Computational models of melanoma.

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research

Clinical update on head and neck cancer: molecular biology and ongoing challenges.

Head and neck squamous cell carcinomas (HNSCCs) are an aggressive, genetically complex and difficult to treat group of cancers. In lieu of truly effective targeted therapies, surgery and radiotherapy represent the primary treatment options for most patients. But these treatments are associated with significant morbidity and a reduction in quality of life. Resistance to both radiotherapy and the only available targeted therapy, and subsequent relapse are common. Research has therefore focussed on identifying biomarkers to stratify patients into clinically meaningful groups and to develop more effective targeted therapies. However, as we are now discovering, the poor response to therapy and aggressive nature of HNSCCs is not only affected by the complex alterations in intracellular signalling pathways but is also heavily influenced by the behaviour of the extracellular microenvironment. The HNSCC tumour landscape is an environment permissive of these tumours' aggressive nature, fostered by the actions of the immune system, the response to tumour hypoxia and the influence of the microbiome. Solving these challenges now rests on expanding our knowledge of these areas, in parallel with a greater understanding of the molecular biology of HNSCC subtypes. This update aims to build on our earlier 2014 review by bringing up to date our understanding of the molecular biology of HNSCCs and provide insights into areas of ongoing research and perspectives for the future

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.

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. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information

Thermodynamically constrained averaging theory for cancer growth modelling

In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three-dimensional biological systems of higher order. The multiplicity and time dependency of cellular system boundaries, mechanical phenomena and spatial concentration gradients affect the intercellular relations and communication of biochemical networks. These environmental effects can be integrated with our promising cancer modelling environment, that is based on thermodynamically constrained averaging theory (TCAT). Especially, the TCAT parameter viscosity can be used as critical player in tumour evolution. Strong cell-cell contacts and a high degree of differentiation make cancer cells viscous and support compact tumour growth with high tumour cell density and accompanied displacement of the extracellular material. In contrast, dedifferentiation and losing of cell-cell contacts make cancer cells more fluid and lead to an infiltrating tumour growth behaviour without resistance due to the ECM. The fast expanding tumour front of the invasive type consumes oxygen and the limited oxygen availability behind the invasive front results automatically in a much smaller average tumour cell density in the tumour core. The proposed modelling technique is most suitable for tumour growth phenomena in stiff tissues like skin or bone with high content of extracellular matrix

FALCON: A Toolbox for the Fast Contextualisation of Logical Networks.

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 contextualise logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualise networks, which includes a pipeline for conducting parameter analysis, knockouts, and easy and fast model investigation. The contextualised models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation: FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON . FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Hypoxia Routes Tryptophan Homeostasis Towards Increased Tryptamine Production

The liver is the central hub for processing and maintaining homeostatic levels of dietary nutrients especially essential amino acids such as tryptophan (Trp). Trp is required not only to sustain protein synthesis but also as a precursor for the production of NAD, neurotransmitters and immunosuppressive metabolites. In light of these roles of Trp and its metabolic products, maintaining homeostatic levels of Trp is essential for health and well-being. The liver regulates global Trp supply by the immunosuppressive enzyme tryptophan-2,3-dioxygenase (TDO2), which degrades Trp down the kynurenine pathway (KP). In the current study, we show that isolated primary hepatocytes when exposed to hypoxic environments, extensively rewire their Trp metabolism by reducing constitutive Tdo2 expression and differentially regulating other Trp pathway enzymes and transporters. Mathematical modelling of Trp metabolism in liver cells under hypoxia predicted decreased flux through the KP while metabolic flux through the tryptamine branch significantly increased. In line, the model also revealed an increased accumulation of tryptamines under hypoxia, at the expense of kynurenines. Metabolic measurements in hypoxic hepatocytes confirmed the predicted reduction in KP metabolites as well as accumulation of tryptamine. Tdo2 expression in cultured primary hepatocytes was reduced upon hypoxia inducible factor (HIF) stabilisation by dimethyloxalylglycine (DMOG), demonstrating that HIFs are involved in the hypoxic downregulation of hepatic Tdo2. DMOG abrogated hepatic luciferase signals in Tdo2 reporter mice, indicating that HIF stability also recapitulates hypoxic rewiring of Trp metabolism in vivo. Also in WT mice HIF stabilization drove homeostatic Trp metabolism away from the KP towards enhanced tryptamine production, leading to enhanced levels of tryptamine in liver, serum and brain. As tryptamines are the most potent hallucinogens known, the observed upregulation of tryptamine in response to hypoxic exposure of hepatocytes may be involved in the generation of hallucinations occurring at high altitude. KP metabolites are known to activate the aryl hydrocarbon receptor (AHR). The AHR-activating properties of tryptamines may explain why immunosuppressive AHR activity is maintained under hypoxia despite downregulation of the KP. In summary our results identify hypoxia as an important factor controlling Trp metabolism in the liver with possible implications for immunosuppressive AHR activation and mental disturbances

Annexin A1 regulates EGFR activity and alters EGFR-containing tumour-derived exosomes in head and neck cancers.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the 6th most common cancer with approximately half a million cases diagnosed each year worldwide. HNSCC has a poor survival rate which has not improved for over 30 years. The molecular pathogenesis of HNSCCs remains largely unresolved; there is high prevalence of p53 mutations and EGFR overexpression; however, the contribution of these molecular changes to disease development and/or progression remains unknown. We have recently identified microRNA miR-196a to be highly overexpressed in HNSCC with poor prognosis. Oncogenic miR-196a directly targets Annexin A1 (ANXA1). Although increased ANXA1 expression levels have been associated with breast cancer development, its role in HNSCC is debatable and its functional contribution to HNSCC development remains unclear. METHODS: ANXA1 mRNA and protein expression levels were determined by RNA Seq analysis and immunohistochemistry, respectively. Gain- and loss-of-function studies were performed to analyse the effects of ANXA1 modulation on cell proliferation, mechanism of activation of EGFR signalling as well as on exosome production and exosomal phospho-EGFR. RESULTS: ANXA1 was found to be downregulated in head and neck cancer tissues, both at mRNA and protein level. Its anti-proliferative effects were mediated through the intracellular form of the protein. Importantly, ANXA1 downregulation resulted in increased phosphorylation and activity of EGFR and its downstream PI3K-AKT signalling. Additionally, ANXA1 modulation affected exosome production and influenced the release of exosomal phospho-EGFR. CONCLUSIONS: ANXA1 acts as a tumour suppressor in HNSCC. It is involved in the regulation of EGFR activity and exosomal phospho-EGFR release and could be an important prognostic biomarker

Impaired serine metabolism complements LRRK2-G2019S pathogenicity in PD patients

Parkinson's disease (PD) is a multifactorial disorder with complex etiology. The most prevalent PD associated mutation, LRRK2-G2019S is linked to familial and sporadic cases. Based on the multitude of genetic predispositions in PD and the incomplete penetrance of LRRK2-G2019S, we hypothesize that modifiers in the patients' genetic background act as susceptibility factors for developing PD. To assess LRRK2-G2019S modifiers, we used human induced pluripotent stem cell-derived neuroepithelial stem cells (NESCs). Isogenic controls distinguish between LRRK2-G2019S dependent and independent cellular phenotypes. LRRK2-G2019S patient and healthy mutagenized lines showed altered NESC self-renewal and viability, as well as impaired serine metabolism. In patient cells, phenotypes were only partly LRRK2-G2019S dependent, suggesting a significant contribution of the genetic background. In this context we identified the gene serine racemase (SRR) as a novel patient-specific, developmental, genetic modifier contributing to the aberrant phenotypes. Its enzymatic product, n-serine, rescued altered cellular phenotypes. Susceptibility factors in the genetic background, such as SRR, could be new targets for early PD diagnosis and treatment.Analytical BioScience

Resolving the Combinatorial Complexity of Smad Protein Complex Formation and Its Link to Gene Expression.

Upon stimulation of cells with transforming growth factor beta (TGF-beta), Smad proteins form trimeric complexes and activate a broad spectrum of target genes. It remains unresolved which of the possible Smad complexes are formed in cellular contexts and how these contribute to gene expression. By combining quantitative mass spectrometry with a computational selection strategy, we predict and provide experimental evidence for the three most relevant Smad complexes in the mouse hepatoma cell line Hepa1-6. Utilizing dynamic pathway modeling, we specify the contribution of each Smad complex to the expression of representative Smad target genes, and show that these contributions are conserved in human hepatoma cell lines and primary hepatocytes. We predict, based on gene expression data of patient samples, increased amounts of Smad2/3/4 proteins and Smad2 phosphorylation as hallmarks of hepatocellular carcinoma and experimentally verify this prediction. Our findings demonstrate that modeling approaches can disentangle the complexity of transcription factor complex formation and its impact on gene expression