miR-196b target screen reveals mechanisms maintaining leukemia stemness with therapeutic potential.

We have shown that antagomiR inhibition of miRNA miR-21 and miR-196b activity is sufficient to ablate MLL-AF9 leukemia stem cells (LSC) in vivo. Here, we used an shRNA screening approach to mimic miRNA activity on experimentally verified miR-196b targets to identify functionally important and therapeutically relevant pathways downstream of oncogenic miRNA in MLL-r AML. We found Cdkn1b (p27Kip1) is a direct miR-196b target whose repression enhanced an embryonic stem cell–like signature associated with decreased leukemia latency and increased numbers of leukemia stem cells in vivo. Conversely, elevation of p27Kip1 significantly reduced MLL-r leukemia self-renewal, promoted monocytic differentiation of leukemic blasts, and induced cell death. Antagonism of miR-196b activity or pharmacologic inhibition of the Cks1-Skp2–containing SCF E3-ubiquitin ligase complex increased p27Kip1 and inhibited human AML growth. This work illustrates that understanding oncogenic miRNA target pathways can identify actionable targets in leukemia

From condition-specific interactions towards the differential complexome of proteins

While capturing the transcriptomic state of a cell is a comparably simple effort with modern sequencing techniques, mapping protein interactomes and complexomes in a sample-specific manner is currently not feasible on a large scale. To understand crucial biological processes, however, knowledge on the physical interplay between proteins can be more interesting than just their mere expression. In this thesis, we present and demonstrate four software tools that unlock the cellular wiring in a condition-specific manner and promise a deeper understanding of what happens upon cell fate transitions. PPIXpress allows to exploit the abundance of existing expression data to generate specific interactomes, which can even consider alternative splicing events when protein isoforms can be related to the presence of causative protein domain interactions of an underlying model. As an addition to this work, we developed the convenient differential analysis tool PPICompare to determine rewiring events and their causes within the inferred interaction networks between grouped samples. Furthermore, we present a new implementation of the combinatorial protein complex prediction algorithm DACO that features a significantly reduced runtime. This improvement facilitates an application of the method for a large number of samples and the resulting sample-specific complexes can ultimately be assessed quantitatively with our novel differential protein complex analysis tool CompleXChange.Das Transkriptom einer Zelle ist mit modernen Sequenzierungstechniken vergleichsweise einfach zu erfassen. Die Ermittlung von Proteininteraktionen und -komplexen wiederum ist in großem Maßstab derzeit nicht möglich. Um wichtige biologische Prozesse zu verstehen, kann das Zusammenspiel von Proteinen jedoch erheblich interessanter sein als deren reine Expression. In dieser Arbeit stellen wir vier Software-Tools vor, die es ermöglichen solche Interaktionen zustandsbezogen zu betrachten und damit ein tieferes Verständnis darüber versprechen, was in der Zelle bei Veränderungen passiert. PPIXpress ermöglicht es vorhandene Expressionsdaten zu nutzen, um die aktiven Interaktionen in einem biologischen Kontext zu ermitteln. Wenn Proteinvarianten mit Interaktionen von Proteindomänen in Verbindung gebracht werden können, kann hierbei sogar alternatives Spleißen berücksichtigen werden. Als Ergänzung dazu haben wir das komfortable Differenzialanalyse-Tool PPICompare entwickelt, welches Veränderungen des Interaktoms und deren Ursachen zwischen gruppierten Proben bestimmen kann. Darüber hinaus stellen wir eine neue Implementierung des Proteinkomplex-Vorhersagealgorithmus DACO vor, die eine deutlich reduzierte Laufzeit aufweist. Diese Verbesserung ermöglicht die Anwendung der Methode auf eine große Anzahl von Proben. Die damit bestimmten probenspezifischen Komplexe können schließlich mit unserem neuartigen Differenzialanalyse-Tool CompleXChange quantitativ bewertet werden

RNA sequencing for the study of splicing

Amongst the many processes that shape the final set of RNA molecules present in eukaryote cells, splicing emerges as the most prominent mechanism for message diversification. In recent years, applications of high throughput sequencing to RNA, known as RNA sequencing, have opened new avenues for the study of transcriptome composition, and have enabled further characterisation of such mechanism. In this thesis, I focus on the application of this technology to the study of human transcript diversity and its potential impact on the protein repertoire. In the first results chapter, I explore the extent of transcriptome diversity by asking whether there is a preference for the production of specific alternative transcripts within each given gene. I show that while many alternative transcripts can be detected, the expression of most protein coding genes tends to be dominated by one single transcript (major transcript). Such findings are validated in the second chapter, and are further used to explore changes in splicing patterns in a disease context. By analysing healthy and tumor samples from kidney cancer patients, I show that most of the detected splicing alterations do not lead to big changes in the relative abundance of major transcripts, at least in a recurrent manner. In addition, I introduce a framework to visualise the most extreme changes in splicing and to evaluate their potential functional impact. In the third chapter, I investigate the role of spliceosome assembly dynamics on the regulation of splice site choice. I show that depletion of PRPF8, a core spliceosomal component, leads to the preferential retention of a subset of introns with weaker splice sites, and also introduces alterations in the rate of co-transcriptional splicing. Finally, in the last chapter, I explore the validation of changes in alternative transcript abundance at the protein level, through the integration of results derived from RNA sequencing datasets with those obtained from proteomics experiments. Altogether, the findings described in this thesis provide a global picture on the extent of alternative splicing in the diversification of the transcriptome, expand current knowledge on the splicing reaction, and open new possibilities for the integration of transcriptomics and proteomics data

Genomic and Post-Genomic Analysis of Human Chromosome 21 in Relation to the Pathogenesis of Trisomy 21 (Down Syndrome)

We performed an innovative systematic meta-analysis of gene expression profiles of whole normal human brain and heart to provide a quantitative transcriptome reference map of it, i.e. a typical reference value of expression for all the known, mapped and uncharacterized (unmapped) transcripts. For this reason, we used the software named TRAM (Transcriptome Mapper), which is able to generate transcriptome maps based on gene expression data from multiple sources. We also analyzed differential gene expression by comparing brain with human foetal brain, with a pool of non-brain tissues and with the three brain sub-region: cerebellum, cerebral cortex and hippocampus, the main regions severely affected with cognitive impairment, as seen in the case of trisomy 21. Data were downloaded from microarray databases, processed and analyzed using TRAM software and validated in vitro by assaying gene expression through several magnitude orders by "Real Time" reverse transcription polymerase chain reaction (RT-PCR). The excellent agreement between in silico and experimental data suggested that our transcriptome maps may be a useful quantitative reference benchmark for gene expression studies related to the human brain and heart. We also generated an integrated quantitative transcriptome map by systematic meta-analysis from all available gene expression profile datasets related to AMKL in paediatric age. The incidence of Acute Megakaryoblastic Leukemia (AMKL) is 500-fold higher in children with Down Syndrome (DS) compared with non-DS children. We present an integrated original model of the DS AMLK transcriptome, providing the identification of genes relevant for its pathophysiology which can potentially be new clinical markers. Finally, computational and molecular analysis of a highly restricted region of chromosome 21, which represents a strong candidate for typical DS features and is considered as intergenic, was performed. Northern Blot analysis and computational biology results show that HR-DSCR contain active loci bidirectionally transcribed