Statistische Analyse von hochdimensionalen toxikologischen Expressionsdaten

Hochdurchsatz-Technologien spielen eine immer größer werdende Rolle in der biologischmedizinischen Forschung. Sie erlauben eine gleichzeitige Messung von Tausenden von biologisch relevanten Messgrößen. So lassen sich zum Beispiel die Expressionen mehrerer Gene mit Hilfe des Microarray-Chips des Herstellers Affymetrix gleichzeitig bestimmen. Die Aufbereitung, Analyse und Interpretation dieser Daten stellen die Wissenschaft jedoch vor vielen Herausforderungen. In der vorliegenden Arbeit nahm man sich zum Ziel, eine Abfolge von statistischen Methoden und Verfahren bereitzustellen, welches dem Anwender ermöglicht, Microarray-Daten sequentiell zu untersuchen. Dazu gehören sowohl deskriptive als auch induktive Analysen. In dieser Arbeit wurde die vorgestellte Verfahrenabfolge ("Pipeline") auf Daten ausgewertet, welche im Rahmen der Toxizitätsstudien erhoben wurden. Die Analysen der zellulären Reaktion sowohl auf steigende Konzentration ("Konzentrationsstudien") als auch auf verschiedene Typen von Substanzen ("Klassifikationsstudien") standen dabei im Vordergrund. Im ersten Schritt wurden die Daten mit Hilfe der Hauptkomponenten- bzw. Clusteranalyse graphisch visualisiert. Dies erlaubte erste Eindrücke bezüglich Datenqualität zu gewinnen. In nächsten Schritten wurden die unterschiedlich exprimierte Gene bestimmt, welche den dynamischen Veränderungen innerhalb der Zelle zu Grunde liegen. Diese "Momentaufnahmen"lassen sich auf Anreicherung innerhalb bekannter biologischer Signaturen testen und somit erste Schlüsse auf zelluläre Prozesse erhalten. Mögliche Quellen der nicht-biologischen Varianz lassen sich mit Hilfe des erörterten ComBat-Verfahrens reduzieren. Bei der Auswertung von Klassifikationsstudien wurden folgende Ergebnisse festgestellt: - Eine Vorauswahl von Prädiktoren erlaubt eine biologische Interpretation und ermöglicht eine sinnvolle Einteilung von Substanzen. Die Klassifikationsgüte wurde dabei im Rahmen einer Kreuzvalidierung bestimmt und auf einem externen Datensatz bestätigt. - Anzahl der technischen Replikate darf zu Gunsten der Vergrößerung der Substanzenanzahl verkleinert werten. - Die analysierten Verfahren zeigen sich gegenüber dem hinzugefügten Rauschen robust. Die gewonnenen Ergebnisse sind teilweise in referierten Zeitschriften veröffentlicht worden

Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach

Developmental neurotoxicity (DNT) and many forms of reproductive toxicity (RT) often manifest themselves in functional deficits that are not necessarily based on cell death, but rather on minor changes relating to cell differentiation or communication. The fields of DNT/RT would greatly benefit from in vitro tests that allow the identification of toxicant-induced changes of the cellular proteostasis, or of its underlying transcriptome network. Therefore, the 'human embryonic stem cell (hESC)- derived novel alternative test systems (ESNATS)' European commission research project established RT tests based on defined differentiation protocols of hESC and their progeny. Valproic acid (VPA) and methylmercury (MeHg) were used as positive control compounds to address the following fundamental questions: (1) Does transcriptome analysis allow discrimination of the two compounds? (2) How does analysis of enriched transcription factor binding sites (TFBS) and of individual probe sets (PS) distinguish between test systems? (3) Can batch effects be controlled? (4) How many DNA microarrays are needed? (5) Is the highest non-cytotoxic concentration optimal and relevant for the study of transcriptome changes? VPA triggered vast transcriptional changes, whereas MeHg altered fewer transcripts. To attenuate batch effects, analysis has been focused on the 500 PS with highest variability. The test systems differed significantly in their responses (\20 % overlap). Moreover, within one test system, little overlap between the PS changed by the two compounds has been observed. However, using TFBS enrichment, a relatively large 'common response' to VPA and MeHg could be distinguished from 'compound-specific' responses. In conclusion, the ESNATS assay battery allows classification of human DNT/RT toxicants on the basis of their transcriptome profiles.EU/FP7/ESNATSDFGDoerenkamp-Zbinden Foundatio

Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach

Developmental neurotoxicity (DNT) and many forms of reproductive toxicity (RT) often manifest themselves in functional deficits that are not necessarily based on cell death, but rather on minor changes relating to cell differentiation or communication. The fields of DNT/RT would greatly benefit from in vitro tests that allow the identification of toxicant-induced changes of the cellular proteostasis, or of its underlying transcriptome network. Therefore, the 'human embryonic stem cell (hESC)-derived novel alternative test systems (ESNATS)' European commission research project established RT tests based on defined differentiation protocols of hESC and their progeny. Valproic acid (VPA) and methylmercury (MeHg) were used as positive control compounds to address the following fundamental questions: (1) Does transcriptome analysis allow discrimination of the two compounds? (2) How does analysis of enriched transcription factor binding sites (TFBS) and of individual probe sets (PS) distinguish between test systems? (3) Can batch effects be controlled? (4) How many DNA microarrays are needed? (5) Is the highest non-cytotoxic concentration optimal and relevant for the study of transcriptome changes? VPA triggered vast transcriptional changes, whereas MeHg altered fewer transcripts. To attenuate batch effects, analysis has been focused on the 500 PS with highest variability. The test systems differed significantly in their responses (<20 % overlap). Moreover, within one test system, little overlap between the PS changed by the two compounds has been observed. However, using TFBS enrichment, a relatively large 'common response' to VPA and MeHg could be distinguished from 'compound-specific' responses. In conclusion, the ESNATS assay battery allows classification of human DNT/RT toxicants on the basis of their transcriptome profiles

Motor neurons control blood vessel patterning in the developing spinal cord

Formation of a precise vascular network within the central nervous system is of critical importance to assure delivery of oxygen and nutrients and for accurate functionality of neuronal networks. Vascularization of the spinal cord is a highly stereotypical process. However, the guidance cues controlling blood vessel patterning in this organ remain largely unknown. Here we describe a new neuro-vascular communication mechanism that controls vessel guidance in the developing spinal cord. We show that motor neuron columns remain avascular during a developmental time window, despite expressing high levels of the pro-angiogenic vascular endothelial growth factor (VEGF). We describe that motor neurons express the VEGF trapping receptor sFlt1 via a Neuropilin-1-dependent mechanism. Using a VEGF gain-of-function approach in mice and a motor neuron-specific sFlt1 loss-of-function approach in chicken, we show that motor neurons control blood vessel patterning by an autocrine mechanism that titrates motor neuron-derived VEGF via their own expression of sFlt1

WEADE: A workflow for enrichment analysis and data exploration.

Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis downstream of the enrichment calculation is missing so far. With WEADE we present a free web application that offers an integrated workflow for the exploration of genomic data combining enrichment analysis with a versatile set of tools to directly compare and intersect experiments or candidate gene lists of any size or origin including cross-species data. Lastly, WEADE supports the graphical representation of output data in the form of functional interaction networks based on prior knowledge, allowing users to go from plain expression data to functionally relevant candidate sub-lists in an interactive and consistent manner

EDI3 links choline metabolism to integrin expression, cell adhesion and spreading

<div><p>Endometrial carcinoma differential 3 (EDI3) was the first member of the glycerophosphodiesterase (GDE) protein family shown to be associated with cancer. Our initial work demonstrated that endometrial and ovarian cancer patients with primary tumors overexpressing EDI3 had a higher risk of developing metastasis and decreased survival. Further analysis indicated that EDI3 cleaves glycerophosphocholine to choline and glycerol-3-phosphate, increases the levels of active PKC, and enhances the migratory activity of tumor cells. Despite these initial findings, EDI3 remained mainly uncharacterized. Therefore, to obtain an overview of processes in which EDI3 may be involved, gene array analysis was performed using MCF-7 breast cancer cells after EDI3 knockdown compared with a non-targeting control siRNA. Several biological motifs were altered, including an enrichment of genes involved in integrin-mediated signaling. More specifically, silencing of EDI3 in MCF-7 and OVCAR-3 cells was associated with reduced expression of the key receptor subunit integrin β1, leading to decreased cell attachment and spreading accompanied by delayed formation of cell protrusions. To confirm these results, we stably overexpressed EDI3 in MCF-7 cells which led to elevated integrin β1 expression associated with enhanced cell attachment and spreading - two processes critical for metastasis. In conclusion, our data provide further insight into the role of EDI3 during cancer progression.</p></div

Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement

Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice