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See detail"Stroma-induced phenotypic plasticity offers phenotype-specific targeting to improve melanoma treatment".
Seip, Kotryna; Jorgensen, Kjetil; Haselager, Marco Vincent et al

in Cancer letters (2018)

Cancer cells' phenotypic plasticity, promoted by stromal cells, contributes to intra-tumoral heterogeneity and affects response to therapy. We have disclosed an association between fibroblast-stimulated ... [more ▼]

Cancer cells' phenotypic plasticity, promoted by stromal cells, contributes to intra-tumoral heterogeneity and affects response to therapy. We have disclosed an association between fibroblast-stimulated phenotype switching and resistance to the clinically used BRAF inhibitor (BRAFi) vemurafenib in malignant melanoma, revealing a challenge in targeting the fibroblast-induced phenotype. Here we compared molecular features and drug sensitivity in melanoma cells grown as co-cultures with fibroblasts versus mono-cultures. In the presence of fibroblasts, melanoma cells switched to the dedifferentiated, mesenchymal-like, inflammatory phenotype that showed reduced sensitivity to the most of 275 tested cancer drugs. Fibroblasts, however, sensitized melanoma cells to PI3K inhibitors (PI3Ki) and particularly the inhibitor of GSK3, AR-A014418 (GSK3i), that showed superior efficacy in co-cultures. The proteome changes induced by the BRAFi+GSK3i combination mimicked changes induced by BRAFi in mono-cultures, and GSK3i in co-cultures. This suggests that the single drug drives the response to the combination treatment, depending on fibroblast presence or absence, consequently, phenotype. We propose that the BRAFi and GSK3i (or PI3Ki) combination exemplifies phenotype-specific combinatorial treatment that should be beneficial in phenotypically heterogeneous tumors rich in stromal interactions. [less ▲]

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See detailFALCON: A Toolbox for the Fast Contextualisation of Logical Networks.
De Landtsheer, Sébastien UL; Trairatphisan, Panuwat UL; Lucarelli, Philippe UL et al

in Bioinformatics (Oxford, England) (2017)

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 ... [more ▼]

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: thomas.sauter@uni.lu. Supplementary information: Supplementary data are available at Bioinformatics online. [less ▲]

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See detailThermodynamically constrained averaging theory for cancer growth modelling
Albrecht, Marco UL; Sciumè, Giuseppe; Lucarelli, Philippe UL et al

in IFAC-PapersOnLine (2016), 49(26), 289-294

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 ... [more ▼]

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. [less ▲]

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See detailBenchmarking procedures for high-throughput context specific reconstruction algorithms
Pacheco, Maria UL; Pfau, Thomas UL; Sauter, Thomas UL

in Frontiers in Physiology (2016)

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction ... [more ▼]

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms. [less ▲]

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See detailA Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST.
Trairatphisan, Panuwat UL; Wiesinger, Monique UL; Bahlawane, Christelle UL et al

in PloS one (2016), 11(5), 0156223

BACKGROUND: Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of ... [more ▼]

BACKGROUND: Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN) as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation. RESULTS AND CONCLUSION: In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF) signalling pathway in Gastrointestinal Stromal Tumour (GIST). We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion. [less ▲]

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See detailL-plastin Ser5 phosphorylation in breast cancer cells and in vitro is mediated by RSK downstream of the ERK/MAPK pathway
Lommel, Maiti UL; Trairatphisan, Panuwat UL; Gäbler, Karoline UL et al

in FASEB Journal (2016)

Deregulated cell migration and invasion are hallmarks of metastatic cancer cells. Phosphorylation on residue Ser5 of the actin-bundling protein L-plastin activates L-plastin and has been reported to be ... [more ▼]

Deregulated cell migration and invasion are hallmarks of metastatic cancer cells. Phosphorylation on residue Ser5 of the actin-bundling protein L-plastin activates L-plastin and has been reported to be crucial for invasion and metastasis. Here, we investigate signal transduction leading to L-plastin Ser5 phosphorylation using 4 human breast cancer cell lines. Whole-genome microarray analysis comparing cell lines with different invasive capacities and corresponding variations in L-plastin Ser5 phosphorylation level revealed that genes of the ERK/MAPK pathway are differentially expressed. It is noteworthy that in vitro kinase assays showed that ERK/MAPK pathway downstream ribosomal protein S6 kinases α-1 (RSK1) and α-3 (RSK2) are able to directly phosphorylate L-plastin on Ser5. Small interfering RNA- or short hairpin RNA-mediated knockdown and activation/inhibition studies followed by immunoblot analysis and computational modeling confirmed that ribosomal S6 kinase (RSK) is an essential activator of L-plastin. Migration and invasion assays showed that RSK knockdown led to a decrease of up to 30% of migration and invasion of MDA-MB-435S cells. Although the presence of L-plastin was not necessary for migration/invasion of these cells, immunofluorescence assays illustrated RSK-dependent recruitment of Ser5-phosphorylated L-plastin to migratory structures. Altogether, we provide evidence that the ERK/MAPK pathway is involved in L-plastin Ser5 phosphorylation in breast cancer cells with RSK1 and RSK2 kinases able to directly phosphorylate L-plastin residue Ser5. [less ▲]

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See detailConstraint based modelling going multicellular
Martins Conde, Patricia UL; Sauter, Thomas UL; Pfau, Thomas UL

in Frontiers in Molecular Biosciences (2016), 3(3),

Constraint based modelling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase ... [more ▼]

Constraint based modelling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modelling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multitissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches. [less ▲]

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See detailIntegrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network
Pacheco, Maria UL; John, Elisabeth UL; Kaoma, Tony et al

in BMC Genomics (2015), 16(809),

Background: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine ... [more ▼]

Background: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Methods: Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. Results: To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. Conclusions: By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming. [less ▲]

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See detailCell type-selective disease-association of genes under high regulatory load
Galhardo, Mafalda Sofia UL; Berninger, Philipp; Nguyen, Thanh-Phuong UL et al

in Nucleic Acids Research (2015), 43(18), 8839-8855

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines ... [more ▼]

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3'UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner. [less ▲]

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See detailTowards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond.
Pfau, Thomas UL; Pacheco, Maria UL; Sauter, Thomas UL

in Briefings in bioinformatics (2015)

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from ... [more ▼]

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted. [less ▲]

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See detailRole of Calprotectin as a Modulator of the IL27-Mediated Proinflammatory Effect on Endothelial Cells
Dorosz, Susann Antonia UL; Ginolhac, Aurélien UL; Kähne, Thilo et al

in Mediators of Inflammation (2015), 2015

An underlying endothelial dysfunction plays a fundamental role in the pathogenesis of cardiovascular events and is the central feature of atherosclerosis. The protein-based communication between ... [more ▼]

An underlying endothelial dysfunction plays a fundamental role in the pathogenesis of cardiovascular events and is the central feature of atherosclerosis. The protein-based communication between leukocytes and inflamed endothelial cells leading to diapedesis has been largely investigated and several key players such as IL6, TNFα, or the damage associated molecular pattern molecule (DAMP) calprotectin are now well identified. However, regarding cytokine IL27, the controversial current knowledge about its inflammatory role and the involved regulatory elements requires clarification. Therefore, we examined the inflammatory impact of IL27 on primary endothelial cells and the potentially modulatory effect of calprotectin on both transcriptome and proteome levels. A qPCR-based screening demonstrated high IL27-mediated gene expression of IL7, IL15, CXCL10, and CXCL11. Calprotectin time-dependent downregulatory effects were observed on IL27-induced IL15 and CXCL10 gene expression. A mass spectrometry-based approach of IL27 ± calprotectin cell stimulation enlightened a calprotectin modulatory role in the expression of 28 proteins, mostly involved in the mechanism of leukocyte transmigration. Furthermore, we showed evidence for STAT1 involvement in this process. Our findings provide new evidence about the IL27-dependent proinflammatory signaling which may be under the control of calprotectin and highlight the need for further investigations on molecules which might have antiatherosclerotic functions. [less ▲]

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See detailConstitutive activation of oncogenic PDGFRalpha-mutant proteins occurring in GIST patients induces receptor mislocalisation and alters PDGFRalpha signalling characteristics.
Bahlawane, Christelle UL; Eulenfeld, Rene; Wiesinger, Monique UL et al

in Cell Communication and Signaling (2015), 13

BACKGROUND: Gastrointestinal stromal tumours (GIST) are mainly characterised by the presence of activating mutations in either of the two receptor tyrosine kinases c-KIT or platelet-derived growth factor ... [more ▼]

BACKGROUND: Gastrointestinal stromal tumours (GIST) are mainly characterised by the presence of activating mutations in either of the two receptor tyrosine kinases c-KIT or platelet-derived growth factor receptor-alpha (PDGFRalpha). Most mechanistic studies dealing with GIST mutations have focused on c-KIT and far less is known about the signalling characteristics of the mutated PDGFRalpha proteins. Here, we study the signalling capacities and corresponding transcriptional responses of the different PDGFRalpha proteins under comparable genomic conditions. RESULTS: We demonstrate that the constitutive signalling via the oncogenic PDGFRalpha mutants favours a mislocalisation of the receptors and that this modifies the signalling characteristics of the mutated receptors. We show that signalling via the oncogenic PDGFRalpha mutants is not solely characterised by a constitutive activation of the conventional PDGFRalpha signalling pathways. In contrast to wild-type PDGFRalpha signal transduction, the activation of STAT factors (STAT1, STAT3 and STAT5) is an integral part of signalling mediated via mutated PDGF-receptors. Furthermore, this unconventional STAT activation by mutated PDGFRalpha is already initiated in the endoplasmic reticulum whereas the conventional signalling pathways rather require cell surface expression of the receptor. Finally, we demonstrate that the activation of STAT factors also translates into a biologic response as highlighted by the induction of STAT target genes. CONCLUSION: We show that the overall oncogenic response is the result of different signatures emanating from different cellular compartments. Furthermore, STAT mediated responses are an integral part of mutated PDGFRalpha signalling. [less ▲]

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See detailThe oncogenic FIP1L1-PDGFRalpha fusion protein displays skewed signaling properties compared to its wild-type PDGFRalpha counterpart.
Haan, Serge UL; Bahlawane, Christelle UL; Wang, Jiali UL et al

in JAK-STAT (2015), 4(1), 1062596

Aberrant activation of oncogenic kinases is frequently observed in human cancers, but the underlying mechanism and resulting effects on global signaling are incompletely understood. Here, we demonstrate ... [more ▼]

Aberrant activation of oncogenic kinases is frequently observed in human cancers, but the underlying mechanism and resulting effects on global signaling are incompletely understood. Here, we demonstrate that the oncogenic FIP1L1-PDGFRalpha kinase exhibits a significantly different signaling pattern compared to its PDGFRalpha wild type counterpart. Interestingly, the activation of primarily membrane-based signal transduction processes (such as PI3-kinase- and MAP-kinase- pathways) is remarkably shifted toward a prominent activation of STAT factors. This diverging signaling pattern compared to classical PDGF-receptor signaling is partially coupled to the aberrant cytoplasmic localization of the oncogene, since membrane targeting of FIP1L1-PDGFRalpha restores activation of MAPK- and PI3K-pathways. In stark contrast to the classical cytokine-induced STAT activation process, STAT activation by FIP1L1-PDGFRalpha does neither require Janus kinase activity nor Src kinase activity. Furthermore, we investigated the mechanism of STAT5 activation via FIP1L1-PDGFRalpha in more detail and found that STAT5 activation does not involve an SH2-domain-mediated binding mechanism. We thus demonstrate that STAT5 activation occurs via a non-canonical activation mechanism in which STAT5 may be subject to a direct phosphorylation by FIP1L1-PDGFRalpha. [less ▲]

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See detailMultiscale model of dynamic neuromodulation integrating neuropeptide-induced signaling pathway activity with membrane electrophysiology.
Makadia, Hirenkumar K.; Anderson, Warren D.; Fey, Dirk et al

in Biophysical journal (2015), 108(1), 211-23

We developed a multiscale model to bridge neuropeptide receptor-activated signaling pathway activity with membrane electrophysiology. Typically, the neuromodulation of biochemical signaling and biophysics ... [more ▼]

We developed a multiscale model to bridge neuropeptide receptor-activated signaling pathway activity with membrane electrophysiology. Typically, the neuromodulation of biochemical signaling and biophysics have been investigated separately in modeling studies. We studied the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream phosphorylation of ion channels. Experiments have shown that AngII binding to the AngII receptor type-1 elicits baseline-dependent regulation of cytosolic Ca(2+) signaling. Our model simulations revealed a baseline Ca(2+)-dependent response to AngII receptor type-1 activation by AngII. Consistent with experimental observations, AngII evoked a rise in Ca(2+) when starting at a low baseline Ca(2+) level, and a decrease in Ca(2+) when starting at a higher baseline. Our analysis predicted that the kinetics of Ca(2+) transport into the endoplasmic reticulum play a critical role in shaping the Ca(2+) response. The Ca(2+) baseline also influenced the AngII-induced excitability changes such that lower Ca(2+) levels were associated with a larger firing rate increase. We examined the relative contributions of signaling kinases protein kinase C and Ca(2+)/Calmodulin-dependent protein kinase II to AngII-mediated excitability changes by simulating activity blockade individually and in combination. We found that protein kinase C selectively controlled firing rate adaptation whereas Ca(2+)/Calmodulin-dependent protein kinase II induced a delayed effect on the firing rate increase. We tested whether signaling kinetics were necessary for the dynamic effects of AngII on excitability by simulating three scenarios of AngII-mediated KDR channel phosphorylation: (1), an increased steady state; (2), a step-change increase; and (3), dynamic modulation. Our results revealed that the kinetics emerging from neuromodulatory activation of the signaling network were required to account for the dynamical changes in excitability. In summary, our integrated multiscale model provides, to our knowledge, a new approach for quantitative investigation of neuromodulatory effects on signaling and electrophysiology. [less ▲]

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See detailThe neural stem cell fate determinant TRIM32 regulates complex behavioral traits
Hillje, Anna-Lena UL; Beckmann, Elisabeth; Pavlou, Maria Angeliki UL et al

in Frontiers in Cellular Neuroscience (2015)

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See detailComparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks
Roume, Hugo UL; Buschart, Anna UL; Muller, Emilie UL et al

in Biofilms and Microbiomes (2015), 1(15007),

BACKGROUND: Mixed microbial communities underpin important biotechnological processes such as biological wastewater treatment (BWWT). A detailed knowledge of community structure and function relationships ... [more ▼]

BACKGROUND: Mixed microbial communities underpin important biotechnological processes such as biological wastewater treatment (BWWT). A detailed knowledge of community structure and function relationships is essential for ultimately driving these systems towards desired outcomes, e.g., the enrichment in organisms capable of accumulating valuable resources during BWWT. METHODS: A comparative integrated omic analysis including metagenomics, metatranscriptomics and metaproteomics was carried out to elucidate functional differences between seasonally distinct oleaginous mixed microbial communities (OMMCs) sampled from an anoxic BWWT tank. A computational framework for the reconstruction of community-wide metabolic networks from multi-omic data was developed. These provide an overview of the functional capabilities by incorporating gene copy, transcript and protein abundances. To identify functional genes, which have a disproportionately important role in community function, we define a high relative gene expression and a high betweenness centrality relative to node degree as gene-centric and network topological features, respectively. RESULTS: Genes exhibiting high expression relative to gene copy abundance include genes involved in glycerolipid metabolism, particularly triacylglycerol lipase, encoded by known lipid accumulating populations, e.g., Candidatus Microthrix parvicella. Genes with a high relative gene expression and topologically important positions in the network include genes involved in nitrogen metabolism and fatty acid biosynthesis, encoded by Nitrosomonas spp. and Rhodococcus spp. Such genes may be regarded as ‘keystone genes’ as they are likely to be encoded by keystone species. CONCLUSION: The linking of key functionalities to community members through integrated omics opens up exciting possibilities for devising prediction and control strategies for microbial communities in the future. [less ▲]

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See detailTranscriptomics profiling of human SGBS adipogenesis
Galhardo, Mafalda Sofia UL; Sinkkonen, Lasse UL; Berninger, Philipp et al

in Genomics Data (2014), 2

Obesity is an ever-growing epidemic where tissue homeostasis is influenced by the differentiation of adipocytes that function in lipid metabolism, endocrine and inflammatory processes. While this ... [more ▼]

Obesity is an ever-growing epidemic where tissue homeostasis is influenced by the differentiation of adipocytes that function in lipid metabolism, endocrine and inflammatory processes. While this differentiation process has been well-characterized in mice, limited data is available from human cells. Applying microarray expression profiling in the human SGBS pre-adipocyte cell line, we identified genes with differential expression during differentiation in combination with constraint-based modeling of metabolic pathway activity. Here we describe the experimental design and quality controls in detail for the gene expression and related results published by Galhardo et al. in Nucleic Acids Research 2014 associated with the data uploaded to NCBI Gene Expression Omnibus (). [less ▲]

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See detailChIP-seq profiling of the active chromatin marker H3K4me3 and PPARγ, CEBPα and LXR target genes in human SGBS adipocytes
Galhardo, Mafalda Sofia UL; Sinkkonen, Lasse UL; Berninger, Philipp et al

in Genomics Data (2014), 2

Transcription factors (TFs) represent key factors to establish a cellular phenotype. It is known that several TFs could play a role in disease, yet less is known so far how their targets overlap. We ... [more ▼]

Transcription factors (TFs) represent key factors to establish a cellular phenotype. It is known that several TFs could play a role in disease, yet less is known so far how their targets overlap. We focused here on identifying the most highly induced TFs and their putative targets during human adipogenesis. Applying chromatin immunoprecipitation coupled with deep sequencing (ChIP-Seq) in the human SGBS pre-adipocyte cell line, we identified genes with binding sites in their vicinity for the three TFs studied, PPARγ, CEBPα and LXR. Here we describe the experimental design and quality controls in detail for the deep sequencing data and related results published by Galhardo et al. in Nucleic Acids Research 2014 [1] associated with the data uploaded to NCBI Gene Expression Omnibus (). [less ▲]

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See detailoptPBN: An Optimisation Toolbox for Probabilistic Boolean Networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in PLoS ONE (2014), 9(7), 980011-15

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only ... [more ▼]

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. Results We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network. Summary The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks. [less ▲]

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