relationships between the selected genes. Other high-throughput techniques such as yeast two-hybrid (Y2H), ChIP-Seq and Chip-Chip but also a myriad of small-scale experiments and predictive computational methods have generated a treasure of interactomics data over the last decade, most of which is now publicly available. By combining this data into a biological interaction network, which contains all molecular pathways that an organisms can utilize and thus is the equivalent of the blueprint of an organisms, it is possible to integrate the omics data obtained from experiments with these biological interaction networks. Biological interaction networks are key to the computational methods presented in this thesis as they enables methods to account for important relations between genes (and gene products). Doing so it is possible to not only identify interesting genes but also to uncover molecular processes important to the phenotype. As the best way to analyze omics data from an interesting phenotype varies widely based on the experimental setup and the available data, multiple methods were developed and applied in the context of this thesis: In a first approach, an existing method (PheNetic) was applied to a consortium of three bacterial species that together are able to efficiently degrade a herbicide but none of the species are able to efficiently degrade the herbicide on their own. For each of the species expression data (RNA-seq) was generated for the consortium and the species in isolation. PheNetic identified molecular pathways which were differentially expressed and likely contribute to a cross-feeding mechanism between the species in the consortium. Having obtained proof-of-concept, PheNetic was adapted to cope with experimental evolution datasets in which, in addition to expression data, genomics data was also available. Two publicly available datasets were analyzed: Amikacin resistance in E. coli and coexisting ecotypes in E.coli. The results allowed to elicit well-known and newly found molecular pathways involved in these phenotypes. Experimental evolution sometimes generates datasets consisting of mutator phenotypes which have high mutation rates. These datasets are hard to analyze due to the large amount of noise (most mutations have no effect on the phenotype). To this end IAMBEE was developed. IAMBEE is able to analyze genomic datasets from evolution experiments even if they contain mutator phenotypes. IAMBEE was tested using an E. coli evolution experiment in which cells were exposed to increasing concentrations of ethanol. The results were validated in the wet-lab. In addition to methods for analysis of causal mutations and mechanisms in bacteria, a method for the identification of causal molecular pathways in cancer was developed. As bacteria and cancerous cells are both clonal, they can be treated similar in this context. The big differences are the amount of data available (many more samples are available in cancer) and the fact that cancer is a complex and heterogenic phenotype. Therefore we developed SSA-ME, which makes use of the concept that a causal molecular pathway has at most one mutation in a cancerous cell (mutual exclusivity). However, enforcing this criterion is computationally hard. SSA-ME is designed to cope with this problem and search for mutual exclusive patterns in relatively large datasets. SSA-ME was tested on cancer data from the TCGA PAN-cancer dataset. From the results we could, in addition to already known molecular pathways and mutated genes, predict the involvement of few rarely mutated genes.nrpages: 246status: publishe