The Role of PDGF on hepatocellular Epithelial to Mesenchymal Transition

Das hepatozelluläre Leberkarzinom (HCC) ist eine der fünf verbreitesten und eine der tödlichsten Krebsarten weltweit. Die häufigste Ursache für die Entstehung von HCC ist eine chronische Infektion mit dem Hepatitis B Virus (HBV). Andere, weniger verbreitete Risikofaktoren sind die Infektion mit dem Hepatis C Virus (HBC), Vergiftung durch Nahrungsverunreinigung (z.B. Aflatoxin), Alkoholismus, Fettsucht oder Metastasierung aus anderen Organen. Diese Risikofaktoren können anhaltend die Leber schädigen und so eine Umgebung schaffen, die Fibrose, Zirrhose und letztlich die Entstehung von HCC fördert. Wir etablierten ein Mausmodell für HCC, bei dem wir immortalisierte p19ARF defiziente, Ha-Ras transformierte Hepatozyten verwendeten, die bei TGF-β Zugabe einen Übergang vom epithelförmigem zum mesenchymalen Zustand (EMT) durchmachen. Diese Zellen erfahren einen morphologischen Wandel, bei welchem sie sich von einem polarisiertem, epitheloiden Phänotyp zu einem fibroblastoiden Phänotyp transformieren. Dieser fibroblastoide Phänotyp besitzt eine veränderte Interaktion mit der extrazellulären Matrix (ECM), welche die Motilität und die Invasivität fördert. Eine Microarrayanalyse der Genexpression während der EMT ergab, dass PDGF Ligand und Rezeptor hochreguliert sind, was auf eine mögliche Beteiligung an der Transformation schliessen lässt. Wir konstruierten einen dominant-negativen PDGF-α Rezeptor (dnPDGFR-α), um die Auswirkungen von gesteigerter PDGF Signaltransduktion zu untersuchen. Dadurch stellten wir eine Beteiligung von PDGF bei der Motilität fest. Weiters konnten wir durch den Rezeptortyrosinkinaseinhibitor STI571 den PDGF Signaltransduktionsweg blockieren. Die Entdeckung von micro RNAs (miRs) als regulatives Netzwerk, und die Erkenntnis, dass PDGF ein miR-140 bindendes Motif im 3' UTR besitzt, sind ein aussichtsreicher Ansatz, um diesen Regulationsmechanismus zu verstehen und in bestehende Krebsmodelle, als auch in neue Strategien für die Krebstherapie zu integrieren.Hepatocellular carcinoma (HCC) is one of the five most common and most deadliest cancers worldwide. A common reason for HCC is the chronic infection with the Hepatitis B virus (HBV). while other risk factors are the infection with Hepatitis C virus (HCV), intoxication by food contaminants (e.g. aflatoxin), alcoholism, obesity or metastases from other organs. These risk factors are able to inflict continuous damage to the liver, which are accompanied by a microenvironment that promotes fibrosis, cirrhosis and finally the formation of HCC. In the present study, we were able to establish a mouse model for HCC using immortalised p19ARF deficient, Ha-Ras transformed hepatocytes, that conducted epithelial to mesenchymal transition (EMT) upon TGF-β administration. These cells performed a morphological switch, in which they transformed from a polarized, epithelial phenotype to a spindle shaped, fibroblastoid phenotype. The fibroblastoid phenotype exhibited a changed extracellular matrix (ECM) interaction, that advocated motility and invasiveness. A microarray analyses of gene expression during EMT revealed that PDGF ligand as well as receptor are upregulated, indicating a possible involvement in the transition process. Therefore we constructed a dominant-negative PDGF-α receptor (dnPDGFR-α), to study the effects of upregulated PDGF signalling. We were able to determine the involvement of autocrine PDGF signalling in motility. In addition we could specifically block PDGF signalling using the receptor tyrosine kinase inhibitor STI571. With the discovery of micro RNAs (miRs) acting as a regulatory network and the insight that PDGF has a miR-140 binding motif in its 3' UTR, we are looking forward to understand and integrate this mechanism of regulation into current cancer models as well as it poses a novel strategy for anti-cancer therapy

Critical Evaluation of Organic Thin-Film Transistor Models

Thin-film transistors (TFTs) represent a wide-spread tool to determine the charge-carrier mobility of materials. Mobilities and further transistor parameters like contact resistances are commonly extracted from the electrical characteristics. However, the trust in such extracted parameters is limited, because their values depend on the extraction technique and on the underlying transistor model. We propose a technique to establish whether a chosen model is adequate to represent the transistor operation. This two-step technique analyzes the electrical measurements of a series of TFTs with different channel lengths. The first step extracts the parameters for each individual transistor by fitting the full output and transfer characteristics to the transistor model. The second step checks whether the channel-length dependence of the extracted parameters is consistent with the model. We demonstrate the merit of the technique for distinct sets of organic TFTs that differ in the semiconductor, the contacts, and the geometry. Independent of the transistor set, our technique consistently reveals that state-of-the-art transistor models fail to reproduce the correct channel-length dependence. Our technique suggests that contemporary transistor models require improvements in terms of charge-carrier-density dependence of the mobility and/or the consideration of uncompensated charges in the transistor channel.Comment: 20 pages, 10 figure

Therapeutic Vulnerabilities in <i>FLT3</i>-Mutant AML Unmasked by Palbociclib

While significant progress has been made in the treatment of acute myeloid leukemia (AML), not all patients can be cured. Mutated in about 1/3 of de novo AML, the FLT3 receptor tyrosine kinase is an attractive target for drug development, activating mutations of the FLT3 map to the juxtamembrane domain (internal tandem duplications, ITD) or the tyrosine kinase domain (TKD), most frequently at codon D835. While small molecule tyrosine kinase inhibitors (TKI) effectively target ITD mutant forms, those on the TKD are not responsive. Moreover, FLT3 inhibition fails to induce a persistent response in patients due to mutational resistance. More potent compounds with broader inhibitory effects on multiple FLT3 mutations are highly desirable. We describe a critical role of CDK6 in the survival of FLT3+ AML cells as palbociclib induced apoptosis not only in FLT3&#8315;ITD+ cells but also in FLT3&#8315;D835Y+ cells. Antineoplastic effects were also seen in primary patient-derived cells and in a xenograft model, where therapy effectively suppressed tumor formation in vivo at clinically relevant concentrations. In cells with FLT3&#8315;ITD or -TKD mutations, the CDK6 protein not only affects cell cycle progression but also transcriptionally regulates oncogenic kinases mediating intrinsic drug resistance, including AURORA and AKT&#8212;a feature not shared by its homolog CDK4. While AKT and AURORA kinase inhibitors have significant therapeutic potential in AML, single agent activity has not been proven overly effective. We describe synergistic combination effects when applying these drugs together with palbociclib which could be readily translated to patients with AML bearing FLT3&#8315;ITD or &#8315;TKD mutations. Targeting synergistically acting vulnerabilities, with CDK6 being the common denominator, may represent a promising strategy to improve AML patient responses and to reduce the incidence of selection of resistance-inducing mutations

Variant profiling of evolving prokaryotic populations.

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at

Variant profiling of evolving prokaryotic populations.

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at

Distributed under Creative Commons CC-BY 4.0 Variant profiling of evolving prokaryotic populations

ABSTRACT Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at

Man-made microbial resistances in built environments

© The Author(s) 2019.Antimicrobial resistance is a serious threat to global public health, but little is known about the effects of microbial control on the microbiota and its associated resistome. Here we compare the microbiota present on surfaces of clinical settings with other built environments. Using state-of-the-art metagenomics approaches and genome and plasmid reconstruction, we show that increased confinement and cleaning is associated with a loss of microbial diversity and a shift from Gram-positive bacteria, such as Actinobacteria and Firmicutes, to Gram-negative such as Proteobacteria. Moreover, the microbiome of highly maintained built environments has a different resistome when compared to other built environments, as well as a higher diversity in resistance genes. Our results highlight that the loss of microbial diversity correlates with an increase in resistance, and the need for implementing strategies to restore bacterial diversity in certain built environments.This work was supported by a grant from the FWF (Austrian Science Fund) and the federal state government of Styria to G.B. (P29285-BBL). Sampling at the cleanroom facility was carried out under a contract with ESA and a subcontract with DLR given to C.M-E (ESTEC contract no. 4000103794/11/NL/EK) and for I.M. the work was supported by a grant from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement 640384)

Critical Evaluation of Organic Thin-Film Transistor Models

The thin-film transistor (TFT) is a popular tool for determining the charge-carrier mobility in semiconductors, as the mobility (and other transistor parameters, such as the contact resistances) can be conveniently extracted from its measured current-voltage characteristics. However, the accuracy of the extracted parameters is quite limited, because their values depend on the extraction technique and on the validity of the underlying transistor model. We propose here a new approach for validating to what extent a chosen transistor model is able to predict correctly the transistor operation. In the two-step fitting approach we have developed, we analyze the measured current-voltage characteristics of a series of TFTs with different channel lengths. In the first step, the transistor parameters are extracted from each individual transistor by fitting the output and transfer characteristics to the transistor model. In the second step, we check whether the channel-length dependence of the extracted parameters is consistent with the underlying model. We present results obtained from organic TFTs fabricated in two different laboratories using two different device architectures, three different organic semiconductors and five different materials combinations for the source and drain contacts. For each set of TFTs, our approach reveals that the state-of-the-art transistor models fail to reproduce correctly the channel-length-dependence of the transistor parameters. Our approach suggests that conventional transistor models require improvements in terms of the charge-carrier-density dependence of the mobility and/or in terms of the consideration of uncompensated charges in the carrier-accumulation channel