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The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data.
Tumor development is known to be a stepwise process involving dynamic changes that affect cellular integrity and cellular behavior. This complex interaction between genomic organization and gene, as well as protein expression is not yet fully understood. Tumor characterization by gene expression analyses is not sufficient, since expression levels are only available as a snapshot of the cell status. So far, research has mainly focused on gene expression profiling or alterations in oncogenes, even though DNA microarray platforms would allow for high-throughput analyses of copy number alterations (CNAs).
During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics.
Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.
To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License.
In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.
The microarray technology has become an established approach for large-scale gene expression analysis with mature protocols for sample, microarray, and data processing. The maize oligonucleotide array (maizearray) is one of the few microarray platforms designed for genome-wide gene expression analysis in Zea mays L. Many datas-ets addressing various genetic, physiological and developmental topics generated with this platform are available. The original 57,452 microarray probes were compiled based on expressed sequence tags (ESTs). Meanwhile the maize genome sequence became available providing the possibility for an improved annotation of the microar-ray probe set. In this study we determined the genome positions of all maizearray probes to obtain current gene annotations and generated current Gene Ontology (GO) annotations. These new data allow tracing redundancy of the probe set and interfering cross-hybridizations, and doubled the number of genes with functional GO data. Our re-annotation will largely improve the functional analysis of available and future datasets generated on this microarray platform.
Carcinogenesis is a complex multifactorial, multistage process, but the precise mechanisms are not well understood. In this study, we performed a genome-wide analysis of the copy number variation (CNV), breakpoint region (BPR) and fragile sites in 2,737 tumor samples from eight tumor entities and in 432 normal samples. CNV detection and BPR identification revealed that BPRs tended to accumulate in specific genomic regions in tumor samples whereas being dispersed genome-wide in the normal samples. Hotspots were observed, at which segments with similar alteration in copy number were overlapped along with BPRs adjacently clustered. Evaluation of BPR occurrence frequency showed that at least one was detected in about and more than 15% of samples for each tumor entity while BPRs were maximal in 12% of the normal samples. 127 of 2,716 tumor-relevant BPRs (termed ‘common BPRs’) exhibited also a noticeable occurrence frequency in the normal samples. Colocalization assessment identified 20,077 CNV-affecting genes and 169 of these being known tumor-related genes. The most noteworthy genes are KIAA0513 important for immunologic, synaptic and apoptotic signal pathways, intergenic non-coding RNA RP11-115C21.2 possibly acting as oncogene or tumor suppressor by changing the structure of chromatin, and ADAM32 likely importance in cancer cell proliferation and progression by ectodomain-shedding of diverse growth factors, and the well-known tumor suppressor gene p53. The BPR distributions indicate that CNV mutations are likely non-random in tumor genomes. The marked recurrence of BPRs at specific regions supports common progression mechanisms in tumors. The presence of hotspots together with common BPRs, despite its small group size, imply a relation between fragile sites and cancer-gene alteration. Our data further suggest that both protein-coding and non-coding genes possessing a range of biological functions might play a causative or functional role in tumor biology. This research enhances our understanding of the mechanisms for tumorigenesis and progression.
Bei Nacktmullen wurde bisher Tumorentstehung nicht beobachtet. Zur Untersuchung der molekularen Ursachen dient der Nacktmull daher als ein wertvoller Modellorganismus für Krebsresistenz. Ziel dieser Arbeit war eine weiterführende Analyse des kürzlich publizierten Genoms des Nacktmulls. Eine Genvorhersage konnte auf Grundlage der vorhandenden Genom- und Transkriptomdaten durchgeführt und ein hypothetisches Netzwerk der Interaktionen der tumorinduzierenden Faktoren erstellt werden. Dieses Netzwerk basiert auf den Tumorsuppressor-Regulationswegen p53 und RB im Menschen. Die Genom- und Transkriptom-Daten des Nacktmulls weisen deutliche Unterschiede zu den Befunden im Menschen auf. Dieser Befund könnte ein erster Anhaltspunkt für die Aufklärung der molekularen Ursachen der Krebsresistenz von Nacktmullen sein.
Für die effektive Haltung von Mikroorganismen, kleinen Pflanzen oder Algen in Bioreaktoren ist die Aufrechterhaltung optimaler Kultivierungsbedingungen, wie beispielsweise pH-Wert, Temperatur oder Nährstoffgehalt, notwendig. Diese Parameter können sich während der Kultivierung ändern, weshalb sie regelmäßig kontrolliert und gegebenenfalls angepasst werden müssen. Wir präsentieren hier den technischen Aufbau und die Softwarerealisierung eines Automatisierungssystems zur autonomen Regulierung des pH-Wertes in Bioreaktoren, in denen die grüne Mikroalge Scenedesmus rubescens kultiviert wird. Dazu wurde ein System mit pH-Sensoren, Signalwandlern und Magnetventilen zur kontrollierten CO2-Begasung aufgebaut. Für die Steuerung und die Datenaufzeichnung diente ein Single-Board-Computer (Raspberry Pi) mit Webeserver. Die Anlage war voll funktionsfähig und konnte über mehrere Tage fehlerlos den pH-Wert auf einen vorgegebenen Wert regeln. Das System ist leicht auch auf Großanlagen und für andere Parameter erweiterbar. Durch die Nutzung eines Single-Board-Computers erfordert die Anlage nur minimalen Platz- und Energiebedarf und ist mit geringen Anschaffungskosten verbunden.