A computational model of thiopurine metabolism

PhD ThesisA computational model of thiopurine metabolism The thiopurines, azathioprine, mercaptopurine (MP) and thioguanine are used as immunosuppressants and in the treatment of leukaemia. These drugs undergo extensive metabolism to form their cytotoxic metabolites that correlate with drug efficacy and the likelihood of side effects. Although these inexpensive drugs are effective in many patients, a deeper understanding of thiopurine metabolism would enable better individualisation of therapy resulting in increased efficacy and safety. A computational model of MP metabolism using data generated from mercaptopurine treated MOLT-4 (human T-ALL cell line) by a novel liquid chromatography mass spectrometry assay was built in CoPaSi. The model qualitatively reproduced published data about the effects of changes in thiopurine methyltransferase activity on MP metabolism. In vitro studies showed that high concentrations of allopurinol, a xanthine oxidase inhibitor, reduced the sensitivity of MOLT-4 cells to MP from 2.9 μM to 43 μM (P<0.001), whereas lower concentrations of allopurinol slightly but not significantly increased the sensitivity of MOLT-4 cells to MP. Combination of MP and allopurinol treatment of MOLT-4 cells resulted in lower concentrations of thioguanine nucleotides and methylated thioinosine monophosphate metabolites compared to MP only treatment, as determined by liquid chromatography mass spectrometry. These data were used to extend the model of MP metabolism to test hypotheses that addition of allopurinol decreased methylated thioinosine monophosphate and increased the concentration of TGNs. The computational model suggested that the mechanism by which allopurinol interacts with MP metabolism is by inhibiting hypoxanthine guanine phosphoribosyl transferase resulting in altered levels of MeTIMP and TGNs

Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesEndothelial dysfunction contributes to sepsis outcome. Metabolic phenotypes associated with endothelial dysfunction are not well characterised in part due to difficulties in assessing endothelial metabolism in situ. Here, we describe the construction of iEC2812, a genome scale metabolic reconstruction of endothelial cells and its application to describe metabolic changes that occur following endothelial dysfunction. Metabolic gene expression analysis of three endothelial subtypes using iEC2812 suggested their similar metabolism in culture. To mimic endothelial dysfunction, an in vitro sepsis endothelial cell culture model was established and the metabotypes associated with increased endothelial permeability and glycocalyx loss after inflammatory stimuli were quantitatively defined through metabolomics. These data and transcriptomic data were then used to parametrize iEC2812 and investigate the metabotypes of endothelial dysfunction. Glycan production and increased fatty acid metabolism accompany increased glycocalyx shedding and endothelial permeability after inflammatory stimulation. iEC2812 was then used to analyse sepsis patient plasma metabolome profiles and predict changes to endothelial derived biomarkers. These analyses revealed increased changes in glycan metabolism in sepsis non-survivors corresponding to metabolism of endothelial dysfunction in culture. The results show concordance between endothelial health and sepsis survival in particular between endothelial cell metabolism and the plasma metabolome in patients with sepsis.RANNIS Landspitali Reykjavik Rigshospitalet Copenhage

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.Icelandic Research Fund Institute for Systems Biology's Translational Research Fellows Progra

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadSince their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage (proliferation, osteogenesis, and adipogenesis) that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs. Keywords: GEM; MSCs; adipogenesis; metabolic differences; metabolic reconstruction; osteogenesis.Icelandic Research Fun

Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients [version 1; peer review: 2 approved]

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas

Inhibition of ATR prevents macropinocytosis driven retraction of neurites and opposes invasion in GBM

Glioblastoma (GBM) is the most common and aggressive type of primary brain tumour and remains incurable despite decades of research. GBM are characterised by highly infiltrative growth patterns that contribute to the profound cognitive and neurological symptoms experienced by patients, and to inevitable recurrence following treatment. Novel treatments that reduce infiltration of the healthy brain have potential to ameliorate clinical symptoms and improve survival. Here, we report a novel role of the Ataxia telangiectasia and Rad 3 related kinase (ATR) in supporting the invasive properties of GBM cells through the regulation of macropinocytosis-driven internalisation of integrin adhesion receptors. We demonstrate that inhibition of ATR opposes GBM migration in vitro, and correspondingly reduces infiltrative behaviour in orthotopic mouse models. These results indicate that ATR inhibition, in addition to its use as a radiosensitiser, may be effective in reducing GBM infiltration and its associated symptoms

Metabolic and Transcriptional Changes across Osteogenic Differentiation of Mesenchymal Stromal Cells.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMesenchymal stromal cells (MSCs) are multipotent post-natal stem cells with applications in tissue engineering and regenerative medicine. MSCs can differentiate into osteoblasts, chondrocytes, or adipocytes, with functional differences in cells during osteogenesis accompanied by metabolic changes. The temporal dynamics of these metabolic shifts have not yet been fully characterized and are suspected to be important for therapeutic applications such as osteogenesis optimization. Here, our goal was to characterize the metabolic shifts that occur during osteogenesis. We profiled five key extracellular metabolites longitudinally (glucose, lactate, glutamine, glutamate, and ammonia) from MSCs from four donors to classify osteogenic differentiation into three metabolic stages, defined by changes in the uptake and secretion rates of the metabolites in cell culture media. We used a combination of untargeted metabolomic analysis, targeted analysis of 13C-glucose labelled intracellular data, and RNA-sequencing data to reconstruct a gene regulatory network and further characterize cellular metabolism. The metabolic stages identified in this proof-of-concept study provide a framework for more detailed investigations aimed at identifying biomarkers of osteogenic differentiation and small molecule interventions to optimize MSC differentiation for clinical applications.Icelandic Research Fun