Open Research Data Portal (ORDP)

ORDP stellt eine einheitliche, Disziplinen überspannende Lösung zur Verwaltung, Archivierung und Publikation von Forschungsdaten dar. Um die Nachhaltigkeit der Tübinger Forschungszentren (Core-Facilities) eScience-Center und Zentrum für Quantitative Biologie (QBIC) signifikant zu steigern, werden die technologisch verwandten, momentan softwaretechnisch und organisatorisch aber getrennt aufgestellten Systeme auf Basis der Portallösung Liferay zusammengeführt

Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy.

BACKGROUND Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only. METHODS To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer. RESULTS For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer. CONCLUSION Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy

Open Research Data Portal - Ein offenes, interdisziplinäres Portal zur Verwaltung, Archivierung und Publikation primärer Forschungsdaten

Im Rahmen des Projektes ORDP sollen die technologisch verwandten, momentan jedoch softwaretechnisch und organisatorisch getrennt aufgestellten Tübinger Core Facilities, Zentrum für Quantitative Biologie und eScience-Center, für ein nachhaltiges Forschungsdatenmanagement zusammengeführt werden. Beide Core Facilities verfolgen vergleichbare Ziele für das Forschungsdatenmanagement, wenngleich für verschiedene Zielgruppen und Datenvolumina. Mit ORDP soll auf Basis des CMS-Systems Liferay eine einheitliche, interdisziplinäre Portallösung zur Verwaltung, Archivierung und Publikation von Forschungsdaten entstehen. Durch die Verwendung von Open-Source-Software und die Veröffentlichung der entwickelten Lösungen als Open-Source-Software ist eine Weiterverwendung sowie eine nachhaltige Weiterentwicklung sichergestellt

Ten simple rules for providing effective bioinformatics research support.

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support

Specific Induction of Double Negative B Cells During Protective and Pathogenic Immune Responses

Double negative (DN) (CD19(+)CD20(low)CD27(-)IgD(-)) B cells are expanded in patients with autoimmune and infectious diseases;however their role in the humoral immune response remains unclear. Using systematic flow cytometric analyses of peripheral blood B cell subsets, we observed an inflated DN B cell population in patients with variety of active inflammatory conditions: myasthenia gravis, Guillain-Barre syndrome, neuromyelitis optica spectrum disorder, meningitis/encephalitis, and rheumatic disorders. Furthermore, we were able to induce DN B cells in healthy subjects following vaccination against influenza and tick borne encephalitis virus. Transcriptome analysis revealed a gene expression profile in DN B cells that clustered with naive B cells, memory B cells, and plasmablasts. Immunoglobulin VH transcriptome sequencing and analysis of recombinant antibodies revealed clonal expansion of DN B cells that were targeted against the vaccine antigen. Our study suggests that DN B cells are expanded in multiple inflammatory neurologic diseases and represent an inducible B cell population that responds to antigenic stimulation, possibly through an extra-follicular maturation pathway

Clinical and Genetic Tumor Characteristics of Responding and Non-Responding Patients to PD-1 Inhibition in Hepatocellular Carcinoma.

Immune checkpoint inhibitors (ICIs) belong to the therapeutic armamentarium in advanced hepatocellular carcinoma (HCC). However, only a minority of patients benefit from immunotherapy. Therefore, we aimed to identify indicators of therapy response. This multicenter analysis included 99 HCC patients. Progression-free (PFS) and overall survival (OS) were studied by Kaplan-Meier analyses for clinical parameters using weighted log-rank testing. Next-generation sequencing (NGS) was performed in a subset of 15 patients. The objective response (OR) rate was 19% median OS (mOS)16.7 months. Forty-one percent reached a PFS > 6 months; these patients had a significantly longer mOS (32.0 vs. 8.5 months). Child-Pugh (CP) A and B patients showed a mOS of 22.1 and 12.1 months, respectively. Ten of thirty CP-B patients reached PFS > 6 months, including 3 patients with an OR. Tumor mutational burden (TMB) could not predict responders. Of note, antibiotic treatment within 30 days around ICI initiation was associated with significantly shorter mOS (8.5 vs. 17.4 months). Taken together, this study shows favorable outcomes for OS with low AFP, OR, and PFS > 6 months. No specific genetic pattern, including TMB, could identify responders. Antibiotics around treatment initiation were associated with worse outcome, suggesting an influence of the host microbiome on therapy success

Ring1b-dependent epigenetic remodelling is an essential prerequisite for pancreatic carcinogenesis

BACKGROUND AND AIMS Besides well-defined genetic alterations, the dedifferentiation of mature acinar cells is an important prerequisite for pancreatic carcinogenesis. Acinar-specific genes controlling cell homeostasis are extensively downregulated during cancer development; however, the underlying mechanisms are poorly understood. Now, we devised a novel in vitro strategy to determine genome-wide dynamics in the epigenetic landscape in pancreatic carcinogenesis. DESIGN With our in vitro carcinogenic sequence, we performed global gene expression analysis and ChIP sequencing for the histone modifications H3K4me3, H3K27me3 and H2AK119ub. Followed by a comprehensive bioinformatic approach, we captured gene clusters with extensive epigenetic and transcriptional remodelling. Relevance of Ring1b-catalysed H2AK119ub in acinar cell reprogramming was studied in an inducible Ring1b knockout mouse model. CRISPR/Cas9-mediated Ring1b ablation as well as drug-induced Ring1b inhibition were functionally characterised in pancreatic cancer cells. RESULTS The epigenome is vigorously modified during pancreatic carcinogenesis, defining cellular identity. Particularly, regulatory acinar cell transcription factors are epigenetically silenced by the Ring1b-catalysed histone modification H2AK119ub in acinar-to-ductal metaplasia and pancreatic cancer cells. Ring1b knockout mice showed greatly impaired acinar cell dedifferentiation and pancreatic tumour formation due to a retained expression of acinar differentiation genes. Depletion or drug-induced inhibition of Ring1b promoted tumour cell reprogramming towards a less aggressive phenotype. CONCLUSIONS Our data provide substantial evidence that the epigenetic silencing of acinar cell fate genes is a mandatory event in the development and progression of pancreatic cancer. Targeting the epigenetic repressor Ring1b could offer new therapeutic options

qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml

Untersuchungen zur Proteinexpressionsdynamik in humanen Tumorzellen nach pharmakologischer Behandlung

Cancer, the multifactorial disease, resulting in uncontrolled growth of malignant cells, is the second most frequent cause of death worldwide. Despite enormous growth in knowledge on cancer pathology, efficient medication still remains elusive. In recent years, global profiling approaches are increasingly important tools to study complex biological problems, such as cancer. One emerging profiling technology is proteomics, the continuously growing research branch of (bio)analytical chemistry that studies the entire set of proteins in a biological system, their modifications and interactions. However, a variety of computational and technological challenges in proteomics are still limiting the broad application of the technology in cancer research. This thesis contributes in three major topics to new methodological approaches for the analysis of proteomics data and to novel insights of the effects of therapeutical treatment in cancer cells. In the first research part, a new method to analyze 2D-Polyacrylamid Gel Electrophoresis (PAGE) proteomics data is introduced. Although the DIGE (Difference Gel Eletrophoresis) technology greatly influenced the quality of 2D-PAGE experiments through the fluorescent labeling of different samples and their common separation in the same 2D gel, the technology is still accompanied with major challenges. In this thesis we provide a solution to one of the major problems, the accurate and automated mapping of protein spots from different DIGE gels. We implemented a novel scoring method and applied a graph-theoretical approach to solve the assignment problem and to ultimately find the protein spots with reproducible regulation on different gels. The second research section presents a new method for the integration of several database search engines for improved peptide identification. Database search for peptide identification belongs to the cornerstones in the processing of shotgun proteomics data. The underlying algorithms from different search engines produce results that overlap in parts and disagree in others. Here we present a new computational framework that combines results from several search algorithms and thereby shows significant gain in peptide identification rates. Our method relies on the normalization of single engine scores and on a weighted, average-like method to combine the identification results from different engines to a common consensus score. This new approach to peptide identification yields up to 63 % more identifications as the single engines alone. In the last research section we present the application of quantitative shotgun proteomics to an important aspect of cancer research, the study of the influence of kinase inhibitors to the global protein expression. Dynamic quantitation of protein expression after kinase inhibitor treatment using SILAC (Stable Isotope Labeling by Amino Acids in Cell culture) opened new insights to the quantitative and dynamic effects of the two multi-kinase inhibitors, sorafenib and LY294002, on the whole proteome. In these experiments, we were able to identify and quantify more than 5,400 proteins and to investigate the protein expression levels at five different time points, revealing unprecedented insights to the kinetic behavior of the proteome as a function of length of treatment. We could show that for both inhibitors several clusters of proteins show similar regulation following inhibitor treatment. We confirm the known regulation of the mTor pathway by the LY294003 inhibitor and we speculate about the influence of LY294002 to DNA replication. Furthermore, the investigations on the kinetic effects of sorafenib treatment revealed known mechanisms, such as the influence to the Rho and Ras mediated cell cycle progression, but opened also new and interesting hypothesis, such as sorafenib's contribution to autophagy induction. Large scale proteomics datasets provide a wealth of information and new ways to study biological systems on a system-wide level.Krebs, die multifaktorielle Krankheit bei der sich pathologisch veränderte Zellen unkontrolliert teilen, ist weltweit die zweithäufigste Todesursache. Trotz des enormen Zuwachses an Wissen über die Entstehung von Krebs, bleiben effiziente Therapiemethoden bislang aus. Globale Profilierungsmethoden haben sich als sehr vielversprechende Ansätze für die Untersuchung von komplexen biologischen Problemen, wie Krebs, erwiesen. Eine dieser neuen Methoden ist die Proteomik, der stetig wachsenden Zweig der (bio)analytischen Chemie, welcher die Gesamtheit der Proteine eines biologischen Systems, sowie ihre Modifikationen und Interaktionen erforscht. Eine Vielzahl von bioinformatischen und technologischen Herausforderungen in der Proteomik verhindern jedoch immer noch den breiten Einsatz dieser Technologie in der Krebsforschung. Im Rahmen dieser Dissertation tragen wir zu drei wichtigen Themengebiete der Proteomik und ihrer Anwendung in der Krebsforschung bei. Wir entwickelten neue methodische Ansätze für die Analyse von proteomischen Daten und wendeten proteomische Methoden an, um ein besseres Verständnis zum Mechanismus von therapeutischen Substanzen in Tumorzellen zu gewinnen. In dem ersten Teil der Forschungsarbeiten stellen wir eine neue Methode für die Analyse von 2D Gel basierten Daten vor. Obwohl die DIGE Technologie durch die Floureszenzmarkierung von verschiedenen Proben und deren gemeinsame Trennung auf einem Gel, einen erheblichen Beitrag zur Verbesserung der Qualität von 2D Gel Experimenten gemacht hat, gibt es nach wie vor noch erhebliche Herausforderungen in der DIGE basierten Proteomanalytik. Diese Dissertation präsentiert eine neue Lösung für eines der größten Probleme der DIGE basierten Proteomik, der akkurate und automatisierte Abgleich von Proteinspots auf verschiedenen DIGE Gelen. Die Implementierung einer neuen Scoring-Methode und die Anwendung von graph-theoretischen Ansätzen zur Lösung des Zuordnungsproblems erlauben das schnelle Finden von Proteinspots, welche auf verschiedenen Gelen reproduzierbar reguliert sind. Das zweite Kapitel der Forschungsarbeiten behandelt eine neue Methode zur Integration von mehreren Datenbanksuchmaschinen zur Verbesserung von Peptididentifizierungsraten. Datenbanksuchen zur Identifizierung von Peptiden gehören zu den Eckpfeilern der Datenprozessierung in der Massenspektrometrie-basierten Proteomik. Die verschiedenen Algorithmen, die den unterschiedlichen Suchmaschinen zu Grunde liegen, annotieren einen Teil der Spektren mit den gleichen Sequenzen, aber schlagen oft unterschiedliche Peptide für einen anderen Teil der Spektren vor. Hier präsentieren wir einen neuen algorithmischen Ansatz, der die Ergebnisse verschiedener Suchmaschinen integriert und dabei einen signifikanten Zuwachs an identifizierten Peptiden erzielt. Unsere Methode beruht auf der Normalisierung der Suchresultate der einzelnen Suchmaschinen und wendet dann ein neues, gewichtetes, dem Durchschnitt ähnliches Maß an, um die verschiedenen Suchresultate zu einem gemeinsamen Konsensus-Score zu verbinden. Dabei konnten wir im Vergleich zu den einzelnen Suchmaschinen bis zu 63 % mehr Peptide identifizieren. In dem folgenden Kapitel präsentieren wir eine Anwendung von Methoden der Massenspektrometrie-basierten Proteomik zu einer wichtigen Fragestellungen in der Krebsforschung. Hierbei wurde die dynamische Veränderung der Proteinexpression in Tumorzellen nach Behandlung mit Kinase-Inhibitoren analysiert. Mit Hilfe der SILAC-Methode wurde die Wirkung der zwei Multi-Kinase-Inhibitoren Sorafenib und LY294002 in humanen Melanomzellen untersucht. In diesen Experimenten gelang es mehr als 5400 Proteine zu identifizieren und zu quantifizieren. Mit unseren experimentellen Untersuchungen in fünf verschiedenen Zeitpunkten erzielten wir eine bisher noch nie dargelegte Einsicht in die Proteinexpressionsdynamik als Funktion der Inhibitionsdauer. Mit Methoden der Clusteranalyse konnten wir zeigen, dass beide Inhibitoren verschiedene Cluster von Proteinen bilden, die in gleicher Weise reguliert sind. Für die Behandlung mit LY294002 konnten wir die bekannte m-Tor Inhibition bestätigen und neue Hypothesen über den Einfluß des Inhibitors auf die DNA Replikation formulieren. In ähnlicher Weise konnten wir durch die Untersuchungen zur Expressionskinetik nach der Behandlung mit Sorafenib bekannte Mechanismen, wie den Einfluß auf die Ras vermittelte Proliferation betätigen, aber unser Datensatz erlaubt auch neue Hypothesen und ermöglicht neue Einblicke, wie den Einfluß von Sorafenib auf die Induktion der Autophagie. Umfassende proteomische Datensätze bieten eine Fülle an Informationen und neue Wege ein biologisches System als Ganzes zu verstehen