Background Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes. Methods Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference). Results We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups. Conclusions We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

Background Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes. Methods Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference). Results We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups. Conclusions We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

Genomweite Bemühungen haben eine große Anzahl langer nichtkodierender RNAs (lncRNAs) identifiziert, obwohl ihre möglichen Funktionen weitgehend rätselhaft bleiben. Hier verwendeten wir ein System zur synchronisierten Blüteninduktion in Arabidopsis, um 4106 blütenbezogene lange intergene RNAs (lincRNAs) zu identifizieren. Blütenbezogene lincRNAs sind typischerweise mit funktionellen Enhancern assoziiert, die bidirektional transkribiert werden und mit verschiedenen funktionellen Genmodulen assoziiert sind, die mit der Entwicklung von Blütenorganen zusammenhängen, die durch Koexpressionsnetzwerkanalyse aufgedeckt wurden. Die Master-regulatorischen Transkriptionsfaktoren (TFs) APETALA1 (AP1) und SEPALLATA3 (SEP3) binden an lincRNA-assoziierte Enhancer. Die Bindung dieser TFs korreliert mit der Zunahme der lincRNA-Transkription und fördert möglicherweise die Zugänglichkeit von Chromatin an Enhancern, gefolgt von der Aktivierung einer Untergruppe von Zielgenen. Darüber hinaus ist die Evolutionsdynamik von lincRNAs in Pflanzen, einschließlich nicht blühender Pflanzen, noch nicht bekannt, und das Expressionsmuster in verschiedenen Pflanzenarten war ziemlich unbekannt. Hier identifizierten wir Tausende von lincRNAs in 26 Pflanzenarten, einschließlich nicht blühender Pflanzen. Ein direkter Vergleich von lincRNAs zeigt, dass die meisten lincRNAs speziesspezifisch sind und das Expressionsmuster von lincRNAs einen hohen Transkriptionsumsatz nahe legt. Darüber hinaus zeigen konservierte lincRNAs eine aktive Regulation durch Transkriptionsfaktoren wie AP1 und SEP3. Konservierte lincRNAs zeigen eine konservierte blütenbezogene Funktionalität sowohl in der Brassicaceae- als auch in der Grasfamilie. Die Evolutionslandschaft von lincRNAs in Pflanzen liefert wichtige Einblicke in die Erhaltung und Funktionalität von lincRNAs. / Genome-wide efforts have identified a large number of long non-coding RNAs (lncRNAs), although their potential functions remain largely enigmatic. Here, we used a system for synchronized floral induction in Arabidopsis to identify 4106 flower-related long intergenic RNAs (lincRNAs). Flower-related lincRNAs are typically associated with functional enhancers which are bi-directionally transcribed and are associated with diverse functional gene modules related to floral organ development revealed by co-expression network analysis. The master regulatory transcription factors (TFs) APETALA1 (AP1) and SEPALLATA3 (SEP3) bind to lincRNA-associated enhancers. The binding of these TFs is correlated with the increase in lincRNA transcription and potentially promotes chromatin accessibility at enhancers, followed by activation of a subset of target genes. Furthermore, the evolutionary dynamics of lincRNAs in plants including non-flowering plants still remain to be elusive and the expression pattern in different plant species was quite unknown. Here, we identified thousands of lincRNAs in 26 plant species including non-flowering plants, and allow us to infer sequence conserved and synteny based homolog lincRNAs, and explore conserved characteristics of lincRNAs during plants evolution. Direct comparison of lincRNAs reveals most lincRNAs are species-specific and the expression pattern of lincRNAs suggests their high evolutionary gain and loss. Moreover, conserved lincRNAs show active regulation by transcriptional factors such as AP1 and SEP3. Conserved lincRNAs demonstrate conserved flower related functionality in both the Brassicaceae and grass family. The evolutionary landscape of lincRNAs in plants provide important insights into the conservation and functionality of lincRNAs.

Hochdurchsatzverfahren quantifizieren eine Vielzahl zellulärer Komponenten, können aber selten deren Interaktionen beschreiben. Daher wurden in den letzten 20 Jahren verschiedenste Netzwerk-Rekonstruktionsmethoden entwickelt. Insbesondere Perturbationsdaten erlauben dabei Rückschlüsse über funktionelle Mechanismen in der Genregulierung, Signal Transduktion, intra-zellulärer Kommunikation und anderen Prozessen zu ziehen. Dennoch bleibt Netzwerkinferenz ein ungelöstes Problem, weil die meisten Methoden auf ungeeigneten Annahmen basieren und die Identifizierbarkeit von Netzwerkkanten nicht aufklären. Diesbezüglich beschreibt diese Dissertation eine neue Rekonstruktionsmethode, die auf einfachen Annahmen von Perturbationsausbreitung basiert. Damit ist sie in verschiedensten Zusammenhängen anwendbar und übertrifft andere Methoden in Standard-Benchmarks. Für MAPK und PI3K Signalwege in einer Adenokarzinom-Zellline generiert sie plausible Netzwerkhypothesen, die unterschiedliche Sensitivitäten von PI3K-Mutanten gegenüber verschiedener Inhibitoren überzeugend erklären. Weiterhin wird gezeigt, dass sich Netzwerk-Identifizierbarkeit durch ein intuitives Max-Flow Problem beschreiben lässt. Dieses analytische Resultat erlaubt effektive, identifizierbare Netzwerke zu ermitteln und das experimentelle Design aufwändiger Perturbationsexperimente zu optimieren. Umfangreiche Tests zeigen, dass der Ansatz im Vergleich zu zufällig generierten Perturbationssequenzen die Anzahl der für volle Identifizierbarkeit notwendigen Perturbationen auf unter ein Drittel senkt. Schließlich beschreibt die Dissertation eine mathematische Weiterentwicklung der Modular Response Analysis. Es wird gezeigt, dass sich das Problem als analytisch lösbare orthogonale Regression approximieren lässt. Dies erlaubt eine drastische Reduzierung des nummerischen Aufwands, womit sich deutlich größere Netzwerke rekonstruieren und neueste Hochdurchsatz-Perturbationsdaten auswerten lassen. / 'Omics' technologies provide extensive quantifications of components of biological systems but rarely characterize the interactions between them. To fill this gap, various network reconstruction methods have been developed over the past twenty years. Using perturbation data, these methods can deduce functional mechanisms in gene regulation, signal transduction, intra-cellular communication and many other cellular processes. Nevertheless, this reverse engineering problem remains essentially unsolved because inferred networks are often based on inapt assumptions, lack interpretability as well as a rigorous description of identifiability. To overcome these shortcoming, this thesis first presents a novel inference method which is based on a simple response logic. The underlying assumptions are so mild that the approach is suitable for a wide range of applications while also outperforming existing methods in standard benchmark data sets. For MAPK and PI3K signalling pathways in an adenocarcinoma cell line, it derived plausible network hypotheses, which explain distinct sensitivities of PI3K mutants to targeted inhibitors. Second, an intuitive maximum-flow problem is shown to describe identifiability of network interactions. This analytical result allows to devise identifiable effective network models in underdetermined settings and to optimize the design of costly perturbation experiments. Benchmarked on a database of human pathways, full network identifiability is obtained with less than a third of the perturbations that are needed in random experimental designs. Finally, the thesis presents mathematical advances within Modular Response Analysis (MRA), which is a popular framework to quantify network interaction strengths. It is shown that MRA can be approximated as an analytically solvable total least squares problem. This insight drastically reduces computational complexity, which allows to model much bigger networks and to handle novel large-scale perturbation data.