A dynamic model of the MYCN regulated DNA damage response in Neuroblastoma

IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014): Workshop on Empowering Systems Medicine Through Optimal Design of Experimentation and Computational Modeling, Belfast, Northern Ireland, 2-5 November 2014Neuroblastoma is the most common the most common cancer in infancy with an extremely heterogeneous phenotype that is mainly driven by the MYCN oncogene. The MYCN transcription factor and its amplification is commonly associated with poor prognosis in patients, although it has also been shown that elevated MYCN levels correlates with apoptosis sensitization in cells. HMGA1 is one of MYCN target genes and is involved in triggering apoptosis through a DNA Damage Response (DDR) by inducing ataxia-telangiectasia-mutated (ATM) gene expression. But HMGA1 is also involved in preventing apoptosis by directly binding HIPK2 and decreasing its presence in the nucleus, therefore decreasing phosphorylation of p53 at serine 46 which is required for the activation of p53 apoptotic targets. In this article, we propose a model in which MYCN protein regulates the HMGA1-ATM-p53 and HMGA1-HIPK2-p53 subsystems. Because the molecular details concerning the HMGA1-HMGA1 interaction are uncertain several possibilities were explored in simulations. Our model points towards an important role of MYCN-dependent regulation of HMGA1 expression levels and the subsequent HIPK2 nuclear/cytoplasmic re-localization and led to experimentally testable predictions that can discern between alternative model structures.  European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

On the personalised modelling of cancer signalling

6th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE 2016), Magdeburg, Germany, 9-12 October 2016Dynamic modelling has long been used to understand fundamental principles of cell signalling and its dysregulation in cancer. More recently these models have also been used to understand the individual risks of cancer patients, and predict their survival probabilities. However, the current methodologies for integrating tumour data and generating patient-specific simulations suffer from the lack of general applicability; they only work for cell signalling models in which only posttranslational protein modifications are considered, so that the total protein concentrations are conserved. Here, we present novel, generally applicable method. The method is based on a simple theoretical framework for modelling gene-regulation, and the indirect estimation of patient-specific parameters from tumour data. Because our method does not require time-invariance of the total-protein concentrations, it can be applied to models of any nature, including the many cancer signalling models involving gene-regulation.European Commission - Seventh Framework Programme (FP7

Establishment of a Real-Time PCR-Based Approach for Accurate Quantification of Bacterial RNA Targets in Water, Using Salmonella as a Model Organism

Quantitative PCR (Q-PCR) is a fast and efficient tool to quantify target genes. In eukaryotic cells, quantitative reverse transcription-PCR (Q-RT-PCR) is also used to quantify gene expression, with stably expressed housekeeping genes as standards. In bacteria, such stable expression of housekeeping genes does not occur, and the use of DNA standards leads to a broad underestimation. Therefore, an accurate quantification of RNA is feasible only by using appropriate RNA standards. We established and validated a Q-PCR method which enables the quantification of not only the number of copies of target genes (i.e., the number of bacterial cells) but also the number of RNA copies. The genes coding for InvA and the 16S rRNA of Salmonella enterica serovar Typhimurium were selected for the evaluation of the method. As DNA standards, amplified fragments of the target genes were used, whereas the same DNA standards were transcribed in vitro for the development of appropriate RNA standards. Salmonella cultures and environmental water samples inoculated with bacteria were then employed for the final testing. Both experimental approaches led to a sensitive, accurate, and reproducible quantification of the selected target genes and RNA molecules by Q-PCR and Q-RT-PCR. It is the first time that RNA standards have been successfully used for a precise quantification of the number of RNA molecules in prokaryotes. This demonstrates the potential of this approach for determining the presence and metabolic activity of pathogenic bacteria in environmental samples

Quantitative reverse transcription polymerase chain reaction analysis of Vibrio cholerae cells entering the viable but non-culturable state and starvation in response to cold shock.

We performed a comparative analysis of the Vibrio cholerae strain El Tor 3083 entering the viable but non-culturable (VBNC) state and starvation after incubation in artificial seawater (ASW) at 4 and 15 degrees C respectively. To this end, we determined bacterial culturability and membrane integrity, as well as the cellular levels of 16S rRNA and mRNA for the tuf, rpoS and relA genes, which were assessed by real-time quantitative reverse transcription polymerase chain reaction (Q-RT-PCR). Bacterial cells entering the VBNC state showed a 154, 5.1 x 10(3), 24- and 23-fold reduction in the number of copies of 16S rRNA and mRNA for tuf, rpoS and relA, in comparison to exponentially growing cells. The differences were less striking between cells in the VBNC and starvation states. The mRNA for relA was selectively increased in VBNC cells (3.2-folds), whereas a 3.9-fold reduction was observed for 16S rRNA. The obtained results confirmed that key activities of the cellular metabolism (i.e. tuf representing protein synthesis, and relA or rpoS stress response) were still detected in bacteria entering the VBNC state and starvation. These data suggest that the new Q-RT-PCR methodology, based on the selected RNA targets, could be successfully exploited for the identification (rRNA) of V. cholerae and assessment of its metabolic activity (tuf, rpoS, relA mRNA) in environmental samples