Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG+ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects

Evaluation of Clinical Risk Factors to Predict High On-Treatment Platelet Reactivity and Outcome in Patients with Stable Coronary Artery Disease (PREDICT-STABLE)

Objectives This study was designed to identify the multivariate effect of clinical risk factors on high on-treatment platelet reactivity (HPR) and 12 months major adverse events (MACE) under treatment with aspirin and clopidogrel in patients undergoing non-urgent percutaneous coronary intervention (PCI). Methods 739 consecutive patients with stable coronary artery disease (CAD) undergoing PCI were recruited. On-treatment platelet aggregation was tested by light transmittance aggregometry. Clinical risk factors and MACE during one-year follow-up were recorded. An independent population of 591 patients served as validation cohort. Results Degree of on-treatment platelet aggregation was influenced by different clinical risk factors. In multivariate regression analysis older age, diabetes mellitus, elevated BMI, renal function and left ventricular ejection fraction were independent predictors of HPR. After weighing these variables according to their estimates in multivariate regression model, we developed a score to predict HPR in stable CAD patients undergoing elective PCI (PREDICT-STABLE Score, ranging 0-9). Patients with a high score were significantly more likely to develop MACE within one year of follow-up, 3.4% (score 0-3), 6.3% (score 4-6) and 10.3% (score 7-9); odds ratio 3.23, P=0.02 for score 7-9 vs. 0-3. This association was confirmed in the validation cohort. Conclusions Variability of on-treatment platelet function and associated outcome is mainly influenced by clinical risk variables. Identification of high risk patients (e.g. with high PREDICT-STABLE score) might help to identify risk groups that benefit from more intensified antiplatelet regimen. Additional clinical risk factor assessment rather than isolated platelet function-guided approaches should be investigated in future to evaluate personalized antiplatelet therapy in stable CAD-patients

A novel EGFR inhibitor acts as potent tool for hypoxia-activated prodrug systems and exerts strong synergistic activity with VEGFR inhibition in vitro and in vivo

Small-molecule EGFR inhibitors have distinctly improved the overall survival especially in EGFR-mutated lung cancer. However, their use is often limited by severe adverse effects and rapid resistance development. To overcome these limitations, a hypoxia-activatable Co(III)-based prodrug (KP2334) was recently synthesized releasing the new EGFR inhibitor KP2187 in a highly tumor-specific manner only in hypoxic areas of the tumor. However, the chemical modifications in KP2187 necessary for cobalt chelation could potentially interfere with its EGFR-binding ability. Consequently, in this study, the biological activity and EGFR inhibition potential of KP2187 was compared to clinically approved EGFR inhibitors. In general, the activity as well as EGFR binding (shown in docking studies) was very similar to erlotinib and gefitinib (while other EGFR-inhibitory drugs behaved different) indicating no interference of the chelating moiety with the EGFR binding. Moreover, KP2187 significantly inhibited cancer cell proliferation as well as EGFR pathway activation in vitro and in vivo. Finally, KP2187 proved to be highly synergistic with VEGFR inhibitors such as sunitinib. This indicates that KP2187releasing hypoxia-activated prodrug systems are promising candidates to overcome the clinically observed enhanced toxicity of EGFR-VEGFR inhibitor combination therapies

Method specific calibration corrects for DNA extraction method effects on relative telomere length measurements by quantitative PCR

Telomere length (TL) is increasingly being used as a biomarker in epidemiological, biomedical and ecological studies. A wide range of DNA extraction techniques have been used in telomere experiments and recent quantitative PCR (qPCR) based studies suggest that the choice of DNA extraction method may influence average relative TL (RTL) measurements. Such extraction method effects may limit the use of historically collected DNA samples extracted with different methods. However, if extraction method effects are systematic an extraction method specific (MS) calibrator might be able to correct for them, because systematic effects would influence the calibrator sample in the same way as all other samples. In the present study we tested whether leukocyte RTL in blood samples from Holstein Friesian cattle and Soay sheep measured by qPCR was influenced by DNA extraction method and whether MS calibration could account for any observed differences. We compared two silica membrane-based DNA extraction kits and a salting out method. All extraction methods were optimized to yield enough high quality DNA for TL measurement. In both species we found that silica membrane-based DNA extraction methods produced shorter RTL measurements than the non-membrane-based method when calibrated against an identical calibrator. However, these differences were not statistically detectable when a MS calibrator was used to calculate RTL. This approach produced RTL measurements that were highly correlated across extraction methods (r > 0.76) and had coefficients of variation lower than 10% across plates of identical samples extracted by different methods. Our results are consistent with previous findings that popular membrane-based DNA extraction methods may lead to shorter RTL measurements than non-membrane-based methods. However, we also demonstrate that these differences can be accounted for by using an extraction method-specific calibrator, offering researchers a simple means of accounting for differences in RTL measurements from samples extracted by different DNA extraction methods within a study

Center for the Study and Observation of the Universe in Portaria of Pilio

Στο πλαίσιο της προσωπικής μου ενασχόλησης με την παρατηρησιακή αστρονομία και κατόπιν συνεννόησης με την Εταιρία Αστρονομίας και Διαστήματος, μια εταιρία με έδρα την πόλη του Βόλου και με πολύπλευρες δράσεις στο ενεργητικό της, αποφάσισα να σχεδιάζω ένα αστεροσκοπείο ερευνητικού αλλά κυρίως εκπαιδευτικού χαρακτήρα στο νομό Μαγνησίας. Ως περιοχή επέμβασης επιλέχθηκε ο οικισμός της Πορταριάς στο Πήλιο, ως τόπος ο οποίος ήδη προσελκύει αρκετούς επισκέπτες και πληροί τις προϋποθέσεις φωτορύπανσης. Το οικόπεδο επέμβασης εντοπίζεται έξω από τον οικισμό, εμφανίζει έντονη κλίση εδάφους και προνομιακή θέα. Σε ό, τι αφορά το κτίριο ως λύση, μεγάλο μέρος του είναι υπόσκαφο, επιχειρώντας κατά το δυνατό να εντάξει την μεγάλη του κλίμακα στον όμορο οικισμό και το φυσικό περιβάλλον. Κύρια στοιχεία του από εμφανές ωπλισμένο σκυρόδεμα αποτελούν το ανηφορικό μονοπάτι εισόδου, η τεθλασμένη μπροστινή όψη και το πυργάκι που φιλοξενεί το κύριο τηλεσκόπιο. Εξίσου χαρακτηριστικά στοιχεία αποτελούν δύο μεταλλικές κατασκευές, η κύρια κατακόρυφη κίνηση του κτιρίου και η εξωτερική εξέδρα θέασης. Χωρικά, το κτίριο περιλαμβάνει αμφιθέατρο, πλανητάριο, αίθουσα κύριου τηλεσκοπίου, βιβλιοθήκη, χώρο εκθέσεων, γραφεία μελέτης δεδομένων, συσκέψεων και διοίκησης, κυλικείο, WC και parking. Σημαντική κρίνεται και η εξωτερική ερασιτεχνική παρατήρηση με πτυσσόμενα τηλεσκόπια στα πλατώματα που δημιουργούνται.Being involved in the observational astronomy and after the consultation of the Astronomy and Space Company, a company based in the city of Volos and with multifaceted action under its belt, I decided to design an observatory, of research but mainly of educational character. The chosen area of intervention was Portaria village, in Pilio mountain, as an location that already attracts lots of visitors and also qualifies the terms of light pollution. The plot of intervention is located outside the village, has sharply inclined terrain and a magnificent view. Regarding the building as an approach, a big part of it is underground, attempting as possible to integrate it’s big scale in the nearby village and in it’s natural environment. It’s main elements by reinforced concrete, are it’s uphill path of entrance, the front façade as a crooked line and the small scale tower that hosts the main telescope. Of equal importance, are elements of metal construction such as the main vertical movement of the building and the outdoor view platform. Spatially, the building includes an auditorium, a planetarium, the main telescope hall, a library, an exhibition space, offices for data research, meetings and management, canteen, WC and parking. Also important is the outdoor amateur observation which can be achieved by foldind telescopes on the plateaus around the building.Θεόδωρος Αθ. Τόλιο

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword “bioinformatics”). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature. © 201

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword “bioinformatics”). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.This article is based upon work from COST Action 17104 STRATAGEM, supported by CoST (European Cooperation in Science and Technology).Peer reviewe