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  • 1
    Abstract: OBJECTIVE: The initial steps of pancreatic regeneration versus carcinogenesis are insufficiently understood. Although a combination of oncogenic Kras and inflammation has been shown to induce malignancy, molecular networks of early carcinogenesis remain poorly defined. DESIGN: We compared early events during inflammation, regeneration and carcinogenesis on histological and transcriptional levels with a high temporal resolution using a well-established mouse model of pancreatitis and of inflammation-accelerated KrasG12D-driven pancreatic ductal adenocarcinoma. Quantitative expression data were analysed and extensively modelled in silico. RESULTS: We defined three distinctive phases-termed inflammation, regeneration and refinement-following induction of moderate acute pancreatitis in wild-type mice. These corresponded to different waves of proliferation of mesenchymal, progenitor-like and acinar cells. Pancreas regeneration required a coordinated transition of proliferation between progenitor-like and acinar cells. In mice harbouring an oncogenic Kras mutation and challenged with pancreatitis, there was an extended inflammatory phase and a parallel, continuous proliferation of mesenchymal, progenitor-like and acinar cells. Analysis of high-resolution transcriptional data from wild-type animals revealed that organ regeneration relied on a complex interaction of a gene network that normally governs acinar cell homeostasis, exocrine specification and intercellular signalling. In mice with oncogenic Kras, a specific carcinogenic signature was found, which was preserved in full-blown mouse pancreas cancer. CONCLUSIONS: These data define a transcriptional signature of early pancreatic carcinogenesis and a molecular network driving formation of preneoplastic lesions, which allows for more targeted biomarker development in order to detect cancer earlier in patients with pancreatitis.
    Type of Publication: Journal article epub ahead of print
    PubMed ID: 27646934
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  • 2
    Keywords: BODY-WEIGHT ; FOOD-INTAKE ; ADIPOSE-TISSUE ; INSULIN-RESISTANCE ; NEUROPEPTIDE-Y ; mTOR ; LONG-TERM DEPRESSION ; IMPAIRS GLUCOSE-METABOLISM ; LEPTIN ACTION ; POMC NEURONS
    Abstract: The role of neuronal noncoding RNAs in energy control of the body is not fully understood. The arcuate nucleus (ARC) of the hypothalamus comprises neurons regulating food intake and body weight. Here we show that Dicer-dependent loss of microRNAs in these neurons of adult (DicerCKO) mice causes chronic overactivation of the signaling pathways involving phosphatidylinositol-3-kinase (PI3K), Akt, and mammalian target of rapamycin (mTOR) and an imbalance in the levels of neuropeptides, resulting in severe hyperphagic obesity. Similarly, the activation of PI3K-Akt-mTOR pathway due to Pten deletion in the adult forebrain leads to comparable weight increase. Conversely, the mTORC1 inhibitor rapamycin normalizes obesity in mice with an inactivated Dicer1 or Pten gene. Importantly, the continuous delivery of oligonucleotides mimicking microRNAs, which are predicted to target PI3K-Akt-mTOR pathway components, to the hypothalamus attenuates adiposity in DicerCKO mice. Furthermore, loss of miR-103 causes strong upregulation of the PI3K-Akt-mTOR pathway in vitro and its application into the ARC of the Dicer-deficient mice both reverses upregulation of Pik3cg, the mRNA encoding the catalytic subunit p110gamma of the PI3K complex, and attenuates the hyperphagic obesity. Our data demonstrate in vivo the crucial role of neuronal microRNAs in the control of energy homeostasis.
    Type of Publication: Journal article published
    PubMed ID: 25100599
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  • 3
    Abstract: Glioblastoma is the most aggressive brain tumor in adults with a median survival below 12 months in population-based studies. The main reason for tumor recurrence and progression is constitutive or acquired resistance to the standard of care of surgical resection followed by radiotherapy with concomitant and adjuvant temozolomide (TMZ/RT--〉TMZ). Here, we investigated the role of microRNA (miRNA) alterations as mediators of alkylator resistance in glioblastoma cells. Using microarray-based miRNA expression profiling of parental and TMZ-resistant cultures of three human glioma cell lines, we identified a set of differentially expressed miRNA candidates. From these, we selected miR-138 for further functional analyses as this miRNA was not only upregulated in TMZ-resistant versus parental cells, but also showed increased expression in vivo in recurrent glioblastoma tissue samples after TMZ/RT--〉TMZ treatment. Transient transfection of miR-138 mimics in glioma cells with low basal miR-138 expression increased glioma cell proliferation. Moreover, miR-138 overexpression increased TMZ resistance in long-term glioblastoma cell lines and glioma initiating cell cultures. The apoptosis regulator BIM was identified as a direct target of miR-138, and its silencing mediated the induced TMZ resistance phenotype. Altered sensitivity to apoptosis played only a minor role in this resistance mechanism. Instead, we identified the induction of autophagy to be regulated downstream of the miR-138/BIM axis and to promote cell survival following TMZ exposure. Our data thus define miR-138 as a glioblastoma cell survival-promoting miRNA associated with resistance to TMZ therapy in vitro and with tumor progression in vivo.
    Type of Publication: Journal article published
    PubMed ID: 26887050
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  • 4
    Abstract: Successful RNAi applications depend on strategies allowing robust and persistent expression of minimal gene silencing triggers without perturbing endogenous gene expression. Here, we propose a novel avenue which is integration of a promoterless shmiRNA, i.e. a shRNA embedded in a micro-RNA (miRNA) scaffold, into an engineered genomic miRNA locus. For proof-of-concept, we used TALE or CRISPR/Cas9 nucleases to site-specifically integrate an anti-hepatitis C virus (HCV) shmiRNA into the liver-specific miR-122/hcr locus in hepatoma cells, with the aim to obtain cellular clones that are genetically protected against HCV infection. Using reporter assays, Northern blotting and qRT-PCR, we confirmed anti-HCV shmiRNA expression as well as miR-122 integrity and functionality in selected cellular progeny. Moreover, we employed a comprehensive battery of PCR, cDNA/miRNA profiling and whole genome sequencing analyses to validate targeted integration of a single shmiRNA molecule at the expected position, and to rule out deleterious effects on the genomes or transcriptomes of the engineered cells. Importantly, a subgenomic HCV replicon and a full-length reporter virus, but not a Dengue virus control, were significantly impaired in the modified cells. Our original combination of DNA engineering and RNAi expression technologies benefits numerous applications, from miRNA, genome and transgenesis research, to human gene therapy.
    Type of Publication: Journal article published
    PubMed ID: 27614072
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  • 5
    Keywords: PARAMETER-ESTIMATION ; RANGE ; LIKELIHOOD
    Abstract: Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.
    Type of Publication: Journal article published
    PubMed ID: 24098642
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  • 6
    Abstract: IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.
    Type of Publication: Journal article published
    PubMed ID: 29062282
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  • 7
    Abstract: The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.
    Type of Publication: Journal article published
    PubMed ID: 29206104
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  • 8
    Abstract: Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify miRNAs involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis-associated miRNAs were identified in The Cancer Genome Atlas lung adenocarcinoma patient cohort (sequencing data; n = 449) and subsequently validated by qRT-PCR in an independent clinical cohort (n = 108). Overexpression of miRNAs located on chromosome 14q32 was associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan-Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma.Implications: This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. Mol Cancer Res; 16(3); 390-402. (c)2018 AACR.
    Type of Publication: Journal article published
    PubMed ID: 29330288
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  • 9
    Abstract: New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.
    Type of Publication: Journal article published
    PubMed ID: 29497170
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  • 10
    Keywords: LIKELIHOOD ; CHAIN MONTE-CARLO ; THERMODYNAMIC INTEGRATION ; METROPOLIS ALGORITHM
    Abstract: In this work we present results of a detailed Bayesian parameter estimation for an analysis of ordinary differential equation models. These depend on many unknown parameters that have to be inferred from experimental data. The statistical inference in a high-dimensional parameter space is however conceptually and computationally challenging. To ensure rigorous assessment of model and prediction uncertainties we take advantage of both a profile posterior approach and Markov chain Monte Carlo sampling. We analyzed a dynamical model of the JAK2/STAT5 signal transduction pathway that contains more than one hundred parameters. Using the profile posterior we found that the corresponding posterior distribution is bimodal. To guarantee efficient mixing in the presence of multimodal posterior distributions we applied a multi-chain sampling approach. The Bayesian parameter estimation enables the assessment of prediction uncertainties and the design of additional experiments that enhance the explanatory power of the model. This study represents a proof of principle that detailed statistical analysis for quantitative dynamical modeling used in systems biology is feasible also in high-dimensional parameter spaces.
    Type of Publication: Journal article published
    PubMed ID: 23602931
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