References of "Pacheco, Maria 50002864"
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See detailThe Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells
Rampler, Evelyn; Egger, Dominik; Schoeny, Harald et al

in Molecules (2019)

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat ... [more ▼]

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat formation as they are the progenitors of adipocytes. In this work, we used human MSCs, received from surgery waste, and differentiated them into fat adipocytes. The combination of several layers of information coming from lipidomics, metabolomics and proteomics enabled network analysis of the biochemical pathways in adipogenesis. Simultaneous analysis of metabolites, lipids, and proteins in cell culture is challenging due to the compound’s chemical difference, so most studies involve separate analysis with unimolecular strategies. In this study, we employed a multimolecular approach using a two–phase extraction to monitor the crosstalk between lipid metabolism and protein-based signaling in a single sample (~105 cells). We developed an innovative analytical workflow including standardization with in-house produced 13C isotopically labeled compounds, hyphenated high-end mass spectrometry (high-resolution Orbitrap MS), and chromatography (HILIC, RP) for simultaneous untargeted screening and targeted quantification. Metabolite and lipid concentrations ranged over three to four orders of magnitude and were detected down to the low fmol (absolute on column) level. Biological validation and data interpretation of the multiomics workflow was performed based on proteomics network reconstruction, metabolic modelling (MetaboAnalyst 4.0), and pathway analysis (OmicsNet). Comparing MSCs and adipocytes, we observed significant regulation of different metabolites and lipids such as triglycerides, gangliosides, and carnitine with 113 fully reprogrammed pathways. The observed changes are in accordance with literature findings dealing with adipogenic differentiation of MSC. These results are a proof of principle for the power of multimolecular extraction combined with orthogonal LC-MS assays and network construction. Considering the analytical and biological validation performed in this study, we conclude that the proposed multiomics workflow is ideally suited for comprehensive follow-up studies on adipogenesis and is fit for purpose for different applications with a high potential to understand the complex pathophysiology of diseases. [less ▲]

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See detailTowards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Sauter, Thomas UL

in Automated Reasoning for Systems Biology and Medicine (2019)

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated ... [more ▼]

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated datasets such as TCGA or METABRIC. These datasets will serve as references to compare new patient samples to in order to assign them to a predefined category (i.e. ‘patients associated with poor prognosis’). Some problems affecting the downstream analysis remain to be solved, the bottleneck is no longer data generation itself but the integration of the existing datasets with the present knowledge. Constraint-based modelling, that only requires the setting of a few parameters, became popular for the integration of high-throughput data in a metabolic context. Notably, context-specific building algorithms that extract a subnetwork from a reference network are largely used to study metabolic changes in various diseases. Reference networks are composed of canonical pathways while extracted subnetworks include only active pathways in the context of interest based on high-throughput data. Even though these algorithms can be part of automated pipelines, to be applied by clinicians, the model-building pipelines must be coupled to a standardized semi-automated analysis workflow based on machine learning approaches to avoid bias and reduce the cost of diagnostics. [less ▲]

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See detailIdentifying and targeting cancer-specific metabolism with network-based drug target prediction
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Ternes, Dominik UL et al

in EBioMedicine (2019), 43(May 2019), 98-106

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the ... [more ▼]

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. Findings Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. Interpretation The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. [less ▲]

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See detailIntegrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells
Greenhalgh, Kacy UL; Ramiro Garcia, Javier UL; Heinken et al

in Cell Reports (2019), 27

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations ... [more ▼]

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combina- torial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebi- otic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarci- nogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal ca- pacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbi- otics-based treatments in the future. [less ▲]

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See detailThe FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models
Pacheco, Maria UL; Sauter, Thomas UL

in Fondi, Marco (Ed.) Metabolic Network Reconstruction and Modeling (2018)

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See detailFast reconsonstruction of compact context-specific network models
Pacheco, Maria UL

Doctoral thesis (2016)

Recent progress in high-throughput data acquisition has shifted the focus from data generation to the processing and understanding of now easily collected patient-specific information. Metabolic models ... [more ▼]

Recent progress in high-throughput data acquisition has shifted the focus from data generation to the processing and understanding of now easily collected patient-specific information. Metabolic models, which have already proven to be very powerful for the integration and analysis of such data sets, might be successfully applied in precision medicine in the near future. Context-specific reconstructions extracted from generic genome-scale models like Reconstruction X (ReconX) (Duarte et al., 2007; Thiele et al., 2013) or Human Metabolic Reconstruction (HMR) (Agren et al., 2012; Mardinoglu et al., 2014a) thereby have the potential to become a diagnostic and treatment tool tailored to the analysis of specific groups of individuals. The use of computational algorithms as a tool for the routinely diagnosis and analysis of metabolic diseases requires a high level of predictive power, robustness and sensitivity. Although multiple context-specific reconstruction algorithms were published in the last ten 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. The aim of this thesis was to create a family of robust and fast algorithms for the building of context-specific models that could be used for the integration of different types of omics data and which should be sensitive enough to be used in the framework of precision medicine. FASTCORE (Vlassis et al., 2014), which was developed in the frame of this thesis is among the first context-specific building algorithms that do not optimize for a biological function and that has a computational time around seconds. Furthermore, FASTCORE is devoid of heuristic parameter settings. FASTCORE requires as input a set of reactions that are known to be active in the context of interest (core reactions) and a genome-scale reconstruction. FASTCORE uses an approximation of the cardinality function to force the core set of reactions to carry a flux above a threshold. Then an L1-minimization is applied to penalize the activation of reactions with low confidence level while still constraining the set of core reactions to carry a flux. The rationale behind FASTCORE is to reconstruct a compact consistent (all the reactions of the model have the potential to carry non zero-flux) output model that contains all the core reactions and a small number of non-core reactions. Then, in order to cope with the non-negligible amount of noise that impede direct comparison within genes, FASTCORE was extended to the FASTCORMICS workflow (Pires Pacheco and Sauter, 2014; Pires Pacheco et al., 2015a) for the building of models via the integration of microarray data . FASTCORMICS was applied to reveal control points regulated by genes under high regulatory load in the metabolic network of monocyte derived macrophages (Pires Pacheco et al., 2015a) and to investigate the effect of the TRIM32 mutation on the metabolism of brain cells of mice (Hillje et al., 2013). The use of metabolic modelling in the frame of personalized medicine, high-throughput data analysis and integration of omics data calls for a significant improvement in quality of existing algorithms and generic metabolic reconstructions used as input for the former. To this aim and to initiate a discussion in the community on how to improve the quality of context-specific reconstruction, benchmarking procedures were proposed and applied to seven recent contextspecific algorithms including FASTCORE and FASTCORMICS (Pires Pacheco et al., 2015a). Further, the problems arising from a lack of standardization of building and annotation pipelines and the use of non-specific identifiers was discussed in the frame of a review. In this review, we also advocated for a switch from gene-centred protein rules (GPR rules) to transcript-centred protein rules (Pfau et al., 2015). [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 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 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 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 detailFast reconstruction of compact context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria UL; Sauter, Thomas UL

in PLoS Computational Biology (2014), 10(1), 1003424

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can ... [more ▼]

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present FASTCORE, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. FASTCORE takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and FASTCORE iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, FASTCORE can form the backbone of many future metabolic network reconstruction algorithms. [less ▲]

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See detailFast reconstruction of compact context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria UL; Sauter, Thomas UL

E-print/Working paper (2013)

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can ... [more ▼]

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present FASTCORE, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. FASTCORE takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and FASTCORE iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a chief rival method. Given its simplicity and its excellent performance, FASTCORE can form the backbone of many future metabolic network reconstruction algorithms. [less ▲]

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See detailFastcore: An algorithm for fast reconstruction of context-specific metabolic network models
Vlassis, Nikos UL; Pacheco, Maria UL; Sauter, Thomas UL

in Proc. 8th BeNeLux Bioinformatics Conference (2013)

Detailed reference viewed: 191 (34 UL)