Publications by authors named "Sebastien De Landtsheer"

Article Synopsis
  • Stratification of cancer patients aims to personalize oncology by predicting drug responses based on the molecular characteristics of cancer cells, utilizing multi-omic data.
  • A machine-learning framework using ensemble learning was developed to analyze this data and predict sensitivity to both common and experimental cancer treatments, with validated accuracy across frequent cancer types.
  • The approach not only identifies key omic layers linked to drug responsiveness but also suggests potential transcriptional markers that could assist clinicians in tailoring patient care.
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  • The circadian clock is a key biological regulator that affects cellular functions and can influence health outcomes, leading to interest in circadian-based therapies.
  • Aligning drug treatments with circadian rhythms can improve effectiveness and reduce side effects, but finding the best dosing times is still a challenge.
  • This research presents a high-throughput method using live imaging to study cancer cells, aiming to identify optimal times and conditions for drug treatments, ultimately enhancing anti-cancer therapies through circadian insights.
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Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins.

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Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms.

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Background: Metastasis is the predominant cause for cancer morbidity and mortality accounting for approximatively 90% of cancer deaths. The actin-bundling protein L-plastin has been proposed as a metastatic marker and phosphorylation on its residue Ser5 is known to increase its actin-bundling activity. We recently showed that activation of the ERK/MAPK signalling pathway leads to L-plastin Ser5 phosphorylation and that the downstream kinases RSK1 and RSK2 are able to directly phosphorylate Ser5.

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  • A significant portion of the global population suffers from metabolic diseases (MD), with projections indicating a potential doubling of cases in the coming decades, leading to additional health challenges like NAFLD and cardiomyopathy.
  • The investigation of genetic factors contributing to MD typically uses biological network analysis but faces issues like data bias and complexity in methodology.
  • The proposed approach introduces a straightforward, parameter-free method that considers the effects of database dependence and network topology, helping to identify key genes linked to MD and suggesting new candidates for further research.
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Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks.

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Metastatic melanoma remains a life-threatening disease because most tumors develop resistance to targeted kinase inhibitors thereby regaining tumorigenic capacity. We show the 2nd generation hexavalent TRAIL receptor-targeted agonist IZI1551 to induce pronounced apoptotic cell death in BRAF melanoma cells. Aiming to identify molecular changes that may confer IZI1551 resistance we combined Dynamic Bayesian Network modelling with a sophisticated regularization strategy resulting in sparse and context-sensitive networks and show the performance of this strategy in the detection of cell line-specific deregulations of a signalling network.

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Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks.

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Motivation: Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically.

Results: We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules.

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A new recombinant form representing a mosaic of HIV-1 subtype B and F1 and designated as CRF42_BF was identified in Luxembourg. We confirmed the inedited nature of CRF42_BF by near full-length genome characterization and retrieved a possible ancestor originating from Brazil. The demographic history of CRF42_BF in Luxembourg using Bayesian coalescent-based methods was investigated.

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Entry of Human Immunodeficiency Virus type 1 (HIV-1) into target cells is mediated by the CD4 receptor and a coreceptor, CCR5 or CXCR4. Maraviroc interferes with HIV entry by binding the CCR5 coreceptor. Virological failure to maraviroc-containing regimens can occur through the emergence of resistance, or through tropism evolution and broadened coreceptor usage.

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Noroviruses (NoV) in 78 wastewater samples from Luxembourg were quantified, cloned, and sequenced in 2008-2009. The concentrations of NoV genogroup II and the relative occurrences of certain genotypes changed significantly during the winter season. NoV genogroup I was frequently detected by real-time reverse transcription-PCR (RT-PCR), albeit at 30-fold lower concentrations than for genogroup II, hampering attempts to assess overall genetic diversity by the cloning/sequencing approach.

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To investigate the presence and persistence of avian influenza virus in African birds, we monitored avian influenza in wild and domestic birds in two different regions in Nigeria. We found low-pathogenic avian influenza (LPAI) H5N2 viruses in three spur-winged geese (Plectropterus gambensis) in the Hadejia-Nguru wetlands. Phylogenetic analyses revealed that all of the genes, except the non-structural (NS) genes, of the LPAI H5N2 viruses were more closely related to genes recently found in wild and domestic birds in Europe.

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Measles and rubella virus cause fever/rash diseases that are difficult to differentiate clinically. Both viruses can be detected in the same clinical specimens and are propagated on the same cell cultures. A single-tube multiplex TaqMan assay is described for the simultaneous and rapid detection of the full spectrum of known genetic variants.

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Although the WHO recommends a comprehensive genetic characterization, little is known about circulating strains and genotypes of rubella virus (RUBV) for many European countries. Studies investigating the genetic diversity of a sizeable number of strains from a certain location are rare. This study presents the first molecular characterization of isolates from Belarus.

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Genetic analysis of highly pathogenic avian influenza (H5N1) viruses from poultry and hooded vultures in Burkina Faso shows that these viruses belong to 1 of 3 sublineages initially found in Nigeria and later in other African countries. Hooded vultures could potentially be vectors or sentinels of influenza subtype H5N1, as are cats and swans elsewhere.

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