Untersuchungen zur Rolle von hand2 in der Schilddrüsenentwicklung des Zebrafischs

Zusammenfassung: Untersuchungen zur Rolle von hand2 in der Schilddrüsenentwicklung des Zebrafischs Die Schilddrüse ist ein endokrines Organ der Wirbeltiere. Erste Untersuchungen legen nahe, dass die Mechanismen ihrer Entwicklung bei den verschiedenen Wirbeltierarten sowohl auf morphologischer, als auch auf molekularer Ebene konserviert sind. Allerdings sind die molekularbiologischen Grundlagen der Schilddrüsenentwicklung noch weitgehend unbekannt. Im Rahmen dieser Doktorarbeit sollten genetische und molekularbiologische Mechanismen der frühen Schilddrüsenentwicklung im Modellorganismus Zebrafisch untersucht werden. Das Schilddrüsenprimordium im Zebrafisch entwickelt sich ab 24 hpf im ventralen Pharynxepithel und befindet sich ventral von der Mittelhirn-Hinterhirn-Grenze. Ferner liegt das Schilddrüsenprimordium asymmetrisch links von der Mittellinie in direktem Kontakt zum ventral, ebenfalls links gelegenen Herz. Ein Hinweis auf eine mögliche Beziehung des Schilddrüsenprimordiums zum Herz zeigt sich auch an Zebrafisch-Herzmutanten. Unter den bekannten Zebrafisch-Herzmutanten weisen viele eine defekte Schilddrüse auf. Eine dieser Mutanten ist die Zebrafisch hands off (han) Mutante, in der das Gen hand2 deletiert ist. Mit dieser Mutante wurden die meisten Experimente in dieser Arbeit durchgeführt. In der hands off Mutante ist zu beobachten, dass kein Schilddrüsenprimordium entsteht. Das Gen hand2 wird im Seitenplattenmesoderm, aus dem sich der myokardiale Teil des Herzens entwickelt, und in den Kiemenbögen exprimiert. Außerdem ist es in einer bilateralen Domäne zwischen dem ersten und zweiten Kiemenbogen, im Endoderm und in den Vorderflossen exprimiert. Durch Transplantation von Wildtypzellen in hands off Mutanten konnte gezeigt werden, dass hand2 in der Entwicklung des Schilddrüsenprimordiums nicht-zellautonom wirkt. Um das hand2 exprimierende Gewebe zu identifizieren, dass im Wildtyp einen nicht-zellautonomen Einfluss auf die Schilddrüse hat, wurde durch die Injektion von Morpholino-Oligonukleotiden die hand2 Kiemenbogenexpression ausgeschaltet. Dabei war zu erkennen, dass das Schilddrüsenprimordium unabhängig von dieser Expressionsdomäne entsteht. Dies deutet darauf hin, dass das Herz eine tragende Rolle bei der Entwicklung der Schilddrüse spielt. Im Herz sind viele sekretierte Faktoren der FGF Familie exprimiert. Um eine Rolle dieser Proteine in der Schilddrüsenentwicklung zu testen, wurden Mikropartikel, die mit FGF1 und FGF2 Proteinen beladen waren, implantiert. Dabei konnte in hands off Mutanten ein Rettung des Schilddrüsenprimordiums beobachtet werden. Daraus kann geschlossen werden, dass FGF-Proteine eine nicht-zellautonome Rolle in der Entwicklung der Schilddrüse "downstream" von hand2 spielen. Ob sie direkte induktive Signale darstellen, konnte aber im Rahmen dieser Arbeit nicht festgestellt werden

PBPK Models for CYP3A4 and P-gp DDI Prediction : A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug-drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole-body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration-time curve (AUC) ratios and 94% of the peak plasma concentration (Cmax) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme-mediated and transportermediated DDIs during model-informed drug development. All presented models are provided open-source and transparently documented

Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions : A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole

Background Drug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the efect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background. Objectives The frst objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners. Methods PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfbrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 diferent DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network. Results The newly developed models show a good performance, accurately describing plasma concentration–time profles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values. Conclusions Whole-body PBPK models of gemfbrozil, repaglinide, and pioglitazone have been built and qualifed for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms

Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

This study provides whole-body physiologically-based pharmacokinetic models of the strong index cytochrome P450 (CYP)1A2 inhibitor and moderate CYP3A4 inhibitor fluvoxamine and of the sensitive CYP1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug-drug interaction (DDI) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP1A2 substrate), rifampicin (moderate CYP1A2 inducer), and midazolam (sensitive index CYP3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve (AUC) ratios (AUC during DDI/AUC control) and seven of seven of the predicted DDI peak plasma concentration (Cmax ) ratios (Cmax during DDI/Cmax control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community

A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach

Early developmental specification of the thyroid gland depends on han-expressing surrounding tissue and on FGF signals

The thyroid is an endocrine gland in all vertebrates that develops from the ventral floor of the anterior pharyngeal endoderm. Unravelling the molecular mechanisms of thyroid development helps to understand congenital hypothyroidism caused by the absence or reduction of this gland in newborn humans. Severely reduced or absent thyroid-specific developmental genes concomitant with the complete loss of the functional gland in the zebrafish hands off (han, hand2) mutant reveals the han gene as playing a novel, crucial role in thyroid development. han-expressing tissues surround the thyroid primordium throughout development. Fate mapping reveals that, even before the onset of thyroid-specific developmental gene expression, thyroid precursor cells are in close contact with han-expressing cardiac lateral plate mesoderm. Grafting experiments show that han is required in surrounding tissue, and not in a cell-autonomous manner, for thyroid development. Loss of han expression in the branchial arches and arch-associated cells after morpholino knock-down of upstream regulator genes does not impair thyroid development, indicating that other han-expressing structures, most probably cardiac mesoderm, are responsible for the thyroid defects in han mutants. The zebrafish ace (fgf8) mutant has similar thyroid defects as han mutants, and chemical suppression of fibroblast growth factor (FGF) signalling confirms that this pathway is required for thyroid development. FGF-soaked beads can restore thyroid development in han mutants, showing that FGFs act downstream of or in parallel to han. These data suggest that loss of FGF-expressing tissue in han mutants is responsible for the thyroid defects

MuSiC: Identifying mutational significance in cancer genomes

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery

Targeting the microenvironment in the treatment of arteriovenous malformations

Extracranial arteriovenous malformations (AVMs) are regarded as rare diseases and are prone to complications such as pain, bleeding, relentless growth, and high volume of shunted blood. Due to the high vascular pressure endothelial cells of AVMs are exposed to mechanical stress. To control symptoms and lesion growth pharmacological treatment strategies are urgently needed in addition to surgery and interventional radiology. AVM cells were isolated from three patients and exposed to cyclic mechanical stretching for 24 h. Thalidomide and bevacizumab, both VEGF inhibitors, were tested for their ability to prevent the formation of circular networks and proliferation of CD31+ endothelial AVM cells. Furthermore, the effect of thalidomide and bevacizumab on stretched endothelial AVM cells was evaluated. In response to mechanical stress, VEGF gene and protein expression increased in patient AVM endothelial cells. Thalidomide and bevacizumab reduced endothelial AVM cell proliferation. Bevacizumab inhibited circular network formation of endothelial AVM cells and lowered VEGF gene and protein expression, even though the cells were exposed to mechanical stress. With promising in vitro results, bevacizumab was used to treat three patients with unresectable AVMs or to prevent regrowth after incomplete resection. Bevacizumab controlled bleeding, pulsation, and pain over the follow up of eight months with no patient-reported side effects. Overall, mechanical stress increases VEGF expression in the microenvironment of AVM cells. The monoclonal VEGF antibody bevacizumab alleviates this effect, prevents circular network formation and proliferation of AVM endothelial cells in vitro. The clinical application of bevacizumab in AVM treatment demonstrates effective symptom control with no side effects

Limited capability of MRI radiomics to predict primary tumor histology of brain metastases in external validation

Background Growing research demonstrates the ability to predict histology or genetic information of various malignancies using radiomic features extracted from imaging data. This study aimed to investigate MRI-based radiomics in predicting the primary tumor of brain metastases through internal and external validation, using oversampling techniques to address the class imbalance. Methods This IRB-approved retrospective multicenter study included brain metastases from lung cancer, melanoma, breast cancer, colorectal cancer, and a combined heterogenous group of other primary entities (5-class classification). Local data were acquired between 2003 and 2021 from 231 patients (545 metastases). External validation was performed with 82 patients (280 metastases) and 258 patients (809 metastases) from the publicly available Stanford BrainMetShare and the University of California San Francisco Brain Metastases Stereotactic Radiosurgery datasets, respectively. Preprocessing included brain extraction, bias correction, coregistration, intensity normalization, and semi-manual binary tumor segmentation. Two-thousand five hundred and twenty-eight radiomic features were extracted from T1w (± contrast), fluid-attenuated inversion recovery (FLAIR), and wavelet transforms for each sequence (8 decompositions). Random forest classifiers were trained with selected features on original and oversampled data (5-fold cross-validation) and evaluated on internal/external holdout test sets using accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). Results Oversampling did not improve the overall unsatisfactory performance on the internal and external test sets. Incorrect data partitioning (oversampling before train/validation/test split) leads to a massive overestimation of model performance. Conclusions Radiomics models’ capability to predict histologic or genomic data from imaging should be critically assessed; external validation is essential

Somatic mutations affect key pathways in lung adenocarcinoma

Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well- classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus are probably involved in carcinogenesis. The frequently mutated genes include tyrosine kinases, among them the EGFR homologue ERBB4; multiple ephrin receptor genes, notably EPHA3; vascular endothelial growth factor receptor KDR; and NTRK genes. These data provide evidence of somatic mutations in primary lung adenocarcinoma for several tumour suppressor genes involved in other cancers - including NF1, APC, RB1 and ATM - and for sequence changes in PTPRD as well as the frequently deleted gene LRP1B. The observed mutational profiles correlate with clinical features, smoking status and DNA repair defects. These results are reinforced by data integration including single nucleotide polymorphism array and gene expression array. Our findings shed further light on several important signalling pathways involved in lung adenocarcinoma, and suggest new molecular targets for treatment.National Human Genome Research InstituteWe thank A. Lash, M.F. Zakowski, M.G. Kris and V. Rusch for intellectual contributions, and many members of the Baylor Human Genome Sequencing Center, the Broad Institute of Harvard and MIT, and the Genome Center at Washington University for support. This work was funded by grants from the National Human Genome Research Institute to E.S.L., R.A.G. and R.K.W.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62885/1/nature07423.pd